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18 pages, 4181 KiB

Open AccessArticle

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

by Shuibo Hu^1,2, Huizeng Liu^1,3,*

, Wenjing Zhao⁴, Tiezhu Shi¹, Zhongwen Hu¹, Qingquan Li¹ and Guofeng Wu^1,2,*

¹ Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities & Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China

² College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China

³ Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China

⁴ South China Institute of Environmental Sciences, the Ministry of Environmental Protection of RPC, Guangzhou 510535, China

Remote Sens. 2018, 10(3), 191; https://doi.org/10.3390/rs10030191 - 8 Mar 2018

Cited by 55 | Viewed by 6967

Abstract

The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton [...] Read more.

The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs), in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD) high performance liquid chromatography (HPLC) database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS), artificial neural networks (ANN), support vector machine (SVM) and random forests (RF), and feature selection techniques, including genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination based on support vector machine (SVM-RFE), for inferring PSCs from remote sensing data. Results showed that: (1) SVM-RFE worked better in selecting sensitive features; (2) RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3) machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4) sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5) the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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25 pages, 5813 KiB

Open AccessArticle

Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China

by Hao Chen^1,2, Wanchang Zhang^1,*, Huiran Gao^1,2 and Ning Nie³

¹ Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China

Remote Sens. 2018, 10(3), 356; https://doi.org/10.3390/rs10030356 - 25 Feb 2018

Cited by 105 | Viewed by 11558

Abstract

Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., [...] Read more.

Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., croplands) over the Songnen and Sanjiang Plain (SNP and SJP), northeast China, during the past 35 years (1980–2015). The relative role of human activities and climatic changes in terms of their impacts on wetlands and agriculture dynamics were quantitatively distinguished and evaluated in different periods based on a seven-stage LULC dataset. Our results indicated that human activities, such as population expansion and socioeconomic development, and institutional policies related to wetlands and agriculture were the main driving forces for LULCC of the SJP and SNP during the past decades, while increasing contributions of climatic changes were also found. Furthermore, as few studies have identified which geographic regions are most at risk, how the future climate changes will spatially and temporally impact wetlands and agriculture, i.e., the suitability of wetlands and agriculture distributions under different future climate change scenarios, were predicted and analyzed using a habitat distribution model (Maxent) at the pixel-scale. The present findings can provide valuable references for policy makers on regional sustainability for food security, water resource rational management, agricultural planning and wetland protection as well as restoration of the region. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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20 pages, 4511 KiB

Open AccessArticle

A Study of Rank Defect and Network Effect in Processing the CMONOC Network on Bernese

by Weiwei Wu^1,*

, Jicang Wu¹ and Guojie Meng²

¹ College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China

² Institute of Earthquake Science, China Earthquake Administration, Beijing 100036, China

Remote Sens. 2018, 10(3), 357; https://doi.org/10.3390/rs10030357 - 25 Feb 2018

Cited by 16 | Viewed by 4627

Abstract

High-precision GPS data processing on Bernese has been employed to routinely resolve daily position solutions of GPS stations in the Crustal Movement Observation Network of China (CMONOC). The rank-deficient problems of the normal equation (NEQ) system and the network effect on the frame [...] Read more.

High-precision GPS data processing on Bernese has been employed to routinely resolve daily position solutions of GPS stations in the Crustal Movement Observation Network of China (CMONOC). The rank-deficient problems of the normal equation (NEQ) system and the network effect on the frame alignment of NEQs in the processing of CMONOC data on Bernese still present difficulties. In this study, we diagnose the rank-deficient problems of the original NEQ, review the efficiency of the controlled datum removal (CDR) method in filtering out the three frame-origin-related datum contents, investigate the reliabilities of the inherited frame orientation and scale information from the fixation of the GPS satellite orbits and the Earth rotation parameters in establishing the NEQ of the CMONOC network on Bernese, and analyze the impact of the network effect on the position time series of GPS stations. Our results confirm the nonsingularity of the original NEQ and the efficiency of the CDR filtering in resolving the rank-deficient problems; show that the frame origin parameters are weakly defined and should be stripped off, while the frame orientation and scale parameters should be retained due to their insufficient redefinition from the minimal constraint (MC) implementation through inhomogeneous and asymmetrical fiducial networks; and reveal the superiority of a globally distributed fiducial network for frame alignment of the reconstructed NEQs via No-Net-Translation (NNT) MC conditions. Finally, we attribute the two apparent discontinuities in the position time series to the terrestrial reference frame (TRF) conversions of the GPS satellite orbits, and identify it as the orbit TRF effect. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

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17 pages, 11263 KiB

Open AccessEditor’s ChoiceArticle

Impacts of Climate Change on Tibetan Lakes: Patterns and Processes

by Dehua Mao¹

, Zongming Wang^1,*

, Hong Yang^2,*

, Huiying Li³, Julian R. Thompson⁴, Lin Li⁵, Kaishan Song¹, Bin Chen⁶, Hongkai Gao^7,8 and Jianguo Wu^7,8,9

¹ Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

² Department of Geography and Environmental Science, University of Reading, Whiteknights, Reading RG6 6AB, UK

³ College of Earth Sciences, Jilin University, Changchun 130021, China

⁴ UCL Department of Geography, University College London, London WC1E 6BT, UK

⁵ Department of Earth Sciences, Indiana University—Purdue University at Indianapolis, Indianapolis, IN 46202, USA

⁶ School of Environment, Beijing Normal University, Beijing 100875, China

⁷ School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA

⁸ Global School of Sustainability, Arizona State University, Tempe, AZ 85287, USA

⁹ Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Remote Sens. 2018, 10(3), 358; https://doi.org/10.3390/rs10030358 - 26 Feb 2018

Cited by 62 | Viewed by 10658

Abstract

High-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and [...] Read more.

High-altitude inland-drainage lakes on the Tibetan Plateau (TP), the earth’s third pole, are very sensitive to climate change. Tibetan lakes are important natural resources with important religious, historical, and cultural significance. However, the spatial patterns and processes controlling the impacts of climate and associated changes on Tibetan lakes are largely unknown. This study used long time series and multi-temporal Landsat imagery to map the patterns of Tibetan lakes and glaciers in 1977, 1990, 2000, and 2014, and further to assess the spatiotemporal changes of lakes and glaciers in 17 TP watersheds between 1977 and 2014. Spatially variable changes in lake and glacier area as well as climatic factors were analyzed. We identified four modes of lake change in response to climate and associated changes. Lake expansion was predominantly attributed to increased precipitation and glacier melting, whereas lake shrinkage was a main consequence of a drier climate or permafrost degradation. These findings shed new light on the impacts of recent environmental changes on Tibetan lakes. They suggest that protecting these high-altitude lakes in the face of further environmental change will require spatially variable policies and management measures. Full article

(This article belongs to the Special Issue Application of Remote Sensing in Hydrological Modeling and Watershed Management)

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16 pages, 3841 KiB

Open AccessArticle

Snow Wetness Retrieved from L-Band Radiometry

by Reza Naderpour^1,*

and Mike Schwank^1,2

¹ Swiss Federal Research Institute WSL, Birmensdorf CH-8903, Switzerland

² Gamma Remote Sensing AG, Gümligen CH-3073, Switzerland

Remote Sens. 2018, 10(3), 359; https://doi.org/10.3390/rs10030359 - 26 Feb 2018

Cited by 25 | Viewed by 5364

Abstract

The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission [...] Read more.

The present study demonstrates the successful use of the high sensitivity of L-band brightness temperatures to snow liquid water in the retrieval of snow liquid water from multi-angular L-band brightness temperatures. The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS), coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on L-band brightness temperatures measured over (i) areas with a metal reflector placed on the ground (“reflector area”—

T_{B, R}

), and (ii) natural snow-covered ground (“natural area”—

T_{B, N}

). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of the aforementioned quasi-simultaneous retrievals. This is verified by the fact that both types of snow liquid water retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. The robust and reliable performance of snow liquid water retrieved from

T_{B, R}

, together with their level of detail, suggest the use of these retrievals as “references” to assess the meaningfulness of the snow liquid water retrievals based on

T_{B, N}

. It is noteworthy that the latter retrievals are achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements, such as ground permittivity

ε_{G}

and snow mass-density

ρ_{S}

. The latter two are estimated in the first retrieval-step employing the well-established two-parameter

(ρ_{S}, ε_{G})

retrieval scheme designed for dry snow conditions and explored in the companion paper that is included in this special issue in terms of its sensitivity with respect to disturbative melting effects. The two-step retrieval approach proposed and investigated here, opens up the possibility of using airborne or spaceborne L-band radiometry to estimate

(ρ_{S}, ε_{G})

and additionally snow liquid water as a new passive L-band data product. Full article

(This article belongs to the Special Issue Snow Remote Sensing)

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24 pages, 6410 KiB

Open AccessArticle

Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data

by Chenghao Zhu¹

, Xiaoli Zhang^1,*, Ning Zhang^2,3, Mohammed Abdelmanan Hassan¹ and Lin Zhao¹

¹ Beijing Key Laboratory of Precision Forestry, Forestry College, Beijing Forestry University, Beijing 100083, China

² Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

³ National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Remote Sens. 2018, 10(3), 360; https://doi.org/10.3390/rs10030360 - 26 Feb 2018

Cited by 16 | Viewed by 5180

Abstract

Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using [...] Read more.

Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5). After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing. Full article

(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)

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29 pages, 4868 KiB

Open AccessArticle

Directional ℓ₀ Sparse Modeling for Image Stripe Noise Removal

by Hong-Xia Dou, Ting-Zhu Huang^*, Liang-Jian Deng, Xi-Le Zhao and Jie Huang

School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China

Remote Sens. 2018, 10(3), 361; https://doi.org/10.3390/rs10030361 - 26 Feb 2018

Cited by 44 | Viewed by 6702

Abstract

Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to [...] Read more.

Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex

ℓ_{0}

sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively. Full article

(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)

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28 pages, 21093 KiB

Open AccessArticle

Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China

by Lei Bai^1,2,3, Chunxiang Shi⁴, Lanhai Li^2,*, Yanfen Yang⁵ and Jing Wu⁶

¹ College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China

² State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

⁴ National Meteorological Information Center, Beijing 100081, China

⁵ State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China

⁶ Lanzhou Central Meteorological Observatory, Lanzhou 730020, China

Remote Sens. 2018, 10(3), 362; https://doi.org/10.3390/rs10030362 - 26 Feb 2018

Cited by 157 | Viewed by 12650

Abstract

Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection [...] Read more.

Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates’ accuracy and valid spatial coverage. Full article

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23 pages, 13740 KiB

Open AccessArticle

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

by He Wang^1,*

, Jing Wang², Jingsong Yang³

, Lin Ren³, Jianhua Zhu¹, Xinzhe Yuan⁴ and Chunhua Xie⁴

¹ National Ocean Technology Center, State Oceanic Administration, Tianjin 300112, China

² Marine Acoustics and Remote Sensing Laboratory, Zhejiang Ocean University, Zhoushan 316000, China

³ State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China

⁴ National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China

Remote Sens. 2018, 10(3), 363; https://doi.org/10.3390/rs10030363 - 26 Feb 2018

Cited by 54 | Viewed by 5436

Abstract

Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open [...] Read more.

Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open ocean. An empirical retrieval algorithm of significant wave height (SWH), termed Quad-Polarized C-band WAVE algorithm for GF-3 wave mode (QPCWAVE_GF3), is developed for quad-polarized SAR measurements from GF-3 in wave mode. QPCWAVE_GF3 model is built using six SAR image and spectrum related parameters. Based on a total of 2576 WaveWatch III (WW3) and GF-3 wave mode match-ups, 12 empirical coefficients of the model are determined for 6 incidence angle modes. The validation of the QPCWAVE_GF3 model is performed through comparisons against independent WW3 modelling hindcasts, and observations from altimeters and buoys from January to October in 2017. The assessment shows a good agreement with root mean square error from 0.5 m to 0.6 m, and scatter index around 20%. In particular, applications of the QPCWAVE_GF3 model in SWH estimation for two storm cases from GF-3 data in wave mode and Quad-Polarization Strip I mode are presented respectively. Results indicate that the proposed algorithm is suitable for SWH estimation from GF-3 wave mode and is promising for other similar data. Full article

(This article belongs to the Section Ocean Remote Sensing)

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19 pages, 6059 KiB

Open AccessArticle

Vertical Deformation Monitoring of the Suspension Bridge Tower Using GNSS: A Case Study of the Forth Road Bridge in the UK

by Qusen Chen^1,2

, Weiping Jiang^1,*, Xiaolin Meng^2,*

, Peng Jiang^3,*, Kaihua Wang³, Yilin Xie² and Jun Ye⁴

¹ School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

² Nottingham Geospatial Institute/Sino-UK Geospatial Engineering Centre, The University of Nottingham, Nottingham NG7 2TU, UK

³ GNSS Research Center, Wuhan University, Wuhan 430079, China

⁴ UbiPOS UK Ltd., Nottingham Geospatial Building, Nottingham NG7 2TU, UK

Remote Sens. 2018, 10(3), 364; https://doi.org/10.3390/rs10030364 - 26 Feb 2018

Cited by 47 | Viewed by 8235

Abstract

The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts [...] Read more.

The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts to reveal its deformation mechanism. Firstly, we designed a strategy for high-precision GNSS data processing aiming at facilitating deformation extraction and analysis. Then, 33 months of vertical deformation time series of the southern tower of the Forth Road Bridge (FRB) in the UK were processed, and the accurate subsidence and the parameters of seasonal signals were estimated based on a classic function model that has been widely studied to analyse GNSS coordinate time series. We found that the subsidence rate is about 4.7 mm/year, with 0.1 mm uncertainty. Meanwhile, a 15-month meteorological dataset was utilised with a thermal expansion model (TEM) to explain the effects of seasonal signals on tower deformation. The amplitude of the annual signals correlated quite well that obtained by the TEM, with the consistency reaching 98.9%, demonstrating that the thermal effect contributes significantly to the annual signals. The amplitude of daily signals displays poor consistency with the ambient temperature data. However, the phase variation tendencies between the daily signals of the vertical deformation and the ambient temperature are highly consistent after February 2016. Finally, the potential contribution of the North Atlantic Drift (NAD) to the characteristics of annual and daily signals is discussed because of the special geographical location of the FRB. Meanwhile, this paper emphasizes the importance of collecting more detailed meteorological and other loading data for the investigation of the vertical deformation mechanism of the bridge towers over time with the support of GNSS. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

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17 pages, 12949 KiB

Open AccessArticle

Regional Land Subsidence Analysis in Eastern Beijing Plain by InSAR Time Series and Wavelet Transforms

by Mingliang Gao^1,2,3,4,*

, Huili Gong^2,3,4,*, Beibei Chen^2,3,4, Xiaojuan Li^2,3,4, Chaofan Zhou^2,3,4, Min Shi^2,3,4, Yuan Si^2,3,4, Zheng Chen⁵

and Guangyao Duan⁶

¹ Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China

² Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China

³ Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China

⁴ Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

⁵ Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

⁶ School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China

Remote Sens. 2018, 10(3), 365; https://doi.org/10.3390/rs10030365 - 26 Feb 2018

Cited by 70 | Viewed by 8306

Abstract

Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence [...] Read more.

Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence rates of more than 100 mm/year. In this study, we employ two SAR datasets acquired by Envisat and TerraSAR-X satellites to investigate the surface deformation in Beijing Plain from 2003 to 2013 based on the multi-temporal InSAR technique. Furthermore, we also use observation wells to provide in situ hydraulic head levels to perform the evolution of land subsidence and spatial-temporal changes of groundwater level. Then, we analyze the accumulated displacement and hydraulic head level time series using continuous wavelet transform to separate periodic signal components. Finally, cross wavelet transform (XWT) and wavelet transform coherence (WTC) are implemented to analyze the relationship between the accumulated displacement and hydraulic head level time series. The results show that the subsidence centers in the northern Beijing Plain is spatially consistent with the groundwater drop funnels. According to the analysis of well based results located in different areas, the long-term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction, while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics (seasonal characteristics) as the groundwater level changes. Moreover, according to the wavelet transformation, the land subsidence time series at monitoring well site lags several months behind the groundwater level change. Full article

(This article belongs to the Special Issue Remote Sensing of Land Subsidence)

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23 pages, 50092 KiB

Open AccessArticle

A New Approach for Monitoring the Terra Nova Bay Polynya through MODIS Ice Surface Temperature Imagery and Its Validation during 2010 and 2011 Winter Seasons

by Giuseppe Aulicino^1,2,*,†

, Manuela Sansiviero^1,*,†, Stephan Paul³, Cinzia Cesarano⁴, Giannetta Fusco^1,5

, Peter Wadhams² and Giorgio Budillon^1,5

¹ Department of Science and Technology—DiST, Università degli Studi di Napoli Parthenope, Centro Direzionale Is. C4, 80143 Napoli, Italy

² Department of Life and Environmental Sciences—DiSVA, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy

³ Alfred Wegener Institute, Am Handelshafen 12, 27570 Bremerhaven, Germany

⁴ Progetto Terra, Via Roma 85, 80054 Gragnano, Italy

⁵ Consorzio Nazionale Interuniversitario per le Scienze del Mare, 00196 Rome, Italy

^† These authors contributed equally to this work.

Remote Sens. 2018, 10(3), 366; https://doi.org/10.3390/rs10030366 - 26 Feb 2018

Cited by 37 | Viewed by 7885

Abstract

Polynyas are dynamic stretches of open water surrounded by ice. They typically occur in remote regions of the Arctic and Antarctic, thus remote sensing is essential for monitoring their dynamics. On regional scales, daily passive microwave radiometers provide useful information about their extent [...] Read more.

Polynyas are dynamic stretches of open water surrounded by ice. They typically occur in remote regions of the Arctic and Antarctic, thus remote sensing is essential for monitoring their dynamics. On regional scales, daily passive microwave radiometers provide useful information about their extent because of their independence from cloud coverage and daylight; nonetheless, their coarse resolution often does not allow an accurate discrimination between sea ice and open water. Despite its sensitivity to the presence of clouds, thermal infrared (TIR) Moderate Resolution Imaging Spectroradiometer (MODIS) provides higher-resolution information (typically 1 km) at large swath widths, several times per day, proving to be useful for the retrieval of the size of polynyas. In this study, we deal with Aqua satellite MODIS observations of a frequently occurring coastal polynya in the Terra Nova Bay (TNB), Ross Sea (Antarctica). The potential of a new methodology for estimating the variability of this polynya through MODIS TIR during the 2010 and 2011 freezing season (April to October) is presented and discussed. The polynya is observed in more than 1600 radiance scenes, after a preliminary filter evaluates and discards cloudy and fog-contaminated scenes. This reduces the useful MODIS swaths to about 50% of the available acquisitions, but a revisit time of less than 24 h is kept for about 90% of the study period. As expected, results show a high interannual variability with an opening/closing fluctuation clearly depending on the regime of the katabatic winds recorded by the automatic weather stations Rita and Eneide along the TNB coast. Retrievals are also validated through a comparison with a set of 196 co-located high-resolution ENVISAT ASAR images. Although our estimations slightly underestimate the ASAR derived extents, a good agreement is found, the linear correlation reaching 0.75 and the average relative error being about 6%. Finally, a sensitivity test on the applied thermal thresholds supports the effectiveness of our setting. Full article

(This article belongs to the Section Ocean Remote Sensing)

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25 pages, 21088 KiB

Open AccessArticle

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

by Keng-Hao Liu^1,*, Shih-Yu Chen²

, Hung-Chang Chien¹ and Meng-Han Lu¹

¹ Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan

² Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan

Remote Sens. 2018, 10(3), 367; https://doi.org/10.3390/rs10030367 - 26 Feb 2018

Cited by 10 | Viewed by 5081

Abstract

Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and [...] Read more.

Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and are sometimes ineffective for applications that require timeliness, such as disaster prevention or target detection. This paper proposes an online BS method that allows us obtain instant BS results in a progressive manner during HSI data transmission, which is carried out under band-interleaved-by-sample/pixel (BIS/BIP) format. Such a revolutionary method is called progressive sample processing of band selection (PSP-BS). In PSP-BS, BS can be done recursively pixel by pixel, so that the instantaneous BS can be achieved without waiting for all the pixels of an image. To develop a PSP-BS algorithm, we proposed PSP-OMPBS, which adopted the recursive version of a self-sparse regression BS method (OMPBS) as a native algorithm. The experiments conducted on two real hyperspectral images demonstrate that PSP-OMPBS can progressively output the BS with extremely low computing time. In addition, the convergence of BS results during transmission can be further accelerated by using a pre-defined pixel transmission sequence. Such a significant advantage not only allows BS to be done in a real-time manner for the future satellite data downlink, but also determines the BS results in advance, without waiting to receive every pixel of an image. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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21 pages, 3895 KiB

Open AccessArticle

Sparse Bayesian Learning Based Three-Dimensional Imaging Algorithm for Off-Grid Air Targets in MIMO Radar Array

by Zekun Jiao^1,2,3

, Chibiao Ding^1,2,*, Xingdong Liang^1,2, Longyong Chen^1,2 and Fubo Zhang^1,2

¹ National Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China

² Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

³ University of the Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2018, 10(3), 369; https://doi.org/10.3390/rs10030369 - 27 Feb 2018

Cited by 17 | Viewed by 5178

Abstract

In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of [...] Read more.

In recent years, the development of compressed sensing (CS) and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of aperture size and number of antennas. Since the signal to be reconstructed is sparse for air targets, many CS-based imaging algorithms using a sparse array are proposed. However, most of those algorithms assume that the scatterers are exactly located at the pre-discretized grids, which will not hold in real scene. Aiming at finding an accurate solution to off-grid target imaging, we propose an off-grid 3D imaging method based on improved sparse Bayesian learning (SBL). Besides, the Bayesian Cramér-Rao Bound (BCRB) for off-grid bias estimator is provided. Different from previous algorithms, the proposed algorithm adopts a three-stage hierarchical sparse prior to introduce more degrees of freedom. Then variational expectation maximization method is applied to solve the sparse recovery problem through iteration, during each iteration joint sparsity is used to improve efficiency. Experimental results not only validate that the proposed method outperforms the existing off-grid imaging methods in terms of accuracy and resolution, but have compared the root mean square error with corresponding BCRB, proving effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)

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17 pages, 2175 KiB

Open AccessArticle

Burned Area Mapping of an Escaped Fire into Tropical Dry Forest in Western Madagascar Using Multi-Season Landsat OLI Data

by Anne C. Axel

Department of Biological Sciences, Marshall University, Huntington, WV 25755, USA

Remote Sens. 2018, 10(3), 371; https://doi.org/10.3390/rs10030371 - 27 Feb 2018

Cited by 25 | Viewed by 9243

Abstract

A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry [...] Read more.

A human-induced fire cleared a large area of tropical dry forest near the Ankoatsifaka Research Station at Kirindy Mitea National Park in western Madagascar over several weeks in 2013. Fire is a major factor in the disturbance and loss of global tropical dry forests, yet remotely sensed mapping studies of fire-impacted tropical dry forests lag behind fire research of other forest types. Methods used to map burns in temperature forests may not perform as well in tropical dry forests where it can be difficult to distinguish between multiple-age burn scars and between bare soil and burns. In this study, the extent of forest lost to stand-replacing fire in Kirindy Mitea National Park was quantified using both spectral and textural information derived from multi-date satellite imagery. The total area of the burn was 18,034 ha. It is estimated that 6% (4761 ha) of the Park’s primary tropical dry forest burned over the period 23 June to 27 September. Half of the forest burned (2333 ha) was lost in the large conflagration adjacent to the Research Station. The best model for burn scar mapping in this highly-seasonal tropical forest and pastoral landscape included the differenced Normalized Burn Ratio (dNBR) and both uni- and multi-temporal measures of greenness. Lessons for burn mapping of tropical dry forest are much the same as for tropical dry forest mapping—highly seasonal vegetation combined with a mixture of background spectral information make for a complicated analysis and may require multi-temporal imagery and site specific techniques. Full article

(This article belongs to the Special Issue Remote Sensing of Wildfire)

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21 pages, 11006 KiB

Open AccessArticle

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

by Mingzhu He^1,*, John S. Kimball^1,2, Marco P. Maneta³, Bruce D. Maxwell⁴

, Alvaro Moreno¹, Santiago Beguería⁵

and Xiaocui Wu⁶

¹ Numerical Terradynamic Simulation Group, College of Forestry & Conservation, University of Montana, Missoula, MT 59812, USA

² Department of Ecosystem and Conservation Sciences, College of Forestry & Conservation, University of Montana, Missoula, MT 59812, USA

³ Department of Geosciences, University of Montana, Missoula, MT 59812, USA

⁴ Department of Land Resources and Environmental Science, Montana State University, Bozeman, MT 59717, USA

⁵ Estación Experimental de Aula Dei, Consejo Superior de Investigaciones Científicas (EEAD-CSIC), 50059 Zaragoza, Spain

⁶ Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA

Remote Sens. 2018, 10(3), 372; https://doi.org/10.3390/rs10030372 - 28 Feb 2018

Cited by 118 | Viewed by 17296

Abstract

Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too [...] Read more.

Accurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008–2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HI_GPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security. Full article

(This article belongs to the Collection Google Earth Engine Applications)

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19 pages, 8987 KiB

Open AccessArticle

Structure Tensor-Based Algorithm for Hyperspectral and Panchromatic Images Fusion

by Jiahui Qu^1,2,*

, Jie Lei^1,2,*

, Yunsong Li^1,2,*, Wenqian Dong²

, Zhiyong Zeng³ and Dunyu Chen⁴

¹ Joint Laboratory of High Speed Multi-source Image Coding and Processing, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China

² State Key Lab. of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China

³ School of Mathematics and Information, Fujian Normal University, Fuzhou 350108, China

⁴ School of Electrical Communication, Yuan Ze University, Taoyuan City 32003, Taiwan

Remote Sens. 2018, 10(3), 373; https://doi.org/10.3390/rs10030373 - 1 Mar 2018

Cited by 32 | Viewed by 6308

Abstract

Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a [...] Read more.

Restricted by technical and budget constraints, hyperspectral (HS) image which contains abundant spectral information generally has low spatial resolution. Fusion of hyperspectral and panchromatic (PAN) images can merge spectral information of the former and spatial information of the latter. In this paper, a new hyperspectral image fusion algorithm using structure tensor is proposed. An image enhancement approach is utilized to sharpen the spatial information of the PAN image, and the spatial details of the HS image is obtained by an adaptive weighted method. Since structure tensor represents structure and spatial information, a structure tensor is introduced to extract spatial details of the enhanced PAN image. Seeing that the HS and PAN images contain different and complementary spatial information for a same scene, a weighted fusion method is presented to integrate the extracted spatial information of the two images. To avoid artifacts at the boundaries, a guided filter is applied to the integrated spatial information image. The injection matrix is finally constructed to reduce spectral and spatial distortion, and the fused image is generated by injecting the complete spatial information. Comparative analyses validate the proposed method outperforms the state-of-art fusion methods, and provides more spatial details while preserving the spectral information. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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16 pages, 1184 KiB

Open AccessArticle

SAR Target Recognition via Incremental Nonnegative Matrix Factorization

by Sihang Dang

, Zongyong Cui, Zongjie Cao^*

and Nengyuan Liu

Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China

Remote Sens. 2018, 10(3), 374; https://doi.org/10.3390/rs10030374 - 1 Mar 2018

Cited by 36 | Viewed by 7797

Abstract

In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing [...] Read more.

In synthetic aperture radar (SAR) target recognition, the amount of target data increases continuously, and thus SAR automatic target recognition (ATR) systems are required to provide updated feature models in real time. Most recent SAR feature extraction methods have to use both existing and new samples to retrain a new model every time new data is acquired. However, this repeated calculation of existing samples leads to an increased computing cost. In this paper, a dynamic feature learning method called incremental nonnegative matrix factorization with L

_{p}

sparse constraints (L

_{p}

-INMF) is proposed as a solution to that problem. In contrast to conventional nonnegative matrix factorization (NMF) whereby existing and new samples are computed to retrain a new model, incremental NMF (INMF) computes only the new samples to update the trained model incrementally, which can improve the computing efficiency. Considering the sparse characteristics of scattering centers in SAR images, we set the updating process under a generic sparse constraint (L

_{p}

) for matrix decomposition of INMF. Thus, L

_{p}

-INMF can extract sparse characteristics in SAR images. Experimental results using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data illustrate that the proposed L

_{p}

-INMF method can not only update models with new samples more efficiently than conventional NMF, but also has a higher recognition rate than NMF and INMF. Full article

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19 pages, 5985 KiB

Open AccessArticle

An Observation Task Chain Representation Model for Disaster Process-Oriented Remote Sensing Satellite Sensor Planning: A Flood Water Monitoring Application

by Chao Yang^†

, Jin Luo, Chuli Hu^*,†, Lu Tian, Jie Li and Ke Wang

¹ Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

^† These authors contributed equally to this work.

Remote Sens. 2018, 10(3), 375; https://doi.org/10.3390/rs10030375 - 1 Mar 2018

Cited by 8 | Viewed by 6129

Abstract

An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning [...] Read more.

An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain) representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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14 pages, 613 KiB

Open AccessArticle

An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource

by Carl Salk^1,2,*

, Steffen Fritz², Linda See²

, Christopher Dresel² and Ian McCallum²

¹ Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, SE-23053 Alnarp, Sweden

² International Institute for Applied Systems Analysis (IIASA), Center for Citizen Science and Earth Observation, Schlossplatz 1, 2361 Laxenburg, Austria

Remote Sens. 2018, 10(3), 376; https://doi.org/10.3390/rs10030376 - 1 Mar 2018

Cited by 16 | Viewed by 5305

Abstract

A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics [...] Read more.

A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map’s legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producers. Full article

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28 pages, 7860 KiB

Open AccessArticle

Optimal, Recursive and Sub-Optimal Linear Solutions to Attitude Determination from Vector Observations for GNSS/Accelerometer/Magnetometer Orientation Measurement

by Zebo Zhou^1,*,†

, Jin Wu^1,2,*,†, Jinling Wang³ and Hassen Fourati⁴

¹ School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 610054, China

² School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China

³ Surveying and Geospatial Engineering, School of Civil and Environmental Engineering, University of New South Wales, Sydney 2052, NSW, Australia

⁴ Institute of Engineering, University Grenoble Alpes, CNRS, Inria, Grenoble INP, GIPSALab, 38000 Grenoble, France

^† Zebo Zhou and Jin Wu contributed equally on the theory and experiment of this paper.

Remote Sens. 2018, 10(3), 377; https://doi.org/10.3390/rs10030377 - 1 Mar 2018

Cited by 18 | Viewed by 5651

Abstract

The integration of the Accelerometer and Magnetometer (AM) provides continuous, stable and accurate attitude information for land-vehicle navigation without magnetic distortion and external acceleration. However, magnetic disturbance and linear acceleration strongly degrade the overall system performance. As an important complement, the Global Navigation [...] Read more.

The integration of the Accelerometer and Magnetometer (AM) provides continuous, stable and accurate attitude information for land-vehicle navigation without magnetic distortion and external acceleration. However, magnetic disturbance and linear acceleration strongly degrade the overall system performance. As an important complement, the Global Navigation Satellite System (GNSS) produces the heading estimates, thus it can potentially benefit the AM system. Such a GNSS/AM system for attitude estimation is mathematically converted to a multi-observation vector pairs matching problem in this paper. The optimal and sub-optimal attitude determination and their time-varying recursive variants are all comprehensively investigated and discussed. The developed methods are named as the Optimal Linear Estimator of Quaternion (OLEQ), Suboptimal-OLEQ (SOLEQ) and Recursive-OLEQ (ROLEQ) for different application scenarios. The theory is established based on our previous contributions, and the multi-vector matrix multiplications are decomposed with the eigenvalue factorization. Some analytical results are proven and given, which provides the reader with a brand new viewpoint of the attitude determination and its evolution. With the derivations of the two-vector case, the n-vector case is then naturally formed. Simulations are carried out showing the advantages of the accuracy, robustness and time consumption of the proposed OLEQs, compared with representative methods. The algorithms are then implemented using the C++ programming language on the designed hardware with a GNSS module, three-axis accelerometer and three-axis magnetometer, giving an effective validation of them in real-world applications. The designed schemes have proven their fast speed and good accuracy in these verification scenarios. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

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21 pages, 7478 KiB

Open AccessArticle

Depolarization Ratio Profiles Calibration and Observations of Aerosol and Cloud in the Tibetan Plateau Based on Polarization Raman Lidar

by Guangyao Dai¹, Songhua Wu^1,2,*

and Xiaoquan Song^1,2

¹ Ocean Remote Sensing Institute, Ocean University of China, Qingdao 266100, China

² Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

Remote Sens. 2018, 10(3), 378; https://doi.org/10.3390/rs10030378 - 1 Mar 2018

Cited by 22 | Viewed by 6636

Abstract

A brief description of the Water vapor, Cloud and Aerosol Lidar (WACAL) system is provided. To calibrate the volume linear depolarization ratio, the concept of “

Δ 90 °

-calibration” is applied in this study. This effective and accurate calibration method is adjusted [...] Read more.

A brief description of the Water vapor, Cloud and Aerosol Lidar (WACAL) system is provided. To calibrate the volume linear depolarization ratio, the concept of “

Δ 90 °

-calibration” is applied in this study. This effective and accurate calibration method is adjusted according to the design of WACAL. Error calculations and analysis of the gain ratio, calibrated volume linear depolarization ratio and particle linear depolarization ratio are provided as well. In this method, the influences of the gain ratio, the rotation angle of the plane of polarization and the polarizing beam splitter are discussed in depth. Two groups of measurements with half wave plate (HWP) at angles of (0

°

, 45

°

) and (22.5

°

, −22.5

°

) are operated to calibrate the volume linear depolarization ratio. Then, the particle linear depolarization ratios measured by WACAL and CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization) during the simultaneous observations were compared. Good agreements are found. The calibration method was applied in the third Tibetan Plateau Experiment of Atmospheric Sciences (TIPEX III) in 2013 and 2014 in China. Vertical profiles of the particle depolarization ratio of clouds and aerosol in the Tibetan Plateau were measured with WACAL in Litang (30.03° N, 100.28° E, 3949 m above sea level (a.s.l.)) in 2013 and Naqu (31.48° N, 92.06° E, 4508 m a.s.l.) in 2014. Then an analysis on the polarizing properties of the aerosol, clouds and cirrus over the Tibetan Plateau is provided. The particle depolarization ratio of cirrus clouds varies from 0.36 to 0.52, with a mean value of 0.44 ± 0.04. Cirrus clouds occurred between 5.2 and 12 km above ground level (a.g.l.). The cloud thickness ranges from 0.12 to 2.55 km with a mean thickness of 1.22 ± 0.70 km. It is found that the particle depolarization ratio of cirrus clouds become larger as the height increases. However, the increase rate of the particle depolarization ratio becomes smaller as the height increases. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Properties)

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16 pages, 10906 KiB

Open AccessFeature PaperArticle

Reconstructing the Roman Site “Aquis Querquennis” (Bande, Spain) from GPR, T-LiDAR and IRT Data Fusion

by Iván Puente^1,2,*

, Mercedes Solla^1,2

, Susana Lagüela^2,3

and Javier Sanjurjo-Pinto⁴

¹ Defense University Center, Plaza de España s/n, 36920 Marín, Spain

² Applied Geotechnologies Research Group, University of Vigo, Rúa Maxwell s/n, Campus Lagoas-Marcosende, 36310 Vigo, Spain

³ Department of Cartographic and Terrain Engineering, University of Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain

⁴ PhD Programme in Protection of the Cultural Heritage, University of Vigo, 36310 Vigo, Spain

Remote Sens. 2018, 10(3), 379; https://doi.org/10.3390/rs10030379 - 1 Mar 2018

Cited by 37 | Viewed by 8623

Abstract

This work presents the three-dimensional (3D) reconstruction of one of the most important archaeological sites in Galicia: “Aquis Querquennis” (Bande, Spain) using in-situ non-invasive ground-penetrating radar (GPR) and Terrestrial Light Detection and Ranging (T-LiDAR) techniques, complemented with infrared thermography. T-LiDAR is [...] Read more.

This work presents the three-dimensional (3D) reconstruction of one of the most important archaeological sites in Galicia: “Aquis Querquennis” (Bande, Spain) using in-situ non-invasive ground-penetrating radar (GPR) and Terrestrial Light Detection and Ranging (T-LiDAR) techniques, complemented with infrared thermography. T-LiDAR is used for the recording of the 3D surface of this particular case and provides high resolution 3D digital models. GPR data processing is performed through the novel software tool “toGPRi”, developed by the authors, which allows the creation of a 3D model of the sub-surface and the subsequent XY images or time-slices at different depths. All these products are georeferenced, in such a way that the GPR orthoimages can be combined with the orthoimages from the T-LiDAR for a complete interpretation of the site. In this way, the GPR technique allows for the detection of the structures of the barracks that are buried, and their distribution is completed with the structure measured by the T-LiDAR on the surface. In addition, the detection of buried elements made possible the identification and labelling of the structures of the surface and their uses. These structures are additionally inspected with infrared thermography (IRT) to determine their conservation condition and distinguish between original and subsequent constructions. Full article

(This article belongs to the Special Issue Recent Advances in GPR Imaging)

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21 pages, 13818 KiB

Open AccessArticle

Remote Sensing and Geo-Archaeological Data: Inland Water Studies for the Conservation of Underwater Cultural Heritage in the Ferrara District, Italy

by Giovanna Bucci^1,2

¹ Cultural Heritage Department, University of Padova, 35122 Padova, Italy

² Confédération Mondiale des Activités Subaquatiques, Federation ITA F07 A.CDCI., 40124 Bologna, Italy

Remote Sens. 2018, 10(3), 380; https://doi.org/10.3390/rs10030380 - 1 Mar 2018

Cited by 8 | Viewed by 5108

Abstract

In the southern area of the Ferrara District, Italy, remote sensing investigations associated with geo-archaeological drilling in underwater archaeological studies, have helped to broad our understanding of the historical evolution and cultural heritage of inland waterways. In working on prototype sites, we have [...] Read more.

In the southern area of the Ferrara District, Italy, remote sensing investigations associated with geo-archaeological drilling in underwater archaeological studies, have helped to broad our understanding of the historical evolution and cultural heritage of inland waterways. In working on prototype sites, we have taken a multidisciplinary approach of surveillance and preventive archaeology, and have collaborated with archaeologists, geologists, hydro-biologists, and engineers. In this area of research, often lakes, lagoons, and rivers are characterized by low visibility. Some Quaternary events have deeply modified Ferrara’s landscape. Analysis of preserved samples from micro-drillings, underwater direct and indirect surveys, and the cataloguing of historical artefacts, are giving to the researchers a remarkable ancient chronology line. Recent studies confirmed anthropization sequences from the 1st Century B.C. to the 6th Century A.D. Waterscape archaeology, a multidisciplinary science devoted to the study of the human use of wetlands and anthropological connection with the water environment, testifies the ways in which people, in the past, constructed and used the water environment. In this article, we describe underwater cultural heritage research using 3D side scan sonar surveys and artifacts analysis, comparing data from direct diving investigations and stratigraphic data from micro-geological drillings on sites of Lago Tramonto, Gambulaga, Portomaggiore (Ferrara). Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)

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21 pages, 11408 KiB

Open AccessArticle

A Relative Radiometric Calibration Method Based on the Histogram of Side-Slither Data for High-Resolution Optical Satellite Imagery

by Mi Wang, Chaochao Chen^*, Jun Pan

, Ying Zhu

and Xueli Chang

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Remote Sens. 2018, 10(3), 381; https://doi.org/10.3390/rs10030381 - 1 Mar 2018

Cited by 27 | Viewed by 6059

Abstract

Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for [...] Read more.

Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for relative radiometric calibration. One technique, often called side-slither, where the sensor axis is rotated 90° in yaw relative to normal acquisitions, has been gaining popularity, being applied to Landsat 8, QuickBird, RapidEye, and other satellites. Side-slither can be more time efficient than some of the traditional methods, as only one acquisition may be required. In addition, the side-slither does not require any onboard calibration hardware, only a satellite capability to yaw and maintain a stable yawed attitude. A relative radiometric calibration method based on histograms of side-slither data is developed. This method has three steps: pre-processing, extraction of key points, and calculation of coefficients. Histogram matching and Otsu’s method are used to extract key points. Three datasets from the Chinese GaoFen-9 satellite were used: one to obtain the relative radiometric coefficients, and the others to verify the coefficients. Root-mean-square deviations of the corrected imagery were better than 0.1%. The maximum streaking metrics was less than 1. This method produced significantly better relative radiometric calibration than the traditional method used for GaoFen-9. Full article

(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)

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17 pages, 3610 KiB

Open AccessArticle

Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites

by Qiang Zhang¹

, Xiang Guo¹, Lizhong Qu^2,* and Qile Zhao^1,3,*

¹ GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China

² School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, No. 15 Yongyuan Road, Beijing 102600, China

³ Collaborative Innovation Center of Geospatial Technology, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China

Remote Sens. 2018, 10(3), 382; https://doi.org/10.3390/rs10030382 - 1 Mar 2018

Cited by 12 | Viewed by 5133

Abstract

The emerging BeiDou navigation satellite system has contributed to global precise positioning and has recently moved toward space-borne applications. However, the contribution of BeiDou on LEO orbit determination applications is limited by the poor precision of the GEO satellite orbit and clock products. [...] Read more.

The emerging BeiDou navigation satellite system has contributed to global precise positioning and has recently moved toward space-borne applications. However, the contribution of BeiDou on LEO orbit determination applications is limited by the poor precision of the GEO satellite orbit and clock products. Current researches suggest that BeiDou GEO satellites should not be included in LEO precise orbit determination. Based on analyzing the characteristics of errors existing in BeiDou GEO orbit products, we propose a feasible method to mitigate the offsets in BeiDou GEO orbit errors by in-flight calibration of the systematic daily constant biases in the along-track and cross-track of BeiDou GEO satellites. The proposed method is investigated and validated using one entire month of onboard BDS data from the Chinese FY-3C satellite. The average daily RMS compared with the GPS-derived orbit indicates that our method achieves 6.2 cm three-dimensional precision. When compared to the solutions that disregard the GEO orbit errors scheme and roughly exclude the GEO scheme, the FY-3C orbit precision has been improved by 89.1% and 20.2%, respectively. The average daily RMS values of phase residuals are about one centimeter for solutions that exclude GEO and that estimate systematic biases in GEO orbits. The calibrated orbits of GEO with the decimeter level in along-track and cross-track can be reconstructed by correcting the orbit biases estimated in the FY-3C precise orbit determination. Statistics of the FY-3C orbit quality, observation residuals, and precision of the recovered GEO orbits demonstrate that calibration of daily orbit biases in GEO can improve the precision of LEO orbit determination and enhance the reliability of the solution. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

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26 pages, 7779 KiB

Open AccessArticle

Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

by Yongguang Zhai^1,2

, Zhongyi Qu^1,* and Lei Hao³

¹ College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

² Insitute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing 100101, China

³ College of Forestry, Inner Mongolia Agricultural University, Hohhot 010018, China

Remote Sens. 2018, 10(3), 383; https://doi.org/10.3390/rs10030383 - 1 Mar 2018

Cited by 34 | Viewed by 7776

Abstract

Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for [...] Read more.

Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method. Full article

(This article belongs to the Special Issue Classification and Feature Extraction for Remote Sensing Image Analysis)

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18 pages, 3540 KiB

Open AccessArticle

Paddy Field Expansion and Aggregation Since the Mid-1950s in a Cold Region and Its Possible Causes

by Fengqin Yan

, Lingxue Yu, Chaobin Yang

and Shuwen Zhang^*

Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China

Remote Sens. 2018, 10(3), 384; https://doi.org/10.3390/rs10030384 - 1 Mar 2018

Cited by 31 | Viewed by 5312

Abstract

Over the last six decades, paddy fields on the Sanjiang Plain have experienced rapid expansion and aggregation. In our study, land use and land cover changes related to paddy fields were studied based on information acquired from topographic maps and remote-sensing images. Paddy [...] Read more.

Over the last six decades, paddy fields on the Sanjiang Plain have experienced rapid expansion and aggregation. In our study, land use and land cover changes related to paddy fields were studied based on information acquired from topographic maps and remote-sensing images. Paddy field expansion and aggregation were investigated through landscape indices and trajectory codes. Furthermore, the possible causes of paddy field expansion and aggregation were explored. Results indicated that such fields have increased by approximately 42,704 ha·y⁻¹ over the past six decades. Approximately 98% of paddy fields in 2015 were converted from other land use types. In general, the gravity center moved 254.51 km toward the northeast, at a rate of approximately 4.17 km·y⁻¹. The cohesion index increased from 96.8208 in 1954 to 99.5656 in 2015, and the aggregation index grew from 91.3533 in 1954 to 93.4448 in 2015, indicating the apparent aggregation of paddy fields on the Sanjiang Plain. Trajectory analyses showed that the transformations from marsh as well as from grassland to dry farmland and then into paddy fields were predominant. Climate warming provided a favorable environment for rice planting. Meanwhile, population growth, technological progress, and government policies drove paddy field expansion and aggregation during the study period. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover/Land Use Mapping at Local and Regional Scales)

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23 pages, 21330 KiB

Open AccessFeature PaperArticle

Drone-Borne Hyperspectral Monitoring of Acid Mine Drainage: An Example from the Sokolov Lignite District

by Robert Jackisch^*

, Sandra Lorenz

, Robert Zimmermann

, Robert Möckel and Richard Gloaguen

Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Division “Exploration Technology”, Chemnitzer Str. 40, 09599 Freiberg, Germany

Remote Sens. 2018, 10(3), 385; https://doi.org/10.3390/rs10030385 - 2 Mar 2018

Cited by 67 | Viewed by 21112

Abstract

This contribution explores the potential of unmanned aerial systems (UAS) to monitor areas affected by acid mine drainage (AMD). AMD is an environmental phenomenon that usually develops in the vicinity of mining operations or in post-mining landscapes. The investigated area covers a re-cultivated [...] Read more.

This contribution explores the potential of unmanned aerial systems (UAS) to monitor areas affected by acid mine drainage (AMD). AMD is an environmental phenomenon that usually develops in the vicinity of mining operations or in post-mining landscapes. The investigated area covers a re-cultivated tailing in the Sokolov lignite district of the Czech Republic. A high abundance of AMD minerals occurs in a confined space of the selected test site and illustrates potential environmental issues. The mine waste material contains pyrite and its consecutive weathering products, mainly iron hydroxides and oxides. These affect the natural pH values of the Earth’s surface. Prior research done in this area relies on satellite and airborne data, and our approach focuses on lightweight drone systems that enables rapid deployment for field campaigns and consequently-repeated surveys. High spatial image resolutions and precise target determination are additional advantages. Four field and flight campaigns were conducted from April to September 2016. For validation, the waste heap was probed in situ for pH, X-ray fluorescence (XRF), and reflectance spectrometry. Ground truth was achieved by collecting samples that were characterized for pH, X-ray diffraction, and XRF in laboratory conditions. Hyperspectral data were processed and corrected for atmospheric, topographic, and illumination effects using accurate digital elevation models (DEMs). High-resolution point clouds and DEMs were built from drone-borne RGB data using structure-from-motion multi-view-stereo photogrammetry. The supervised classification of hyperspectral image (HSI) data suggests the presence of jarosite and goethite minerals associated with the acidic environmental conditions (pH range 2.3–2.8 in situ). We identified specific iron absorption bands in the UAS-HSI data. These features were confirmed by ground-truth spectroscopy. The distribution of in situ pH data validates the UAS-based mineral classification results. Evaluation of the applied methods demonstrates that drone surveying is a fast, non-invasive, inexpensive technique for multi-temporal environmental monitoring of post-mining landscapes. Full article

(This article belongs to the Special Issue The Use of UAVs in the Raw Material Sector, from Mineral Exploration to Post-Mining Monitoring)

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13 pages, 679 KiB

Open AccessFeature PaperArticle

The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds

by Richard Müller^*, Stephane Haussler and Matthias Jerg

German Weather Service, Frankfurter Str 135, 63067 Offenbach, Germany

Remote Sens. 2018, 10(3), 386; https://doi.org/10.3390/rs10030386 - 2 Mar 2018

Cited by 12 | Viewed by 5467

Abstract

This study is motivated by the great importance of Cbs for aviation safety. The study investigates the role of Numerical Weather Prediction (NWP) filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of about 30 different experiments, covering Central Europe. These [...] Read more.

This study is motivated by the great importance of Cbs for aviation safety. The study investigates the role of Numerical Weather Prediction (NWP) filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of about 30 different experiments, covering Central Europe. These experiments compile different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures (BT). As stability filters, parameters from Numerical Weather Prediction (NWP) are used. The application of the stability filters restricts the detection of Cbs to regions with a labile atmosphere. Various NWP filter settings are investigated in the experiments. The brightness temperature information results from the infrared (IR) Spinning Enhanced Visible and InfraRed Image (SEVIRI) instrument on-board of the Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. Various satellite channels and BT thresholds are applied in the different experiments. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60% for the investigated summer period in 2016. The false alarms result from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70% for 2016. CSI is increased from about 70 to about 75% by application of NWP filtering for the other investigated summer period in 2017. A brief review and reflection of the literature clarify that the function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) work well as a stability filter. Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

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18 pages, 9790 KiB

Open AccessArticle

Assessment of the Structural Integrity of the Roman Bridge of Alcántara (Spain) Using TLS and GPR

by Juan Pedro Cortés Pérez

, José Juan De Sanjosé Blasco

, Alan D. J. Atkinson^*

and Luis Mariano Del Río Pérez

Escuela Politécnica, Universidad de Extremadura, 10003 Cáceres, Spain

Remote Sens. 2018, 10(3), 387; https://doi.org/10.3390/rs10030387 - 2 Mar 2018

Cited by 20 | Viewed by 8938

Abstract

The Roman bridge of Alcántara is the largest in Spain. Its preservation is of the utmost importance and to this end different aspects must be studied. The most prominent is the assessment of its structure, and this is especially important as the bridge [...] Read more.

The Roman bridge of Alcántara is the largest in Spain. Its preservation is of the utmost importance and to this end different aspects must be studied. The most prominent is the assessment of its structure, and this is especially important as the bridge remains in use. This paper documents the way the assessment of structural safety was carried out. The assessment methodology of existing structures was applied. The preliminary assessment was based on bibliographic data and non-destructive techniques. The geometric data of the bridge were obtained by Terrestrial Laser Scanning (TLS), which made possible the analysis of its deformations and assessment of its structure. Ground-Penetrating Radar (GPR) was also used with different antennae to work at different depths and spatial resolutions with the aim of analysing structural elements. From the above information, the assessment of structural safety was made using the limit analysis method by applying the historical works carried out on it and those described in the regulation of obligatory compliance in Spain (IAP11), studying the sensitivity of safety to the most relevant parameters. The state of preservation and structural integrity of the bridge is discussed and conclusions are drawn on the areas of greatest risk and the bases for the following assessment phase of preservation of the bridge. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Archaeological Heritage)

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24 pages, 3432 KiB

Open AccessArticle

Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

by Siti Najja Mohd Zad¹, Zed Zulkafli^1,*

and Farrah Melissa Muharram²

¹ Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia

² Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400 Serdang, Malaysia

Remote Sens. 2018, 10(3), 388; https://doi.org/10.3390/rs10030388 - 2 Mar 2018

Cited by 44 | Viewed by 6659

Abstract

The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of [...] Read more.

The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction. Full article

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21 pages, 2799 KiB

Open AccessArticle

Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy

by Zachary Tane^1,2,*, Dar Roberts¹

, Sander Veraverbeke^3,4

, Ángeles Casas⁵, Carlos Ramirez² and Susan Ustin⁶

¹ Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA

² United States Department of Agriculture, Forest Service, Pacific Southwest Region, Remote Sensing Lab, McClellan, CA 95652, USA

³ Faculty of Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands

⁴ Department of Earth System Science, University of California Irvine, Irvine, CA 92697, USA

⁵ Independent Researcher

⁶ Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California Davis, Davis, CA 95616, USA

Remote Sens. 2018, 10(3), 389; https://doi.org/10.3390/rs10030389 - 2 Mar 2018

Cited by 53 | Viewed by 6763

Abstract

Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember [...] Read more.

Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolution WorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r² values as high as 0.741 for the MESMA ash fractions compared to classified WorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-off between accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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17 pages, 3884 KiB

Open AccessArticle

Understanding Temporal and Spatial Distribution of Crop Residue Burning in China from 2003 to 2017 Using MODIS Data

by Yan Zhuang^1,2,†, Ruiyuan Li^1,2,†, Hao Yang³, Danlu Chen^1,2, Ziyue Chen^1,2,*

, Bingbo Gao³ and Bin He^1,2

¹ State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, 19 Xinjiekouwai Street, Haidian, Beijing 100875, China

² Joint Center for Global Change Studies, Beijing 100875, China

³ National Engineering Research Center for Information Technology in Agriculture, 11 Shuguang Huayuan Middle Road, Beijing 100097, China

^† There authors contributed equally to this work.

Remote Sens. 2018, 10(3), 390; https://doi.org/10.3390/rs10030390 - 2 Mar 2018

Cited by 63 | Viewed by 6884

Abstract

Crop residue burning, which is a convenient approach to process excessive crop straws, has a negative impact on local and regional air quality and soil structures. China, as a major agricultural country with a large population, should take more effective measures to control [...] Read more.

Crop residue burning, which is a convenient approach to process excessive crop straws, has a negative impact on local and regional air quality and soil structures. China, as a major agricultural country with a large population, should take more effective measures to control crop residue burning. In this case, a better understanding of long-term spatio-temporal variations of crop residue burning in China is required. The MODIS products MOD14A1/MYD14A1 were employed in this research. Meanwhile, due to the vast territory of China, we divided the study area into seven regions based on the national administrative divisions to examine crop residue burning in each region, respectively. The temporal analysis of crop residue burning in different regions demonstrates a fluctuated, but generally upward, trend from 2003 to 2017. For monthly variations of crop residue burning in different regions, detected fire spots in June mainly concentrated in Central China (CC), East China (EC), and North China (NC). A majority of detected fire spots in Northeast China (NEC) and Northwest China (NWC) appeared in April and October. For other months, a small number of fire spots were distributed in all regions in a scattered manner. Furthermore, from a spatio-temporal perspective, this research revealed that crop residue burning in NEC was the most active among all regions both in spring and autumn. For summer, EC holds a larger proportion of burning spots than other regions. For winter, the number of burning spots in most regions was close. This research conducts a comprehensive analysis of crop residue burning in China at both a national and regional scale. The methodology and results from this research provide useful reference for better monitoring and controlling crop residue burning in China. Full article

(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))

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21 pages, 5996 KiB

Open AccessArticle

Sparse Subspace Clustering-Based Feature Extraction for PolSAR Imagery Classification

by Bo Ren^1,2, Biao Hou^1,2,*

, Jin Zhao^1,2 and Licheng Jiao^1,2

¹ Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China

² Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an 710071, China

Remote Sens. 2018, 10(3), 391; https://doi.org/10.3390/rs10030391 - 2 Mar 2018

Cited by 11 | Viewed by 5691

Abstract

Features play an important role in the learning technologies and pattern recognition methods for polarimetric synthetic aperture (PolSAR) image interpretation. In this paper, based on the subspace clustering algorithms, we combine sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property [...] Read more.

Features play an important role in the learning technologies and pattern recognition methods for polarimetric synthetic aperture (PolSAR) image interpretation. In this paper, based on the subspace clustering algorithms, we combine sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property of PolSAR data. In this algorithm framework, the features are projected through the projection matrix with the sparse or/and the low rank characteristic in the low dimensional space. Meanwhile, different kinds of manifold graphs explore the geometry structure of PolSAR data to make the projected feature more discriminative. Those learned matrices, that are constrained by the sparsity and low rank terms can search for a few points from the samples and capture the global structure. The proposed algorithms aim at constructing a projection matrix from the subspace clustering algorithms to achieve the features benefiting for the subsequent PolSAR image classification. Experiments test the different combinations of those constraints. It demonstrates that the proposed algorithms outperform other state-of-art linear and nonlinear approaches with better quantization and visualization performance in PolSAR data from spaceborne and airborne platforms. Full article

(This article belongs to the Special Issue Classification and Feature Extraction for Remote Sensing Image Analysis)

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16 pages, 4861 KiB

Open AccessArticle

Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India

by Aku Riihelä^1,*

, Viivi Kallio¹, Sarvesh Devraj², Anu Sharma³ and Anders V. Lindfors¹

¹ Finnish Meteorological Institute, Erik Palménin aukio 1, P.O. Box 503, FI-00101 Helsinki, Finland

² The Energy and Resources Institute, Darbari Seth Block, IHC Complex, Lodhi Road, New Delhi 110003, India

³ TERI School of Advanced Studies, Vasant Kunj Institutional Area 10, New Delhi 110070, India

Remote Sens. 2018, 10(3), 392; https://doi.org/10.3390/rs10030392 - 3 Mar 2018

Cited by 26 | Viewed by 7543

Abstract

We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface [...] Read more.

We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface solar radiation. The results indicate that SARAH-E consistently overestimates surface solar radiation, with a mean bias of 21.9 W/m². The results are complicated by the fact that the estimation bias is stable between 1999 and 2009 with a mean of 19.6 W/m² but increases sharply thereafter as a result of rapidly decreasing (dimming) surface measurements of solar radiation. In addition, between 1999 and 2009, both in situ measurements and SARAH-E estimates described a statistically significant (at 95% confidence interval) trend of approximately −0.6 W/m²/year, but diverged strongly afterward. We investigated the cause of decreasing solar radiation at one site (Pune) by simulating clear-sky irradiance with local measurements of water vapor and aerosols as input to a radiative transfer model. The relationship between simulated and measured irradiance appeared to change post-2009, indicating that measured changes in the clear-sky aerosol loading are not sufficient to explain the rapid dimming in measured total irradiance. Besides instrumentation biases, possible explanations in the diverging measurements and retrievals of solar radiation may be found in the aerosol climatology used for SARAH-E generation. However, at present, we have insufficient data to conclusively identify the cause of the increasing retrieval bias. Users of the datasets are advised to be aware of the increasing bias when using the post-2009 data. Full article

(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing)

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14 pages, 5573 KiB

Open AccessArticle

Impact of Sea Ice Drift Retrieval Errors, Discretization and Grid Type on Calculations of Ice Deformation

by Jakob Griebel^1,* and Wolfgang Dierking^1,2

¹ The Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bussestraße 24, 27570 Bremerhaven, Germany

² Center for Integrated Remote Sensing and Forecasting for Arctic Operations, Arctic University of Norway, Sykehusvegen 21, 9019 Tromsø, Norway

Remote Sens. 2018, 10(3), 393; https://doi.org/10.3390/rs10030393 - 3 Mar 2018

Cited by 10 | Viewed by 3827

Abstract

We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the [...] Read more.

We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the local ice mass balance and the interaction between atmosphere, ice, and ocean, and constitutes a hazard to marine traffic and operations. Due to numerical effects, the results of deformation retrievals may predict, e.g., openings and closings of the ice cover that do not exist in reality. We focus specifically on fields of ice drift obtained from synthetic aperture radar (SAR) imagery and analyze the Propagated Drift Retrieval Error (PDRE) and the Boundary Definition Error (BDE). From the theory of error propagation, the PDRE for the calculated deformation parameters can be estimated. To quantify the BDE, we devise five different grid types and compare theoretical expectation and numerical results for different deformation parameters assuming three scenarios: pure divergence, pure shear, and a mixture of both. Our findings for both sources of error help to set up optimal deformation retrieval schemes and are also useful for other applications working with vector fields and scalar parameters derived therefrom. Full article

(This article belongs to the Section Ocean Remote Sensing)

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20 pages, 7442 KiB

Open AccessArticle

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

by Xiu Jin¹

, Lu Jie², Shuai Wang¹, Hai Jun Qi¹ and Shao Wen Li^1,*

¹ College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China

² College of Agronomy, Anhui Agriculture University, Hefei 230036, China

Remote Sens. 2018, 10(3), 395; https://doi.org/10.3390/rs10030395 - 4 Mar 2018

Cited by 167 | Viewed by 9915

Abstract

Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern [...] Read more.

Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN). After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU) has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery. Full article

(This article belongs to the Special Issue Classification and Feature Extraction for Remote Sensing Image Analysis)

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20 pages, 4600 KiB

Open AccessArticle

Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks

by Jiaojiao Li^1,*

, Bobo Xi¹, Yunsong Li¹, Qian Du² and Keyan Wang¹

¹ The State Key Lab. of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China

² The Department of Electronic and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA

Remote Sens. 2018, 10(3), 396; https://doi.org/10.3390/rs10030396 - 4 Mar 2018

Cited by 91 | Viewed by 7030

Abstract

With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification [...] Read more.

With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification process to enhance classification accuracy greatly. In this paper, a novel hyperspectral classification framework based on an optimal DBN and a novel texture feature enhancement (TFE) is proposed. Through band grouping, sample band selection and guided filtering, the texture features of hyperspectral data are improved. After TFE, the optimal DBN is employed on the hyperspectral reconstructed data for feature extraction and classification. Experimental results demonstrate that the proposed classification framework outperforms some state-of-the-art classification algorithms, and it can achieve outstanding hyperspectral classification performance. Furthermore, our proposed TFE method can play a significant role in improving classification accuracy. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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20 pages, 7167 KiB

Open AccessArticle

Remote Sensing of River Erosion on the Colville River, North Slope Alaska

by Cole Payne^*, Santosh Panda

and Anupma Prakash

Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA

Remote Sens. 2018, 10(3), 397; https://doi.org/10.3390/rs10030397 - 5 Mar 2018

Cited by 34 | Viewed by 8989

Abstract

The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to [...] Read more.

The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to use remote sensing techniques to map and quantify erosion rates and the volume of land loss at selected bluff sites along the main channel of the Colville, and to assess the suitability of automated methods of regional erosion monitoring. We used orthomosaics from high resolution aerial photos acquired in 1955 and 1979/1982, as well as high resolution WorldView-2 images from 2015 to quantify long-term erosion rates and the cubic volume of erosion. We found that, at the selected sites, erosion rates averaged 1 to 3.5 m per year. The erosion rate remained the same at one site and increased from 1955 to 2015 at two of the four sites. We estimated the volume of land loss to be in the magnitude of 166,000 m³ to 2.5 million m³ at our largest site. We also found that estimates of erosion were comparable for manual hand-digitized and automated methods, suggesting our automated method was effective and can be extended to monitor erosion at other sites along river systems that are bordered by bluffs. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)

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16 pages, 5634 KiB

Open AccessArticle

Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations

by Ana F. Militino^1,2,3,*

, M. Dolores Ugarte^1,2,3

and Unai Pérez-Goya¹

¹ Department of Statistics and Operations Research, Public University of Navarre, 31006 Pamplona, Spain

² Institute for Advanced Materials (InaMat), Public University of Navarre, 31006 Pamplona, Spain

³ Department of Mathematics, UNED Pamplona, 31006 Pamplona, Spain

Remote Sens. 2018, 10(3), 398; https://doi.org/10.3390/rs10030398 - 5 Mar 2018

Cited by 4 | Viewed by 7050

Abstract

Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show [...] Read more.

Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal. Full article

(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)

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19 pages, 3714 KiB

Open AccessArticle

Calibration of GLONASS Inter-Frequency Code Bias for PPP Ambiguity Resolution with Heterogeneous Rover Receivers

by Yanyan Liu¹, Shengfeng Gu^2,* and Qingquan Li¹

¹ Institute of Urban Smart Transportation & Safety Maintenance , Shenzhen University, Shenzhen 518060, China

² GNSS Research Center, Wuhan University, Wuhan 430079, China

Remote Sens. 2018, 10(3), 399; https://doi.org/10.3390/rs10030399 - 5 Mar 2018

Cited by 6 | Viewed by 5272

Abstract

Integer ambiguity resolution (IAR) is important for rapid initialization of precise point positioning (PPP). Whereas many studies have been limited to Global Positioning System (GPS) alone, there is a strong need to add Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) to the PPP-IAR solution. However, [...] Read more.

Integer ambiguity resolution (IAR) is important for rapid initialization of precise point positioning (PPP). Whereas many studies have been limited to Global Positioning System (GPS) alone, there is a strong need to add Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) to the PPP-IAR solution. However, the frequency-division multiplexing of GLONASS signals causes inter-frequency code bias (IFCB) in the receiving equipment. The IFCB causes GLONASS wide-lane uncalibrated phase delay (UPD) estimation with heterogeneous receiver types to fail, so GLONASS ambiguity is therefore traditionally estimated as float values in PPP. A two-step method of calibrating GLONASS IFCB is proposed in this paper, such that GLONASS PPP-IAR can be performed with heterogeneous receivers. Experimental results demonstrate that with the proposed method, GLONASS PPP ambiguity resolution can be achieved across a variety of receiver types. For kinematic PPP with mixed receiver types, the fixing percentage within 10 min is only 33.5% for GPS-only. Upon adding GLONASS, the percentage improves substantially, to 84.9%. Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

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19 pages, 7113 KiB

Open AccessArticle

Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

by Chao Dong^1,2

, Jinghong Liu^1,* and Fang Xu¹

¹ Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

² University of Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2018, 10(3), 400; https://doi.org/10.3390/rs10030400 - 5 Mar 2018

Cited by 69 | Viewed by 6384

Abstract

Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main [...] Read more.

Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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18 pages, 5011 KiB

Open AccessArticle

In-Flight Retrieval of SCIAMACHY Instrument Spectral Response Function

by Mourad Hamidouche^* and Günter Lichtenberg

Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany

Remote Sens. 2018, 10(3), 401; https://doi.org/10.3390/rs10030401 - 5 Mar 2018

Cited by 11 | Viewed by 4425

Abstract

The instrument Spectral Response Function (ISRF) has a strong impact on spectral calibration and the atmospheric trace gases retrievals. An accurate knowledge or a fine characterization of the ISRF shape and its FWHM (Full width at half maximum) as well as its temporal [...] Read more.

The instrument Spectral Response Function (ISRF) has a strong impact on spectral calibration and the atmospheric trace gases retrievals. An accurate knowledge or a fine characterization of the ISRF shape and its FWHM (Full width at half maximum) as well as its temporal behavior is therefore crucial. Designing a strategy for the characterization of the ISRF both on ground and in-flight is critical for future missions, such as the spectral imagers in the Copernicus program. We developed an algorithm to retrieve the instrument ISRF in-flight. Our method uses solar measurements taken in-flight by the instrument to fit a parameterized ISRF from on ground based calibration, and then retrieves the shape and FWHM of the actual in-flight ISRF. With such a strategy, one would be able to derive and monitor the ISRF during the commissioning and operation of spectrometer imager missions. We applied our method to retrieve the SCIAMACHY instrument ISRF in its different channels. We compared the retrieved ones with the on ground estimated ones. Besides some peculiarities found in SCIAMACHY channel 8, the ISRF results in other channels were relatively consistent and stable over time in most cases. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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20 pages, 5631 KiB

Open AccessArticle

Hydrological Variability and Changes in the Arctic Circumpolar Tundra and the Three Largest Pan-Arctic River Basins from 2002 to 2016

by Kazuyoshi Suzuki^1,*

, Koji Matsuo², Dai Yamazaki^1,3, Kazuhito Ichii⁴

, Yoshihiro Iijima⁵, Fabrice Papa⁶

, Yuji Yanagi¹ and Tetsuya Hiyama⁷

¹ Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan

² Geospatial Information Authority of Japan, Kitasato 1-ban, Tsukuba, Ibaraki 305-0816, Japan

³ Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

⁴ Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

⁵ Graduate School of Bioresources, Mie University, 1577 Kurima-Machiya-Cho, Tsu, Mie 514-8507, Japan

⁶ Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, IRD, CNES, CNRS, UPS, 31400 Toulouse, France

⁷ Institute for Space-Earth Environmental Research, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan

Remote Sens. 2018, 10(3), 402; https://doi.org/10.3390/rs10030402 - 6 Mar 2018

Cited by 32 | Viewed by 9000

Abstract

The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In this paper, we analyze the spatiotemporal variations in [...] Read more.

The Arctic freshwater budget is critical for understanding the climate in the northern regions. However, the hydrology of the Arctic circumpolar tundra region (ACTR) and the largest pan-Arctic rivers are still not well understood. In this paper, we analyze the spatiotemporal variations in the terrestrial water storage (TWS) of the ACTR and three of the largest pan-Arctic river basins (Lena, Mackenzie, Yukon). To do this, we utilize monthly Gravity Recovery and Climate Experiment (GRACE) data from 2002 to 2016. Together with global land reanalysis, and river runoff data, we identify declining TWS trends throughout the ACTR that we attribute largely to increasing evapotranspiration driven by increasing summer air temperatures. In terms of regional changes, large and significant negative trends in TWS are observed mainly over the North American continent. At basin scale, we show that, in the Lena River basin, the autumnal TWS signal persists until the spring of the following year, while in the Mackenzie River basin, the TWS level in the autumn and winter has no significant impact on the following year. As expected global warming is expected to be particularly significant in the northern regions, our results are important for understanding future TWS trends, with possible further decline. Full article

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19 pages, 5249 KiB

Open AccessArticle

An Orthogonal Projection Algorithm to Suppress Interference in High-Frequency Surface Wave Radar

by Zezong Chen^1,*, Fei Xie²

, Chen Zhao²

and Chao He²

¹ School of Electronic Information and Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan 430072, China

² School of Electronic Information, Wuhan University, Wuhan 430072, China

Remote Sens. 2018, 10(3), 403; https://doi.org/10.3390/rs10030403 - 6 Mar 2018

Cited by 10 | Viewed by 5564

Abstract

High-frequency surface wave radar (HFSWR) has been widely applied in sea-state monitoring, and its performance is known to suffer from various unwanted interferences and clutters. Radio frequency interference (RFI) from other radiating sources and ionospheric clutter dominate the various types of unwanted signals [...] Read more.

High-frequency surface wave radar (HFSWR) has been widely applied in sea-state monitoring, and its performance is known to suffer from various unwanted interferences and clutters. Radio frequency interference (RFI) from other radiating sources and ionospheric clutter dominate the various types of unwanted signals because the HF band is congested with many users and the ionosphere propagates interference from distant sources. In this paper, various orthogonal projection schemes are summarized, and three new schemes are proposed for interference cancellation. Simulations and field data recorded by experimental multi-frequency HFSWR from Wuhan University are used to evaluate the cancellation performances of these schemes with respect to both RFI and ionospheric clutter. The processing results may provide a guideline for identifying the appropriate orthogonal projection cancellation schemes in various HFSWR applications. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)

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14 pages, 6006 KiB

Open AccessFeature PaperArticle

A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests

by Gregory P. Asner^1,*

, Roberta E. Martin¹

, Lisa M. Keith², Wade P. Heller³, Marc A. Hughes³, Nicholas R. Vaughn¹

, R. Flint Hughes⁴ and Christopher Balzotti¹

¹ Department of Global Ecology, Carnegie Institution for Science, 260 Panama St, Stanford, CA 94305, USA

² USDA Agricultural Research Service, Hilo, HI 96720, USA

³ College of Tropical Agriculture and Human Resources, University of Hawaiʻi at Manoa, Hilo, HI 96720, USA

⁴ Institute of Pacific Islands Forestry, US Forest Service, Hilo, HI 96720, USA

Remote Sens. 2018, 10(3), 404; https://doi.org/10.3390/rs10030404 - 6 Mar 2018

Cited by 43 | Viewed by 10974

Abstract

Pathogenic invasions are a major source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Two novel [...] Read more.

Pathogenic invasions are a major source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Two novel species of the fungal genus Ceratocystis have spread rapidly across humid and mesic forests of Hawaiʻi Island, causing widespread mortality of the keystone endemic canopy tree species, Metrosideros polymorpha (common name: ʻōhiʻa). The process, known as Rapid Ohia Death (ROD), causes browning of canopy leaves in weeks to months following infection by the pathogen. An operational mapping approach is needed to track the spread of the disease. We combined field studies of leaf spectroscopy with laboratory chemical studies and airborne remote sensing to develop a spectral signature for ROD. We found that close to 80% of ROD-infected plants undergo marked decreases in foliar concentrations of chlorophyll, water and non-structural carbohydrates, which collectively result in strong consistent changes in leaf spectral reflectance in the visible (400–700 nm) and shortwave-infrared (1300–2500 nm) wavelength regions. Leaf-level results were replicated at the canopy level using airborne laser-guided imaging spectroscopy, with quantitative spectral separability of normal green-leaf canopies from suspected ROD-infected brown-leaf canopies in the visible and shortwave-infrared spectrum. Our results provide the spectral–chemical basis for detection, mapping and monitoring of the spread of ROD in native Hawaiian forests. Full article

(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)

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17 pages, 18707 KiB

Open AccessArticle

Modeling Wildfire-Induced Permafrost Deformation in an Alaskan Boreal Forest Using InSAR Observations

by Yusuf Eshqi Molan¹

, Jin-Woo Kim¹

, Zhong Lu^1,*

, Bruce Wylie²

and Zhiliang Zhu³

¹ Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75205, USA

² Earth Resources Observation and Science Center, United States Geological Survey, Sioux Falls, SD 57198, USA

³ United States Geological Survey, Reston, VA 20192, USA

Remote Sens. 2018, 10(3), 405; https://doi.org/10.3390/rs10030405 - 6 Mar 2018

Cited by 25 | Viewed by 6772

Abstract

The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using [...] Read more.

The discontinuous permafrost zone is one of the world’s most sensitive areas to climate change. Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)

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18 pages, 37551 KiB

Open AccessArticle

Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks

by Guangming Wu¹

, Xiaowei Shao¹, Zhiling Guo¹, Qi Chen^1,2,*, Wei Yuan¹

, Xiaodan Shi¹, Yongwei Xu¹ and Ryosuke Shibasaki¹

¹ Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan

² Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China

Remote Sens. 2018, 10(3), 407; https://doi.org/10.3390/rs10030407 - 6 Mar 2018

Cited by 189 | Viewed by 12420

Abstract

Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing [...] Read more.

Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing more accurate segmentation results is still critical for further applications such as automatic mapping. In this study, a multi-constraint fully convolutional network (MC–FCN) model is proposed to perform end-to-end building segmentation. Our MC–FCN model consists of a bottom-up/top-down fully convolutional architecture and multi-constraints that are computed between the binary cross entropy of prediction and the corresponding ground truth. Since more constraints are applied to optimize the parameters of the intermediate layers, the multi-scale feature representation of the model is further enhanced, and hence higher performance can be achieved. The experiments on a very-high-resolution aerial image dataset covering 18 km

^{2}

and more than 17,000 buildings indicate that our method performs well in the building segmentation task. The proposed MC–FCN method significantly outperforms the classic FCN method and the adaptive boosting method using features extracted by the histogram of oriented gradients. Compared with the state-of-the-art U–Net model, MC–FCN gains 3.2% (0.833 vs. 0.807) and 2.2% (0.893 vs. 0.874) relative improvements of Jaccard index and kappa coefficient with the cost of only 1.8% increment of the model-training time. In addition, the sensitivity analysis demonstrates that constraints at different positions have inconsistent impact on the performance of the MC–FCN. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing)

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18 pages, 13614 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Secular Ground Motions in Istanbul from a Long-Term InSAR Time-Series (1992–2017)

by Gokhan Aslan^1,2,*

, Ziyadin Cakır³

, Semih Ergintav⁴

, Cécile Lasserre^1,5 and François Renard^1,6

¹ Université Grenoble-Alpes, CNRS, IRD, IFSTTAR, ISTerre, 38000 Grenoble, France

² Eurasia Institute of Earth Sciences, Istanbul Technical University, 34469 Istanbul, Turkey

³ Department of Geological Engineering, Istanbul Technical University, 34469 Istanbul, Turkey

⁴ Kandilli Observatory and Earthquake Research Institute (KOERI), Bogazici University, 34684 Istanbul, Turkey

⁵ Université de Lyon, UCBL, ENSL, CNRS, LGL-TPE, 69622 Villeurbanne, France

⁶ Physics of Geological Processes (PGP), The NJORD Centre, Department of Geosciences, UiO, NO-0316 Oslo, Norway

Remote Sens. 2018, 10(3), 408; https://doi.org/10.3390/rs10030408 - 6 Mar 2018

Cited by 50 | Viewed by 9256

Abstract

The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture [...] Read more.

The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture radar (InSAR) time series analysis is a very powerful tool for the operational mapping of ground deformation related to urban subsidence and landslide phenomena. With an analysis spanning almost 25 years of satellite radar observations, we compute an InSAR time series of data from multiple satellites (European Remote Sensing satellites ERS-1 and ERS-2, Envisat, Sentinel-1A, and its twin sensor Sentinel-1B) in order to investigate the spatial extent and rate of ground deformation in the megacity of Istanbul. By combining the various multi-track InSAR datasets (291 images in total) and analysing persistent scatterers (PS-InSAR), we present mean velocity maps of ground surface displacement in selected areas of Istanbul. We identify several sites along the terrestrial and coastal regions of Istanbul that underwent vertical ground subsidence at varying rates, from 5 ± 1.2 mm/yr to 15 ± 2.1 mm/yr. The results reveal that the most distinctive subsidence patterns are associated with both anthropogenic factors and relatively weak lithologies along the Haramirede valley in particular, where the observed subsidence is up to 10 ± 2 mm/yr. We show that subsidence has been occurring along the Ayamama river stream at a rate of up to 10 ± 1.8 mm/yr since 1992, and has also been slowing down over time following the restoration of the river and stream system. We also identify subsidence at a rate of 8 ± 1.2 mm/yr along the coastal region of Istanbul, which we associate with land reclamation, as well as a very localised subsidence at a rate of 15 ± 2.3 mm/yr starting in 2016 around one of the highest skyscrapers of Istanbul, which was built in 2010. Full article

(This article belongs to the Special Issue Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography)

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31 pages, 11955 KiB

Open AccessArticle

Integration of PSI, MAI, and Intensity-Based Sub-Pixel Offset Tracking Results for Landslide Monitoring with X-Band Corner Reflectors—Italian Alps (Corvara)

by Mehdi Darvishi^1,2,*

, Romy Schlögel^1,3

, Lorenzo Bruzzone² and Giovanni Cuozzo¹

¹ Institute for Earth Observation, Eurac Research, 39100 Bolzano-Bozen, Italy

² Department of Information Engineering and Computer Science, University of Trento, 38122 Trento, Italy

³ ESA Climate Office, European Centre for Space Applications and Telecommunications (ECSAT), Didcot OX11 0FD, UK

Remote Sens. 2018, 10(3), 409; https://doi.org/10.3390/rs10030409 - 6 Mar 2018

Cited by 23 | Viewed by 7209

Abstract

This paper presents an analysis of the integration between interferometric and intensity-offset tracking-based SAR remote sensing for landslide hazard mitigation in the Italian Alps. Despite the advantages of Synthetic Aperture Radar Interferometry (InSAR) methods for quantifying landslide deformation, some limitations remain. The temporal [...] Read more.

This paper presents an analysis of the integration between interferometric and intensity-offset tracking-based SAR remote sensing for landslide hazard mitigation in the Italian Alps. Despite the advantages of Synthetic Aperture Radar Interferometry (InSAR) methods for quantifying landslide deformation, some limitations remain. The temporal decorrelation, the 1-D Line Of Sight (LOS) observation restriction, the high velocity rate and the multi-directional movement properties make it difficult to monitor accurately complex landslides in areas covered by vegetation. Therefore, complementary and integrated approaches, such as offset tracking-based techniques, are needed to overcome these InSAR limitations for monitoring ground surface deformations. As sub-pixel offset tracking is highly sensitive to data spatial resolution, the latest generations of SAR sensors, such as TerraSAR-X and COSMO-SkyMed, open interesting perspective for a more accurate hazard assessment. In this paper, we consider high-resolution X-band data acquired by the COSMO-SkyMed (CSK) constellation for Permanent Scatterers Interferometry (PSI), Multi-Aperture Interferometry (MAI) and offset tracking processing. We analyze the offset tracking techniques considering area and feature-based matching algorithms to evaluate their applicability to CSK data by improving sub-pixel offset estimations. To this end, PSI and MAI are used for extracting LOS and azimuthal displacement components. Then, four well-known area-based and five feature-based matching algorithms (taken from computer vision) are applied to 16 X-band corner reflectors. Results show that offset estimation accuracy can be considerably improved up to less than 3% of the pixel size using the combination of the different feature-based detectors and descriptors. A sensitivity analysis of these techniques applied to CSK data to monitor complex landslides in the Italian Alps provides indications on advantages and disadvantages of each of them. Full article

(This article belongs to the Special Issue Radar Interferometry for Geohazards)

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24 pages, 3900 KiB

Open AccessArticle

Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery

by Xi Gong^1,2, Zhong Xie^1,2, Yuanyuan Liu^1,*

, Xuguo Shi¹

and Zhuo Zheng¹

¹ Department of Information Engineering, China University of Geosciences, Wuhan 430075, China

² National Engineering Research Center of Geographic Information System, Wuhan 430075, China

Remote Sens. 2018, 10(3), 410; https://doi.org/10.3390/rs10030410 - 6 Mar 2018

Cited by 43 | Viewed by 6144

Abstract

Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises). This paper [...] Read more.

Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises). This paper proposes a deep salient feature based anti-noise transfer network (DSFATN) method that effectively enhances and explores the high-level features for RS scene classification in different scales and noise conditions. In DSFATN, a novel discriminative deep salient feature (DSF) is introduced by saliency-guided DSF extraction, which conducts a patch-based visual saliency (PBVS) algorithm using “visual attention” mechanisms to guide pre-trained CNNs for producing the discriminative high-level features. Then, an anti-noise network is proposed to learn and enhance the robust and anti-noise structure information of RS scene by directly propagating the label information to fully-connected layers. A joint loss is used to minimize the anti-noise network by integrating anti-noise constraint and a softmax classification loss. The proposed network architecture can be easily trained with a limited amount of training data. The experiments conducted on three different scale RS scene datasets show that the DSFATN method has achieved excellent performance and great robustness in different scales and noise conditions. It obtains classification accuracy of 98.25%, 98.46%, and 98.80%, respectively, on the UC Merced Land Use Dataset (UCM), the Google image dataset of SIRI-WHU, and the SAT-6 dataset, advancing the state-of-the-art substantially. Full article

(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)

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19 pages, 2257 KiB

Open AccessArticle

A Lookup-Table-Based Approach to Estimating Surface Solar Irradiance from Geostationary and Polar-Orbiting Satellite Data

by Hailong Zhang¹

, Chong Huang², Shanshan Yu¹, Li Li¹, Xiaozhou Xin^1,*

and Qinhuo Liu^1,*

¹ State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, and Beijing Normal University, Beijing 100101, China

² State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2018, 10(3), 411; https://doi.org/10.3390/rs10030411 - 7 Mar 2018

Cited by 20 | Viewed by 8897

Abstract

Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving [...] Read more.

Incoming surface solar irradiance (SSI) is essential for calculating Earth’s surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m², −5.9 W/m², and 14.9 W/m² for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m² (16.8%), 127.6 W/m² (55.1%), and 99.5 W/m² (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m². The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for ‘clearest sky’ will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness. Full article

(This article belongs to the Special Issue Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li)

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28 pages, 5385 KiB

Open AccessArticle

Modification of Local Urban Aerosol Properties by Long-Range Transport of Biomass Burning Aerosol

by Iwona S. Stachlewska^1,*

, Mateusz Samson^1,2, Olga Zawadzka¹

, Kamila M. Harenda², Lucja Janicka¹, Patryk Poczta^2,3, Dominika Szczepanik¹, Birgit Heese⁴, Dongxiang Wang¹, Karolina Borek¹, Eleni Tetoni^1,5,6, Emmanouil Proestakis⁵, Nikolaos Siomos⁷, Anca Nemuc⁸, Bogdan H. Chojnicki², Krzysztof M. Markowicz¹, Aleksander Pietruczuk⁹

, Artur Szkop⁹, Dietrich Althausen⁴, Kerstin Stebel¹⁰

, Dirk Schuettemeyer¹¹ and Claus Zehner¹² Show full author list Hide full author list

¹ Institute of Geophysics, Faculty of Physics, University of Warsaw, 02-093 Warsaw, Poland

² Department of Meteorology, Faculty of Environmental Engineering and Spatial Management, Poznan University of Life Sciences, 60-649 Poznan, Poland

³ Department of Grassland and Natural Landscape Sciences, Faculty of Agronomy and Bioengineering, Poznan University of Life Sciences, 60-632 Poznan, Poland

⁴ Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany

⁵ Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, 15236 Athens, Greece

⁶ Division of Environmental Physics and Meteorology, Faculty of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece

⁷ Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

⁸ National Institute for Research and Development in Optoelectronics, 077125 Magurele, Romania

⁹ Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland

¹⁰ Atmosphere and Climate Department, Norwegian Institute for Air Research, 2027 Kjeller, Norway

¹¹ European Space Research and Technology Centre, European Space Agency, 2201 Noordwijk, The Netherlands

¹² European Space Research Institute, European Space Agency, 00044 Frascati, Italy

Show full affiliation list Hide full affiliation list

Remote Sens. 2018, 10(3), 412; https://doi.org/10.3390/rs10030412 - 7 Mar 2018

Cited by 40 | Viewed by 9874

Abstract

During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the [...] Read more.

During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the European Aerosol Research Lidar Network (EARLINET network) urban site in Warsaw, Poland. During 24–30 August 2016, the lidar-derived products (boundary layer height, aerosol optical depth, Ångström exponent, lidar ratio, depolarization ratio) were analysed in terms of air mass transport (HYSPLIT model), aerosol load (CAMS data) and type (NAAPS model) and confronted with active and passive remote sensing at the ground level (PolandAOD, AERONET, WIOS-AQ networks) and aboard satellites (SEVIRI, MODIS, CATS sensors). Optical properties for less than a day-old fresh biomass burning aerosol, advected into Warsaw’s boundary layer from over Ukraine, were compared with the properties of long-range transported 3–5 day-old aged biomass burning aerosol detected in the free troposphere over Warsaw. Analyses of temporal changes of aerosol properties within the boundary layer, revealed an increase of aerosol optical depth and Ångström exponent accompanied by an increase of surface PM₁₀ and PM_2.5. Intrusions of advected biomass burning particles into the urban boundary layer seem to affect not only the optical properties observed but also the top height of the boundary layer, by moderating its increase. Full article

(This article belongs to the Special Issue Aerosol Remote Sensing)

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18 pages, 3058 KiB

Open AccessArticle

Radiometric Evaluation of SNPP VIIRS Band M11 via Sub-Kilometer Intercomparison with Aqua MODIS Band 7 over Snowy Scenes

by Mike Chu^1,2,*, Junqiang Sun^1,3

and Menghua Wang¹

¹ NOAA/NESDIS Center for Satellite Applications and Research, E/RA3, 5830 University Research Ct., College Park, MD 20740, USA

² Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA

³ Global Science and Technology, 7855 Walker Drive, Suite 200, Greenbelt, MD 20770, USA

Remote Sens. 2018, 10(3), 413; https://doi.org/10.3390/rs10030413 - 8 Mar 2018

Cited by 3 | Viewed by 4449

Abstract

A refined intersensor comparison study is carried out to evaluate the radiometric stability of the 2257 nm channel (M11) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. This study is initiated as part of [...] Read more.

A refined intersensor comparison study is carried out to evaluate the radiometric stability of the 2257 nm channel (M11) of the first Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. This study is initiated as part of the examination into the performance of key shortwave infrared (SWIR) bands for SNPP VIIRS ocean color data processing and applications, with Band M11 playing key role over turbid and inland waters. The evaluation utilizes simultaneous nadir overpasses (SNOs) to compare SNPP VIIRS Band M11 against Band 7 of the MODerate-resolution Imaging Spectroradiometer (MODIS) in the Aqua satellite over concurrently observed scenes. The standard result of the radiance comparison is a seemingly uncontrolled and inconsistent time series unsuitable for further analyses, in great contrast to other matching band-pairs whose radiometric comparisons are typically stable around 1.0 within 1% variation. The mismatching relative spectral response (RSR) between the two respective bands, with SNPP VIIRS M11 at 2225 to 2275 nm and Aqua MODIS B7 at 2125 to 2175 nm, is demonstrated to be the cause of the large variation because of the different dependence of the spectral responses of the two bands over identical scenes. A consistent radiometric comparison time series, however, can be extracted from SNO events that occur over snowy surfaces. A customized selection and analysis procedure successfully identifies the snowy scenes within the SNO events and builds a stable comparison time series. Particularly instrumental for the success of the comparison is the use of the half-kilometer spatial resolution data of Aqua MODIS B7 that significantly enhances the statistics. The final refined time series of Aqua MODIS B7 radiance over the SNPP VIIRS M11 radiance is stable at around 0.39 within 2.5% showing no evidence of drift. The radiometric ratio near 0.39 suggests the strong presence of medium-grained snow of a mixed-snow condition in those SNO scenes leading to successful comparison. The multi-year stability indicates the correctness of the on-orbit RSB calibration of SNPP VIIRS M11 whose result does not suffer from long-term drift. Full article

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16 pages, 11914 KiB

Open AccessArticle

Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features

by Longlong Yu^1,2,*

, Jinhe Su^1,2, Chun Li^1,2, Le Wang^1,2, Ze Luo¹ and Baoping Yan¹

¹ Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China

² University of Chinese Academy of Sciences, Beijing 100190, China

Remote Sens. 2018, 10(3), 414; https://doi.org/10.3390/rs10030414 - 8 Mar 2018

Cited by 19 | Viewed by 5624

Abstract

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a [...] Read more.

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data. Full article

(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)

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17 pages, 2668 KiB

Open AccessArticle

Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification

by Chen Ding^*

, Ying Li, Yong Xia, Lei Zhang and Yanning Zhang

Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710129, China

Remote Sens. 2018, 10(3), 415; https://doi.org/10.3390/rs10030415 - 8 Mar 2018

Cited by 9 | Viewed by 6106

Abstract

Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each cluster without further training. Thus the amount of [...] Read more.

Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each cluster without further training. Thus the amount of labeled samples necessitated for training can be greatly reduced. One key property of those kernels is their spatial size which determines their capacity in detecting local patterns and is expected to be task-specific. However, most of literatures determine the spatial size of those kernels in a heuristic way. To address this problem, we propose to automatically determine the kernel size in order to better adapt the K-means neural network for hyperspectral imagery classification. Specifically, a novel kernel-size determination scheme is developed by measuring the clustering performance of local patches with different sizes. With the kernel of determined size, more discriminative local patterns can be detected in the hyperspectral imagery, with which the classification performance of K-means neural network can be obviously improved. Experimental results on two datasets demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Deep Learning for Target Object Detection and Identification in Remote Sensing Data)

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23 pages, 7883 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies

by Luigi Ranghetti^1,*

, Elisa Cardarelli²

, Mirco Boschetti¹

, Lorenzo Busetto¹

and Mauro Fasola²

¹ Institute for Electromagnetic Sensing of Environment, Consiglio Nazionale delle Ricerche, Via Bassini 15, 20133 Milan, Italy

² Department of Earth and Environmental Sciences, Università di Pavia, Via Ferrata 9, 27100 Pavia, Italy

Remote Sens. 2018, 10(3), 416; https://doi.org/10.3390/rs10030416 - 8 Mar 2018

Cited by 25 | Viewed by 6567

Abstract

The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of [...] Read more.

The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of submerged paddies strictly depends on crop management practices: in this framework, the recent diffusion of rice seeding in dry conditions has led to a reduction of flooded surfaces during spring and could have contributed to the observed decline of the populations of some waterbird species that exploit rice fields as foraging habitat. In order to test the existence and magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period, MODIS remotely-sensed data were used to estimate the extent of the average flooded surface and the proportion of flooded rice fields in the years 2000–2016 during the nesting period of waterbirds. A general reduction of flooded rice fields during the rice-sowing season was observed, averaging

- 0.86 \pm 0.20

% per year (p-value < 0.01). Overall, the loss in submerged surface area during the sowing season reached

44 %

of the original extent in 2016, with a peak of

78 %

in the sub-districts to the east of the Ticino River. Results highlight the usefulness of remote sensing data and techniques to map and monitor water dynamics within rice cropping systems. These techniques could be of key importance to analyze the effects at the regional scale of the recent increase of dry-seeded rice cultivations on watershed recharge and water runoff and to interpret the decline of breeding waterbirds via a loss of foraging habitat. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)

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20 pages, 15823 KiB

Open AccessArticle

A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery

by Weiwei Sun^1,2,*, Long Tian³, Yan Xu³, Bo Du⁴ and Qian Du³

¹ Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

² State Key Laboratory of Information Engineering on Survey, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

³ Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA

⁴ School of Computer Science, Wuhan University, Wuhan 430079, China

Remote Sens. 2018, 10(3), 417; https://doi.org/10.3390/rs10030417 - 8 Mar 2018

Cited by 45 | Viewed by 4903

Abstract

This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns [...] Read more.

This paper proposes a randomized subspace learning based anomaly detector (RSLAD) for hyperspectral imagery (HSI). Improved from robust principal component analysis, the RSLAD assumes that the background matrix is low-rank, and the anomaly matrix is sparse with a small portion of nonzero columns (i.e., column-wise). It also assumes the anomalies do not lie in the column subspace of the background and aims to find a randomized subspace of the background to detect the anomalies. First, random techniques including random sampling and random Hadamard projections are implemented to construct a coarse randomized columns subspace of the background with reduced computational cost. Second, anomaly columns are searched and removed from the coarse randomized column subspace by solving a series of least squares problems, resulting in a purified randomized column subspace. Third, the nonzero columns in the anomaly matrix are located by projecting all the pixels on the orthogonal subspace of the purified subspace, and the anomalies are finally detected based on the L₂ norm of the columns in the anomaly matrix. The detection performance of RSLAD is compared with four state-of-the-art methods, including global Reed-Xiaoli (GRX), local RX (LRX), collaborative-representation based detector (CRD), and low-rank and sparse matrix decomposition base anomaly detector (LRaSMD). Experimental results show good detection performance of RSLAD with lower computational cost. Therefore, the proposed RSLAD offers an alternative option for hyperspectral anomaly detection. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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28 pages, 4546 KiB

Open AccessArticle

Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans

by Susanne Kratzer^1,*

and Gerald Moore²

¹ Department of Ecology, Environment and Plant Sciences, Stockholm University, 10691 Stockholm, Sweden

² Bio-Optika, Crofters, Middle Dimson, Gunnislake PL18 9NQ, UK

Remote Sens. 2018, 10(3), 418; https://doi.org/10.3390/rs10030418 - 8 Mar 2018

Cited by 40 | Viewed by 6026

Abstract

In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS [...] Read more.

In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS Reference Data Set (RDS), covering a wide range of optical provinces. Ternary plots of relative absorption at 442 nm showed CDOM dominance over phytoplankton and non-algal particle absorption (NAP). At 670 nm, the distribution of Baltic measurements was not different from case 1 waters and the retrieval of Chl a was shown to be improved by red-ratio algorithms. For correct retrieval of CDOM from Medium Resolution Imaging Spectrometer (MERIS) data, a different CDOM slope over the Baltic region is required. The CDOM absorption slope, S_CDOM, was significantly higher in the northwestern Baltic Sea: 0.018 (±0.002) compared to 0.016 (±0.005) for the RDS. Chl a-specific absorption and a_d _[SPM]*(442) and its spectral slope did not differ significantly. The comparison to the MERIS Reference Model Document (RMD) showed that the S_NAP slope was generally much higher (0.011 ± 0.003) than in the RMD (0.0072 ± 0.00108), and that the SPM scattering slope was also higher (0.547 ± 0.188) vs. 0.4. The SPM-specific scattering was much higher (1.016 ± 0.326 m² g⁻¹) vs. 0.578 m² g⁻¹ in RMD. SPM retrieval could be improved by applying the local specific scattering. A novel method was implemented to derive the phase function (PF) from AC9 and VSF-3 data.

\tilde{b}

was calculated fitting a Fournier–Forand PF to the normalized VSF data.

\tilde{b}

was similar to Petzold, but the PF differed in the backwards direction. Some of the sIOPs showed a bimodal distribution, indicating different water types—e.g., coastal vs. open sea. This seems to be partially caused by the distribution of inorganic particles that fall out relatively close to the coast. In order to improve remote sensing retrieval from Baltic Sea data, one should apply different parameterization to these distinct water types, i.e., inner coastal waters that are more influenced by scattering of inorganic particles vs. open sea waters that are optically dominated by CDOM absorption. Full article

(This article belongs to the Special Issue Remote Sensing of Water Quality)

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14 pages, 5866 KiB

Open AccessArticle

Shortwave Radiation Affected by Agricultural Practices

by Jerzy Cierniewski^1,*, Jakub Ceglarek¹, Arnon Karnieli², Eyal Ben-Dor³, Sławomir Królewicz¹ and Cezary Kaźmierowski¹

¹ Department of Soil Science and Remote Sensing of Soils, Adam Mickiewicz University in Poznań, Bogumiła Krygowskiego 10, 61-680 Poznań, Poland

² Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben Gurion, University of the Negev, Sede Boker Campus, Sede-Boker 84990, Israel

³ The Remote Sensing Laboratory, Department of Geography, School of Earth Science Faculty of Exact Science, Tel Aviv University, Tel Aviv 69989, Israel

Remote Sens. 2018, 10(3), 419; https://doi.org/10.3390/rs10030419 - 9 Mar 2018

Cited by 11 | Viewed by 4214

Abstract

The albedo of bare soil depends on its organic matter, iron oxide, carbonate contents, and reflectance geometry, features considered stable over time, and also depends on salinity, moisture and roughness, which change dynamically due to agricultural practices. This paper deals with the quantitative [...] Read more.

The albedo of bare soil depends on its organic matter, iron oxide, carbonate contents, and reflectance geometry, features considered stable over time, and also depends on salinity, moisture and roughness, which change dynamically due to agricultural practices. This paper deals with the quantitative estimation of the amount of shortwave radiation that could be reflected by air-dried bare soils in clear-sky conditions within arable lands in Israel throughout the year, assuming that they were shaped by a plough, a disk harrow, or a smoothing harrow. An area of bare soils was extracted from Landsat 8 images, within the contours of arable lands. The radiation reflected from the bare soils was calculated by equations predicting variations in their half-diurnal albedo as the solar zenith angle function. Accordingly, laboratory reflectance data of Israeli soil samples were used. The results clearly showed annual variation in the amount of short-wave radiation reflected from all bare soils within arable lands. The minimum radiation occurred in the winter, between the 1st and 70th day of the year (DOY), and the maximum was identified in the summer between 200th and 250th DOY. This could reach about 3–5 PJ/day and 16–23 PJ/day, respectively. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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18 pages, 21235 KiB

Open AccessArticle

Evaluation of Three Parametric Models for Estimating Directional Thermal Radiation from Simulation, Airborne, and Satellite Data

by Xiangyang Liu^1,2

, Bo-Hui Tang^1,2,*

and Zhao-Liang Li^1,2,3

¹ State Key Laboratory of Resources and Environment Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Remote Sens. 2018, 10(3), 420; https://doi.org/10.3390/rs10030420 - 9 Mar 2018

Cited by 30 | Viewed by 4878

Abstract

An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models—the Roujean–Lagouarde (RL) model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the [...] Read more.

An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models—the Roujean–Lagouarde (RL) model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the Vinnikov model—at canopy and pixel scale using simulation, airborne, and satellite data. The results at canopy scale showed that (1) the three models could describe directional anisotropy well and the Vinnikov model performed the best, especially for erectophile canopy or low leaf area index (LAI); (2) the three models reached the highest fitting accuracy when the LAI varied from 1 to 2; and (3) the capabilities of the three models were all restricted by the hotspot effect, plant height, plant spacing, and three-dimensional structure. The analysis at pixel scale indicated a consistent result that the three models presented a stable effect both on verification and validation, but the Vinnikov model had the best ability in the erectophile canopy (savannas and grassland) and low LAI (barren or sparsely vegetated) areas. Therefore, the Vinnikov model was calibrated for different land cover types to instruct the angular correction of LST. Validation with the Surface Radiation Budget Network (SURFRAD)-measured LST demonstrated that the root mean square (RMSE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product could be decreased by 0.89 K after angular correction. In addition, the corrected LST showed better spatial uniformity and higher angular correlation. Full article

(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis)

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23 pages, 11775 KiB

Open AccessArticle

Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images

by Dong Zhao¹, Xinwen Cheng^1,*, Hongping Zhang¹, Yanfei Niu¹, Yangyang Qi¹ and Haitao Zhang²

¹ Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China

² College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China

Remote Sens. 2018, 10(3), 421; https://doi.org/10.3390/rs10030421 - 9 Mar 2018

Cited by 20 | Viewed by 5486

Abstract

It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness [...] Read more.

It is important to detect floating oil slicks after spill accidents, and hyperspectral remote sensing technology is capable of achieving this task. Traditional methods mainly utilize the spectral indices of hydrocarbons to detect floating oil slicks, but are poor at distinguishing the thickness of oil slicks and cannot detect sheens. Since the spectra of oil slicks should be affected by seawater as well as oil, this paper investigated the use of spectral indices of hydrocarbons and seawater to identify different thicknesses of oil slicks. In this research, a measurement, called index separability (IS), was proposed for quantitatively evaluating the identification ability of these spectral indices. Based on the evaluation results, experiments were conducted to validate the applicability of these spectral indices. The results show that the spectral indices of hydrocarbons are more suitable for detecting continuous true color oil slicks and emulsions and that spectral indices of seawater are more suitable for sheens and seawater. In addition, the spectral indices of hydrocarbons and seawater are complementary for detecting oil slicks. Finally, combining the spectral indices of hydrocarbons and seawater is conducive to achieving more accurate oil slick recognition results. Full article

(This article belongs to the Special Issue Oil Spill Remote Sensing)

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17 pages, 13949 KiB

Open AccessArticle

Estimation of Daily Average Downward Shortwave Radiation over Antarctica

by Yingji Zhou^1,2

, Guangjian Yan^1,2,*

, Jing Zhao³, Qing Chu^1,2, Yanan Liu^1,2, Kai Yan^1,2

, Yiyi Tong^1,2, Xihan Mu^1,2

, Donghui Xie^1,2

and Wuming Zhang^1,2

¹ State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

² Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;

³ Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518000, China

Remote Sens. 2018, 10(3), 422; https://doi.org/10.3390/rs10030422 - 9 Mar 2018

Cited by 12 | Viewed by 6774

Abstract

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values [...] Read more.

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R²) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m² (8.52%). As for the traditional sinusoidal method, the R² and RMSE are 0.68 and 70.32 W/m² (18.59%), respectively The R² and RMSE of the second method are 0.96 and 25.27 W/m² (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m² (15.87%)). Full article

(This article belongs to the Special Issue Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li)

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17 pages, 9651 KiB

Open AccessArticle

UAV Capability to Detect and Interpret Solar Radiation as a Potential Replacement Method to Hemispherical Photography

by Azadeh Abdollahnejad^1,*

, Dimitrios Panagiotidis¹

, Peter Surový¹

and Iva Ulbrichová²

¹ Department of Forest management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences (CULS), Kamýcká 129, Prague 165 21, Czech Republic

² Department of Silviculture, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences (CULS), Kamýcká 129, Prague 165 21, Czech Republic

Remote Sens. 2018, 10(3), 423; https://doi.org/10.3390/rs10030423 - 9 Mar 2018

Cited by 11 | Viewed by 5507

Abstract

Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the [...] Read more.

Solar radiation is one of the most significant environmental factors that regulates the rate of photosynthesis, and consequently, growth. Light intensity in the forest can vary both spatially and temporally, so precise assessment of canopy and potential solar radiation can significantly influence the success of forest management actions, for example, the establishment of natural regeneration. In this case study, we investigated the possibilities and perspectives of close-range photogrammetric approaches for modeling the amount of potential direct and diffuse solar radiation during the growing seasons (spring–summer), by comparing the performance of low-cost Unmanned Aerial Vehicle (UAV) RGB imagery vs. Hemispherical Photography (HP). Characterization of the solar environment based on hemispherical photography has already been widely used in botany and ecology for a few decades, while the UAV method is relatively new. Also, we compared the importance of several components of potential solar irradiation and their impact on the regeneration of Pinus sylvestris L. For this purpose, a circular fisheye objective was used to obtain hemispherical images to assess sky openness and direct/diffuse photosynthetically active flux density under canopy average for the growing season. Concerning the UAV, a Canopy Height Model (CHM) was constructed based on Structure from Motion (SfM) algorithms using Photoscan professional. Different layers such as potential direct and diffuse radiation, direct duration, etc., were extracted from CHM using ArcGIS 10.3.1 (Esri: California, CA, USA). A zonal statistics tool was used in order to extract the digital data in tree positions and, subsequently, the correlation between potential solar radiation layers and the number of seedlings was evaluated. The results of this study showed that there is a high relation between the two used approaches (HP and UAV) with R² = 0.74. Finally, potential diffuse solar radiation derived from both methods had the highest significant relation (−8.06% bias) and highest impact in the modeling of pine regeneration. Full article

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25 pages, 4021 KiB

Open AccessArticle

Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years

by Simon Munier^1,*

, Dominique Carrer¹, Carole Planque¹, Fernando Camacho², Clément Albergel¹

and Jean-Christophe Calvet¹

¹ CNRM, UMR 3589, Météo France, 42 av Gaspard Coriolis, 31000 Toulouse, France

² EOLAB, Parc Cientific Universitat de Valencia, C/Catedratico Agustin Escardino, 9, E-46980 Paterna, Valencia, Spain

Remote Sens. 2018, 10(3), 424; https://doi.org/10.3390/rs10030424 - 9 Mar 2018

Cited by 37 | Viewed by 18787

Abstract

The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the [...] Read more.

The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the Leaf Area Index (LAI) seasonal and interannual variability at the global scale and in a consistent way over the last decades. However, the coarse spatial resolution of these satellite-derived products does not permit distinguishing vegetation types within mixed pixels. Considering only the dominant type per pixel has two main drawbacks: the LAI of the dominant vegetation type is contaminated by spurious signal from other vegetation types and at the global scale, significant areas of individual vegetation types are neglected. In this study, we first developed a Kalman Filtering (KF) approach to disaggregate the satellite-derived LAI from GEOV1 over nine main vegetation types, including grasslands and crops as well as evergreen, broadleaf and coniferous forests. The KF approach permits the separation of distinct LAI values for individual vegetation types that coexist within a pixel. The disaggregated LAI product, called LAI-MC (Multi-Cover), consists of world-wide LAI maps provided every 10 days for each vegetation type over the 1999–2015 period. A trend analysis of the original GEOV1 LAI product and of the disaggregated LAI time series was conducted using the Mann-Kendall test. Resulting trends of the GEOV1 LAI (which accounts for all vegetation types) compare well with previous regional or global studies, showing a greening over a large part of the globe. When considering each vegetation type individually, the largest global trend from LAI-MC is found for coniferous forests (0.0419 m

^{2}

m

^{- 2}

yr

^{- 1}

) followed by summer crops (0.0394 m

^{2}

m

^{- 2}

yr

^{- 1}

), while winter crops and grasslands show the smallest global trends (0.0261 m

^{2}

m

^{- 2}

yr

^{- 1}

and 0.0279 m

^{2}

m

^{- 2}

yr

^{- 1}

, respectively). The LAI-MC presents contrasting trends among the various vegetation types within the same pixel. For instance, coniferous and broadleaf forests experience a marked greening in the North-East of Europe while crops and grasslands show a browning. In addition, trends from LAI-MC can significantly differ (by up to 50%) from trends obtained with GEOV1 by considering only the dominant vegetation type over each pixel. These results demonstrate the usefulness of the disaggregation method compared to simple ones. LAI-MC may provide a new tool to monitor and quantify tendencies of LAI per vegetation type all over the globe. Full article

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17 pages, 7705 KiB

Open AccessArticle

Comparison of Wind Speeds from Spaceborne Microwave Radiometers with In Situ Observations and ECMWF Data over the Global Ocean

by Lei Zhang¹

, Hanqing Shi¹, Zhenzhan Wang^2,3,*, Hong Yu¹, Xiaobin Yin⁴ and Qixiang Liao¹

¹ Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

² Key Laboratory of Microwave Remote Sensing, Chinese Academy of Sciences, Beijing 100190, China

³ National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China

⁴ Beijing Piesat Information Technology Co. Ltd., Beijing 100195, China

Remote Sens. 2018, 10(3), 425; https://doi.org/10.3390/rs10030425 - 9 Mar 2018

Cited by 17 | Viewed by 5915

Abstract

This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of [...] Read more.

This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of 0.82 m/s for WindSat, 1.45 m/s for SSMIS F16, 1.39 m/s for SSMIS F17, 1.43 m/s for AMSR-E, and 1.45 m/s for AMSR2. The overall mean bias for each satellite is typically <0.25 m/s when averaged over all selected buoys for a given study time. The satellite wind speeds are underestimated with respect to the buoy observations at a band of the tropical Pacific Ocean from −8°S to 4°N. The mean buoy–satellite difference as a function of year is always <0.4 m/s, except for SSMIS F16. The selected satellite wind speeds show an obvious seasonal characteristic at high latitudes. In comparison with the ECMWF data, some obviously positive differences exist at high southern latitudes in January and at high northern latitudes in July. Full article

(This article belongs to the Special Issue Remote Sensing of Essential Climate Variables and Their Applications)

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19 pages, 7369 KiB

Open AccessArticle

Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean

by Ittai Herrmann^1,2,*

, Steven K. Vosberg², Prabu Ravindran¹, Aditya Singh¹, Hao-Xun Chang³

, Martin I. Chilvers³, Shawn P. Conley² and Philip A. Townsend¹

¹ Department of Forest & Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, USA

² Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI 53706, USA

³ Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA

Remote Sens. 2018, 10(3), 426; https://doi.org/10.3390/rs10030426 - 9 Mar 2018

Cited by 55 | Viewed by 8274

Abstract

Pre-visual detection of crop disease is critical for food security. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were spectrally measured throughout a growing season to assess the capacity of leaf [...] Read more.

Pre-visual detection of crop disease is critical for food security. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were spectrally measured throughout a growing season to assess the capacity of leaf and canopy level spectral measurements to detect non-visual foliage symptoms induced by Fusarium virguliforme (Fv, which causes sudden death syndrome). Canopy reflectance measurements were made using the Piccolo Doppio dual field-of-view, two-spectrometer (400 to 1630 nm) system on a tractor. Leaf level measurements were obtained, in different plots, using a handheld spectrometer (400 to 2500 nm). Partial least squares discriminant analysis (PLSDA) was applied to the spectroscopic data to discriminate between Fv-inoculated and control plants. Canopy and leaf spectral data allowed identification of Fv infection, prior to visual symptoms, with classification accuracy of 88% and 91% for calibration, 79% and 87% for cross-validation, and 82% and 92% for validation, respectively. Differences in wavelengths important to prediction by canopy vs. leaf data confirm that there are different bases for accurate predictions among methods. Partial least square regression (PLSR) was used on a late-stage canopy level data to predict soybean seed yield, with calibration, cross-validation and validation R² values 0.71, 0.59 and 0.62 (p < 0.01), respectively, and validation root mean square error of 0.31 t·ha⁻¹. Spectral data from the tractor mounted system are thus sensitive to the expression of Fv root infection at canopy scale prior to canopy symptoms, suggesting such systems may be effective for precision agricultural research and management. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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25 pages, 5363 KiB

Open AccessArticle

A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability

by Carsten Montzka^1,*

, Kathrina Rötzer¹

, Heye R. Bogena¹

, Nilda Sanchez²

and Harry Vereecken¹

¹ Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), 52428 Jülich, Germany

² Instituto Hispano Luso de Investigaciones Agrarias, University of Salamanca, 37185 Salamanca, Spain

Remote Sens. 2018, 10(3), 427; https://doi.org/10.3390/rs10030427 - 9 Mar 2018

Cited by 55 | Viewed by 8062

Abstract

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the [...] Read more.

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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41 pages, 13919 KiB

Open AccessEditor’s ChoiceArticle

A New Algorithm for the On-Board Compression of Hyperspectral Images

by Raúl Guerra^*

, Yubal Barrios

, María Díaz

, Lucana Santos, Sebastián López and Roberto Sarmiento

Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain

Remote Sens. 2018, 10(3), 428; https://doi.org/10.3390/rs10030428 - 9 Mar 2018

Cited by 41 | Viewed by 9874

Abstract

Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and [...] Read more.

Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA), is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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13 pages, 3226 KiB

Open AccessArticle

Mapping Asphaltic Roads’ Skid Resistance Using Imaging Spectroscopy

by Nimrod Carmon^1,* and Eyal Ben-Dor²

¹ Porter School of Environmental Studies, Tel-Aviv University, Tel-Aviv 6997801, Israel

² Department of Geography, School of Earth Science, Faculty of Exact Science, Tel-Aviv University, Tel-Aviv 6997801, Israel

Remote Sens. 2018, 10(3), 430; https://doi.org/10.3390/rs10030430 - 10 Mar 2018

Cited by 24 | Viewed by 6329

Abstract

The purpose of this study is to evaluate a realistic feasibility of using hyperspectral remote sensing (also termed imaging spectroscopy) airborne data for mapping asphaltic roads’ transportation safety. This is done by quantifying the road-tire friction, an attribute responsible for vehicle control and [...] Read more.

The purpose of this study is to evaluate a realistic feasibility of using hyperspectral remote sensing (also termed imaging spectroscopy) airborne data for mapping asphaltic roads’ transportation safety. This is done by quantifying the road-tire friction, an attribute responsible for vehicle control and emergency stopping. We engaged in a real-life operational scenario, where the roads’ friction was modeled against the reflectance information extracted directly from the image. The asphalt pavement’s dynamic friction coefficient was measured by a standardized technique using a Dynatest 6875H (Dynatest Consulting Inc., Westland, MI, USA) Friction Measuring System, which uses the common test-wheel retardation method. The hyperspectral data was acquired by the SPECIM AisaFenix 1K (Specim, Spectral Imaging Ltd., Oulu, Finland) airborne system, covering the entire optical range (350–2500 nm), over a selected study site, with roads characterized by different aging conditions. The spectral radiance data was processed to provide apparent surface reflectance using ground calibration targets and the ACORN-6 atmospheric correction package. Our final dataset was comprised of 1370 clean asphalt pixels coupled with geo-rectified in situ friction measurement points. We developed a partial least squares regression model using PARACUDA-II spectral data mining engine, which uses an automated outlier detection procedure and dual validation routines—a full cross-validation and an iterative internal validation based on a Latin Hypercube sampling algorithm. Our results show prediction capabilities of R² = 0.632 for full cross-validation and R² = 0.702 for the best available model in internal validation, both with significant results (p < 0.0001). Using spectral assignment analysis, we located the spectral bands with the highest weight in the model and discussed their possible physical and chemical assignments. The derived model was applied back on the hyperspectral image to predict and map the friction values of every road pixel in the scene. Combining the standard method with imaging spectroscopy may provide the required expansion of the available data to furnish decision makers with a full picture of the roads’ status. This technique’s limitations originate mainly in compositional variations between different roads, and the requirement for the application of multiple calibrations between scenes. Possible improvements could be achieved by using more spectral regions (e.g., thermal) and additional remote sensing techniques (e.g., LIDAR) as well as new platforms (e.g., UAV). Full article

(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)

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14 pages, 4045 KiB

Open AccessArticle

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

by Jordi Cristóbal^1,2,*

, Juan C. Jiménez-Muñoz³

, Anupma Prakash²

, Cristian Mattar⁴, Dražen Skoković³ and José A. Sobrino³

¹ Asiaq—Greenland Survey, Postbox 1003, 3900 Nuuk, Greenland

² Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Dr., Fairbanks, AK 99775-7320, USA

³ GCU/IPL, University of València, Catedrático José Beltrán 2, 46980 Paterna Valencia, Spain

⁴ Universidad de Aysén, Obispo Vielmo 62, 5950000 Coyhaique, Chile

Remote Sens. 2018, 10(3), 431; https://doi.org/10.3390/rs10030431 - 10 Mar 2018

Cited by 140 | Viewed by 12437

Abstract

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, [...] Read more.

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (T_a) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of T_a together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and T_a, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and T_a or only w models that yielded higher error and bias. Full article

(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis)

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Figure 1

21 pages, 6916 KiB

Open AccessArticle

kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

by Yang Bai^1,2, Ping Tang² and Changmiao Hu^2,*

¹ University of the Chinese Academy of Sciences, Beijing 100049, China

² Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2018, 10(3), 432; https://doi.org/10.3390/rs10030432 - 10 Mar 2018

Cited by 23 | Viewed by 5175

Abstract

Radiation normalization is an essential pre-processing step for generating high-quality satellite sequence images. However, most radiometric normalization methods are linear, and they cannot eliminate the regular nonlinear spectral differences. Here we introduce the well-established kernel canonical correlation analysis (kCCA) into radiometric normalization for [...] Read more.

Radiation normalization is an essential pre-processing step for generating high-quality satellite sequence images. However, most radiometric normalization methods are linear, and they cannot eliminate the regular nonlinear spectral differences. Here we introduce the well-established kernel canonical correlation analysis (kCCA) into radiometric normalization for the first time to overcome this problem, which leads to a new kernel method. It can maximally reduce the image differences among multi-temporal images regardless of the imaging conditions and the reflectivity difference. It also perfectly eliminates the impact of nonlinear changes caused by seasonal variation of natural objects. Comparisons with the multivariate alteration detection (CCA-based) normalization and the histogram matching, on Gaofen-1 (GF-1) data, indicate that the kCCA-based normalization can preserve more similarity and better correlation between an image-pair and effectively avoid the color error propagation. The proposed method not only builds the common scale or reference to make the radiometric consistency among GF-1 image sequences, but also highlights the interesting spectral changes while eliminates less interesting spectral changes. Our method enables the application of GF-1 data for change detection, land-use, land-cover change detection etc. Full article

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21 pages, 2465 KiB

Open AccessArticle

A Topography-Informed Morphology Approach for Automatic Identification of Forest Gaps Critical to the Release of Avalanches

by Jochen Ruben Breschan^*, Andreas Gabriel and Monika Frehner

Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland

Remote Sens. 2018, 10(3), 433; https://doi.org/10.3390/rs10030433 - 10 Mar 2018

Cited by 5 | Viewed by 4280

Abstract

Human assets in Alpine regions are prone to gravitational natural hazards such as rock fall, shallow landslides and avalanches. Forests make up a substantial share in that landscape and can mitigate those hazards. Management of avalanche protection forests must cope with avalanches potentially [...] Read more.

Human assets in Alpine regions are prone to gravitational natural hazards such as rock fall, shallow landslides and avalanches. Forests make up a substantial share in that landscape and can mitigate those hazards. Management of avalanche protection forests must cope with avalanches potentially released in forest gaps, which can damage downslope forests. The Swiss guidelines “Sustainability and success monitoring in protection forests” prescribe forest-gap extents in slope-line direction critical to the release of avalanches in forested areas. This article proposes a topography-informed morphology approach (TIMA) to automate the detection of critical gaps based on a digital terrain model and a canopy height model (CHM) derived from airborne LiDAR-data. TIMA uses complementary information about topography to probe forest gaps computed from the CHM with templates meeting critical-gap extents adjusted to local topography. The method was applied to a test site in Klosters-Serneus (Switzerland). The comparison of a critical-gap map with the results of a field assessment at 19 sample locations resulted in 84% overall accuracy. Moreover, plausibility of gap detection could be improved by including linear features forest roads and torrent channels in TIMA to account for decoupled snow layer resulting from abrupt breaks on the hillslope. If the TIMA concept can be successfully applied to the case of avalanches, this would encourage its use in assessing other gravitational natural hazard processes. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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24 pages, 19335 KiB

Open AccessArticle

A Sliding Window-Based Joint Sparse Representation (SWJSR) Method for Hyperspectral Anomaly Detection

by Seyyed Reza Soofbaf¹, Mahmod Reza Sahebi^1,*

and Barat Mojaradi²

¹ Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran 19667-15433, Iran

² Department of Geomatics Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran

Remote Sens. 2018, 10(3), 434; https://doi.org/10.3390/rs10030434 - 10 Mar 2018

Cited by 20 | Viewed by 6631

Abstract

In this paper, a new sliding window-based joint sparse representation (SWJSR) anomaly detector for hyperspectral data is proposed. The main contribution of this paper is to improve the judgments about the probability of anomaly presence in signals using the integration of information gathered [...] Read more.

In this paper, a new sliding window-based joint sparse representation (SWJSR) anomaly detector for hyperspectral data is proposed. The main contribution of this paper is to improve the judgments about the probability of anomaly presence in signals using the integration of information gathered during transition of sliding window for each pixel. In this method, each pixel experiences different spatial positions with respect to the spatial neighbors through the transition of this sliding window. In each position, an optimized local background dictionary is formed using a K-Singular Value Decomposition (K-SVD) algorithm and the recovery error of sparse estimation for each pixel is calculated using a simultaneous orthogonal matching pursuit algorithm (SOMP). Thus, the votes of each signal in terms of the anomaly presence in each spatial neighborhood are calculated and the variance of these recovery errors is considered as the detection criterion. The experimental results of the proposed SWJSR method on both synthetic and real datasets proved its higher performance compared to the Global RX (GRX), Local RX (LRX), Collaborative Representation Detector (CRD), Background Joint Sparse Representation (BJSR), Causal RX Detector (CR-RXD, CK-RXD), and Sliding Local RX(SLRX) detectors with an average efficiency improvement of about 7.5%, 14.25%, 8.2%, 8.25%, 6.45%, 6.5%, and 3.6%, respectively, in comparison to the mentioned algorithms. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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26 pages, 31050 KiB

Open AccessArticle

High Turbidity Solis Clear Sky Model: Development and Validation

by Pierre Ineichen

Department F.-A. Forel for Environmental and Aquatic Sciences, Institute for Environmental Sciences, University of Geneva, 1205 Genève, Switzerland

Remote Sens. 2018, 10(3), 435; https://doi.org/10.3390/rs10030435 - 10 Mar 2018

Cited by 20 | Viewed by 6314

Abstract

The Solis clear sky model is a spectral scheme based on radiative transfer calculations and the Lambert–Beer relation. Its broadband version is a simplified fast analytical version; it is limited to broadband aerosol optical depths lower than 0.45, which is a weakness when [...] Read more.

The Solis clear sky model is a spectral scheme based on radiative transfer calculations and the Lambert–Beer relation. Its broadband version is a simplified fast analytical version; it is limited to broadband aerosol optical depths lower than 0.45, which is a weakness when applied in countries with very high turbidity such as China or India. In order to extend the use of the original simplified version of the model for high turbidity values, we developed a new version of the broadband Solis model based on radiative transfer calculations, valid for turbidity values up to 7, for the three components, global, beam, and diffuse, and for the four aerosol types defined by Shettle and Fenn. A validation of low turbidity data acquired in Geneva shows slightly better results than the previous version. On data acquired at sites presenting higher turbidity data, the bias stays within ±4% for the beam and the global irradiances, and the standard deviation around 5% for clean and stable condition data and around 12% for questionable data and variable sky conditions. Full article

(This article belongs to the Special Issue Solar Radiation, Modelling and Remote Sensing)

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17 pages, 3756 KiB

Open AccessArticle

Optimal Interpolation of Precipitable Water Using Low Earth Orbit and Numerical Weather Prediction Data

by Jun-Hyung Heo, Geun-Hyeok Ryu^* and Jae-Dong Jang

National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon-gun 27803, Korea

Remote Sens. 2018, 10(3), 436; https://doi.org/10.3390/rs10030436 - 10 Mar 2018

Cited by 11 | Viewed by 5536

Abstract

The National Meteorological Satellite Center/Korean Meteorological Administration (NMSC/KMA) receives data directly from low Earth orbit (LEO) satellites (including NOAA-18,19; MetOp-A,B; and Suomi-NPP), and generates Level 2 products (e.g., temperature and humidity profile) in near real time. Total precipitable water (TPW) and layer precipitable [...] Read more.

The National Meteorological Satellite Center/Korean Meteorological Administration (NMSC/KMA) receives data directly from low Earth orbit (LEO) satellites (including NOAA-18,19; MetOp-A,B; and Suomi-NPP), and generates Level 2 products (e.g., temperature and humidity profile) in near real time. Total precipitable water (TPW) and layer precipitable water (LPW) are also generated using the retrieved humidity profiles. Today, forecasters need meteorologically-significant data fields composited from all available data sources, not multiple maps of observations from individual sources. Hence, TPW and LPW are reproduced using the optimal interpolation (OI) method with numerical weather prediction (NWP) data, in order to generate composite precipitable water (PW) products. In the OI procedure, PW data retrieved from the LEO satellites serve as observation data, while PW data from NWP serve as background data. Error covariances are estimated using a new approach, which considers correlations between observation errors to describe the characteristics of the errors better. Both background and observation error covariance matrices may have non-zero off-diagonal components. The composite PW products are validated using radiosonde data. The validation results for optimally-interpolated LPW (OI LPW) are much better than those for optimally-interpolated TPW (OI TPW). Generally, the OI LPW validation results are better than those for observation and background data; OI LPW data are ~5–10% more accurate than background data. Optimally-interpolated PW (OI PW) fields are applied to the correction of NWP forecast fields and the prediction of severe weather. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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24 pages, 4325 KiB

Open AccessArticle

Potential Investigation of Linking PROSAIL with the Ross-Li BRDF Model for Vegetation Characterization

by Xiaoning Zhang^1,2

, Ziti Jiao^1,2,*, Yadong Dong^1,2

, Hu Zhang³, Yang Li^1,2, Dandan He^1,2, Anxin Ding^1,2, Siyang Yin^1,2, Lei Cui^1,2 and Yaxuan Chang^1,2

¹ State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

² Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

³ College of Urban and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China

Remote Sens. 2018, 10(3), 437; https://doi.org/10.3390/rs10030437 - 10 Mar 2018

Cited by 50 | Viewed by 5624

Abstract

Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to [...] Read more.

Methods that link different models for investigating the retrieval of canopy biophysical/structural variables have been substantially adopted in the remote sensing community. To retrieve global biophysical parameters from multiangle data, the kernel-driven bidirectional reflectance distribution function (BRDF) model has been widely applied to satellite multiangle observations to model (interpolate/extrapolate) the bidirectional reflectance factor (BRF) in an arbitrary direction of viewing and solar geometries. Such modeled BRFs, as an essential information source, are then input into an inversion procedure that is devised through a large number of simulation analyses from some widely used physical models that can generalize such an inversion relationship between the BRFs (or their simple algebraic composite) and the biophysical/structural parameter. Therefore, evaluation of such a link between physical models and kernel-driven models contributes to the development of such inversion procedures to accurately retrieve vegetation properties, particularly based on the operational global BRDF parameters derived from satellite multiangle observations (e.g., MODIS). In this study, the main objective is to investigate the potential for linking a popular physical model (PROSAIL) with the widely used kernel-driven Ross-Li models. To do this, the BRFs and albedo are generated by the physical PROSAIL in a forward model, and then the simulated BRFs are input into the kernel-driven BRDF model for retrieval of the BRFs and albedo in the same viewing and solar geometries. To further strengthen such an investigation, a variety of field-measured multiangle reflectances have also been used to investigate the potential for linking these two models. For simulated BRFs generated by the PROSAIL model at 659 and 865 nm, the two models are generally comparable to each other, and the resultant root mean square errors (RMSEs) are 0.0092 and 0.0355, respectively, although some discrepancy in the simulated BRFs can be found at large average leaf angle (ALA) values. Unsurprisingly, albedos generated by the method are quite consistent, and 99.98% and 97.99% of the simulated white sky albedo (WSA) has a divergence less than 0.02. For the field measurements, the kernel-driven model presents somewhat better model-observation congruence than the PROSAIL model. The results show that these models have an overall good consistency for both field-measured and model-simulated BRFs. Therefore, there is potential for linking these two models for looking into the retrieval of canopy biophysical/structural variables through a simulation method, particularly from the current archive of the global routine MODIS BRDF parameters that were produced by the kernel-driven BRDF model; however, erectophile vegetation must be further examined. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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21 pages, 6690 KiB

Open AccessArticle

Object-Based Mapping of Aboveground Biomass in Tropical Forests Using LiDAR and Very-High-Spatial-Resolution Satellite Data

by Yasumasa Hirata^1,*, Naoyuki Furuya², Hideki Saito³, Chealy Pak⁴, Chivin Leng⁵, Heng Sokh⁶, Vuthy Ma⁶, Tsuyoshi Kajisa⁷, Tetsuji Ota⁸ and Nobuya Mizoue⁸

¹ Principal Research Director, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan

² Hokkaido Research Center, Forestry and Forest Products Research Institute, 7 Hitsujigaoka, Toyohiraku, Sapporo 062-8516, Japan

³ Department of Forest Management, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan

⁴ Forestry Administration, No 40, Preah Norodom Blvd, Phnom Penh 12205, Cambodia

⁵ Ministry of Environment, No 503, Tonle Bassac, Chamkarmon, Phnom Penh 12301, Cambodia

⁶ Forest-Wildlife Research and Development Institute, Forestry Administration, Khan Sen Sok, Phnom Penh 12157, Cambodia

⁷ Faculty of Agriculture, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-8580, Japan

⁸ Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan

Remote Sens. 2018, 10(3), 438; https://doi.org/10.3390/rs10030438 - 11 Mar 2018

Cited by 23 | Viewed by 6817

Abstract

Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes [...] Read more.

Developing countries that intend to implement the United Nations REDD-plus (Reducing Emissions from Deforestation and forest Degradation, and the role of forest conservation, sustainable management of forests, and enhancement of forest carbon stocks) framework and obtain economic incentives are required to estimate changes in forest carbon stocks based on the IPCC guidelines. In this study, we developed a method to support REDD-plus implementation by estimating tropical forest aboveground biomass (AGB) by combining airborne LiDAR with very-high-spatial-resolution satellite data. We acquired QuickBird satellite images of Kampong Thom, Cambodia in 2011 and airborne LiDAR measurements in some parts of the same area. After haze reduction and atmospheric correction of the satellite data, we calibrated reflectance values from the mean reflectance of the objects (obtained by segmentation from areas of overlap between dates) to reduce the effects of the observation angle and solar elevation. Then, we performed object-based classification using the satellite data (overall accuracy = 77.0%, versus 92.9% for distinguishing forest from non-forest land). We used a two-step method to estimate AGB and map it in a tropical environment in Cambodia. First, we created a multiple-regression model to estimate AGB from the LiDAR data and plotted field-surveyed AGB values against AGB values predicted by the LiDAR-based model (R² = 0.90, RMSE = 38.7 Mg/ha), and calculated reflectance values in each band of the satellite data for the analyzed objects. Then, we created a multiple-regression model using AGB predicted by the LiDAR-based model as the dependent variable and the mean and standard deviation of the reflectance values in each band of the satellite data as the explanatory variables (R² = 0.73, RMSE = 42.8 Mg/ha). We calculated AGB of all objects, divided the results into density classes, and mapped the resulting AGB distribution. Our results suggest that this approach can provide the forest carbon stock per unit area values required to support REDD-plus. Full article

(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)

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19 pages, 7987 KiB

Open AccessArticle

Speckle Suppression Based on Sparse Representation with Non-Local Priors

by Shuaiqi Liu^1,2,3,*

, Qi Hu^1,2,3, Pengfei Li^1,2,3, Jie Zhao^1,2,3, Chong Wang^4,5,* and Zhihui Zhu⁶

¹ College of Electronic and Information Engineering, Hebei University, Baoding 071000, China

² Machine Vision Engineering Research Center of Hebei Province, Baoding 071000, China

³ Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071002, China

⁴ Institute of Geophysics and Geomatics, China University of Geosciences, Beijing 100083, China

⁵ Bureau of Economic Geology, University of Texas at Austin, Austin, TX 78713, USA

⁶ Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA

Remote Sens. 2018, 10(3), 439; https://doi.org/10.3390/rs10030439 - 11 Mar 2018

Cited by 17 | Viewed by 5007

Abstract

As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on [...] Read more.

As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on classical sparse representation, we propose a non-local speckle suppression algorithm that combines the non-local prior knowledge of the image into the sparse representation. The proposed algorithm first applies shearlet to sparsely represent the input image. We then incorporate the non-local priors as constraints into the image sparse representation de-noising problem. The denoised image is obtained by utilizing an alternating minimization algorithm to solve the corresponding constrained de-noising problem. The experimental results show that the proposed algorithm can not only significantly remove speckle noise, but also improve the visual effect and retain the texture information of the image better. Full article

(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)

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13 pages, 2874 KiB

Open AccessArticle

Urban Heat Island Analysis over the Land Use Zoning Plan of Bangkok by Means of Landsat 8 Imagery

by Chaiyapon Keeratikasikorn¹ and Stefania Bonafoni^2,*

¹ Department of Computer Science, Khon Kaen University, Khon Kaen 40002, Thailand

² Department of Engineering, University of Perugia, via Duranti 93, 06125 Perugia, Italy

Remote Sens. 2018, 10(3), 440; https://doi.org/10.3390/rs10030440 - 11 Mar 2018

Cited by 95 | Viewed by 20516

Abstract

Surface urban heat island (SUHI) maps retrieved from spaceborne sensor data are increasingly recognized as an efficient scientific support to be considered in sustainable urban planning. By means of reflective and thermal data from Landsat 8 imagery in the time interval 2014–2016, this [...] Read more.

Surface urban heat island (SUHI) maps retrieved from spaceborne sensor data are increasingly recognized as an efficient scientific support to be considered in sustainable urban planning. By means of reflective and thermal data from Landsat 8 imagery in the time interval 2014–2016, this work deals with the SUHI pattern identification within the different land use categories of Bangkok city plan. This study first provides an overview of the SUHI phenomenon in Bangkok, then singles out the surface heating behavior in each land use category. To describe the SUHI dynamics within the different classes, the main statistics of the SUHI intensity (mean, standard deviation, maximum and minimum) are computed. Overall, the analysis points out that the categories placed in the city core (high-density residential; commercial; historical and military classes) exhibit the highest mean SUHI intensities (around 4 °C); whilst the vegetated pixels exert a less cool effect with respect to the greenery of categories mainly placed farther from the city center. The proposed analysis can help to identify if the land use plan requires targeted future actions for the SUHI mitigation; or if the maintenance of the current urban development model is in line with the environmental sustainability. Full article

(This article belongs to the Special Issue Urban Heat Island Remote Sensing)

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26 pages, 4324 KiB

Open AccessArticle

Classification of Hyperspectral Images by SVM Using a Composite Kernel by Employing Spectral, Spatial and Hierarchical Structure Information

by Yi Wang^*

and Hexiang Duan

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

Remote Sens. 2018, 10(3), 441; https://doi.org/10.3390/rs10030441 - 12 Mar 2018

Cited by 46 | Viewed by 8404

Abstract

In this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, [...] Read more.

In this paper, we introduce a novel classification framework for hyperspectral images (HSIs) by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS), marker-based hierarchical segmentation (MHSEG) and algebraic multigrid (AMG) are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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28 pages, 4302 KiB

Open AccessArticle

Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA

by Karis Tenneson^1,*, Matthew S. Patterson^1,2

, Thomas Mellin³, Mark Nigrelli⁴, Peter Joria³ and Brent Mitchell¹

¹ RedCastle Resources, Inc. Contractor to USDA Forest Service Geospatial Technology and Applications Center, Salt Lake City, UT 84119, USA

² Department of Urban Design and Planning, College of the Built Environment, University of Washington, Seattle, WA 98195, USA

³ USDA Forest Service, Southwestern Regional Office, Albuquerque, NM 87102, USA

⁴ USDA Forest Service, Coconino National Forest, Flagstaff, AZ 86001, USA

Remote Sens. 2018, 10(3), 442; https://doi.org/10.3390/rs10030442 - 12 Mar 2018

Cited by 9 | Viewed by 4688

Abstract

Historical forest management practices in the southwestern US have left forests prone to high-severity, stand-replacement fires. Reducing the cost of forest-fire management and reintroducing fire to the landscape without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). [...] Read more.

Historical forest management practices in the southwestern US have left forests prone to high-severity, stand-replacement fires. Reducing the cost of forest-fire management and reintroducing fire to the landscape without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to increase ability of managers to monitor AGB and other forest metrics at reduced cost. We developed a regional lidar-based statistical model to estimate AGB for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. Model selection was performed using Bayesian model averaging (BMA) to reduce researcher bias, fully explore the model space, and avoid overfitting. The selected model includes measures of canopy height, canopy density, and height distribution. The model selected with BMA explains 71% of the variability in field-estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model is structured in accordance with previously described local models, and performs equivalently to these smaller scale models. We have demonstrated the effectiveness of lidar for developing cost-effective, robust regional AGB models for monitoring and planning adaptively at the landscape scale. Full article

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20 pages, 18501 KiB

Open AccessArticle

Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

by Fen Chen^1,2,*, Ruilong Ren¹, Tim Van de Voorde^3,4, Wenbo Xu^1,2, Guiyun Zhou¹ and Yan Zhou¹

¹ School of Resources and Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, China

² Center for Information Geoscience, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, Sichuan, China

³ Department of Geography, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

⁴ Department of Geography, Ghent University, Krijgslaan 281, S8, 9000 Ghent, Belgium

Remote Sens. 2018, 10(3), 443; https://doi.org/10.3390/rs10030443 - 12 Mar 2018

Cited by 89 | Viewed by 10990

Abstract

Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster [...] Read more.

Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method. Full article

(This article belongs to the Special Issue Deep Learning for Target Object Detection and Identification in Remote Sensing Data)

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23 pages, 7450 KiB

Open AccessArticle

Scene Classification Based on a Deep Random-Scale Stretched Convolutional Neural Network

by Yanfei Liu, Yanfei Zhong^*

, Feng Fei, Qiqi Zhu and Qianqing Qin

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Remote Sens. 2018, 10(3), 444; https://doi.org/10.3390/rs10030444 - 12 Mar 2018

Cited by 83 | Viewed by 8946

Abstract

With the large number of high-resolution images now being acquired, high spatial resolution (HSR) remote sensing imagery scene classification has drawn great attention but is still a challenging task due to the complex arrangements of the ground objects in HSR imagery, which leads [...] Read more.

With the large number of high-resolution images now being acquired, high spatial resolution (HSR) remote sensing imagery scene classification has drawn great attention but is still a challenging task due to the complex arrangements of the ground objects in HSR imagery, which leads to the semantic gap between low-level features and high-level semantic concepts. As a feature representation method for automatically learning essential features from image data, convolutional neural networks (CNNs) have been introduced for HSR remote sensing image scene classification due to their excellent performance in natural image classification. However, some scene classes of remote sensing images are object-centered, i.e., the scene class of an image is decided by the objects it contains. Although previous methods based on CNNs have achieved comparatively high classification accuracies compared with the traditional methods with handcrafted features, they do not consider the scale variation of the objects in the scenes. This makes it difficult to directly utilize CNNs on those remote sensing images belonging to object-centered classes to extract features that are robust to scale variation, leading to wrongly classified scene images. To solve this problem, scene classification based on a deep random-scale stretched convolutional neural network (SRSCNN) for HSR remote sensing imagery is proposed in this paper. In the proposed method, patches with a random scale are cropped from the image and stretched to the specified scale as the input to train the CNN. This forces the CNN to extract features that are robust to the scale variation. Furthermore, to further improve the performance of the CNN, a robust scene classification strategy is adopted, i.e., multi-perspective fusion. The experimental results obtained using three datasets—the UC Merced dataset, the Google dataset of SIRI-WHU, and the Wuhan IKONOS dataset—confirm that the proposed method performs better than the traditional scene classification methods. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing)

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20 pages, 7810 KiB

Open AccessArticle

Hyperspectral Pansharpening Based on Intrinsic Image Decomposition and Weighted Least Squares Filter

by Wenqian Dong

, Song Xiao^*, Yunsong Li^* and Jiahui Qu

State Key laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China

Remote Sens. 2018, 10(3), 445; https://doi.org/10.3390/rs10030445 - 12 Mar 2018

Cited by 7 | Viewed by 5057

Abstract

Component substitution (CS) and multiresolution analysis (MRA) based methods have been adopted in hyperspectral pansharpening. The major contribution of this paper is a novel CS-MRA hybrid framework based on intrinsic image decomposition and weighted least squares filter. First, the panchromatic (P) [...] Read more.

Component substitution (CS) and multiresolution analysis (MRA) based methods have been adopted in hyperspectral pansharpening. The major contribution of this paper is a novel CS-MRA hybrid framework based on intrinsic image decomposition and weighted least squares filter. First, the panchromatic (P) image is sharpened by the Gaussian-Laplacian enhancement algorithm to enhance the spatial details, and the weighted least squares (WLS) filter is performed on the enhanced P image to extract the high-frequency information of the P image. Then, the MTF-based deblurring method is applied to the interpolated hyperspectral (HS) image, and the intrinsic image decomposition (IID) is adopted to decompose the deblurred interpolated HS image into the illumination and reflectance components. Finally, the detail map is generated by making a proper compromise between the high-frequency information of the P image and the spatial information preserved in the illumination component of the HS image. The detail map is further refined by the information ratio of different bands of the HS image and injected into the deblurred interpolated HS image. Experimental results indicate that the proposed method achieves better fusion results than several state-of-the-art hyperspectral pansharpening methods. This demonstrates that a combination of an IID technique and a WLS filter is an effective way for hyperspectral pansharpening. Full article

(This article belongs to the Special Issue Hyperspectral Imaging and Applications)

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21 pages, 22099 KiB

Open AccessArticle

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

by Yuanxin Jia^1,2

, Yong Ge^1,*, Feng Ling³

, Xian Guo¹, Jianghao Wang¹

, Le Wang⁴, Yuehong Chen⁵

and Xiaodong Li³

¹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China

⁴ Department of Geography, The State University of New York, Buffalo, NY 14261, USA

⁵ School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China

Remote Sens. 2018, 10(3), 446; https://doi.org/10.3390/rs10030446 - 12 Mar 2018

Cited by 68 | Viewed by 12539

Abstract

Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, [...] Read more.

Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions. Full article

(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)

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22 pages, 8070 KiB

Open AccessArticle

Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data

by Seonyoung Park¹

, Jungho Im^1,*

, Seohui Park¹, Cheolhee Yoo¹

, Hyangsun Han²

and Jinyoung Rhee³

¹ School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea

² Department of Polar Remote Sensing, Korea Polar Research Institute, Incheon 21990, Korea

³ Climate Research Department, APEC Climate Center, Busan 48058, Korea

Remote Sens. 2018, 10(3), 447; https://doi.org/10.3390/rs10030447 - 12 Mar 2018

Cited by 144 | Viewed by 13413

Abstract

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps [...] Read more.

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach. Full article

(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)

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18 pages, 11112 KiB

Open AccessArticle

A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka

by Niranga Alahacoon, Karthikeyan Matheswaran, Peejush Pani

and Giriraj Amarnath^*

International Water Management Institute (IWMI), 127 Sunil Mawatha, Pelawatte, Colombo 10120, Sri Lanka

Remote Sens. 2018, 10(3), 448; https://doi.org/10.3390/rs10030448 - 13 Mar 2018

Cited by 45 | Viewed by 9885

Abstract

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) [...] Read more.

Critical information on a flood-affected area is needed in a short time frame to initiate rapid response operations and develop long-term flood management strategies. This study combined rainfall trend analysis using Asian Precipitation—Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) gridded rainfall data with flood maps derived from Synthetic Aperture Radar (SAR) and multispectral satellite to arrive at holistic spatio-temporal patterns of floods in Sri Lanka. Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data were used to map flood extents for emergency relief operations while eight-day Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data for the time period from 2001 to 2016 were used to map long term flood-affected areas. The inundation maps produced for rapid response were published within three hours upon the availability of satellite imagery in web platforms, with the aim of supporting a wide range of stakeholders in emergency response and flood relief operations. The aggregated time series of flood extents mapped using MODIS data were used to develop a flood occurrence map (2001–2016) for Sri Lanka. Flood hotpots identified using both optical and synthetic aperture average of 325 km² for the years 2006–2015 and exceptional flooding in 2016 with inundation extent of approximately 1400 km². The time series rainfall data explains increasing trend in the extreme rainfall indices with similar observation derived from satellite imagery. The results demonstrate the feasibility of using multi-sensor flood mapping approaches, which will aid Disaster Management Center (DMC) and other multi-lateral agencies involved in managing rapid response operations and preparing mitigation measures. Full article

(This article belongs to the Special Issue Remote Sensing for Flood Mapping and Monitoring of Flood Dynamics)

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22 pages, 5155 KiB

Open AccessArticle

Climate and Spring Phenology Effects on Autumn Phenology in the Greater Khingan Mountains, Northeastern China

by Yuanyuan Fu¹

, Hong S. He^1,2,*, Jianjun Zhao¹

, David R. Larsen², Hongyan Zhang¹, Michael G. Sunde² and Shengwu Duan²

¹ School of Geographical Sciences, Northeast Normal University, Changchun 130024, China

² School of Natural Resources, University of Missouri, Columbia, MO 65211, USA

Remote Sens. 2018, 10(3), 449; https://doi.org/10.3390/rs10030449 - 13 Mar 2018

Cited by 69 | Viewed by 8117

Abstract

Vegetation phenology plays a key role in terrestrial ecosystem nutrient and carbon cycles and is sensitive to global climate change. Compared with spring phenology, which has been well studied, autumn phenology is still poorly understood. In this study, we estimated the date of [...] Read more.

Vegetation phenology plays a key role in terrestrial ecosystem nutrient and carbon cycles and is sensitive to global climate change. Compared with spring phenology, which has been well studied, autumn phenology is still poorly understood. In this study, we estimated the date of the end of the growing season (EOS) across the Greater Khingan Mountains, China, from 1982 to 2015 based on the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index third-generation (NDVI3g) dataset. The temporal correlations between EOS and climatic factors (e.g., preseason temperature, preseason precipitation), as well as the correlation between autumn and spring phenology, were investigated using partial correlation analysis. Results showed that more than 94% of the pixels in the Greater Khingan Mountains exhibited a delayed EOS trend, with an average rate of 0.23 days/y. Increased preseason temperature resulted in earlier EOS in most of our study area, except for the semi-arid grassland region in the south, where preseason warming generally delayed EOS. Similarly, EOS in most of the mountain deciduous coniferous forest, forest grassland, and mountain grassland forest regions was earlier associated with increased preseason precipitation, but for the semi-arid grassland region, increased precipitation during the preseason mainly led to delayed EOS. However, the effect of preseason precipitation on EOS in most of the Greater Khingan Mountains was stronger than that of preseason temperature. In addition to the climatic effects on EOS, we also found an influence of spring phenology on EOS. An earlier SOS led to a delayed EOS in most of the study area, while in the southern of mountain deciduous coniferous forest region and northern of semi-arid grassland region, an earlier SOS was often followed by an earlier EOS. These findings suggest that both climatic factors and spring phenology should be incorporated into autumn phenology models in order to improve prediction accuracy under present and future climate change scenarios. Full article

(This article belongs to the Special Issue Land Surface Phenology )

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22 pages, 5639 KiB

Open AccessFeature PaperArticle

The Development of a Low-Cost, Near Infrared, High-Temperature Thermal Imaging System and Its Application to the Retrieval of Accurate Lava Lake Temperatures at Masaya Volcano, Nicaragua

by Thomas Charles Wilkes^1,2,*

, Leigh Russell Stanger²

, Jon Raffe Willmott²

, Tom David Pering¹

, Andrew John Samuel McGonigle^1,3

and Rebecca Anne England¹

¹ Department of Geography, The University of Sheffield, Winter Street, Sheffield S10 2TN, UK

² Department of Electronic and Electrical Engineering, The University of Sheffield, Portobello Centre, Pitt Street, Sheffield S1 4ET, UK

³ School of Geosciences, The University of Sydney, Sydney 2006, Australia

Remote Sens. 2018, 10(3), 450; https://doi.org/10.3390/rs10030450 - 13 Mar 2018

Cited by 11 | Viewed by 7380

Abstract

Near infrared thermal cameras can provide useful low-cost imaging systems for high temperature applications, as an alternative to ubiquitous mid-/long-wavelength infrared systems. Here, we present a new Raspberry Pi-based near infrared thermal camera for use at temperatures of ≈>500 °C. We discuss in [...] Read more.

Near infrared thermal cameras can provide useful low-cost imaging systems for high temperature applications, as an alternative to ubiquitous mid-/long-wavelength infrared systems. Here, we present a new Raspberry Pi-based near infrared thermal camera for use at temperatures of ≈>500 °C. We discuss in detail the building of the optical system, calibration using a Sakuma-Hattori model and quantification of uncertainties in remote temperature retrievals. We then present results from the deployment of the system on Masaya Volcano, Nicaragua, where the active lava lake was imaged. Temperatures reached a maximum of 1104 ± 14 °C and the lake radiative power output was found to range between 30 and 45 MW. To the best of our knowledge, this is the first published ground-based data on the thermal characteristics of this relatively nascent lava lake, which became visible in late 2015. Full article

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24 pages, 32944 KiB

Open AccessArticle

Object-Based Features for House Detection from RGB High-Resolution Images

by Renxi Chen^1,*

, Xinhui Li² and Jonathan Li³

¹ School of Earth Science and Engineering, Hohai University, Nanjing 211100, China

² School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

³ Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L3G1, Canada

Remote Sens. 2018, 10(3), 451; https://doi.org/10.3390/rs10030451 - 13 Mar 2018

Cited by 64 | Viewed by 12197

Abstract

Automatic building extraction from satellite images, an open research topic in remote sensing, continues to represent a challenge and has received substantial attention for decades. This paper presents an object-based and machine learning-based approach for automatic house detection from RGB high-resolution images. The [...] Read more.

Automatic building extraction from satellite images, an open research topic in remote sensing, continues to represent a challenge and has received substantial attention for decades. This paper presents an object-based and machine learning-based approach for automatic house detection from RGB high-resolution images. The images are first segmented by an algorithm combing a thresholding watershed transformation and hierarchical merging, and then shadows and vegetation are eliminated from the initial segmented regions to generate building candidates. Subsequently, the candidate regions are subjected to feature extraction to generate training data. In order to capture the characteristics of house regions well, we propose two kinds of new features, namely edge regularity indices (ERI) and shadow line indices (SLI). Finally, three classifiers, namely AdaBoost, random forests, and Support Vector Machine (SVM), are employed to identify houses from test images and quality assessments are conducted. The experiments show that our method is effective and applicable for house identification. The proposed ERI and SLI features can improve the precision and recall by 5.6% and 11.2%, respectively. Full article

(This article belongs to the Special Issue Classification and Feature Extraction for Remote Sensing Image Analysis)

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18 pages, 20529 KiB

Open AccessArticle

The Effect of Surface Waves on Airborne Lidar Bathymetry (ALB) Measurement Uncertainties

by Matthew Birkebak^1,2, Firat Eren^1,*

, Shachak Pe’eri³ and Neil Weston³

¹ Center for Coastal and Ocean Mapping, University of New Hampshire, 24 Colovos Road, Durham, NH 03824, USA

² Labsphere Inc., 231 Shaker St., North Sutton, NH 03260, USA

³ National Oceanic and Atmospheric Administration (NOAA), 1315 East West Highway, Silver Spring, MD 20910, USA

Remote Sens. 2018, 10(3), 453; https://doi.org/10.3390/rs10030453 - 13 Mar 2018

Cited by 27 | Viewed by 5657

Abstract

Airborne Lidar Bathymetry (ALB) provides a rapid means of data collection that provides seamless digital elevation maps across land and water. However, environmental factors such as water surface induce significant uncertainty in the ALB measurements. In this study, the effect of water surface [...] Read more.

Airborne Lidar Bathymetry (ALB) provides a rapid means of data collection that provides seamless digital elevation maps across land and water. However, environmental factors such as water surface induce significant uncertainty in the ALB measurements. In this study, the effect of water surface on the ALB measurements is characterized both theoretically and empirically. Theoretical analysis includes Monte Carlo ray-tracing simulations that evaluate different environmental and hardware conditions such as wind speed, laser beam footprint diameter and off-nadir angle that are typically observed in ALB survey conditions. The empirical study includes development of an optical detector array to measure and analyze the refraction angle of the laser beam under a variety of environmental and hardware conditions. The results suggest that the refraction angle deviations (

2 σ

) in the along-wind direction vary between 3–5° when variations in wind speed, laser beam footprint size and the laser beam incidence angle are taken into account. Full article

(This article belongs to the Special Issue Advances in Undersea Remote Sensing)

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20 pages, 14838 KiB

Open AccessArticle

An Efficient Maximum Likelihood Estimation Approach of Multi-Baseline SAR Interferometry for Refined Topographic Mapping in Mountainous Areas

by Yuting Dong¹, Houjun Jiang², Lu Zhang^1,3

and Mingsheng Liao^1,3,*

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

² College of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

³ Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

Remote Sens. 2018, 10(3), 454; https://doi.org/10.3390/rs10030454 - 14 Mar 2018

Cited by 18 | Viewed by 5508

Abstract

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry [...] Read more.

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2. Full article

(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)

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15 pages, 4840 KiB

Open AccessArticle

A Human Settlement Composite Index (HSCI) Derived from Nighttime Luminosity Associated with Imperviousness and Vegetation Indexes

by Ting Ma^1,3,6,*

, Tao Xu^1,4, Lin Huang^2,3,* and Alicia Zhou⁵

¹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

² State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100012, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

⁴ Information Science Academy, China Electronics Technology Group Corporation, Beijing 100081, China

⁵ Department of Mathematics & Statistics, College of Art and Science, Boston University, Boston, MA 02215, USA

⁶ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Remote Sens. 2018, 10(3), 455; https://doi.org/10.3390/rs10030455 - 14 Mar 2018

Cited by 17 | Viewed by 6230

Abstract

Satellite-derived nighttime light data have been increasingly used for studying urbanization and socioeconomic dynamics, because there are notable quantitative relationships between anthropogenic nocturnal radiance and the degree of human activity over time and space at different scales. When considering the visible impacts of [...] Read more.

Satellite-derived nighttime light data have been increasingly used for studying urbanization and socioeconomic dynamics, because there are notable quantitative relationships between anthropogenic nocturnal radiance and the degree of human activity over time and space at different scales. When considering the visible impacts of saturation and over-glow effects from original nighttime light images, several composite indexes, which mainly include the introduction of vegetation index, have been studied to improve the application of nighttime light data for investigating the spatial patterns in human settlements. To overcome the shortcomings of previous composite indexes, especially in areas of highly intensified human activity, such as urban, non-man-made surfaces, and low density human activity, such as in rural residential sites, we propose a new human settlement composite index (HSCI). The establishment of this proposed HSCI is based on a combination of three different remote sensing datasets: nighttime light brightness (derived from the Defense Meteorological Satellite Program, DMSP), the normalized difference vegetation index (NDVI, derived from the Moderate Resolution Imaging Spectroradiometer, MODIS), and the percent impervious surface area (PISA, derived from the GlobeLand30 land cover and land use dataset produced from Landsat data). We defined the calculation of HSCI as the arithmetic mean of the normalized difference urban index and normalized difference imperviousness index with respect to both the magnitude of socioeconomic activity and the distribution of artificial surface across human settlement, respectively. Analysis results clearly demonstrate the utility of HSCI in delineating spatial patterns for different kinds of human settlement, particularly for identifying non-man-made surfaces in urbanized areas, various densities of human activities in peripheral areas and small human settlements in rural and remote areas. Our method and findings provide an effective way to investigate human settlements with a nighttime brightness-based composite index, as well as valuable insights into further studies of the composite index related to nocturnal luminosity data. Full article

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18 pages, 9509 KiB

Open AccessArticle

A Unified Algorithm for Channel Imbalance and Antenna Phase Center Position Calibration of a Single-Pass Multi-Baseline TomoSAR System

by Yuncheng Bu^1,2

, Xingdong Liang^1,*, Yu Wang^1,*

, Fubo Zhang¹ and Yanlei Li¹

¹ National Key Laboratory of Science and Technology on Microwave Imaging, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

² University of Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2018, 10(3), 456; https://doi.org/10.3390/rs10030456 - 14 Mar 2018

Cited by 8 | Viewed by 4965

Abstract

The multi-baseline synthetic aperture radar (SAR) tomography (TomoSAR) system is employed in such applications as disaster remote sensing, urban 3-D reconstruction, and forest carbon storage estimation. This is because of its 3-D imaging capability in a single-pass platform. However, a high 3-D resolution [...] Read more.

The multi-baseline synthetic aperture radar (SAR) tomography (TomoSAR) system is employed in such applications as disaster remote sensing, urban 3-D reconstruction, and forest carbon storage estimation. This is because of its 3-D imaging capability in a single-pass platform. However, a high 3-D resolution of TomoSAR is based on the premise that the channel imbalance and antenna phase center (APC) position are precisely known. If this is not the case, the 3-D resolution performance will be seriously degraded. In this paper, a unified algorithm for channel imbalance and APC position calibration of a single-pass multi-baseline TomoSAR system is proposed. Based on the maximum likelihood method, as well as the least squares and the damped Newton method, we can calibrate the channel imbalance and APC position. The algorithm is suitable for near-field conditions, and no phase unwrapping operation is required. The effectiveness of the proposed algorithm has been verified by simulation and experimental results. Full article

(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)

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24 pages, 8800 KiB

Open AccessArticle

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

by Tao Liu^1,2,* and Amr Abd-Elrahman^1,2

¹ School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA

² Gulf Coast Research Center, University of Florida, Plant City, FL 33563, USA

Remote Sens. 2018, 10(3), 457; https://doi.org/10.3390/rs10030457 - 14 Mar 2018

Cited by 34 | Viewed by 6281

Abstract

Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, [...] Read more.

Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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16 pages, 4020 KiB

Open AccessArticle

Unmanned Aerial Vehicle-Based Traffic Analysis: A Case Study for Shockwave Identification and Flow Parameters Estimation at Signalized Intersections

by Muhammad Arsalan Khan^*

, Wim Ectors

, Tom Bellemans, Davy Janssens and Geert Wets

UHasselt-Hasselt University, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium

Remote Sens. 2018, 10(3), 458; https://doi.org/10.3390/rs10030458 - 14 Mar 2018

Cited by 81 | Viewed by 9285

Abstract

Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles (UAVs), or drones, have become increasingly popular. However, the civil applications of this technology, particularly for traffic data collection and analysis, still need to be thoroughly explored. For this purpose, the authors previously [...] Read more.

Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles (UAVs), or drones, have become increasingly popular. However, the civil applications of this technology, particularly for traffic data collection and analysis, still need to be thoroughly explored. For this purpose, the authors previously proposed a detailed methodological framework for the automated UAV video processing in order to extract multi-vehicle trajectories at a particular road segment. In this paper, however, the main emphasis is on the comprehensive analysis of vehicle trajectories extracted via a UAV-based video processing framework. An analytical methodology is presented for: (i) the automatic identification of flow states and shockwaves based on processed UAV trajectories, and (ii) the subsequent extraction of various traffic parameters and performance indicators in order to study flow conditions at a signalized intersection. The experimental data to analyze traffic flow conditions was obtained in the city of Sint-Truiden, Belgium. The generation of simplified trajectories, shockwaves, and fundamental diagrams help in analyzing the interrupted-flow conditions at a signalized four-legged intersection using UAV-acquired data. The analysis conducted on such data may serve as a benchmark for the actual traffic-specific applications of the UAV-acquired data. The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. The future research will mainly focus on further extensions of UAV-based traffic applications. Full article

(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))

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16 pages, 8570 KiB

Open AccessArticle

Proof of Feasibility of the Sea State Monitoring from Data Collected in Medium Pulse Mode by a X-Band Wave Radar System

by Giovanni Ludeno^1,*

, Francesco Raffa²

, Francesco Soldovieri¹

and Francesco Serafino³

¹ Institute for Electromagnetic Sensing of the Environment, National Research Council, via Diocleziano 328, I-80124 Napoli, Italy

² Institute of Geosciences and Earth Resources, National Research Council, Via G. Moruzzi, I-56124 Pisa, Italy

³ Institute of Biometeorology of the National Research Council, via Giovanni Caproni 8, I-50145 Florence, Italy

Remote Sens. 2018, 10(3), 459; https://doi.org/10.3390/rs10030459 - 15 Mar 2018

Cited by 4 | Viewed by 4379

Abstract

X-band marine radars can be exploited to estimate the sea state parameters and surface current. However, to pursue this aim, they are set in such a way as to radiate a very short pulse to exploit the maximum spatial resolution. However, this condition [...] Read more.

X-band marine radars can be exploited to estimate the sea state parameters and surface current. However, to pursue this aim, they are set in such a way as to radiate a very short pulse to exploit the maximum spatial resolution. However, this condition strongly limits the use of radar as an anti-collision system during navigation. Consequently, a continuous change of radar scale is needed to perform both the operations of waves and current estimations and target tracking activities. The goal of this manuscript is to investigate the possibility of using marine radar working in a medium pulse mode to estimate the sea state parameters and surface current, while assuring suitable anti-collision performance. Specifically, we compare the capabilities of the X-band radar for sea state monitoring when it works in short and medium pulse modes and we present the results of a comparison based on data collected during two experimental campaigns. The provided results show that there is good agreement about the estimation of wave parameters and the surface current field that make us hopeful that, in principle, it is possible to use the medium pulse mode to achieve information about sea state with a reasonable degradation. Full article

(This article belongs to the Special Issue Ocean Surface Currents: Progress in Remote Sensing and Validation)

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17 pages, 7066 KiB

Open AccessArticle

Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery

by Samuel Hislop^1,2,3,*, Simon Jones¹, Mariela Soto-Berelov¹, Andrew Skidmore^2,4

, Andrew Haywood⁵

and Trung H. Nguyen^1,3

¹ School of Science, RMIT University, Melbourne, VIC 3000, Australia

² Faculty for Geo-Information Science and Earth Observation (ITC), University of Twente, 7522 NB Enschede, The Netherlands

³ Cooperative Research Centre for Spatial Information (CRCSI), Carlton, VIC 3053, Australia

⁴ Department of Environmental Science, Macquarie University, Sydney, NSW 2109, Australia

⁵ European Forest Institute, Barcelona 08025, Spain

Remote Sens. 2018, 10(3), 460; https://doi.org/10.3390/rs10030460 - 15 Mar 2018

Cited by 128 | Viewed by 14539

Abstract

Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel [...] Read more.

Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass’s delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy ‘greenness’, are not as reliable, with values returning to pre-fire levels in 3–5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8–10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and limitations of the various indices that these methods rely on. Full article

(This article belongs to the Section Forest Remote Sensing)

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22 pages, 10384 KiB

Open AccessArticle

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

by Xueyi Shang¹, Xibing Li^1,*, Antonio Morales-Esteban², Gualberto Asencio-Cortés³

and Zewei Wang⁴

¹ School of Resources and Safety Engineering, Central South University, Changsha 410083, China

² Department of Building Structures and Geotechnical Engineering, University of Seville, 41004 Sevilla, Spain

³ Department of Computer Science, Pablo de Olavide University of Seville, 41013 Sevilla, Spain

⁴ School of Earthquake Sciences and Engineering, Sysu, Sun Yat-Sen University, Guangzhou 510275, China

Remote Sens. 2018, 10(3), 461; https://doi.org/10.3390/rs10030461 - 15 Mar 2018

Cited by 25 | Viewed by 7723

Abstract

Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts’ seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and [...] Read more.

Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts’ seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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21 pages, 38333 KiB

Open AccessArticle

Ground Deformations around the Toktogul Reservoir, Kyrgyzstan, from Envisat ASAR and Sentinel-1 Data—A Case Study about the Impact of Atmospheric Corrections on InSAR Time Series

by Julia Neelmeijer^1,2,*, Tilo Schöne¹

, Robert Dill¹, Volker Klemann¹

and Mahdi Motagh^1,2

¹ GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany

² Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, 30167 Hannover, Germany

Remote Sens. 2018, 10(3), 462; https://doi.org/10.3390/rs10030462 - 15 Mar 2018

Cited by 24 | Viewed by 7600

Abstract

We present ground deformations in response to water level variations at the Toktogul Reservoir, located in Kyrgyzstan, Central Asia. Ground deformations were measured by Envisat Advanced Synthetic Aperture Radar (ASAR) and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) imagery covering the time periods [...] Read more.

We present ground deformations in response to water level variations at the Toktogul Reservoir, located in Kyrgyzstan, Central Asia. Ground deformations were measured by Envisat Advanced Synthetic Aperture Radar (ASAR) and Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) imagery covering the time periods 2004–2009 and 2014–2016, respectively. The net reservoir water level, as measured by satellite radar altimetry, decreased approximately 60 m (∼13.5 km³) from 2004–2009, whereas, for 2014–2016, the net water level increased by approximately 51 m (∼11.2 km³). The individual Small BAseline Subset (SBAS) interferograms were heavily influenced by atmospheric effects that needed to be minimized prior to the time-series analysis. We tested several approaches including corrections based on global numerical weather model data, such as the European Centre for Medium-RangeWeather Forecasts (ECMWF) operational forecast data, the ERA-5 reanalysis, and the ERA-Interim reanalysis, as well as phase-based methods, such as calculating a simple linear dependency on the elevation or the more sophisticated power-law approach. Our findings suggest that, for the high-mountain Toktogul area, the power-law correction performs the best. Envisat descending time series for the period of water recession reveal mean line-of-sight (LOS) uplift rates of 7.8 mm/yr on the northern shore of the Toktogul Reservoir close to the Toktogul city area. For the same area, Sentinel-1 ascending and descending time series consistently show a subsidence behaviour due to the replenishing of the water reservoir, which includes intra-annual LOS variations on the order of 30mm. A decomposition of the LOS deformation rates of both Sentinel-1 orbits revealed mean vertical subsidence rates of 25 mm/yr for the common time period of March 2015–November 2016, which is in very good agreement with the results derived from elastic modelling based on the TEA12 Earth model. Full article

(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)

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23 pages, 14148 KiB

Open AccessArticle

Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

by Shaohua Qiu^1,*

, Gongjian Wen¹, Jia Liu^2,*, Zhipeng Deng³

and Yaxiang Fan¹

¹ Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China

² College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China

³ College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Remote Sens. 2018, 10(3), 464; https://doi.org/10.3390/rs10030464 - 15 Mar 2018

Cited by 12 | Viewed by 5359

Abstract

Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods [...] Read more.

Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10× and 2.5× for airplane and ship, and a detection speedup of maximal 7.2×, 4.1× and 2.5× on three test sets, respectively. Full article

(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)

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14 pages, 30158 KiB

Open AccessArticle

Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach

by Ting Ma^1,2,3,*

, Zhan Yin^1,2 and Alicia Zhou⁴

¹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

⁴ Department of Mathematics & Statistics, College of Art and Science, Boston University, Boston, MA 02215, USA

Remote Sens. 2018, 10(3), 465; https://doi.org/10.3390/rs10030465 - 15 Mar 2018

Cited by 29 | Viewed by 7206

Abstract

As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher [...] Read more.

As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher spatial resolution and fewer over-glow and saturation effects, nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument with day/night band (DNB), which is on the Suomi National Polar-Orbiting Partnership satellite (Suomi-NPP), may further improve our understanding of spatiotemporal dynamics and socioeconomic activities, particularly at the local scale. Capturing and identifying spatial patterns in human settlements from VIIRS images, however, is still challenging due to the lack of spatially explicit texture characteristics, which are usually crucial for general image classification methods. In this study, we propose a watershed-based partition approach by combining a second order exponential decay model for the spatial delineation of human settlements with VIIRS-derived nighttime light images. Our method spatially partitions the human settlement into five different types of sub-regions: high, medium-high, medium, medium-low and low lighting areas with different degrees of human activity. This is primarily based on the local coverage of locally maximum radiance signals (watershed-based) and the rank and magnitude of the nocturnal radiance signal across the whole region, as well as remotely sensed building density data and social media-derived human activity information. The comparison results for the relationship between sub-regions with various density nighttime brightness levels and human activities, as well as the densities of different types of interest points (POIs), show that our method can distinctly identify various degrees of human activity based on artificial nighttime radiance and ancillary data. Furthermore, the analysis results across 99 cities in 10 urban agglomerations in China reveal inter-regional variations in partition thresholds and human settlement patterns related to the urban size and form. Our partition method and relative results can provide insight into the further application of VIIRS DNB nighttime light data in spatially delineated urbanization processes and socioeconomic activities in human settlements. Full article

(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)

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24 pages, 5337 KiB

Open AccessArticle

Coherent Focused Lidars for Doppler Sensing of Aerosols and Wind

by Chris Hill

Malvern Lidar Consultants, Great Malvern, Worcestershire WR14 1YE, UK

Remote Sens. 2018, 10(3), 466; https://doi.org/10.3390/rs10030466 - 16 Mar 2018

Cited by 21 | Viewed by 8074

Abstract

Many coherent lidars are used today with aerosol targets for detailed studies of e.g., local wind speed and turbulence. Fibre-optic lidars operating near 1.5 μm dominate the wind energy market, with hundreds now installed worldwide. Here, we review some of the beam/target physics [...] Read more.

Many coherent lidars are used today with aerosol targets for detailed studies of e.g., local wind speed and turbulence. Fibre-optic lidars operating near 1.5 μm dominate the wind energy market, with hundreds now installed worldwide. Here, we review some of the beam/target physics for these lidars and discuss practical problems. In a monostatic Doppler lidar with matched local oscillator and transmit beams, focusing of the beam gives rise to a spatial sensitivity along the beam direction that depends on the inverse of beam area; for Gaussian beams, this sensitivity follows a Lorentzian function. At short range, the associated probe volume can be extremely small and contain very few scatterers; we describe predictions and simulations for few-scatterer and multi-scatterer sensing. We review the single-particle mode (SPM) and volume mode (VM) modelling of Frehlich et al. and some numerical modelling of lidar detector time series and statistics. Interesting behaviour may be observed from a modern coherent lidar used at short ranges (e.g., in a wind tunnel) and/or with weak aerosol seeding. We also review some problems (and solutions) for Doppler-sign-insensitive lidars. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Conditions for Wind Energy Applications)

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15 pages, 3238 KiB

Open AccessArticle

Potential of Combining Optical and Dual Polarimetric SAR Data for Improving Mangrove Species Discrimination Using Rotation Forest

by Hongsheng Zhang^1,2, Ting Wang¹, Mingfeng Liu¹, Mingming Jia³

, Hui Lin^1,2,4,*, LM Chu⁵ and Adam Thomas Devlin¹

¹ Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

² Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China

³ Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China

⁴ Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China

⁵ School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

Remote Sens. 2018, 10(3), 467; https://doi.org/10.3390/rs10030467 - 16 Mar 2018

Cited by 73 | Viewed by 8119

Abstract

Classification of mangrove species using satellite images is important for investigating the spatial distribution of mangroves at community and species levels on local, regional and global scales. Hence, studies of mangrove deforestation and reforestation are imperative to support the conservation of mangrove forests. [...] Read more.

Classification of mangrove species using satellite images is important for investigating the spatial distribution of mangroves at community and species levels on local, regional and global scales. Hence, studies of mangrove deforestation and reforestation are imperative to support the conservation of mangrove forests. However, accurate discrimination of mangrove species remains challenging due to many factors such as data resolution, species number and spectral confusion between species. In this study, three different combinations of datasets were designed from Worldview-3 and Radarsat-2 data to classify four mangrove species, Kandelia obovate (KO), Avicennia marina (AM), Acanthus ilicifolius (AI) and Aegiceras corniculatum (AC). Then, the Rotation Forest (RoF) method was employed to classify the four mangrove species. Results indicated the benefits of dual polarimetric SAR data with an improvement of accuracy by 2–3%, which can be useful for more accurate large-scale mapping of mangrove species. Moreover, the difficulty of classifying different mangrove species, in order of increasing difficulty, was identified as KO < AM < AI < AC. Dual polarimetric SAR data are recognized to improve the classification of AI and AC species. Although this improvement is not remarkable, it is consistent for all three methods. The improvement can be particularly important for large-scale mapping of mangrove forest at the species level. These findings also provide useful guidance for future studies using multi-source satellite data for mangrove monitoring and conservation. Full article

(This article belongs to the Section Forest Remote Sensing)

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18 pages, 4302 KiB

Open AccessArticle

The Spatiotemporal Response of Soil Moisture to Precipitation and Temperature Changes in an Arid Region, China

by Yunqian Wang^1,2,3,4, Jing Yang^1,6,*, Yaning Chen¹, Anqian Wang^1,2 and Philippe De Maeyer^3,5

¹ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ Department of Geography, Ghent University, 9000 Ghent, Belgium

⁴ Sino-Belgian Joint Laboratory of Geo-Information, Urumqi 830011, China

⁵ Sino-Belgian Joint Laboratory of Geo-Information, 9000 Ghent, Belgium

⁶ National Institute of Water and Atmospheric Research, Christchurch 8000, New Zealand

Remote Sens. 2018, 10(3), 468; https://doi.org/10.3390/rs10030468 - 16 Mar 2018

Cited by 70 | Viewed by 7760

Abstract

Soil moisture plays a crucial role in the hydrological cycle and climate system. The reliable estimation of soil moisture in space and time is important to monitor and even predict hydrological and meteorological disasters. Here we studied the spatiotemporal variations of soil moisture [...] Read more.

Soil moisture plays a crucial role in the hydrological cycle and climate system. The reliable estimation of soil moisture in space and time is important to monitor and even predict hydrological and meteorological disasters. Here we studied the spatiotemporal variations of soil moisture and explored the effects of precipitation and temperature on soil moisture in different land cover types within the Tarim River Basin from 2001 to 2015, based on high-spatial-resolution soil moisture data downscaled from the European Space Agency’s (ESA) Climate Change Initiative (CCI) soil moisture data. The results show that the spatial average soil moisture increased slightly from 2001 to 2015, and the soil moisture variation in summer contributed most to regional soil moisture change. For the land cover, the highest soil moisture occurred in the forest and the lowest value was found in bare land, and soil moisture showed significant increasing trends in grassland and bare land during 2001~2015. Both partial correlation analysis and multiple linear regression analysis demonstrate that in the study area precipitation had positive effects on soil moisture, while temperature had negative effects, and precipitation made greater contributions to soil moisture variations than temperature. The results of this study can be used for decision making for water management and allocation. Full article

(This article belongs to the Special Issue Retrieval, Validation and Application of Satellite Soil Moisture Data)

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16 pages, 2277 KiB

Open AccessArticle

Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR

by Nazi Wang, Tianhe Xu^*

, Fan Gao and Guochang Xu

Institute of Space Science, Shandong University, Weihai 264209, China

Remote Sens. 2018, 10(3), 470; https://doi.org/10.3390/rs10030470 - 16 Mar 2018

Cited by 40 | Viewed by 8272

Abstract

Ground-based GNSS-R (global navigation satellite system reflectometry) can provide the absolute vertical distance from a GNSS antenna to the reflective surface of the ocean in a common height reference frame, given that vertical crustal motion at a GNSS station can be determined using [...] Read more.

Ground-based GNSS-R (global navigation satellite system reflectometry) can provide the absolute vertical distance from a GNSS antenna to the reflective surface of the ocean in a common height reference frame, given that vertical crustal motion at a GNSS station can be determined using direct GNSS signals. This technique offers the advantage of enabling ground-based sea level measurements to be more accurately determined compared with traditional tide gauges. Sea level changes can be retrieved from multipath effects on GNSS, which is caused by interference of the GNSS L-band microwave signals (directly from satellites) with reflections from the environment that occur before reaching the antenna. Most of the GNSS observation types, such as pseudo-range, carrier-phase and signal-to-noise ratio (SNR), suffer from this multipath effect. In this paper, sea level altimetry determinations are presented for the first time based on geometry-free linear combinations of the carrier phase at low elevation angles from a fixed global positioning system (GPS) station. The precision of the altimetry solutions are similar to those derived from GNSS SNR data. There are different types of observation and reflector height retrieval methods used in the data processing, and to analyze the performance of the different methods, five sea level determination strategies are adopted. The solutions from the five strategies are compared with tide gauge measurements near the GPS station, and the results show that sea level changes determined from GPS SNR and carrier phase combinations for the five strategies show good agreement (correlation coefficient of 0.97–0.98 and root-mean-square error values of <0.2 m). Full article

(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)

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23 pages, 4262 KiB

Open AccessArticle

Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data

by Haobo Lyu^1,†, Hui Lu^1,2,*

, Lichao Mou^3,4,†, Wenyu Li¹, Jonathon Wright^1,2

, Xuecao Li⁵

, Xinlu Li^1,6, Xiao Xiang Zhu^3,4

, Jie Wang⁷, Le Yu^1,2

and Peng Gong^1,2

¹ Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China

² Joint Center for Global Change Studies, Beijing 100875, China

³ Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling 82234, Germany

⁴ Signal Processing in Earth Observation, Technical University of Munich (TUM), Munich 80333, Germany

⁵ Department of Geological & Atmospheric Science, Iowa State University, Ames, IA 50014, USA

⁶ National Space Science Center, Chinese Academy of Sciences, Beijing 10019, China

⁷ State Key Lab of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China

^† These authors contributed equally to this work.

Remote Sens. 2018, 10(3), 471; https://doi.org/10.3390/rs10030471 - 17 Mar 2018

Cited by 64 | Viewed by 9297

Abstract

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this [...] Read more.

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984–2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York, Melbourne, and Munich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984–2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy to maximize information gain from limited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984–2016 is 89% for Beijing, 94% for New York, 93% for Melbourne, and 89% for Munich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing)

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19 pages, 6621 KiB

Open AccessArticle

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

by Zhiyong Lv¹, Tongfei Liu¹

, Yiliang Wan^2,3,*

, Jón Atli Benediktsson⁴

and Xiaokang Zhang⁵

¹ School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China

² College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China

³ Key Laboratory of Geospatial Big Data Mining and Application, Hunan Province, Changsha 410081, China

⁴ Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland

⁵ School of remote sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Remote Sens. 2018, 10(3), 472; https://doi.org/10.3390/rs10030472 - 17 Mar 2018

Cited by 51 | Viewed by 9378

Abstract

In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the [...] Read more.

In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images. Full article

(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)

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21 pages, 3340 KiB

Open AccessArticle

Urban Built-Up Area Boundary Extraction and Spatial-Temporal Characteristics Based on Land Surface Temperature Retrieval

by Lin Wang^1,2, Jianghong Zhu^3,*, Yanqing Xu^4,* and Zhanqi Wang³

¹ Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

² University of Chinese Academy of Sciences, Beijing 100049, China

³ School of public Administration, China University of Geosciences, Wuhan 430079, China

⁴ Department of Geography and Planning, University of Toledo, Toledo, OH 43606, USA

Remote Sens. 2018, 10(3), 473; https://doi.org/10.3390/rs10030473 - 17 Mar 2018

Cited by 26 | Viewed by 6696

Abstract

The analysis of the spatial and temporal characteristics of urban built-up area is conducive to the rational formulation of urban land use strategy, scientific planning and rational distribution of modern urban development. Based on the remote sensing data in four separate years (1999, [...] Read more.

The analysis of the spatial and temporal characteristics of urban built-up area is conducive to the rational formulation of urban land use strategy, scientific planning and rational distribution of modern urban development. Based on the remote sensing data in four separate years (1999, 2004, 2010 and 2014), this research identified and inspected the urban built-up area boundary based on the temperature retrieval method. Combined with the second land investigation data and Google map data in Jingzhou, this paper used the qualitative and quantitative analysis methods to analyze the spatial-temporal characteristics of Jingzhou urban built-up area expansion over the past 15 years. The analysis shows that the entire spatial form of the urban built-up area has been evolving towards a compact and orderly state. On this basis, the urban area-population elasticity coefficient and algometric growth model were used to determine the reasonability of the urban sprawl. The results show that the expansion of built-up area in Jingzhou is not keeping up with the speed of population growth. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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19 pages, 4438 KiB

Open AccessEditor’s ChoiceArticle

Evaluating Eight Global Reanalysis Products for Atmospheric Correction of Thermal Infrared Sensor—Application to Landsat 8 TIRS10 Data

by Xiangchen Meng^1,2

and Jie Cheng^1,2,3,*

¹ State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

² Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

³ U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA

Remote Sens. 2018, 10(3), 474; https://doi.org/10.3390/rs10030474 - 19 Mar 2018

Cited by 38 | Viewed by 6950

Abstract

Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) [...] Read more.

Global reanalysis products have been widely used for correcting the atmospheric effects of thermal infrared data, but their performances have not been comprehensively evaluated. In this paper, we evaluate eight global reanalysis products (NCEP/FNL; NCEP/DOE Reanalysis2; MERRA-3; MERRA-6; MERRA2-3; MERRA2-6; JRA-55; and ERA-Interim) commonly used in the atmospheric correction of Landsat 8 TIRS10 data by referencing global radiosonde observations collected from 163 stations. The atmospheric parameters (atmospheric transmittance, upward radiance, and downward radiance) simulated with MERRA-6 and ERA-Interim were accurate than those simulated with other reanalysis products for different water vapor contents and surface elevations. When global reanalysis products were applied to retrieve land surface temperature (LST) from simulated Landsat 8 TIRS10 data, ERA-Interim and MERRA-6 were accurate than other reanalysis products. The overall LST biases and RMSEs between the retrieved LSTs and LSTs that were used to generate the top-of-atmosphere radiances were less than 0.2 K and 1.09 K, respectively. When eight reanalysis products were used to estimate LSTs from thirty-two Landsat 8 TIRS10 images covering the Heihe River basin in China, the various reanalysis products showed similar validation accuracies for LSTs with low water vapor contents. The biases ranged from 0.07 K to 0.24 K, and the STDs (RMSEs) ranged from 1.93 K (1.93 K) to 2.02 K (2.04 K). Considering the above evaluation results, MERRA-6 and ERA-Interim are recommended for thermal infrared data atmospheric corrections. Full article

(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis)

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17 pages, 2424 KiB

Open AccessArticle

Global Validation of MODIS C6 and C6.1 Merged Aerosol Products over Diverse Vegetated Surfaces

by Muhammad Bilal¹

, Majid Nazeer²

, Zhongfeng Qiu^1,*

, Xiaoli Ding³

and Jing Wei⁴

¹ School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

² Earth and Atmospheric Remote Sensing Lab. (EARL), Department of Meteorology, COMSATS Institute of Information Technology, Islamabad 45550, Pakistan

³ Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong, China

⁴ College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

Remote Sens. 2018, 10(3), 475; https://doi.org/10.3390/rs10030475 - 19 Mar 2018

Cited by 60 | Viewed by 6218

Abstract

In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTB_C6 and DTB_C6.1) at 0.55 µm were validated from 2004–2014 against Aerosol Robotic Network (AERONET) Version 2 [...] Read more.

In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTB_C6 and DTB_C6.1) at 0.55 µm were validated from 2004–2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non–vegetated (NDVI_P ≤ 0.3), moderately–vegetated (0.3 < NDVI_M ≤ 0.5) and densely–vegetated (NDVI_D > 0.5) surfaces. The DTB_C6 and DTB_C6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI > 0.3, (ii) use the DB AOD retrievals for NDVI < 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTB_SMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DT_C6.1 and DB_C6.1 AOD retrievals or the available one for all the NDVI values. For NDVI_P surfaces, results showed that the DTB_C6 and DTB_C6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVI_M surfaces, retrieval errors and poor quality in DTB_C6 and DTB_C6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVI_D surfaces, DTB_C6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTB_C6.1 AOD retrievals was significantly improved compared to the DTB_C6, but still more improvements are required over NDVI_P, NDVI_M and NDVI_D surfaces of Asia, NDVI_M and NDVI_D surfaces of North America_, and NDVI_M surfaces of South America. The performance of the DTB_SMS retrievals was better than the DTB_C6 and DTB_C6.1 retrievals with 11–13% (31%) greater number of coincident observations, 6–9% (14–22%) greater percentage of retrievals within the EE, and 30–100% (46–100%) smaller relative mean bias compared to the DTB_C6.1 (DTB_C6) at a global scale. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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13 pages, 7457 KiB

Open AccessArticle

IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method

by Xuebing Zhang¹

, Enhedelihai Nilot², Xuan Feng^2,*, Qianci Ren³ and Zhijia Zhang¹

¹ School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China

² College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China

³ College of Earth Sciences, Guilin University of Technology, Guilin 541006, China

Remote Sens. 2018, 10(3), 476; https://doi.org/10.3390/rs10030476 - 19 Mar 2018

Cited by 22 | Viewed by 5105

Abstract

Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing and imaging. With respect to signal processing, GPR data are [...] Read more.

Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing and imaging. With respect to signal processing, GPR data are typically non-stationary, which limits the applications of these methods moving forward. Empirical mode decomposition (EMD) provides alternative solutions with a fresh perspective. With EMD, GPR data are decomposed into a set of sub-components, i.e., the intrinsic mode functions (IMFs). However, the mode-mixing effect may also bring some negatives. To utilize the IMFs’ benefits, and avoid the negatives of the EMD, we introduce a new decomposition scheme termed variational mode decomposition (VMD) for GPR data processing for imaging. Based on the decomposition results of the VMD, we propose a new method which we refer as “the IMF-slice”. In the proposed method, the IMFs are generated by the VMD trace by trace, and then each IMF is sorted and recorded into different profiles (i.e., the IMF-slices) according to its center frequency. Using IMF-slices, the GPR data can be divided into several IMF-slices, each of which delineates a main vibration mode, and some subsurface layers and geophysical events can be identified more clearly. The effectiveness of the proposed method is tested using synthetic benchmark signals, laboratory data and the field dataset. Full article

(This article belongs to the Special Issue Recent Advances in GPR Imaging)

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20 pages, 2388 KiB

Open AccessArticle

Climate Extremes and Their Impacts on Interannual Vegetation Variabilities: A Case Study in Hubei Province of Central China

by Weizhe Chen¹

, Chunju Huang^1,2,*

, Lunche Wang²

and Dongmei Li^2,3

¹ State Key Laboratory of Biogeology and Environmental Geology, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China

² Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China

³ Xinjiang Institute of the Land Source Planning, Urumchi 830000, China

Remote Sens. 2018, 10(3), 477; https://doi.org/10.3390/rs10030477 - 19 Mar 2018

Cited by 18 | Viewed by 6275

Abstract

As the frequency and intensity of climate extremes are likely to be substantially modified in upcoming decades due to climate warming, an evaluation of the response of interannual vegetation variabilities to climate extremes is imperative. This study comprehensively analyzed the spatio-temporal variabilities of [...] Read more.

As the frequency and intensity of climate extremes are likely to be substantially modified in upcoming decades due to climate warming, an evaluation of the response of interannual vegetation variabilities to climate extremes is imperative. This study comprehensively analyzed the spatio-temporal variabilities of 21 temperature and precipitation indices across Hubei Province in Central China based on daily meteorological records for the period 1961–2015. To quantify the sensitivity of the vegetation to climate indices in the study area, we correlated climate indices with three vegetation indicators: leaf area index, normalized difference vegetation index, and gross primary productivity. The results indicated that warm-related indices exerted considerable increasing trends, especially for summer days at a rate of 0.35 days year⁻¹ (p < 0.01). In addition, the trends of 18 indices during 1982–2015 were larger than those during 1961–2015, indicating accelerated climate changes in Hubei Province. Spatially, extreme precipitation showed increases in the eastern regions of the study area and decreases in the western regions. Correlation analyses revealed that warm anomalies of the Atlantic Multidecadal Oscillation resulted in extreme warm conditions and extreme precipitation in the study area. Stepwise linear regression analyses identified three temperature indices and three precipitation indices, which were mostly correlated with the three ecosystem variables at the site scale. Further multiple regressions demonstrated the main negative impacts caused by frost days, warm spell duration, extremely heavy precipitation, and consecutive dry days on the terrestrial ecosystem in Hubei Province. Our study provides an improved understanding of the effects of climate extremes on terrestrial ecosystems and can also offer a basis for the management of mitigating damage from climate extremes. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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14 pages, 4162 KiB

Open AccessArticle

Calculating Viewing Angles Pixel by Pixel in Optical Remote Sensing Satellite Imagery Using the Rational Function Model

by Kai Xu^1,2

, Guo Zhang^1,2,*

, Qingjun Zhang^2,3

and Deren Li^1,2

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

² Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

³ China Academy of Space Technology, Beijing 100094, China

Remote Sens. 2018, 10(3), 478; https://doi.org/10.3390/rs10030478 - 19 Mar 2018

Cited by 1 | Viewed by 8503

Abstract

In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms [...] Read more.

In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms for calculating sensor-viewing angles focus only on the center-point pixel or are complicated and are not well known. Thus, this study aims to provide a simple and general method to estimate the sensor viewing angles pixel by pixel. The Rational Function Model (RFM) is already widely used in high-resolution satellite imagery, and, thus, a method is proposed for calculating the sensor viewing angles based on the space-vector information for the observed light implied in the RFM. This method can calculate independently the sensor-viewing angles in a pixel-by-pixel fashion, regardless of the specific form of the geometric model, even for geometrically corrected imageries. The experiments reveal that the calculated values differ by approximately 10⁻⁴⁰ for the Gaofen-1 (GF-1) Wide-Field-View-1 (WFV-1) sensor, and by ~10⁻⁷⁰ for the Ziyuan-3 (ZY3-02) panchromatic nadir (NAD) sensor when compared to the values that are calculated using the Rigorous Sensor Model (RSM), and the discrepancy is analyzed. Generally, the viewing angles for each pixel in imagery are calculated accurately with the proposed method. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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20 pages, 5929 KiB

Open AccessArticle

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

by Yongsheng Hong^1,2,3, Yiyun Chen^1,2,3,*

, Lei Yu^4,5, Yanfang Liu^1,6,*, Yaolin Liu^1,6, Yong Zhang⁷, Yi Liu^1,2,3

and Hang Cheng^1,2,3

¹ School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

² State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences, Nanjing 210008, China

³ Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

⁴ School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

⁵ Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China

⁶ Key Laboratory of Geographic Information System of the Ministry of Education, Wuhan University, Wuhan 430079, China

⁷ School of Public Finance and Administration, Anhui University of Finance and Economics, Bengbu 233030, China

Remote Sens. 2018, 10(3), 479; https://doi.org/10.3390/rs10030479 - 19 Mar 2018

Cited by 107 | Viewed by 7649

Abstract

Visible and near-infrared (VIS–NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS–NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays [...] Read more.

Visible and near-infrared (VIS–NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS–NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS–NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R² = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R² = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil. Full article

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15 pages, 5987 KiB

Open AccessArticle

Generating Continental Scale Pixel-Based Surface Reflectance Composites in Coastal Regions with the Use of a Multi-Resolution Tidal Model

by Stephen Sagar^1,*

, Claire Phillips¹, Biswajit Bala¹, Dale Roberts^1,2

and Leo Lymburner¹

¹ National Earth and Marine Observations Branch, Geoscience Australia, Symonston, ACT 2609, Australia

² Research School of Finance, Actuarial Studies, and Statistics, Australian National University, Acton, ACT 2601, Australia

Remote Sens. 2018, 10(3), 480; https://doi.org/10.3390/rs10030480 - 20 Mar 2018

Cited by 13 | Viewed by 8202

Abstract

Generating continental-scale pixel composites in dynamic coastal and estuarine environments presents a unique challenge, as the application of a temporal or seasonal approach to composite generation is confounded by tidal influences. We demonstrate how this can be resolved using an approach to compositing [...] Read more.

Generating continental-scale pixel composites in dynamic coastal and estuarine environments presents a unique challenge, as the application of a temporal or seasonal approach to composite generation is confounded by tidal influences. We demonstrate how this can be resolved using an approach to compositing that provides robust composites of multi-type environments. In addition to the visual aesthetics of the images created, we demonstrate the utility of these composites for further interpretation and analysis. This is enabled by the manner in which our approach captures the spatial variation in tidal dynamics through the use of a Voronoi mesh, and preserves the band relationships within the modelled spectra at each pixel. Case studies are presented which include continental-scale mosaics of the Australian coastline at high and low tide, and tailored examples demonstrating the potential of the tidally constrained composites to address a range of coastal change detection and monitoring applications. We conclude with a discussion on the potential applications of the composite products and method in the coastal and marine environment, as well as further development directions for our tidal modelling framework. Full article

(This article belongs to the Section Ocean Remote Sensing)

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17 pages, 2640 KiB

Open AccessArticle

Real-Time Tropospheric Delay Retrieval from Multi-GNSS PPP Ambiguity Resolution: Validation with Final Troposphere Products and a Numerical Weather Model

by Cuixian Lu¹, Xin Li^2,*, Junlong Cheng², Galina Dick¹, Maorong Ge¹, Jens Wickert¹ and Harald Schuh¹

¹ German Research Centre for Geosciences GFZ, Telegrafenberg, 14473 Potsdam, Germany

² School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Remote Sens. 2018, 10(3), 481; https://doi.org/10.3390/rs10030481 - 20 Mar 2018

Cited by 28 | Viewed by 6507

Abstract

The multiple global navigation satellite systems (multi-GNSS) bring great opportunity for the real-time retrieval of high-quality zenith tropospheric delay (ZTD), which is a critical quality for atmospheric science and geodetic applications. In this contribution, a multi-GNSS precise point positioning (PPP) ambiguity resolution (AR) [...] Read more.

The multiple global navigation satellite systems (multi-GNSS) bring great opportunity for the real-time retrieval of high-quality zenith tropospheric delay (ZTD), which is a critical quality for atmospheric science and geodetic applications. In this contribution, a multi-GNSS precise point positioning (PPP) ambiguity resolution (AR) analysis approach is developed for real-time tropospheric delay retrieval. To validate the proposed multi-GNSS ZTD estimates, we collected and processed data from 30 Multi-GNSS Experiment (MGEX) stations; the resulting real-time tropospheric products are evaluated by using standard post-processed troposphere products and European Centre for Medium-Range Weather Forecasts analysis (ECMWF) data. An accuracy of 4.5 mm and 7.1 mm relative to the Center for Orbit Determination in Europe (CODE) and U.S. Naval Observatory (USNO) products is achievable for real-time tropospheric delays from multi-GNSS PPP ambiguity resolution after an initialization process of approximately 5 min. Compared to Global Positioning System (GPS) results, the accuracy of retrieved zenith tropospheric delay from multi-GNSS PPP-AR is improved by 16.7% and 31.7% with respect to USNO and CODE final products. The GNSS-derived ZTD time-series exhibits a great agreement with the ECMWF data for a long period of 30 days. The average root mean square (RMS) of the real-time zenith tropospheric delay retrieved from multi-GNSS PPP-AR is 12.5 mm with respect to ECMWF data while the accuracy of GPS-only results is 13.3 mm. Significant improvement is also achieved in terms of the initialization time of the multi-GNSS tropospheric delays, with an improvement of 50.7% compared to GPS-only fixed solutions. All these improvements demonstrate the promising prospects of the multi-GNSS PPP-AR method for time-critical meteorological applications. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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28 pages, 7048 KiB

Open AccessFeature PaperArticle

Noise Reduction in Hyperspectral Imagery: Overview and Application

by Behnood Rasti^1,*

, Paul Scheunders², Pedram Ghamisi³

, Giorgio Licciardi⁴ and Jocelyn Chanussot⁵

¹ Keilir Institute of Technology (KIT), Grænásbraut 910, 235 Reykjanesbær, Iceland; The Department of Electrical and Computer Engineering, University of Iceland, Sæmundargata 2, 101 Reykjavik, Iceland

² Visionlab, University of Antwerp (CDE) Universiteitsplein 1 (N Building), B-2610 Antwerp, Belgium

³ German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, SAR Signal Processing, Oberpfaffenhofen, 82234 Wessling, Germany

⁴ Hypatia Research Consortium, 00133 Roma, Italy

⁵ GIPSA-lab, Grenoble INP, CNRS, University Grenoble Alpes, 38000 Grenoble, France

Remote Sens. 2018, 10(3), 482; https://doi.org/10.3390/rs10030482 - 20 Mar 2018

Cited by 275 | Viewed by 19505

Abstract

Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization [...] Read more.

Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step. Full article

(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)

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26 pages, 17420 KiB

Open AccessArticle

Evaluation of Groundwater Storage Variations Estimated from GRACE Data Assimilation and State-of-the-Art Land Surface Models in Australia and the North China Plain

by Natthachet Tangdamrongsub^1,*

, Shin-Chan Han¹, Siyuan Tian^2,3, Hannes Müller Schmied^4,5

, Edwin H. Sutanudjaja⁶, Jiangjun Ran⁷

and Wei Feng⁷

¹ School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia

² Research School of Earth Sciences, Australian National University, Canberra, ACT 2601, Australia

³ Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia

⁴ Institute of Physical Geography, Goethe-University Frankfurt, 60438 Frankfurt , Germany

⁵ Senckenberg Biodiversity and Climate Research Centre (SBiK-F), 60325 Frankfurt, Germany

⁶ Department of Physical Geography, Faculty of Geosciences, Utrecht University, 3584 CS Utrecht, The Netherlands

⁷ State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China

Remote Sens. 2018, 10(3), 483; https://doi.org/10.3390/rs10030483 - 20 Mar 2018

Cited by 59 | Viewed by 11987

Abstract

The accurate knowledge of the groundwater storage variation (ΔGWS) is essential for reliable water resource assessment, particularly in arid and semi-arid environments (e.g., Australia, the North China Plain (NCP)) where water storage is significantly affected by human activities and spatiotemporal climate variations. The [...] Read more.

The accurate knowledge of the groundwater storage variation (ΔGWS) is essential for reliable water resource assessment, particularly in arid and semi-arid environments (e.g., Australia, the North China Plain (NCP)) where water storage is significantly affected by human activities and spatiotemporal climate variations. The large-scale ΔGWS can be simulated from a land surface model (LSM), but the high model uncertainty is a major drawback that reduces the reliability of the estimates. The evaluation of the model estimate is then very important to assess its accuracy. To improve the model performance, the terrestrial water storage variation derived from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is commonly assimilated into LSMs to enhance the accuracy of the ΔGWS estimate. This study assimilates GRACE data into the PCRaster Global Water Balance (PCR-GLOBWB) model. The GRACE data assimilation (DA) is developed based on the three-dimensional ensemble Kalman smoother (EnKS 3D), which considers the statistical correlation of all extents (spatial, temporal, vertical) in the DA process. The ΔGWS estimates from GRACE DA and four LSM simulations (PCR-GLOBWB, the Community Atmosphere Biosphere Land Exchange (CABLE), the Water Global Assessment and Prognosis Global Hydrology Model (WGHM), and World-Wide Water (W3)) are validated against the in situ groundwater data. The evaluation is conducted in terms of temporal correlation, seasonality, long-term trend, and detection of groundwater depletion. The GRACE DA estimate shows a significant improvement in all measures, notably the correlation coefficients (respect to the in situ data) are always higher than the values obtained from model simulations alone (e.g., ~0.15 greater in Australia, and ~0.1 greater in the NCP). GRACE DA also improves the estimation of groundwater depletion that the models cannot accurately capture due to the incorrect information of the groundwater demand (in, e.g., PCR-GLOBWB, WGHM) or the unavailability of a groundwater consumption routine (in, e.g., CABLE, W3). In addition, this study conducts the inter-comparison between four model simulations and reveals that PCR-GLOBWB and CABLE provide a more accurate ΔGWS estimate in Australia (subject to the calibrated parameter) while PCR-GLOBWB and WGHM are more accurate in the NCP (subject to the inclusion of anthropogenic factors). The analysis can be used to declare the status of the ΔGWS estimate, as well as itemize the possible improvements of the future model development. Full article

(This article belongs to the Special Issue Remote Sensing of Groundwater from River Basin to Global Scales)

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29 pages, 25739 KiB

Open AccessArticle

Independent Assessment of Sentinel-3A Wet Tropospheric Correction over the Open and Coastal Ocean

by Maria Joana Fernandes^1,2,*

and Clara Lázaro^1,2

¹ Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal

² Centro Interdisciplinar de Investigação Marinha e Ambiental (CIIMAR/CIMAR), Universidade do Porto, 4050-123 Porto, Portugal

Remote Sens. 2018, 10(3), 484; https://doi.org/10.3390/rs10030484 - 20 Mar 2018

Cited by 29 | Viewed by 6121

Abstract

Launched on 16 February 2016, Sentinel-3A (S3A) carries a two-band microwave radiometer (MWR) similar to that of Envisat, and is aimed at the precise retrieval of the wet tropospheric correction (WTC) through collocated measurements using the Synthetic Aperture Radar Altimeter (SRAL) instrument. This [...] Read more.

Launched on 16 February 2016, Sentinel-3A (S3A) carries a two-band microwave radiometer (MWR) similar to that of Envisat, and is aimed at the precise retrieval of the wet tropospheric correction (WTC) through collocated measurements using the Synthetic Aperture Radar Altimeter (SRAL) instrument. This study aims at presenting an independent assessment of the WTC derived from the S3A MWR over the open and coastal ocean. Comparisons with other four MWRs show Root Mean Square (RMS) differences (cm) of S3A with respect to these sensors of 1.0 (Global Precipitation Measurement (GPM) Microwave Imager, GMI), 1.2 (Jason-2), 1.3 (Jason-3), and 1.5 (Satellite with ARgos and ALtika (SARAL)). The linear fit with respect to these MWR shows scale factors close to 1 and small offsets, indicating a good agreement between all these sensors. In spite of the short analysis period of 10 months, a stable temporal evolution of the S3A WTC has been observed. In line with the similar two-band instruments aboard previous European Space Agency (ESA) altimetric missions, strong ice and land contamination can be observed, the latter mainly found up to 20–25 km from the coast. Comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) and an independent WTC derived only from third party data are also shown, indicating good overall performance. However, improvements in both the retrieval algorithm and screening of invalid MWR observations are desirable to achieve the quality of the equivalent WTC from Jason-3. The outcome of this study is a deeper knowledge of the measurement capabilities and limitations of the type of MWR aboard S3A and of the present WTC retrieval algorithms. Full article

(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)

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24 pages, 4908 KiB

Open AccessArticle

Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

by Estrella Olmedo^1,*, Isabelle Taupier-Letage²

, Antonio Turiel¹

and Aida Alvera-Azcárate³

¹ Department of Physical Oceanography, Institute of Marine Sciences, CSIC, Barcelona Expert Center, Pg. Marítim 37-49, Barcelona E-08003, Spain

² Aix Marseille Université, CNRS/INSU, Université de Toulon, IRD, Mediterranean Institute of Oceanography (MIO), F-83507 La Seyne, Marseille, France

³ Département d’astrophys., géophysique et océanographie (AGO), GeoHydrodynamics and Environment Research (GHER), Université de Liège, Allée du 6 Août, 17 Sart Tilman, Liège 4000, Belgium

Remote Sens. 2018, 10(3), 485; https://doi.org/10.3390/rs10030485 - 20 Mar 2018

Cited by 40 | Viewed by 9248

Abstract

A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean [...] Read more.

A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR). Full article

(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)

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24 pages, 7335 KiB

Open AccessArticle

On Signal Modeling of Moon-Based Synthetic Aperture Radar (SAR) Imaging of Earth

by Zhen Xu^1,2

and Kun-Shan Chen^1,*

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² University of Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2018, 10(3), 486; https://doi.org/10.3390/rs10030486 - 20 Mar 2018

Cited by 41 | Viewed by 7999

Abstract

The Moon-based Synthetic Aperture Radar (Moon-Based SAR), using the Moon as a platform, has a great potential to offer global-scale coverage of the earth’s surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, [...] Read more.

The Moon-based Synthetic Aperture Radar (Moon-Based SAR), using the Moon as a platform, has a great potential to offer global-scale coverage of the earth’s surface with a high revisit cycle and is able to meet the scientific requirements for climate change study. However, operating in the lunar orbit, Moon-Based SAR imaging is confined within a complex geometry of the Moon-Based SAR, Moon, and Earth, where both rotation and revolution have effects. The extremely long exposure time of Moon-Based SAR presents a curved moving trajectory and the protracted time-delay in propagation makes the “stop-and-go” assumption no longer valid. Consequently, the conventional SAR imaging technique is no longer valid for Moon-Based SAR. This paper develops a Moon-Based SAR theory in which a signal model is derived. The Doppler parameters in the context of lunar revolution with the removal of ‘stop-and-go’ assumption are first estimated, and then characteristics of Moon-Based SAR imaging’s azimuthal resolution are analyzed. In addition, a signal model of Moon-Based SAR and its two-dimensional (2-D) spectrum are further derived. Numerical simulation using point targets validates the signal model and enables Doppler parameter estimation for image focusing. Full article

(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)

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21 pages, 3482 KiB

Open AccessArticle

Using a Similarity Matrix Approach to Evaluate the Accuracy of Rescaled Maps

by Peijun Sun^1,2,*

and Russell G. Congalton³

¹ State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

² Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

³ Department of Natural Resources & the Environment, University of New Hampshire, Durham, NH 03824, USA

Remote Sens. 2018, 10(3), 487; https://doi.org/10.3390/rs10030487 - 20 Mar 2018

Cited by 5 | Viewed by 4550

Abstract

Rescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using [...] Read more.

Rescaled maps have been extensively utilized to provide data at the appropriate spatial resolution for use in various Earth science models. However, a simple and easy way to evaluate these rescaled maps has not been developed. We propose a similarity matrix approach using a contingency table to compute three measures: overall similarity (OS), omission error (OE), and commission error (CE) to evaluate the rescaled maps. The Majority Rule Based aggregation (MRB) method was employed to produce the upscaled maps to demonstrate this approach. In addition, previously created, coarser resolution land cover maps from other research projects were also available for comparison. The question of which is better, a map initially produced at coarse resolution or a fine resolution map rescaled to a coarse resolution, has not been quantitatively investigated. To address these issues, we selected study sites at three different extent levels. First, we selected twelve regions covering the continental USA, then we selected nine states (from the whole continental USA), and finally we selected nine Agriculture Statistical Districts (ASDs) (from within the nine selected states) as study sites. Crop/non-crop maps derived from the USDA Crop Data Layer (CDL) at 30 m as base maps were used for the upscaling and existing maps at 250 m and 1 km were utilized for the comparison. The results showed that a similarity matrix can effectively provide the map user with the information needed to assess the rescaling. Additionally, the upscaled maps can provide higher accuracy and better represent landscape pattern compared to the existing coarser maps. Therefore, we strongly recommend that an evaluation of the upscaled map and the existing coarser resolution map using a similarity matrix should be conducted before deciding which dataset to use for the modelling. Overall, extending our understanding on how to perform an evaluation of the rescaled map and investigation of the applicability of the rescaled map compared to the existing land cover map is necessary for users to most effectively use these data in Earth science models. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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22 pages, 38594 KiB

Open AccessArticle

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

by Ling Hu^1,2, Wenjie Fan^1,2,*, Huazhong Ren^1,2

, Suhong Liu³, Yaokui Cui^1,2 and Peng Zhao^1,2

¹ Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

² Beijing Key Lab of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, China

³ Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Remote Sens. 2018, 10(3), 488; https://doi.org/10.3390/rs10030488 - 20 Mar 2018

Cited by 44 | Viewed by 7095

Abstract

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright [...] Read more.

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas. Full article

(This article belongs to the Special Issue Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li)

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Review

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30 pages, 5292 KiB

Open AccessReview

Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments

by Jianguang Wen^1,2,3,*, Qiang Liu^2,4

, Qing Xiao^1,3, Qinhuo Liu^1,2,3

, Dongqin You^1,2, Dalei Hao^1,3

, Shengbiao Wu^1,3

and Xingwen Lin^1,3

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² Joint Center for Global Change Studies (JCGCS), Beijing 100875, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

⁴ College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

Remote Sens. 2018, 10(3), 370; https://doi.org/10.3390/rs10030370 - 27 Feb 2018

Cited by 135 | Viewed by 12202

Abstract

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in [...] Read more.

Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF) has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites. Full article

(This article belongs to the Special Issue Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li)

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Other

Jump to: Research, Review

1 pages, 185 KiB

Open AccessErratum

Erratum: Maresma, A., et al. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sens. 2017, 9, 648

by Ángel Maresma^1,†, Mar Ariza², Elías Martínez¹, Jaume Lloveras¹ and José A. Martínez-Casasnovas^2,*

¹ Department of Field Crops and Forest Science, Agrotecnio Center, University of Lleida, Av. Rovira Roure 191, Lleida 25198, Spain

² Research Group in AgroICT and Precision Agriculture, Agrotecnio Center, University of Lleida, Av. Rovira Roure 191, Lleida 25198, Spain

^† Current Affiliation: Department of Animal Science, Cornell University, Ithaca, NY 14853, USA.

Remote Sens. 2018, 10(3), 368; https://doi.org/10.3390/rs10030368 - 27 Feb 2018

Cited by 5 | Viewed by 2791

Abstract

After publication of the research paper [1], the authors noticed an error and wish to make the following correction.[...] Full article

18 pages, 2489 KiB

Open AccessEditor’s ChoiceLetter

Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training

by Darren Pouliot^1,*, Rasim Latifovic², Jon Pasher¹ and Jason Duffe¹

¹ Environment and Climate Change Canada, Landscape Science and Technology, Ottawa, ON K1A 0H3, Canada

² Natural Resources Canada, Earth Sciences Sector, Canada Center for Remote Sensing, Ottawa, ON K1A 0E4, Canada

Remote Sens. 2018, 10(3), 394; https://doi.org/10.3390/rs10030394 - 3 Mar 2018

Cited by 82 | Viewed by 11095

Abstract

Landsat is a fundamental data source for understanding historical change and its effect on environmental processes. In this research we test shallow and deep convolution neural networks (CNNs) for Landsat image super-resolution enhancement, trained using Sentinel-2, in three study sites representing boreal forest, [...] Read more.

Landsat is a fundamental data source for understanding historical change and its effect on environmental processes. In this research we test shallow and deep convolution neural networks (CNNs) for Landsat image super-resolution enhancement, trained using Sentinel-2, in three study sites representing boreal forest, tundra, and cropland/woodland environments. The analysis sought to assess baseline performance and determine the capacity for spatial and temporal extension of the trained CNNs. This is not a data fusion approach and a high-resolution image is only needed to train the CNN. Results show improvement with the deeper network generally achieving better results. For spatial and temporal extension, the deep CNN performed the same or better than the shallow CNN, but at greater computational cost. Results for temporal extension were influenced by change potentiality reducing the performance difference between the shallow and deep CNN. Visual examination revealed sharper images regarding land cover boundaries, linear features, and within-cover textures. The results suggest that spatial enhancement of the Landsat archive is feasible, with optimal performance where CNNs can be trained and applied within the same spatial domain. Future research will assess the enhancement on time series and associated land cover applications. Full article

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22 pages, 1361 KiB

Open AccessProject Report

IEA Wind Task 32: Wind Lidar
Identifying and Mitigating Barriers to the Adoption of Wind Lidar

by Andrew Clifton^1,*

, Peter Clive², Julia Gottschall³, David Schlipf⁴, Eric Simley⁵, Luke Simmons⁶, Detlef Stein⁷, Davide Trabucchi⁸, Nikola Vasiljevic⁹

and Ines Würth¹⁰

¹ WindForS, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany

² Wood-Clean Energy, 2nd Floor, St. Vincent Plaza, 319 St. Vincent Street, Glasgow G2 5LP, UK

³ Fraunhofer Institute for Wind Energy Systems IWES, Am Seedeich 45, 27572 Bremerhaven, Germany

⁴ Stuttgart Wind Energy, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany

⁵ Envision Energy USA Ltd., 1201 Louisiana St. Suite 500, Houston, TX 77002, USA

⁶ DNV GL—Measurements, 1501 9th Avenue, Suite 900, Seattle, WA 98001, USA

⁷ Multiversum GmbH, Shanghaiallee 9, 20457 Hamburg, Germany

⁸ ForWind, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany

⁹ Department for Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark

¹⁰ Stuttgart Wind Energy, University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany

Remote Sens. 2018, 10(3), 406; https://doi.org/10.3390/rs10030406 - 6 Mar 2018

Cited by 43 | Viewed by 12648

Abstract

IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex [...] Read more.

IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Conditions for Wind Energy Applications)

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16 pages, 6248 KiB

Open AccessTechnical Note

Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction

by Xiaodan Ma¹, Jiarui Feng¹, Haiou Guan^1,* and Gang Liu^2,*

¹ College of Electrical and Information, Heilongjiang Bayi Agricultural University, DaQing 163319, China

² Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China

Remote Sens. 2018, 10(3), 429; https://doi.org/10.3390/rs10030429 - 10 Mar 2018

Cited by 36 | Viewed by 7495

Abstract

Improving the speed and accuracy of chlorophyll (Ch1) content prediction in different light areas of apple trees is a central priority for understanding the growth response to light intensity and in turn increasing the primary production of apples. In vitro assessment by wet [...] Read more.

Improving the speed and accuracy of chlorophyll (Ch1) content prediction in different light areas of apple trees is a central priority for understanding the growth response to light intensity and in turn increasing the primary production of apples. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time-consuming. Over the years, alternative methods—both rapid and nondestructive—were explored, and many vegetation indices (VIs) were developed to retrieve Ch1 content at the canopy level from meter- to decameter-scale reflectance observations, which have lower accuracy due to the possible confounding influence of the canopy structure. Thus, the spatially continuous distribution of Ch1 content in different light areas within an apple tree canopy remains unresolved. Therefore, the objective of this study is to develop methods for Ch1 content estimation in areas of different light intensity by using 3D models with color characteristics acquired by a 3D laser scanner with centimeter spatial resolution. Firstly, to research relative light intensity (RLI), canopies were scanned with a FARO Focus3D 120 laser scanner on a calm day without strong light intensity and then divided into 180 cube units for each canopy according to actual division methods in three-dimensional spaces based on distance information. Meanwhile, four different types of RLI were defined as 0–30%, 30–60%, 60–85%, and 85–100%, respectively, according to the actual division method for tree canopies. Secondly, Ch1 content in the 180 cubic units of each apple tree was measured by a leaf chlorophyll meter (soil and plant analyzer development, SPAD). Then, color characteristics were extracted from each cubic area of the 3D model and calculated by two color variables, which could be regarded as effective indicators of Ch1 content in field crop areas. Finally, to address the complexity and fuzziness of relationships between the color characteristics and Ch1 content of apple tree canopies (which could not be expressed by an accurate mathematical model), a three-layer artificial neural network (ANN) was constructed as a predictive model to find Ch1 content in different light areas in apple tree canopies. The results indicated that the mean highest and mean lowest value of Ch1 content distributed in 60–85% and 0–30% of RLI areas, respectively, and that there was no significant difference between adjacent RLI areas. Additionally, color characteristics changed regularly as the RLI rose within canopies. Moreover, the prediction of Ch1 content was strongly correlated with those of actual measurements (R = 0.9755) by the SPAD leaf chlorophyll meter. In summary, the color characteristics in 3D apple tree canopies combined with ANN technology could be used as a potential rapid technique for predicting Ch1 content in different areas of light in apple tree canopies. Full article

(This article belongs to the Special Issue Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery)

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13 pages, 14851 KiB

Open AccessTechnical Note

Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

by Yun-Long Kong

, Qingqing Huang^*, Chengyi Wang, Jingbo Chen, Jiansheng Chen and Dongxu He

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2018, 10(3), 452; https://doi.org/10.3390/rs10030452 - 13 Mar 2018

Cited by 73 | Viewed by 9662

Abstract

A satellite image time series (SITS) contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and [...] Read more.

A satellite image time series (SITS) contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests) and human activities (for example, deforestation and urbanisation) will disturb this pattern and cause a relatively profound change on the Earth’s surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM) networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1) illustrate the effectiveness and stability of the proposed approach for online disturbance detection. Full article

(This article belongs to the Special Issue Deep Learning for Target Object Detection and Identification in Remote Sensing Data)

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13 pages, 1038 KiB

Open AccessTechnical Note

A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product on a 3 km Spatial Grid

by Muhammad Bilal¹

, Zhongfeng Qiu^1,*

, James R. Campbell²

, Scott N. Spak³

, Xiaojing Shen⁴ and Majid Nazeer⁵

¹ School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

² Naval Research Laboratory, Monterey, CA 93943, USA

³ School of Urban & Regional Planning, University of Iowa, Iowa City, IA 52242, USA

⁴ School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

⁵ Earth and Atmospheric Remote Sensing Lab (EARL), Department of Meteorology, COMSATS Institute of Information Technology, Islamabad 45550, Pakistan

Remote Sens. 2018, 10(3), 463; https://doi.org/10.3390/rs10030463 - 15 Mar 2018

Cited by 56 | Viewed by 7923

Abstract

In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT_3K) and 10 km (DT_10K), and at 10 km resolution (DB [...] Read more.

In Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) aerosol products, the Dark Target (DT) and Deep Blue (DB) algorithms provide aerosol optical depth (AOD) observations at 3 km (DT_3K) and 10 km (DT_10K), and at 10 km resolution (DB_10K), respectively. In this study, the DB_10K is resampled to 3 km grid (DB_3K) using the nearest neighbor interpolation technique and merged with DT_3K to generate a new DT and DB merged aerosol product (DTB_3K) on a 3 km grid using Simplified Merge Scheme (SMS). The goal is to supplement DB_10K with high-resolution information over dense vegetation regions where DT_3K is susceptible to error. SMS is defined as “an average of the DT_3K and DB_3K AOD retrievals or the available one with the highest quality flag”. The DT_3K and DTB_3K AOD retrievals are validated from 2008 to 2012 against cloud-screened and quality-assured AOD from 19 AERONET sites located in Europe. Results show that the percentage of DTB_3K retrievals within the expected error (EE = ± (0.05 + 20%)) and data counts are increased by 40% and 11%, respectively, and the root mean square error and the mean bias are decreased by 26% and 54%, respectively, compared to the DT_3K retrievals. These results suggest that the DTB_3K product is a robust improvement over DT_3K alone, and can be used operationally for air quality and climate-related studies as a high-resolution supplement to the current MODIS product suite. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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3 pages, 189 KiB

Open AccessAddendum

Addendum: Hochstaffl, P. et al. Validation of Carbon Monoxide Total Columns from SCIAMACHY with NDACC/TCCON Ground-Based Measurements. Remote Sens. 2018, 10, 223

by Philipp Hochstaffl^1,*

, Franz Schreier¹

, Günter Lichtenberg¹

and Sebastian Gimeno García^1,2

¹ DLR—German Aerospace Center, Remote Sensing Technology Institute, 82234 Oberpfaffenhofen, Germany

² EUMETSAT—European Organisation for the Exploitation of Meteorological Satellites, 64283 Darmstadt, Germany

Remote Sens. 2018, 10(3), 469; https://doi.org/10.3390/rs10030469 - 16 Mar 2018

Viewed by 3669

Abstract

It was brought to our attention that, due to a recent change of the Total Carbon Column Observing Network (TCCON) Data Use Policy, citation of the individual TCCON dataset references used in the publication published in Remote Sensing [1] is now mandatory.[...] Full article

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