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32 pages, 35867 KiB

Open AccessArticle

Development of Geospatial and Temporal Characteristics for Hispaniola’s Lake Azuei and Enriquillo Using Landsat Imagery

by Mahrokh Moknatian¹, Michael Piasecki^1,* and Jorge Gonzalez²

¹ Civil Engineering Department, City College of New York, New York, NY 10031, USA

² Mechanical Engineering Department, City College of New York, New York, NY 10031, USA

Remote Sens. 2017, 9(6), 510; https://doi.org/10.3390/rs9060510 - 24 May 2017

Cited by 16 | Viewed by 8373

Abstract

In this paper, we used Landsat imagery for water body identification to create a novel 36-year surface area extent time series for lakes Azuei (Haiti) and Enriquillo (Dominican Republic) aimed at illuminating the dramatic temporal changes of these two lakes not just at [...] Read more.

In this paper, we used Landsat imagery for water body identification to create a novel 36-year surface area extent time series for lakes Azuei (Haiti) and Enriquillo (Dominican Republic) aimed at illuminating the dramatic temporal changes of these two lakes not just at yearly but at monthly or even sub-monthly scales. We used the Normalized Difference Water Index (NDWI) to extract water features and we also used spatial differentiation and thresholding techniques to remove clouds and associated shadows from the scene that were then passed through gap filling algorithms to complete and extract the lake extent polygons. We also explored the challenges that arrive from trying to combine RS-based Digital Elevation Model data with locally collected bathymetric data to yield a seamless representation of the topographic features of the rift valley that contains the two lakes. This “bathtub” model was then meshed with the lake extent polygons to compute lake volumes, maximum depths, and geospatially referenced lake levels rating curves. We used this data to examine the lakes and their geospatial characteristics in the context of the lakes’ growth/shrinking patterns. While we did not carry out a full hydrologic analysis we attempted to illuminate how specific lake levels cause what type of flooding and especially answered the questions if (a) Lake Azuei would ever spill into Lake Enriquillo, and (b) what the maximum lake levels need to be before spilling into neighboring watersheds. Full article

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34 pages, 12465 KiB

Open AccessArticle

Independent System Calibration of Sentinel-1B

by Marco Schwerdt^1,*, Kersten Schmidt¹

, Núria Tous Ramon¹, Patrick Klenk¹, Nestor Yague-Martinez¹, Pau Prats-Iraola¹, Manfred Zink¹

and Dirk Geudtner²

¹ German Aerospace Center (DLR), Microwaves and Radar Institute, Oberpfaffenhofen, D-82234 Wessling, Germany

² European Space Agency ESTEC, Department Directorate of Earth Observation Programmes, Keplerlaan 1, P.O. Box 299, 2200 AG Noordwijk, The Netherlands

Remote Sens. 2017, 9(6), 511; https://doi.org/10.3390/rs9060511 - 23 May 2017

Cited by 78 | Viewed by 8424

Abstract

Sentinel-1B is the second of two C-Band Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 mission, launched in April 2016—two years after the launch of the first satellite, Sentinel-1A. In addition to the commissioning of Sentinel-1B executed by the European Space Agency (ESA), [...] Read more.

Sentinel-1B is the second of two C-Band Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 mission, launched in April 2016—two years after the launch of the first satellite, Sentinel-1A. In addition to the commissioning of Sentinel-1B executed by the European Space Agency (ESA), an independent system calibration was performed by the German Aerospace Center (DLR) on behalf of ESA. Based on an efficient calibration strategy and the different calibration procedures already developed and applied for Sentinel-1A, extensive measurement campaigns were executed by initializing and aligning DLR’s reference targets deployed on the ground. This paper describes the different activities performed by DLR during the Sentinel-1B commissioning phase and presents the results derived from the analysis and the evaluation of a multitude of data takes and measurements. Full article

(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)

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15 pages, 7793 KiB

Open AccessArticle

Integrated System for Auto-Registered Hyperspectral and 3D Structure Measurement at the Point Scale

by Huijie Zhao¹, Shaoguang Shi¹, Xingfa Gu²

, Guorui Jia^1,*

and Lunbao Xu¹

¹ School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China

² Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

Remote Sens. 2017, 9(6), 512; https://doi.org/10.3390/rs9060512 - 23 May 2017

Cited by 6 | Viewed by 5348

Abstract

Hyperspectral and 3D structure measurement are among the active research areas of remote sensing in recent years. The combination of these two kinds of information can provide improved outcomes distinctly, which is widely used in vegetation physiology, precision agriculture and radiative transfer modeling. [...] Read more.

Hyperspectral and 3D structure measurement are among the active research areas of remote sensing in recent years. The combination of these two kinds of information can provide improved outcomes distinctly, which is widely used in vegetation physiology, precision agriculture and radiative transfer modeling. However, the registration and synchronization has been overlooked in data acquisition. The mismatched characteristics have limited the potential application of the hyperspectral and 3D structure data as a complete data set. This paper proposes a laboratory prototype which can integrate the hyperspectral and 3D structure measurement at the point scale. The prism dispersion and laser triangulation ranging are performed in a common optical path as a result of the coplanar design of the critical optical devices. The hyperspectral data and depth data of the same object point are acquired from the same focal plane, which makes the data auto-registered spatially and temporally. Test experiment verifies the accuracy of the data provided by the prototype and the actual measurement experiment demonstrates the feasibility of the design in vegetation observation. Full article

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

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17 pages, 3642 KiB

Open AccessArticle

In-Flight Calibration of GF-1/WFV Visible Channels Using Rayleigh Scattering

by Xingfeng Chen^1,2

, Jin Xing³, Li Liu⁴, Zhengqiang Li^1,*, Xiaodong Mei^1,5,*, Qiaoyan Fu⁴, Yisong Xie¹, Bangyu Ge¹

, Kaitao Li¹

and Hua Xu¹

¹ State Environmental Protection Key Laboratory of Satellite Remote Sensing Applications, State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China

² Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230031, China

³ Earth Observation Program Center, China National Space Administration, Beijing 100101, China

⁴ China Centre for Resources Satellite Data and Application, Beijing 100094, China

⁵ College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

Remote Sens. 2017, 9(6), 513; https://doi.org/10.3390/rs9060513 - 23 May 2017

Cited by 17 | Viewed by 5774

Abstract

China is planning to launch more and more optical remote-sensing satellites with high spatial resolution and multistep gains. Field calibration, the current operational method of satellite in-flight radiometric calibration, still does not have enough capacity to meet these demands. Gaofen-1 (GF-1), as the [...] Read more.

China is planning to launch more and more optical remote-sensing satellites with high spatial resolution and multistep gains. Field calibration, the current operational method of satellite in-flight radiometric calibration, still does not have enough capacity to meet these demands. Gaofen-1 (GF-1), as the first satellite of the Chinese High-resolution Earth Observation System, has been specially arranged to obtain 22 images over clean ocean areas using the Wide Field Viewing camera. Following this, Rayleigh scattering calibration was carried out for the visible channels with these images after the appropriate data processing steps. To guarantee a high calibration precision, uncertainty was analyzed in advance taking into account ozone, aerosol optical depth (AOD), seawater salinity, chlorophyll concentration, wind speed and solar zenith angle. AOD and wind speed were found to be the biggest error sources, which were also closely coupled to the solar zenith angle. Therefore, the best sample data for Rayleigh scattering calibration were selected at the following solar zenith angle of 19–22° and wind speed of 5–13 m/s to reduce the reflection contributed by the water surface. The total Rayleigh scattering calibration uncertainties of visible bands are 2.44% (blue), 3.86% (green), and 4.63% (red) respectively. Compared with the recent field calibration results, the errors are −1.69% (blue), 1.83% (green), and −0.79% (red). Therefore, the Rayleigh scattering calibration can become an operational in-flight calibration method for the high spatial resolution satellites. Full article

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20 pages, 6040 KiB

Open AccessArticle

Trends in Greenness and Snow Cover in Alaska’s Arctic National Parks, 2000–2016

by David K. Swanson

Arctic Inventory and Monitoring Network, National Park Service, Fairbanks, AK 99709, USA

Remote Sens. 2017, 9(6), 514; https://doi.org/10.3390/rs9060514 - 23 May 2017

Cited by 19 | Viewed by 6680

Abstract

In cold-limited arctic environments, the duration and timing of the snow cover and the vegetation green season have major ecological implications. I monitored the phenology of snow cover and greenness using MODIS Terra satellite data for the years 2000 to 2016 in the [...] Read more.

In cold-limited arctic environments, the duration and timing of the snow cover and the vegetation green season have major ecological implications. I monitored the phenology of snow cover and greenness using MODIS Terra satellite data for the years 2000 to 2016 in the 5 National Parks of northern Alaska, USA. Mann-Kendall trend tests showed that the end of the continuous snow season and midpoint of spring green-up became significantly earlier in parts of the study area over the 16-year period. Using the observed relationship between thaw degree-days at Kotzebue, Alaska and dates of snow-off and half green-up in nearby lowland tundra for the 16 years of MODIS data, I reconstructed the dates of snow-off and half green-up from long-term Kotzebue weather records back to 1937. The average snow-off and green-up dates probably became earlier by about 6 days over this 80-year time interval. Remote sensing of fall vegetation senescence and establishment of the snow cover were less reliable than the spring events due to cloudiness and low sun angles. The annual maximum normalized difference vegetation index (NDVI) generally did not increase significantly from 2001 to 2016, except in places where vegetation was recovering from forest fires. Full article

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

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17 pages, 4753 KiB

Open AccessArticle

Using Space-Time Features to Improve Detection of Forest Disturbances from Landsat Time Series

by Eliakim Hamunyela^*, Johannes Reiche

, Jan Verbesselt

and Martin Herold

Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands

Remote Sens. 2017, 9(6), 515; https://doi.org/10.3390/rs9060515 - 23 May 2017

Cited by 29 | Viewed by 7463

Abstract

Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between [...] Read more.

Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the trade-off between spatial and temporal accuracy. Timely detection of forest disturbance is often accompanied by many false detections, and existing approaches for reducing false detections either compromise the temporal accuracy or amplify the omission error for forest disturbances. Here, we propose to use a set of space-time features to reduce false detections. We first detect potential forest disturbances in the Landsat time series based on two consecutive negative anomalies, and subsequently use space-time features to confirm forest disturbances. A probability threshold is used to discriminate false detections from forest disturbances. We demonstrated this approach in the UNESCO Kafa Biosphere Reserve located in the southwest of Ethiopia by detecting forest disturbances between 2014 and 2016. Our results show that false detections are reduced significantly without compromising temporal accuracy. The user’s accuracy was at least 26% higher than the user’s accuracies obtained when using only temporal information (e.g., two consecutive negative anomalies) to confirm forest disturbances. We found the space-time features related to change in spatio-temporal variability, and spatio-temporal association with non-forest areas, to be the main predictors for forest disturbance. The magnitude of change and two consecutive negative anomalies, which are widely used to distinguish real changes from false detections, were not the main predictors for forest disturbance. Overall, our findings indicate that using a set of space-time features to confirm forest disturbances increases the capacity to reject many false detections, without compromising the temporal accuracy. Full article

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19 pages, 6742 KiB

Open AccessArticle

Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System

by Regina Camara Lins¹, Jean-Michel Martinez²

, David Da Motta Marques³

, José Almir Cirilo¹ and Carlos Ruberto Fragoso^4,*

¹ Department of Civil Engineering, Federal University of Pernambuco, 50670-901 Recife, Brazil

² Géosciences Environnement Toulouse (GET), Unité Mixte de Recherche 5563, IRD/CNRS/Université Toulouse III, 31400 Toulouse, France

³ Hydraulic Research Institute, Federal University of Rio Grande do Sul, CP 15029 Porto Alegre, Brazil

⁴ Center for Technology, Federal University of Alagoas, 57072-970 Maceió, Brazil

Remote Sens. 2017, 9(6), 516; https://doi.org/10.3390/rs9060516 - 24 May 2017

Cited by 54 | Viewed by 8903

Abstract

Remote estimation of chlorophyll-a in turbid and productive estuaries is difficult due to the optical complexity of Case 2 waters. Although recent advances have been obtained with the use of empirical approaches for estimating chlorophyll-a in these environments, the understanding of the relationship [...] Read more.

Remote estimation of chlorophyll-a in turbid and productive estuaries is difficult due to the optical complexity of Case 2 waters. Although recent advances have been obtained with the use of empirical approaches for estimating chlorophyll-a in these environments, the understanding of the relationship between spectral reflectance and chlorophyll-a is based mainly on temperate and subtropical estuarine systems. The potential to apply standard NIR-Red models to productive tropical estuaries remains underexplored. Therefore, the purpose of this study is to evaluate the performance of several approaches based on multispectral data to estimate chlorophyll-a in a productive tropical estuarine-lagoon system, using in situ measurements of remote sensing reflectance, R_rs. The possibility of applying algorithms using simulated satellite bands of modern and recent launched sensors was also evaluated. More accurate retrievals of chlorophyll-a (r² > 0.80) based on field datasets were found using NIR-Red three-band models. In addition, enhanced chlorophyll-a retrievals were found using the two-band algorithm based on bands of recently launched satellites such as Sentinel-2/MSI and Sentinel-3/OLCI, indicating a promising application of these sensors to remotely estimate chlorophyll-a for coming decades in turbid inland waters. Our findings suggest that empirical models based on optical properties involving water constituents have strong potential to estimate chlorophyll-a using multispectral data from satellite, airborne or handheld sensors in productive tropical estuaries. Full article

(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)

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17 pages, 11274 KiB

Open AccessArticle

Plume Segmentation from UV Camera Images for SO₂ Emission Rate Quantification on Cloud Days

by Matías Osorio^1,*, Nicolás Casaballe¹, Gastón Belsterli¹, Miguel Barreto², Álvaro Gómez², José A. Ferrari¹ and Erna Frins¹

¹ Instituto de Física, Facultad de Ingeniería, Universidad de la República, J. Herrera y Reissig 565, Montevideo 11200, Uruguay

² Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, J. Herrera y Reissig 565, Montevideo 11200, Uruguay

Remote Sens. 2017, 9(6), 517; https://doi.org/10.3390/rs9060517 - 24 May 2017

Cited by 20 | Viewed by 7111

Abstract

We performed measurements of SO₂ emissions with a high UV sensitive dual-camera optical system. Generally, in order to retrieve the two-dimensional SO₂ emission rates of a source, e.g., the slant column density of a plume emitted by a stack, one needs [...] Read more.

We performed measurements of SO₂ emissions with a high UV sensitive dual-camera optical system. Generally, in order to retrieve the two-dimensional SO₂ emission rates of a source, e.g., the slant column density of a plume emitted by a stack, one needs to acquire four images with UV cameras: two images including the emitting stack at wavelengths with high and negligible absorption features (λ_on/off), and two additional images of the background intensity behind the plume, at the same wavelengths as before. However, the true background intensity behind a plume is impossible to obtain from a remote measurement site at rest, and thus, one needs to find a way to approximate the background intensity. Some authors have presented methods to estimate the background behind the plume from two emission images. However, those works are restricted to dealing with clear sky, or almost homogeneously illuminated days. The purpose of this work is to present a new approach using background images constructed from emission images by an automatic plume segmentation and interpolation procedure, in order to estimate the light intensity behind the plume. We compare the performance of the proposed approach with the four images method, which uses, as background, sky images acquired at a different viewing direction. The first step of the proposed approach involves the segmentation of the SO₂ plume from the background. In clear sky days, we found similar results from both methods. However, when the illumination of the sky is non homogeneous, e.g., due to lateral sun illumination or clouds, there are appreciable differences between the results obtained by both methods. We present results obtained in a series of measurements of SO₂ emissions performed on a cloudy day from a stack of an oil refinery in Montevideo City, Uruguay. The results obtained with the UV cameras were compared with scanning DOAS measurements, yielding a good agreement. Full article

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

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21 pages, 14487 KiB

Open AccessArticle

Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica

by Fengming Hui^1,2, Tiancheng Zhao¹, Xinqing Li¹, Mohammed Shokr³

, Petra Heil⁴, Jiechen Zhao⁵, Lin Zhang⁵ and Xiao Cheng^1,2,*

¹ State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

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

³ Science and Technology Branch, Environment Canada, Toronto, ON M3H5T4, Canada

⁴ Australian Antarctic Division and Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, TAS 7001, Australia

⁵ Key Laboratory of Research on Marine Hazards Forecasting (SOA), National Marine Environmental Forecasting Center, Beijing 100081, China

Remote Sens. 2017, 9(6), 518; https://doi.org/10.3390/rs9060518 - 24 May 2017

Cited by 28 | Viewed by 7623

Abstract

Sea ice adversely impacts nautical, logistical and scientific missions in polar regions. Ship navigation benefits from up-to-date sea ice analyses at both regional and local scales. This study presents a satellite-based sea ice navigation system (SatSINS) that integrates observations and scientific output from [...] Read more.

Sea ice adversely impacts nautical, logistical and scientific missions in polar regions. Ship navigation benefits from up-to-date sea ice analyses at both regional and local scales. This study presents a satellite-based sea ice navigation system (SatSINS) that integrates observations and scientific output from remote sensing and meteorological data to develop optimum marine navigational routes in sea ice-covered waters, especially in areas where operational ice information is usually scarce. The system and its applications are presented in the context of a decision-making process to optimize the routing of the RV Xuelong during her passage through Prydz Bay, East Antarctica during three trips in the austral spring of 2011–2013. The study assesses scientifically-generated remote sensing ice parameters for their operational use in marine navigation. Evaluation criteria involve identification of priority parameters, their spatio-temporal requirements in relation to navigational needs, and their level of accuracy in conjunction with the severity of ice conditions. Coarse-resolution ice concentration maps are sufficient to delineate ice edge and develop a safe route when ice concentration is less than 70%, provided that ice dynamics, estimated from examining the cyclonic pattern, is not severe. Otherwise, fine-resolution radar data should be used to identify and avoid deformed ice. Satellite data lagging one day behind the actual location of the ship was sufficient in most cases although the proposed route may have to be adjusted. To evaluate the utility of SatSINS, deviation of the actual route from the proposed route was calculated and found to range between 165 m to about 16.0 km with standard deviations of 2.8–6.1 km. Growth of land-fast ice has proven to be an essential component of the system as it was estimated using a thermodynamic model with input from a meteorological station. Full article

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17 pages, 6018 KiB

Open AccessArticle

Ground-Level NO₂ Concentrations over China Inferred from the Satellite OMI and CMAQ Model Simulations

by Jianbin Gu^1,2, Liangfu Chen^1,*, Chao Yu^1,3,*, Shenshen Li¹, Jinhua Tao¹, Meng Fan¹, Xiaozhen Xiong⁴

, Zifeng Wang¹, Huazhe Shang¹ and Lin Su¹

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 the Chinese Academy of Sciences, Beijing 100049, China

³ State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100101, China

⁴ NOAA/NESDIS/Center for Satellite Applications and Research, College Park, MD 20740, USA

Remote Sens. 2017, 9(6), 519; https://doi.org/10.3390/rs9060519 - 24 May 2017

Cited by 65 | Viewed by 8543

Abstract

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO₂) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO₂ columns rather than ground-level NO [...] Read more.

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO₂) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO₂ columns rather than ground-level NO₂ concentrations, but actually ground-level NO₂ concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO₂ concentrations using the total column of NO₂ observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year’s worth of data from 2014 was processed and the results compared with ground-based NO₂ measurements from a network of China’s National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO₂ concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m³, which was improved to a standard deviation of 18.90 μg/m³ between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO₂ concentrations over China. Full article

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

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18 pages, 7065 KiB

Open AccessArticle

Recent Deceleration of the Ice Elevation Change of Ecology Glacier (King George Island, Antarctica)

by Michał Pętlicki^1,2,*, Joanna Sziło¹, Shelley MacDonell³, Sebastián Vivero^3,†

and Robert J. Bialik^1,4

¹ Institute of Geophysics, Polish Academy of Sciences, ul. Księcia Janusza 64, 01-452 Warsaw, Poland

² Glaciology Laboratory, Centro de Estudios Cientificos (CECs), Av. Arturo Prat 514, 5110466 Valdivia, Chile

³ Centro de Estudios Avanzados en Zonas Aridas, Raul Bitran 1305, 1720010 La Serena, Chile

⁴ Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawińskiego 5a, 02-106 Warsaw, Poland

^† Current address: Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Géopolis, Quartier Mouline, CH-1015 Lausanne, Switzerland.

Remote Sens. 2017, 9(6), 520; https://doi.org/10.3390/rs9060520 - 24 May 2017

Cited by 48 | Viewed by 10678

Abstract

Glacier change studies in the Antarctic Peninsula region, despite their importance for global sea level rise, are commonly restricted to the investigation of frontal position changes. Here we present a long term (37 years; 1979–2016) study of ice elevation changes of the Ecology [...] Read more.

Glacier change studies in the Antarctic Peninsula region, despite their importance for global sea level rise, are commonly restricted to the investigation of frontal position changes. Here we present a long term (37 years; 1979–2016) study of ice elevation changes of the Ecology Glacier, King George Island (

62^{\circ}

11^{'}

S,

58^{\circ}

29^{'}

W). The glacier covers an area of 5.21

{km}^{2}

and is located close to the H. Arctowski Polish Antarctic Station, and therefore has been an object of various multidisciplinary studies with subject ranging from glaciology, meteorology to glacial microbiology. Hence, it is of great interest to assess its current state and put it in a broader context of recent glacial change. In order to achieve that goal, we conducted an analysis of archival cartographic material and combined it with field measurements of proglacial lagoon hydrography and state-of-art geodetic surveying of the glacier surface with terrestrial laser scanning and satellite imagery. Overall mass loss was largest in the beginning of 2000s, and the rate of elevation change substantially decreased between 2012–2016, with little ice front retreat and no significant surface lowering. Ice elevation change rate for the common ablation area over all analyzed periods (1979–2001–2012–2016) has decreased from −1.7 ± 0.4 m/year in 1979–2001 and −1.5 ± 0.5 m/year in 2001–2012 to −0.5 ± 0.6 m/year in 2012–2016. This reduction of ice mass loss is likely related to decreasing summer temperatures in this region of the Antarctic Peninsula. Full article

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

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18 pages, 26144 KiB

Open AccessArticle

Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data

by Haifeng Tian^1,2

, Wang Li¹, Mingquan Wu^1,*, Ni Huang¹, Guodong Li³, Xiang Li^1,2 and Zheng Niu^1,2,*

¹ The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, Datun Road, Chaoyang, Beijing 100101, China

² College of Resource and Environment, University of Chinese Academy of Sciences, Yuquan Road 19, Shijingshan, Beijing 100049, China

³ College of Environment and Planning, Henan University, Jinmingdadao Road, Kaifeng 475004, China

Remote Sens. 2017, 9(6), 521; https://doi.org/10.3390/rs9060521 - 24 May 2017

Cited by 61 | Viewed by 10579

Abstract

Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. [...] Read more.

Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km² and 3729.19 km² during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km² (with a change rate of 35%) and 197.58 km² (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)

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24 pages, 10872 KiB

Open AccessArticle

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

by Yu Liu^1,2,*, Duc Minh Nguyen¹, Nikos Deligiannis¹

, Wenrui Ding^2,3 and Adrian Munteanu¹

¹ ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

² School of Electronic and Information Engineering, Beihang University, 37 Xueyuan Rd., Haidian District, Beijing 100191, China

³ Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Remote Sens. 2017, 9(6), 522; https://doi.org/10.3390/rs9060522 - 25 May 2017

Cited by 139 | Viewed by 15369

Abstract

A new convolution neural network (CNN) architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN) design being structured into encoding and decoding stages. By taking advantage of recent advances in [...] Read more.

A new convolution neural network (CNN) architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN) design being structured into encoding and decoding stages. By taking advantage of recent advances in CNN designs, we use the composed inception module to replace common convolutional layers, providing the network with multi-scale receptive areas with rich context. Additionally, in order to reduce spatial ambiguities in the up-sampling stage, skip connections with residual units are also employed to feed forward encoding-stage information directly to the decoder. Moreover, overlap inference is employed to alleviate boundary effects occurring when high resolution images are inferred from small-sized patches. Finally, we also propose a post-processing method based on weighted belief propagation to visually enhance the classification results. Extensive experiments based on the Vaihingen and Potsdam datasets demonstrate that the proposed architectures outperform three reference state-of-the-art network designs both numerically and visually. Full article

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

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22 pages, 8025 KiB

Open AccessArticle

Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring

by Hiroyuki Tsutsui^*

and Takashi Maeda^*

Earth Observation Research Center, Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan

Remote Sens. 2017, 9(6), 523; https://doi.org/10.3390/rs9060523 - 25 May 2017

Cited by 7 | Viewed by 6504

Abstract

Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave [...] Read more.

Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth. Full article

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

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25 pages, 6268 KiB

Open AccessArticle

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

by Guosheng Zhong^1,*, Xiufeng Wang², Meng Guo³

, Hiroshi Tani²

, Anthony R. Chittenden⁴, Shuai Yin¹, Zhongyi Sun¹ and Shinji Matsumura⁵

¹ Graduate School of Agriculture, Hokkaido University, Sapporo 0608589, Japan

² Research Faculty of Agriculture, Hokkaido University, Sapporo 0608589, Japan

³ School of Geographical Sciences, Northeast Normal University, Changchun 130024, China

⁴ Faculty of Fisheries Sciences, Hokkaido University, Hakodate 0418611, Japan

⁵ Faculty of Agriculture, Kagawa University, Sapporo 0608589, Japan

Remote Sens. 2017, 9(6), 524; https://doi.org/10.3390/rs9060524 - 25 May 2017

Cited by 5 | Viewed by 5395

Abstract

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target [...] Read more.

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI_2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI_2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AOD_AERONET). Full article

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20 pages, 17477 KiB

Open AccessArticle

Satellite Monitoring the Spatial-Temporal Dynamics of Desertification in Response to Climate Change and Human Activities across the Ordos Plateau, China

by Qiang Guo^1,2

, Bihong Fu^1,*

, Pilong Shi¹, Thomas Cudahy³

, Jing Zhang¹

and Huan Xu^1,2

¹ 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 CSIRO Mineral Resources, Australian Resources Research Centre, 26 Dick Perry Avenue, Kensington, Western Australia 6151, Australia

Remote Sens. 2017, 9(6), 525; https://doi.org/10.3390/rs9060525 - 25 May 2017

Cited by 88 | Viewed by 10566

Abstract

The Ordos Plateau, a typical semi-arid area in northern China, has experienced severe wind erosion events that have stripped the agriculturally important finer fraction of the topsoil and caused dust events that often impact the air quality in northern China and the surrounding [...] Read more.

The Ordos Plateau, a typical semi-arid area in northern China, has experienced severe wind erosion events that have stripped the agriculturally important finer fraction of the topsoil and caused dust events that often impact the air quality in northern China and the surrounding regions. Both climate change and human activities have been considered key factors in the desertification process. This study used multi-spectral Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) remote sensing data collected in 2000, 2006, 2010 and 2015 to generate a temporal series of the modified soil-adjusted vegetation index (MSAVI), bare soil index (BSI) and albedo products in the Ordos Plateau. Based on these satellite products and the decision tree method, we quantitatively assessed the desertification status over the past 15 years since 2000. Furthermore, a quantitative method was used to assess the roles of driving forces in desertification dynamics using net primary productivity (NPP) as a commensurable indicator. The results showed that the area of non-desertification land increased from 6647 km² in 2000 to 15,961 km² in 2015, while the area of severe desertification land decreased from 16,161 km² in 2000 to 8,331 km² in 2015. During the period 2006–2015, the effect of human activities, especially the ecological recovery projects implemented in northern China, was the main cause of desertification reversion in this region. Therefore, ecological recovery projects are still required to promote harmonious development between nature and human society in ecologically fragile regions like the Ordos Plateau. Full article

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13 pages, 2412 KiB

Open AccessArticle

Exploring Relationships among Tree-Ring Growth, Climate Variability, and Seasonal Leaf Activity on Varying Timescales and Spatial Resolutions

by Upasana Bhuyan^1,*, Christian Zang², Sergio M. Vicente-Serrano³ and Annette Menzel^1,4

¹ Ecoclimatology, Department of Ecology and Ecosystem Management, Technische Universität München, Freising 85354, Germany

² Land Surface-Atmosphere Interactions, Department of Ecology and Ecosystem Management, Technische Universität München, Freising 85354, Germany

³ Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Zaragoza 50059, Spain

⁴ Institute for Advanced Study, Technische Universität München, Garching 85748, Germany

Remote Sens. 2017, 9(6), 526; https://doi.org/10.3390/rs9060526 - 25 May 2017

Cited by 46 | Viewed by 8486

Abstract

In the first section of this study, we explored the relationship between ring width index (RWI) and normalized difference vegetation index (NDVI) time series on varying timescales and spatial resolutions, hypothesizing positive associations between RWI and current and previous- year NDVI at 69 [...] Read more.

In the first section of this study, we explored the relationship between ring width index (RWI) and normalized difference vegetation index (NDVI) time series on varying timescales and spatial resolutions, hypothesizing positive associations between RWI and current and previous- year NDVI at 69 forest sites scattered in the Northern Hemisphere. We noted that the relationship between RWI and NDVI varies over space and between tree types (deciduous versus coniferous), bioclimatic zones, cumulative NDVI periods, and spatial resolutions. The high-spatial-resolution NDVI (MODIS) reflected stronger growth patterns than those with coarse-spatial-resolution NDVI (GIMMS3g). In the second section, we explore the link between RWI, climate and NDVI phenological metrics (in place of NDVI) for the same forest sites using random forest models to assess the complicated and nonlinear relationships among them. The results are as following (a) The model using high-spatial-resolution NDVI time series explained a higher proportion of the variance in RWI than that of the model using coarse-spatial-resolution NDVI time series. (b) Amongst all NDVI phenological metrics, summer NDVI sum could best explain RWI followed by the previous year’s summer NDVI sum and the previous year’s spring NDVI sum. (c) We demonstrated the potential of NDVI metrics derived from phenology to improve the existing RWI-climate relationships. However, further research is required to investigate the robustness of the relationship between NDVI and RWI, particularly when more tree-ring data and longer records of the high-spatial-resolution NDVI become available. Full article

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

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19 pages, 7948 KiB

Open AccessArticle

Optical Cloud Pixel Recovery via Machine Learning

by Subrina Tahsin, Stephen C. Medeiros^*

, Milad Hooshyar and Arvind Singh

Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA

Remote Sens. 2017, 9(6), 527; https://doi.org/10.3390/rs9060527 - 25 May 2017

Cited by 27 | Viewed by 7826

Abstract

Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial [...] Read more.

Remote sensing derived Normalized Difference Vegetation Index (NDVI) is a widely used index to monitor vegetation and land use change. NDVI can be retrieved from publicly available data repositories of optical sensors such as Landsat, Moderate Resolution Imaging Spectro-radiometer (MODIS) and several commercial satellites. Studies that are heavily dependent on optical sensors are subject to data loss due to cloud coverage. Specifically, cloud contamination is a hindrance to long-term environmental assessment when using information from satellite imagery retrieved from visible and infrared spectral ranges. Landsat has an ongoing high-resolution NDVI record starting from 1984. Unfortunately, this long time series NDVI data suffers from the cloud contamination issue. Though both simple and complex computational methods for data interpolation have been applied to recover cloudy data, all the techniques have limitations. In this paper, a novel Optical Cloud Pixel Recovery (OCPR) method is proposed to repair cloudy pixels from the time-space-spectrum continuum using a Random Forest (RF) trained and tested with multi-parameter hydrologic data. The RF-based OCPR model is compared with a linear regression model to demonstrate the capability of OCPR. A case study in Apalachicola Bay is presented to evaluate the performance of OCPR to repair cloudy NDVI reflectance. The RF-based OCPR method achieves a root mean squared error of 0.016 between predicted and observed NDVI reflectance values. The linear regression model achieves a root mean squared error of 0.126. Our findings suggest that the RF-based OCPR method is effective to repair cloudy pixels and provides continuous and quantitatively reliable imagery for long-term environmental analysis. Full article

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15 pages, 2994 KiB

Open AccessArticle

Comparison of Three Theoretical Methods for Determining Dry and Wet Edges of the LST/FVC Space: Revisit of Method Physics

by Hao Sun^*, Yanmei Wang, Weihan Liu, Shuyun Yuan and Ruwei Nie

College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China

Remote Sens. 2017, 9(6), 528; https://doi.org/10.3390/rs9060528 - 26 May 2017

Cited by 26 | Viewed by 5617

Abstract

Land surface temperature and fractional vegetation coverage (LST/FVC) space is a classical model for estimating evapotranspiration, soil moisture, and drought monitoring based on remote sensing. One of the key issues in its utilization is to determine its boundaries, i.e., the dry and wet [...] Read more.

Land surface temperature and fractional vegetation coverage (LST/FVC) space is a classical model for estimating evapotranspiration, soil moisture, and drought monitoring based on remote sensing. One of the key issues in its utilization is to determine its boundaries, i.e., the dry and wet edges. In this study, we revisited and compared three methods that were presented by Moran et al. (1994), Long et al. (2012), and Sun (2016) for calculating the dry and wet edges theoretically. Results demonstrated that: (1) for the dry edge, the Sun method is equal to the Long method and they have greater vegetation temperature than that of the Moran method. (2) With respect to the wet edge, there are greater differences among the three methods. Generally, Long’s wet edge is a horizontal line equaling air temperature. Sun’s wet edge is an oblique line and is higher than that of the Long’s. Moran’s wet edge intersects them with a higher soil temperature and a lower vegetation temperature. (3) The Sun and Long methods are simpler in calculation and can circumvent some complex parameters as compared with the Moran method. Moreover, they outperformed the Moran method in a comparison of estimating latent heat flux (LE), where determination coefficients varied between 0.45 ~ 0.66 (Sun), 0.47 ~ 0.68 (Long), and 0.39 ~ 0.57 (Moran) among field stations. Full article

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16 pages, 7569 KiB

Open AccessArticle

Evaluating Urban Land Carrying Capacity Based on the Ecological Sensitivity Analysis: A Case Study in Hangzhou, China

by Jinyeu Tsou¹, Yanfei Gao¹, Yuanzhi Zhang^2,3,*, Sun Genyun⁴

, Jinchang Ren⁵ and Yu Li⁶

¹ Center for Housing Innovations, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; jinyeutsou@cuhk.edu.hk (J.Y.T.); gaoyanfei2016@163.com (Y.G.)

² National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China

³ Key Lab of Lunar Science and Deep-Space Exploration, Chinese Academy of Sciences, Beijing 100012, China

⁴ School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China

⁵ Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK

⁶ Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Remote Sens. 2017, 9(6), 529; https://doi.org/10.3390/rs9060529 - 25 May 2017

Cited by 73 | Viewed by 10299

Abstract

Abstract: In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and [...] Read more.

Abstract: In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China. Full article

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

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16 pages, 7493 KiB

Open AccessArticle

Global Analysis of Bioclimatic Controls on Ecosystem Productivity Using Satellite Observations of Solar-Induced Chlorophyll Fluorescence

by Nima Madani^1,2,*, John S. Kimball^1,2, Lucas A. Jones^1,2, Nicholas C. Parazoo³ and Kaiyu Guan⁴

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 Jet Propulsion Laboratory, 4800 Oak Grove Drive, Mail Stop 200-233, Pasadena, CA 91109, USA

⁴ Department of Natural Resources and Environmental Sciences and National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

Remote Sens. 2017, 9(6), 530; https://doi.org/10.3390/rs9060530 - 26 May 2017

Cited by 66 | Viewed by 10208

Abstract

Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, [...] Read more.

Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, including carbon flux towers. Recent studies have shown that satellite observations of Solar-Induced chlorophyll Fluorescence (SIF) are highly correlated with ecosystem Gross Primary Productivity (GPP). Here, we use a relatively long-term global SIF observational record from the Global Ozone Monitoring Experiment-2 (GOME-2) sensors to investigate the relationships between SIF, used as a proxy for GPP, and selected bio-climatic factors constraining plant growth at the global scale. We compared the satellite SIF retrievals with collocated GPP observations from a global network of tower carbon flux monitoring sites and surface meteorological data from model reanalysis, including soil moisture, Vapor Pressure Deficit (VPD), and minimum daily air temperature (Tmin). We found strong correspondence (R² > 80%) between SIF and GPP monthly climatologies for tower sites characterized by mixed, deciduous broadleaf, evergreen needleleaf forests, and croplands. For other land cover types including savanna, shrubland, and evergreen broadleaf forest, SIF showed significant but higher variability in correlations between sites. In order to analyze temperature and moisture related effects on ecosystem productivity, we divided SIF by photosynthetically active radiation (SIF_p) and examined partial correlations between SIF_p and the climatic factors across a global range of flux tower sites, and over broader regional and global extents. We found that productivity in arid ecosystems is more strongly controlled by soil water content to an extent that soil moisture explains a higher proportion of the seasonal cycle in productivity than VPD. At the global scale, ecosystem productivity is affected by joint climatic constraint factors so that VPD, Tmin, and soil moisture were significant (p < 0.05) controls over 60, 59, and 35 percent of the global domain, respectively. Our study identifies and confirms dominant climate control factors influencing productivity at the global scale indicated from satellite SIF observations. The results are generally consistent with climate response characteristics indicated from sparse global tower observations, while providing more extensive coverage for verifying and refining global carbon and climate model assumptions and predictions. Full article

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

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13 pages, 2347 KiB

Open AccessArticle

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

by Sam D. Cooper¹, David P. Roy^1,*

, Crystal B. Schaaf² and Ian Paynter²

¹ Geospatial Sciences Center of Excellence & Department of Geography, South Dakota State University, Brookings, SD 57007, USA

² School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA

Remote Sens. 2017, 9(6), 531; https://doi.org/10.3390/rs9060531 - 26 May 2017

Cited by 100 | Viewed by 10064

Abstract

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) [...] Read more.

Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGB_grass r² = 0.42, AGB_total r² = 0.32) than the TLS (AGB_grass r² = 0.46, AGB_total r² = 0.57) or SfM (AGB_grass r² = 0.54, AGB_total r² = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems. Full article

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16 pages, 10757 KiB

Open AccessArticle

The Relationship between Urban Land Surface Material Fractions and Brightness Temperature Based on MESMA

by Tao Chen^1,*

, Xujia Zhang² and Ruiqing Niu¹

¹ Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

² Fujian Surveying and Mapping Institute, Fuzhou 350003, China

Remote Sens. 2017, 9(6), 532; https://doi.org/10.3390/rs9060532 - 27 May 2017

Cited by 5 | Viewed by 4791

Abstract

The relationship between urban land surface material fractions (ULSMFs) and brightness temperature has long attracted attention in research on urban environments. In this paper, a multiple endmember spectral mixture analysis (MESMA) method was applied to extract vegetation-impervious surface-soil (V-I-S) fractions in each pixel, [...] Read more.

The relationship between urban land surface material fractions (ULSMFs) and brightness temperature has long attracted attention in research on urban environments. In this paper, a multiple endmember spectral mixture analysis (MESMA) method was applied to extract vegetation-impervious surface-soil (V-I-S) fractions in each pixel, and the surface brightness temperature was derived by using the radiation in the upper atmosphere, on the basis of Landsat 8 images. Then, a clustering analysis, ternary triangular chart (TTC), and a multivariate statistical analysis were applied to ascertain the relationship between the fractions in each pixel and the land surface brightness temperature (LSBT). The hypsometric TTC, as well as the geographical distribution features of the LSBT, revealed that the changes in LSBT were associated with the high fractions of impervious surfaces (or vegetation), in addition to the temperature distribution differences across locations with varying land-cover types. The data fitting results showed that the comprehensive endmember fractions of V-I-S explained 98.6% of fluctuating LSBT, and the impervious surface fraction had a positive impact on the LSBT, whereas the fraction of vegetation had a negative impact on the LSBT. Full article

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19 pages, 2152 KiB

Open AccessArticle

Extended Data-Based Mechanistic Method for Improving Leaf Area Index Time Series Estimation with Satellite Data

by Hongmin Zhou^1,2, Jindi Wang^1,2,*

, Shunlin Liang^1,3

and Zhiqiang Xiao^1,2

¹ The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

² Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China

³ Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

Remote Sens. 2017, 9(6), 533; https://doi.org/10.3390/rs9060533 - 26 May 2017

Cited by 12 | Viewed by 4760

Abstract

Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. [...] Read more.

Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. This study proposes an extended data-based mechanistic method (EDBM) for estimating LAI time series from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data-based mechanistic model is universalized to supply the LAI background information, and then the vegetation canopy radiative-transfer model (PROSAIL) is coupled to calculate reflectances with the same observation geometry as MODIS reflectance data. The ensemble Kalman filter (ENKF) is introduced to improve LAI estimation based on the difference between simulated and observed reflectances. Field measurements from seven Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites and reference maps from the Imagine-S project La Albufera, Spain site were used to validate the model. The results demonstrate that when compared with field measurements, the LAI time-series estimates obtained using this approach were superior to those obtained with the MODIS 500 m resolution LAI product. The root mean square errors (RMSE) of the MODIS LAI product and of the LAI estimated with the proposed method were 1.26 and 0.5, respectively. When compared with reference LAI maps, the results indicate that the estimated LAI is spatially and temporally consistent with LAI reference maps. The average differences between EDBM and the LAI reference map on the selected four days was 0.32. Full article

(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)

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22 pages, 3169 KiB

Open AccessArticle

Optimization of a Deep Convective Cloud Technique in Evaluating the Long-Term Radiometric Stability of MODIS Reflective Solar Bands

by Qiaozhen Mu^1,*, Aisheng Wu¹, Xiaoxiong Xiong², David R. Doelling³, Amit Angal¹, Tiejun Chang¹ and Rajendra Bhatt⁴

¹ Science Systems and Applications, Inc., 10210 Greenbelt Road, Lanham, MD 20706, USA

² Sciences and Exploration Directorate, NASA/GSFC, Greenbelt, MD 20771, USA

³ NASA Langley Research Center, 21 Langley Blvd. MS 420, Hampton, VA 23681, USA

⁴ Science Systems and Applications Inc., 1 Enterprise Pkwy, Suite 200, Hampton, VA 23666, USA

Remote Sens. 2017, 9(6), 535; https://doi.org/10.3390/rs9060535 - 27 May 2017

Cited by 32 | Viewed by 5044

Abstract

MODIS reflective solar bands are calibrated on-orbit using a solar diffuser and near-monthly lunar observations. To monitor the performance and effectiveness of the on-orbit calibrations, pseudo-invariant targets such as deep convective clouds (DCCs), Libya-4, and Dome-C are used to track the long-term stability [...] Read more.

MODIS reflective solar bands are calibrated on-orbit using a solar diffuser and near-monthly lunar observations. To monitor the performance and effectiveness of the on-orbit calibrations, pseudo-invariant targets such as deep convective clouds (DCCs), Libya-4, and Dome-C are used to track the long-term stability of MODIS Level 1B product. However, the current MODIS operational DCC technique (DCCT) simply uses the criteria set for the 0.65-µm band. We optimize several critical DCCT parameters including the 11-µm IR-band Brightness Temperature (BT11) threshold for DCC identification, DCC core size and uniformity to help locate DCCs at convection centers, data collection time interval, and probability distribution function (PDF) bin increment for each channel. The mode reflectances corresponding to the PDF peaks are utilized as the DCC reflectances. Results show that the BT11 threshold and time interval are most critical for the Short Wave Infrared (SWIR) bands. The Bidirectional Reflectance Distribution Function model is most effective in reducing the DCC anisotropy for the visible channels. The uniformity filters and PDF bin size have minimal impacts on the visible channels and a larger impact on the SWIR bands. The newly optimized DCCT will be used for future evaluation of MODIS on-orbit calibration by MODIS Characterization Support Team. Full article

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18 pages, 16933 KiB

Open AccessArticle

Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing

by Long Li¹

, Xin Huang^2,3,*, Jiayi Li^2,* and Dawei Wen³

¹ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographic Environment of Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China

² School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

³ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Remote Sens. 2017, 9(6), 536; https://doi.org/10.3390/rs9060536 - 27 May 2017

Cited by 27 | Viewed by 6486

Abstract

Canopy layer heat islands (CLHIs) in urban areas are a growing problem. In recent decades, the key issues have been how to monitor CLHIs at a large scale, and how to optimize the urban landscape to mitigate CLHIs. Taking the city of Wuhan [...] Read more.

Canopy layer heat islands (CLHIs) in urban areas are a growing problem. In recent decades, the key issues have been how to monitor CLHIs at a large scale, and how to optimize the urban landscape to mitigate CLHIs. Taking the city of Wuhan as a case study, we examine the spatiotemporal trends of the CLHI along urban-rural gradients, including the intensity and footprint, based on satellite observations and ground weather station data. The results show that CLHI intensity (CLHII) decays exponentially and significantly along the urban-rural gradients, and the CLHI footprint varies substantially and especially in winter. We then quantify the driving factors of the CLHI by establishing multiple linear regression (MLR) models with the assistance of ZY-3 satellite data (with a spatial resolution of 2.5 m), and obtain five main findings: (1) built-up area had a significant positive effect on daily mean CLHII in summer and a negative effect in winter; (2) vegetation had significant inhibiting effects on daily mean CLHII in both summer and winter; (3) absolute humidity has a significant inhibiting effect on daily mean CLHII in summer and a positive effect in winter; (4) anthropogenic heat emissions exacerbated the daily mean CLHII by about 0.19 °C (90% confidence interval −0.06–0.44 °C) on 17 September 2013 and by about 0.06 °C (−0.06–0.19 °C) on 23 January 2014; and (5) if most of the urban area is transformed into roads (i.e., an extreme case), we estimate that the daily mean CLHII would reach 1.41 °C (0.38–2.44 °C) on 17 September 2013 and 0.14 °C (0.08–0.2 °C) on 23 January 2014 in Wuhan metropolitan area. Overall, the results provide new insights into quantifying the CLHI and its driving factors, to enhance our understanding of urban heat islands. Full article

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24 pages, 31892 KiB

Open AccessArticle

An Enhanced Satellite-Based Algorithm for Detecting and Tracking Dust Outbreaks by Means of SEVIRI Data

by Francesco Marchese^1,*

, Filomena Sannazzaro², Alfredo Falconieri¹

, Carolina Filizzola¹

, Nicola Pergola¹

and Valerio Tramutoli²

¹ National Research Council, Institute of Methodologies for Environmental Analysis, C. da S. Loja, 85050 Tito Scalo (Pz), Italy

² School of Engineering, University of Basilicata, Via dell’Ateneo Lucano, 10, 85100 Potenza, Italy

Remote Sens. 2017, 9(6), 537; https://doi.org/10.3390/rs9060537 - 27 May 2017

Cited by 27 | Viewed by 7197

Abstract

Dust outbreaks are meteorological phenomena of great interest for scientists and authorities (because of their impact on the climate, environment, and human activities), which may be detected, monitored, and characterized from space using different methods and procedures. Among the recent dust detection algorithms, [...] Read more.

Dust outbreaks are meteorological phenomena of great interest for scientists and authorities (because of their impact on the climate, environment, and human activities), which may be detected, monitored, and characterized from space using different methods and procedures. Among the recent dust detection algorithms, the RST_DUST multi-temporal technique has provided good results in different geographic areas (e.g., Mediterranean basin; Arabian Peninsula), exhibiting a better performance than traditional split window methods, in spite of some limitations. In this study, we present an optimized configuration of this technique, which better exploits data provided by Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation (MSG) satellites to address those issues (e.g., sensitivity reduction over arid and semi-arid regions; dependence on some meteorological clouds). Three massive dust events affecting Europe and the Mediterranean basin in May 2008/2010 are analysed in this work, using information provided by some independent and well-established aerosol products to assess the achieved results. The study shows that the proposed algorithm, christened eRST_DUST (i.e., enhanced RST_DUST), which provides qualitative information about dust outbreaks, is capable of increasing the trade-off between reliability and sensitivity. The results encourage further experimentations of this method in other periods of the year, also exploiting data provided by different satellite sensors, for better evaluating the advantages arising from the use of this dust detection technique in operational scenarios. Full article

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30 pages, 6839 KiB

Open AccessArticle

Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations

by Richard Beck^1,*, Min Xu¹, Shengan Zhan¹

, Hongxing Liu¹, Richard A. Johansen¹

, Susanna Tong¹, Bo Yang¹, Song Shu¹, Qiusheng Wu¹

, Shujie Wang¹, Kevin Berling¹, Andrew Murray¹

, Erich Emery², Molly Reif³, Joseph Harwood³, Jade Young⁴, Mark Martin⁵, Garrett Stillings⁵, Richard Stumpf⁶

, Haibin Su⁷, Zhaoxia Ye⁸ and Yan Huang⁹ Show full author list Hide full author list

¹ Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA

² U.S. Army Corps of Engineers, Great Lakes and Ohio River Division, Cincinnati, OH 45202, USA

³ U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS 39556, USA

⁴ U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY 40202, USA

⁵ Kentucky Department of Environmental Protection, Division of Water, Frankfort, KY 40601, USA

⁶ National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD 20910, USA

⁷ Department of Physics and Geosciences, Texas A & M Kingsville, Kingsville, TX 78363-8202, USA

⁸ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

⁹ School of Geographic Sciences, Key Laboratory of Geographic Information Science, East China Normal University, Shanghai 200241, China

Remote Sens. 2017, 9(6), 538; https://doi.org/10.3390/rs9060538 - 29 May 2017

Cited by 43 | Viewed by 11608

Abstract

We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km² reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from [...] Read more.

We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km² reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r² = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation. Full article

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18 pages, 9359 KiB

Open AccessArticle

Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China

by Mingyue Liu^1,2,†, Huiying Li^3,†, Lin Li⁴, Weidong Man^1,2

, Mingming Jia^1,*

, Zongming Wang^1,* and Chunyan Lu⁵

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

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

³ College of Earth Sciences, Jilin University, Changchun 130061, China

⁴ Department of Earth Sciences, Indiana University-Purdue University, Indianapolis, IN 46202, USA

⁵ College of Computer and Information, Fujian Agriculture and Forestry University, Fuzhou 350002, China

^† These authors contributed equally to this work.

Remote Sens. 2017, 9(6), 539; https://doi.org/10.3390/rs9060539 - 30 May 2017

Cited by 86 | Viewed by 7755

Abstract

Spartina alterniflora (S. alterniflora) is one of the most harmful invasive plants in China. Google Earth (GE), as a free software, hosts high-resolution imagery for many areas of the world. To explore the use of GE imagery for monitoring S. alterniflora [...] Read more.

Spartina alterniflora (S. alterniflora) is one of the most harmful invasive plants in China. Google Earth (GE), as a free software, hosts high-resolution imagery for many areas of the world. To explore the use of GE imagery for monitoring S. alterniflora invasion and developing an understanding of the invasion process of S. alterniflora in the Zhangjiang Estuary, the object-oriented method and visual interpretation were applied to GE, SPOT-5, and Gaofen-1 (GF-1) images. In addition, landscape metrics of S. alterniflora patches adjacent to mangrove forests were calculated and mangrove gaps were recorded by checking whether S. alterniflora exists. The results showed that from 2003–2015, the areal extent of S. alterniflora in the Zhangjiang Estuary increased from 57.94 ha to 116.11 ha, which was mainly converted from mudflats and moved seaward significantly. Analyses of the S. alterniflora expansion patterns in the six subzones indicated that the expansion trends varied with different environmental circumstances and human activities. Land reclamation, mangrove replantation, and mudflat aquaculture caused significant losses of S. alterniflora. The number of invaded gaps increased and S. alterniflora patches adjacent to mangrove forests became much larger and more aggregated during 2003–2015 (the class area increased from 12.13 ha to 49.76 ha and the aggregation index increased from 91.15 to 94.65). We thus concluded that S. alterniflora invasion in the Zhangjiang Estuary had seriously increased and that measures should be taken considering the characteristics shown in different subzones. This study provides an example of applying GE imagery to monitor invasive plants and illustrates that this approach can aid in the development of governmental policies employed to control S. alterniflora invasion. Full article

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17 pages, 2885 KiB

Open AccessArticle

Urbanization Effects on Vegetation and Surface Urban Heat Islands in China’s Yangtze River Basin

by Rui Yao¹, Lunche Wang^1,*

, Xuan Gui¹, Yukun Zheng¹, Haoming Zhang¹ and Xin Huang^2,3,*

¹ Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China

² School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

³ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Remote Sens. 2017, 9(6), 540; https://doi.org/10.3390/rs9060540 - 30 May 2017

Cited by 103 | Viewed by 8074

Abstract

In the context of rapid urbanization, systematic research about temporal trends of urbanization effects (UEs) on urban environment is needed. In this study, MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature (LST) data and enhanced vegetation index (EVI) data were used to analyze [...] Read more.

In the context of rapid urbanization, systematic research about temporal trends of urbanization effects (UEs) on urban environment is needed. In this study, MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature (LST) data and enhanced vegetation index (EVI) data were used to analyze the temporal trends of UEs on vegetation and surface urban heat islands (SUHIs) at 10 big cities in Yangtze River Basin (YRB), China during 2001–2016. The urban and rural areas in each city were derived from MODIS land cover data and nighttime light data. It was found that the UEs on vegetation and SUHIs were increasingly significant in YRB, China. The ∆EVI (the UEs on vegetation, urban EVI minus rural EVI) decreased significantly (p < 0.05) in 9, 7 and 5 out of 10 cities for annual, summer and winter, respectively. The annual daytime and nighttime SUHI intensity (SUHII; urban LST minus rural LST) increased significantly (p < 0.05) in 10 and 4 out of 10 cities, respectively. The increasing rate of daytime SUHII and the decreasing rate of ∆EVI in old urban areas were much less than the whole urban area (0.034 °C/year vs. 0.077 °C/year for annual daytime SUHII; 0.00209/year vs. 0.00329/year for ∆EVI). The correlation analyses indicated that the annual and summer daytime SUHII were significantly negatively correlated with ∆EVI in most cities. The decreasing ∆EVI may also contribute to the increasing nighttime SUHII. In addition, the significant negative correlations (r < −0.5, p < 0.1) between inter-annual linear slope of ∆EVI and SUHII were observed, which suggested that the cities with higher decreasing rates of ∆EVI may show higher increasing rates of SUHII. Full article

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20 pages, 1646 KiB

Open AccessArticle

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

by Elham Kordi Ghasrodashti^1,*, Azam Karami^2,3,*, Rob Heylen³ and Paul Scheunders³

¹ Department of Electrical and Electronics Engineering, Shiraz University of Technology, 13876-71557 Shiraz, Iran

² Department of Physics, Shahid Bahonar University of Kerman, 7616914111 Kerman, Iran

³ Vision Lab, University of Antwerp, 2610 Antwerp, Belgium

Remote Sens. 2017, 9(6), 541; https://doi.org/10.3390/rs9060541 - 31 May 2017

Cited by 36 | Viewed by 6649

Abstract

In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral [...] Read more.

In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR) or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently. Full article

(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)

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17 pages, 1779 KiB

Open AccessArticle

Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea

by JongCheol Pyo¹, Yakov Pachepsky², Sang-Soo Baek¹, YongSeong Kwon¹, MinJeong Kim¹, Hyuk Lee³, Sanghyun Park³, YoonKyung Cha⁴, Rim Ha³

, Gibeom Nam³, Yongeun Park^1,* and Kyung Hwa Cho^1,*

¹ School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Korea

² Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD 20705, USA

³ Water Quality Assessment Research Division, National Institute of Environmental Research, Environmental Research Complex, Incheon 22689, Korea

⁴ School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 130-743, Korea

Remote Sens. 2017, 9(6), 542; https://doi.org/10.3390/rs9060542 - 30 May 2017

Cited by 25 | Viewed by 5596

Abstract

Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal [...] Read more.

Several semi-analytical algorithms have been developed to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in inland waters. This study aimed at identifying the influence of algorithm parameters on the output variables and searching optimal parameter values. The optimal parameters of seven semi-analytical algorithms were applied to estimate the Chl-a and PC concentrations. The absorption coefficient measurements were coupled with pigment measurements to calibrate the algorithm parameters. For sensitivity analysis, the elementary effect test was conducted to analyze the influence of the algorithm parameters. The sensitivity analysis results showed that the parameters in the Y function and specific absorption coefficient were the most sensitive parameters. Then, the parameters were optimized via a single-objective optimization that involved one objective function being minimized and a multi-objective optimization that contained more than one objective function. The single-objective optimization led to substantial errors in absorption coefficients. In contrast, the multi-objective optimization improved the algorithm performance with respect to both the absorption coefficient estimates and pigment concentration estimates. The optimized parameters of the absorption coefficient reflected the high-particulate content in waters of the Baekje reservoir using an infrared backscattering wavelength and relatively high value of Y. Moreover, the results indicate the value of measuring the site-specific absorption if site-specific optimization of semi-analyical algorithm parameters was envisioned. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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15 pages, 8162 KiB

Open AccessArticle

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

by Friederike Gnädinger and Urs Schmidhalter^*

Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Freising 85354, Germany

Remote Sens. 2017, 9(6), 544; https://doi.org/10.3390/rs9060544 - 31 May 2017

Cited by 158 | Viewed by 13867

Abstract

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need [...] Read more.

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3- to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R² = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes. Full article

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23 pages, 10587 KiB

Open AccessArticle

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

by Ning Liu^1,2,*, Richard J. Harper^1,2, Rebecca N. Handcock^1,3, Bradley Evans⁴, Stanley J. Sochacki¹, Bernard Dell^1,2

, Lewis L. Walden¹ and Shirong Liu^2,*

¹ School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia

² Key Laboratory of Forest Ecology and Environment of State Forestry Administration, Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 10091, China

³ Edith Cowan University, Joondalup,WA 6027, Australia

⁴ School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia

Remote Sens. 2017, 9(6), 545; https://doi.org/10.3390/rs9060545 - 1 Jun 2017

Cited by 15 | Viewed by 7259

Abstract

Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is [...] Read more.

Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant- and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R²: 0.76–0.89) and spectral-based vegetation indices (R²: 0.77–0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands. Full article

(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)

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23 pages, 8349 KiB

Open AccessArticle

Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Composting Algorithms, Spectral Indices, and Sensors

by Eric W. Chance^1,*

, Kelly M. Cobourn¹

, Valerie A. Thomas¹

, Blaine C. Dawson² and Alejandro N. Flores²

¹ Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, 310 West Campus Drive, Blacksburg, VA 24061-0324, USA

² Department of Geosciences, Boise State University, 1910 University Drive, Boise, ID 83725-1535, USA

Remote Sens. 2017, 9(6), 546; https://doi.org/10.3390/rs9060546 - 1 Jun 2017

Cited by 13 | Viewed by 6157

Abstract

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the [...] Read more.

There are pressing concerns about the interplay between agricultural productivity, water demand, and water availability in semi-arid to arid regions of the world. Currently, irrigated agriculture is the dominant water user in these regions and is estimated to consume approximately 80% of the world’s diverted freshwater resources. We develop an improved irrigated land-use mapping algorithm that uses the seasonal maximum value of a spectral index to distinguish between irrigated and non-irrigated parcels in Idaho’s Snake River Plain. We compare this approach to two alternative algorithms that differentiate between irrigated and non-irrigated parcels using spectral index values at a single date or the area beneath spectral index trajectories for the duration of the agricultural growing season. Using six different pixel and county-scale error metrics, we evaluate the performance of these three algorithms across all possible combinations of two growing seasons (2002 and 2007), two datasets (MODIS and Landsat 5), and three spectral indices, the Normalized Difference Vegetation Index, Enhanced Vegetation Index and Normalized Difference Moisture Index (NDVI, EVI, and NDMI). We demonstrate that, on average, the seasonal-maximum algorithm yields an improvement in classification accuracy over the accepted single-date approach, and that the average improvement under this approach is a 60% reduction in county scale root mean square error (RMSE), and modest improvements of overall accuracy in the pixel scale validation. The greater accuracy of the seasonal-maximum algorithm is primarily due to its ability to correctly classify non-irrigated lands in riparian and developed areas of the study region. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)

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12 pages, 3883 KiB

Open AccessArticle

Post-Deepwater Horizon Oil Spill Monitoring of Louisiana Salt Marshes Using Landsat Imagery

by Yu Mo^*, Michael S. Kearney and J. C. Alexis Riter

Department of Environmental Science and Technology, University of Maryland, 1426 Animal Sci./Ag. Engr. Bldg., College Park, MD 20742, USA

Remote Sens. 2017, 9(6), 547; https://doi.org/10.3390/rs9060547 - 1 Jun 2017

Cited by 19 | Viewed by 6291

Abstract

The Deepwater Horizon oil spill, the second largest marine oil spill in history, contaminated over a thousand kilometers of coastline in the Louisiana salt marshes and seriously threatened this valuable ecosystem. Measuring the impacts of the oil spill over the large and complex [...] Read more.

The Deepwater Horizon oil spill, the second largest marine oil spill in history, contaminated over a thousand kilometers of coastline in the Louisiana salt marshes and seriously threatened this valuable ecosystem. Measuring the impacts of the oil spill over the large and complex coast calls for the application of remote sensing techniques. This study develops a method for post-Deepwater Horizon oil spill monitoring of the damaged marsh vegetation using Landsat imagery. This study utilizes 10 years of Landsat data, from 2005 to 2014, to examine the longevity of the oil spill’s impacts on the marsh vegetation. AVIRIS data collected between 2010 and 2012 are used to validate the Landsat results. Landsat imagery documents the significant effect of oiling on the Normalized Difference Vegetation Index (NDVI) of the marsh vegetation in 2010 and 2011 (p < 0.01 in both cases). These results are corroborated by the AVIRIS data, which recorded the most severe impact in May 2011 followed by progressive recovery in October 2011 and October 2012. The Landsat imagery, combined with relevant environmental information and appropriate statistical tools, provides a robust and low-cost method for long-term post-oil spill monitoring of the marshes, revealing that the major aboveground impacts (at 30 m scale) of the Deepwater Horizon oil spill on Louisiana salt marshes lasted for two years. The method presented is applicable for other hazardous events whenever pre-event referencing and long-term post-event monitoring are desired, thereby offering an effective and economical tool for disaster management. Full article

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20 pages, 8345 KiB

Open AccessArticle

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

by Lianru Gao¹

, Bin Zhao^1,2, Xiuping Jia³

, Wenzhi Liao⁴ and Bing Zhang^1,2,*

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 School of Engineering and Information Technology, The University of New South Wales, Canberra Campus, Bruce ACT 2006, Australia

⁴ Department of Telecommunications and Information Processing, Ghent University, Ghent 9000, Belgium

Remote Sens. 2017, 9(6), 548; https://doi.org/10.3390/rs9060548 - 1 Jun 2017

Cited by 58 | Viewed by 7963

Abstract

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data [...] Read more.

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy. Full article

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

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19 pages, 14318 KiB

Open AccessArticle

UAS-Based Change Detection of the Glacial and Proglacial Transition Zone at Pasterze Glacier, Austria

by Gernot Seier^1,*

, Andreas Kellerer-Pirklbauer¹

, Matthias Wecht¹, Simon Hirschmann¹, Viktor Kaufmann², Gerhard K. Lieb¹

and Wolfgang Sulzer¹

¹ Department of Geography and Regional Science, University of Graz, 8010 Graz, Austria

² Institute of Geodesy, Graz University of Technology, 8010 Graz, Austria

Remote Sens. 2017, 9(6), 549; https://doi.org/10.3390/rs9060549 - 1 Jun 2017

Cited by 30 | Viewed by 10223

Abstract

Glacier-related applications of unmanned aircraft systems (UAS) in high mountain regions with steep topography are relatively rare. This study makes a contribution to the lack of UAS applications in studying alpine glaciers in the European Alps. We transferred an established workflow of UAS-based [...] Read more.

Glacier-related applications of unmanned aircraft systems (UAS) in high mountain regions with steep topography are relatively rare. This study makes a contribution to the lack of UAS applications in studying alpine glaciers in the European Alps. We transferred an established workflow of UAS-based change detection procedures to Austria’s largest glacier, the Pasterze Glacier. We focused on a selected part of the glacier tongue and its proglacial vicinity to obtain detailed knowledge of (i) the behavior of a lateral crevasse field, (ii) the evolution of glacier surface structures and velocity fields, (iii) glacier ablation behavior and the current glacier margin, and (iv) proglacial dead ice conditions and dead ice ablation. Based on two UAS flight campaigns, accomplished in 2016 (51 days apart), we produced digital elevation models (DEMs) and orthophotos with a ground sampling distance (GSD) of 0.15 m using Structure-from-Motion (SfM) photogrammetry. Electrical resistivity tomography (ERT) profiling was additionally conducted in the proglacial area. Results indicate distinct changes in the crevasse field with massive ice collapses, rapid glacier recession, surface lowering (mean of −0.9 m), and ice disintegration at the margins, calculated degree day factors on the order of −7 to −11 mm d⁻¹·°C⁻¹ for clean ice parts, and minimal changes of the debris-covered dead ice in the proglacial area. With this contribution we highlight the benefit of UAS in comparison to commonly used terrestrial methods and satellite-related approaches. Full article

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16 pages, 4151 KiB

Open AccessArticle

Impervious Surface Information Extraction Based on Hyperspectral Remote Sensing Imagery

by Fei Tang^1,*

and Hanqiu Xu²

¹ Island Research Center, State Oceanic Administration, Pingtan 350400, China

² College of Environment and Resources, Fuzhou University, Fuzhou 350116, China

Remote Sens. 2017, 9(6), 550; https://doi.org/10.3390/rs9060550 - 1 Jun 2017

Cited by 26 | Viewed by 6116

Abstract

The retrieval of impervious surface information is a hot topic in remote sensing. However, researches on impervious surface retrieval from hyperspectral remote sensing imagery are rare. This paper illustrates a case study of information extraction from urban impervious surfaces based on hyperspectral remote [...] Read more.

The retrieval of impervious surface information is a hot topic in remote sensing. However, researches on impervious surface retrieval from hyperspectral remote sensing imagery are rare. This paper illustrates a case study of information extraction from urban impervious surfaces based on hyperspectral remote sensing imagery that is intended to improve the image spectral resolution of impermeable materials. Fuzhou, Guangzhou, and Hangzhou were selected as test areas and EO-1 Hyperion images were used as data sources. The impervious surface features were retrieved from remote sensing images using linear spectral mixture analysis. A stepwise discriminant analysis was performed to select feature bands for impervious surface retrieval. A standard deviation analysis, correlation analysis, and principal component analysis were then carried out for each of those up to 158 valid Hyperion spectral bands. Eleven feature bands were selected using the stepwise discriminant analysis and a new image called Hyperion’ was formed. The impervious surface was then retrieved from Hyperion’. The results indicate that the extraction accuracy and coverage accuracy are high in all three test areas. Tests of eleven feature band combinations selected in different areas show very good representations of the band combinations in impervious surface retrieval, and can thus be used as optimal band combinations for impervious surface retrieval. Full article

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20 pages, 7025 KiB

Open AccessArticle

Monitoring the Arctic Seas: How Satellite Altimetry Can Be Used to Detect Open Water in Sea-Ice Regions

by Felix L. Müller^*

, Denise Dettmering

, Wolfgang Bosch and Florian Seitz

Deutsches Geodätisches Forschungsinstitut, Technische Universität München, Arcisstraße 21, 80333 Munich, Germany

Remote Sens. 2017, 9(6), 551; https://doi.org/10.3390/rs9060551 - 1 Jun 2017

Cited by 24 | Viewed by 8354

Abstract

Open water areas surrounded by sea ice significantly influence the ocean-ice-atmosphere interaction and contribute to Arctic climate change. Satellite altimetry can detect these ice openings and enables one to estimate sea surface heights and further altimetry data derived products. This study introduces an [...] Read more.

Open water areas surrounded by sea ice significantly influence the ocean-ice-atmosphere interaction and contribute to Arctic climate change. Satellite altimetry can detect these ice openings and enables one to estimate sea surface heights and further altimetry data derived products. This study introduces an innovative, unsupervised classification approach for detecting open water areas in the Greenland Sea based on high-frequency data from Envisat and SARAL. Altimetry radar echoes, also called waveforms, are analyzed regarding different surface conditions. Six waveform features are defined to cluster radar echoes into different groups indicating open water and sea ice waveforms. Therefore, the partitional clustering algorithm K-medoids and the memory-based classification method K-nearest neighbor are employed, yielding an internal misclassification error of about 2%. A quantitative comparison with several SAR images reveals a consistency rate of 76.9% for SARAL and 70.7% for Envisat. These numbers strongly depend on the quality of the SAR images and the time lag between the measurements of both techniques. For a few examples, a consistency rate of more than 90% and a true water detection rate of 94% can be demonstrated. The innovative classification procedure can be used to detect water areas with different spatial extents and can be applied to all available pulse-limited altimetry datasets. Full article

(This article belongs to the Special Issue Satellite Altimetry for Earth Sciences)

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17 pages, 3331 KiB

Open AccessArticle

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

by Tobias Ahsbahs^*

, Merete Badger, Ioanna Karagali

and Xiaoli Guo Larsén

DTU Wind Energy, Roskilde 4000, Denmark

Remote Sens. 2017, 9(6), 552; https://doi.org/10.3390/rs9060552 - 2 Jun 2017

Cited by 36 | Viewed by 6340

Abstract

High-accuracy wind data for coastal regions is needed today, e.g., for the assessment of wind resources. Synthetic Aperture Radar (SAR) is the only satellite borne sensor that has enough resolution to resolve wind speeds closer than 10 km to shore but the Geophysical [...] Read more.

High-accuracy wind data for coastal regions is needed today, e.g., for the assessment of wind resources. Synthetic Aperture Radar (SAR) is the only satellite borne sensor that has enough resolution to resolve wind speeds closer than 10 km to shore but the Geophysical Model Functions (GMF) used for SAR wind retrieval are not fully validated here. Ground based scanning light detection and ranging (LiDAR) offer high horizontal resolution wind velocity measurements with high accuracy, also in the coastal zone. This study, for the first time, examines accuracies of SAR wind retrievals at 10 m height with respect to the distance to shore by validation against scanning LiDARs. Comparison of 15 Sentinel-1A wind retrievals using the GMF called C-band model 5.N (CMOD5.N) versus LiDARs show good agreement. It is found, when nondimenionalising with a reference point, that wind speed reductions are between 4% and 8% from 3 km to 1 km from shore. Findings indicate that SAR wind retrievals give reliable wind speed measurements as close as 1 km to the shore. Comparisons of SAR winds versus two different LiDAR configurations yield root mean square error (RMSE) of 1.31

{ms}^{- 1}

and 1.42

{ms}^{- 1}

for spatially averaged wind speeds. Full article

(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)

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13 pages, 1853 KiB

Open AccessArticle

In-Orbit Spectral Response Function Correction and Its Impact on Operational Calibration for the Long-Wave Split-Window Infrared Band (12.0 μm) of FY-2G Satellite

by Qiang Guo

and Xuan Feng^*

National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China

Remote Sens. 2017, 9(6), 553; https://doi.org/10.3390/rs9060553 - 8 Jun 2017

Cited by 5 | Viewed by 5242

Abstract

During the early stage of the G satellite of the Fengyun-2 series (FY-2G), severe cold biases up to ~2.3 K occur in its measurements in the 12.0 μm (IR2) band, which demonstrate time- and scene-dependent characteristics. Similar cold biases in water vapor and [...] Read more.

During the early stage of the G satellite of the Fengyun-2 series (FY-2G), severe cold biases up to ~2.3 K occur in its measurements in the 12.0 μm (IR2) band, which demonstrate time- and scene-dependent characteristics. Similar cold biases in water vapor and carbon dioxide absorption bands of other satellites are considered to be caused by either ice contamination (physical method) or spectral response function (SRF) shift (empirical method). Simulations indicate that this cold bias of FY-2G indeed suffers from equivalent SRF shift as a whole towards the longer wavelength direction. To overcome it, a novel approach combining both physical and empirical methods is proposed. With the possible ice thicknesses tested before launch, the ice contamination effect is alleviated, while the shape of the SRF can be modified in a physical way. The remaining unknown factors for cold bias are removed by shifting the convolved SRF with an ice transmittance spectrum. Two parameters, i.e., the ice thickness (5 μm) and the shifted value (+0.15 μm), are estimated by inter-calibration with reference instruments, and the modification coefficient is also calculated (0.9885) for the onboard blackbody calibration. Meanwhile, the updated SRF was released online on 23 March 2016. For the period between July 2015 and December 2016, the monthly biases of the FY-2G IR2 band remain oscillating around zero, the majorities (~89%) of which are within ±1.0 K, while its mean monthly absolute bias is around 0.6 K. Nevertheless, the cold bias phenomenon of the IR2 band no longer exists. The combination method can be referred by other corrections for cold biases. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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15 pages, 15769 KiB

Open AccessArticle

Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection

by Alessandro C. Mondini

CNR IRPI, Via Della Madonna Alta 126, 06128 Perugia, Italy

Remote Sens. 2017, 9(6), 554; https://doi.org/10.3390/rs9060554 - 2 Jun 2017

Cited by 64 | Viewed by 8639

Abstract

Landslides cause damages and affect victims worldwide, but landslide information is lacking. Even large events may not leave records when they happen in remote areas or simply do not impact with vulnerable elements. This paper proposes a procedure to measure spatial autocorrelation changes [...] Read more.

Landslides cause damages and affect victims worldwide, but landslide information is lacking. Even large events may not leave records when they happen in remote areas or simply do not impact with vulnerable elements. This paper proposes a procedure to measure spatial autocorrelation changes induced by event landslides in a multi-temporal series of synthetic aperture radar (SAR) intensity Sentinel-1 images. The procedure first measures pixel-based changes between consecutive couples of SAR intensity images using the Log-Ratio index, then it follows the temporal evolution of the spatial autocorrelation inside the Log-Ratio layers using the Moran’s I index and the semivariance. When an event occurs, the Moran’s I index and the semivariance increase compared to the values measured before and after the event. The spatial autocorrelation growth is due to the local homogenization of the soil response caused by the event landslide. The emerging clusters of autocorrelated pixels generated by the event are localized by a process of optimal segmentation of the log-ratio layers. The procedure was used to intercept an event that occurred in August 2015 in Myanmar, Tozang area, when strong rainfall precipitations triggered a number of landslides. A prognostic use of the method promises to increase the availability of information about the number of events at the regional scale, and to facilitate the production of inventory maps, yielding useful results to study the phenomenon for model tuning, landslide forecast model validation, and the relationship between triggering factors and number of occurred events. Full article

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

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16 pages, 4801 KiB

Open AccessArticle

An Improved Aerosol Optical Depth Retrieval Algorithm for Moderate to High Spatial Resolution Optical Remotely Sensed Imagery

by Bo Zhong^1,*

, Shanlong Wu¹, Aixia Yang^1,2 and Qinhuo Liu^1,2

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

² College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Remote Sens. 2017, 9(6), 555; https://doi.org/10.3390/rs9060555 - 2 Jun 2017

Cited by 18 | Viewed by 4977

Abstract

To extract quantitative land information accurately and monitor the air pollution at city scale from moderate to high spatial resolution (MHSR) with a resolution no coarser than 30 m, optical remotely sensed imagery and aerosol parameters, especially aerosol optical depth (AOD), are a [...] Read more.

To extract quantitative land information accurately and monitor the air pollution at city scale from moderate to high spatial resolution (MHSR) with a resolution no coarser than 30 m, optical remotely sensed imagery and aerosol parameters, especially aerosol optical depth (AOD), are a necessary step. In this paper, we introduce a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial resolution imagery under general atmosphere and land surface conditions. This algorithm has been improved in the following three aspects: (i) it has been developed for most of the moderate to high spatial resolution remotely sensed imagery; (ii) it can be applied to all kinds of land surface types including bright surface; and (iii) it is completely automatic. This algorithm is therefore suitable for operational applications. The derived AOD in Beijing from Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Huan Jing 1 (HJ-1/CCD) data is validated with AErosol Robotic NETwork (AERONET) ground measurements at Beijng and Xianghe stations. Full article

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22 pages, 3640 KiB

Open AccessArticle

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

by Salem Ibrahim Salem^1,2,*

, Hiroto Higa³, Hyungjun Kim¹

, Komatsu Kazuhiro⁴, Hiroshi Kobayashi⁵, Kazuo Oki¹ and Taikan Oki¹

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

² Faculty of Engineering, Alexandria University, Lotfy El-Sied St. Off Gamal Abd El-Naser-Alexandria, Alexandria 11432, Egypt

³ Faculty of Urban Innovation, Yokohama National University, Tokiwadai 79-5, Hodogaya, Yokohama, Kanagawa 240-8501, Japan

⁴ National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan

⁵ Graduate School of Interdisciplinary Research, University of Yamanashi, 4-4-37 Takeda, Kofu, Yamanashi 400-8510, Japan

Remote Sens. 2017, 9(6), 556; https://doi.org/10.3390/rs9060556 - 3 Jun 2017

Cited by 25 | Viewed by 7669

Abstract

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption [...] Read more.

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms’ indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R² = 0.692, root mean square error (RMSE) = 21.4 mg m⁻³), Lake Kasumigaura (R² = 0.866, RMSE = 11.3 mg m⁻³), and MERIS data over Lake Kasumigaura (R² = 0.57, RMSE = 36.5 mg m⁻³). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura. Full article

(This article belongs to the Section Ocean Remote Sensing)

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27 pages, 7701 KiB

Open AccessArticle

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

by Hasituya and Zhongxin Chen^*

Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, China. (AGRIRS)/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No. 12, Zhongguancun Nan Dajie, Haidian District, Beijing 100081, China

Remote Sens. 2017, 9(6), 557; https://doi.org/10.3390/rs9060557 - 3 Jun 2017

Cited by 59 | Viewed by 6422

Abstract

Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) [...] Read more.

Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer’s, and user’s accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively. Full article

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17 pages, 5189 KiB

Open AccessArticle

Multiobjective Optimized Endmember Extraction for Hyperspectral Image

by Rong Liu¹, Bo Du^2,* and Liangpei Zhang¹

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

² School of Computer, Wuhan University, Wuhan 430079, China

Remote Sens. 2017, 9(6), 558; https://doi.org/10.3390/rs9060558 - 3 Jun 2017

Cited by 19 | Viewed by 5827

Abstract

Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods [...] Read more.

Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, where several different optimization objectives have been proposed from different perspectives. In all of these methods, only one objective function has to be optimized, which represents a specific characteristic of endmembers. However, one single-objective function may not be able to express all the characteristics of endmembers from various aspects, which would not be powerful enough to provide satisfactory unmixing results because of the complexity of remote sensing images. In this paper, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is utilized to tackle the problem of EE, where two objective functions, namely, volume maximization (VM) and root-mean-square error (RMSE) minimization are simultaneously optimized. Experimental results on two real hyperspectral images show the superiority of the proposed MODPSO with respect to the single objective D-PSO method, and MODPSO still needs further improvement on the optimization of the VM with respect to other approaches. Full article

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

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29 pages, 3161 KiB

Open AccessArticle

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

by Yong Chen, Ting-Zhu Huang^*

, Xi-Le Zhao, Liang-Jian Deng^* and Jie Huang

School of Mathematical Sciences/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China

Remote Sens. 2017, 9(6), 559; https://doi.org/10.3390/rs9060559 - 3 Jun 2017

Cited by 72 | Viewed by 12852

Abstract

Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe [...] Read more.

Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments. Full article

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16 pages, 3259 KiB

Open AccessArticle

Underwater Topography Detection in Coastal Areas Using Fully Polarimetric SAR Data

by Xiaolin Bian^1,2, Yun Shao¹, Wei Tian¹, Shiang Wang³, Chunyan Zhang^1,*, Xiaochen Wang^1,2 and Zhixin Zhang^1,2

¹ Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

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

³ National Ocean Technology Center, Tianjin 300112, China

Remote Sens. 2017, 9(6), 560; https://doi.org/10.3390/rs9060560 - 4 Jun 2017

Cited by 18 | Viewed by 9009

Abstract

Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while [...] Read more.

Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while less prior information is required. The method is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. First, the surface scattering component is obtained by polarization decomposition. Then, wave fields are retrieved from the two-dimensional (2D) spectra by the Fast Fourier Transformation (FFT). Finally, shallow water depths are estimated from the dispersion relation. Applicability and effectiveness of the proposed methodology are tested by using C-band fine quad-polarization mode RADARSAT-2 SAR data over the near-shore area of the Hainan province, China. By comparing with the values from an official electronic navigational chart (ENC), the estimated water depths are in good agreement with them. The average relative error of the detected results from the scattering mechanisms based method and single polarization SAR data are 9.73% and 11.53% respectively. The validation results indicate that the scattering mechanisms based methodology is more effective than only using the single polarization SAR data for underwater topography detection, and will inspire further research on underwater topography detection with fully polarimetric SAR data. Full article

(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)

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31 pages, 17003 KiB

Open AccessArticle

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

by Hauke Beck^*

and Martin Kühn

ForWind, Institute of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany

Remote Sens. 2017, 9(6), 561; https://doi.org/10.3390/rs9060561 - 4 Jun 2017

Cited by 57 | Viewed by 9805

Abstract

Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the [...] Read more.

Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions. Full article

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17 pages, 2618 KiB

Open AccessArticle

Assessment of Satellite-Derived Surface Reflectances by NASA’s CAR Airborne Radiometer over Railroad Valley Playa

by Said Kharbouche^1,*, Jan-Peter Muller¹

, Charles K. Gatebe²

, Tracy Scanlon³ and Andrew C. Banks³

¹ Imaging Group, Mullard Space Science Laboratory, University College London, Department of Space and Climate Physics, Holmbury St Mary, RH5-6NT, UK

² NASA GSFC and Universities Space Research Association, Greenbelt, MD 20771, USA

³ National Physical Laboratory, Teddington TW11-0LW, UK

Remote Sens. 2017, 9(6), 562; https://doi.org/10.3390/rs9060562 - 5 Jun 2017

Cited by 10 | Viewed by 5200

Abstract

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May [...] Read more.

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada, USA (38.504°N, 115.692°W). The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CAR’s land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol effects in the EO derived reflectances. Full article

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27 pages, 27250 KiB

Open AccessArticle

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

by Takeshi Inomata^1,*

, Flory Pinzón², José Luis Ranchos³, Tsuyoshi Haraguchi⁴, Hiroo Nasu⁵, Juan Carlos Fernandez-Diaz⁶

, Kazuo Aoyama⁷ and Hitoshi Yonenobu⁸

¹ School of Anthropology, University of Arizona, Tucson, AZ 85721-0030, USA

² Ceibal-Petexbatun Archaeological Project, Guatemala City 01005, Guatemala

³ Escuela de Historia, Universidad de San Carlos, Guatemala City 01012, Guatemala

⁴ Graduate School of Science Biology and Geosciences, Osaka City University, Osaka 558-8585, Japan

⁵ Faculty of Biosphere-Geosphere Science, Okayama University of Science, Okayama 700-0005, Japan

⁶ NSF National Center for Airborne Laser Mapping (NCALM), University of Houston, Houston, TX 77204-5059, USA

⁷ Faculty of Humanities, Ibaraki University, Mito 310-8512, Japan

⁸ Graduate School of Education, Naruto University of Education, Naruto 772-8502, Japan

Remote Sens. 2017, 9(6), 563; https://doi.org/10.3390/rs9060563 - 5 Jun 2017

Cited by 68 | Viewed by 17135

Abstract

The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of [...] Read more.

The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 × 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic. Full article

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19 pages, 8117 KiB

Open AccessArticle

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

by Yajie Wang¹ and Dengsheng Lu^1,2,*

¹ Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental & Resource Sciences, Zhejiang Agriculture and Forestry University, Lin An 311300, Hangzhou, China

² Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48823, USA

Remote Sens. 2017, 9(6), 564; https://doi.org/10.3390/rs9060564 - 6 Jun 2017

Cited by 16 | Viewed by 5632

Abstract

Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to [...] Read more.

Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to date there are no suitable approaches available for mapping Torreya forest distribution, especially the new Torreya plantations, due to the complex landscapes. This research used high spatial resolution Chinese Gaofen (GF-1) and Ziyuan (ZY-3) satellite images and digital elevation model (DEM) data to extract old Torreya forests and new Torreya plantations using a newly proposed expert rules-based approach. Different variables such as spectral bands, vegetation indices, textural images, and DEM-derived variables were examined, and separability analyses of different land covers were explored. An expert rules-based approach was developed for the extraction of old Torreya forests and new Torreya plantations. The accuracy assessment using field survey data and Google Earth images indicates that this newly-proposed approach can effectively distinguish both old Torreya forests and new Torreya plantations from other land covers with producer’s accuracies of 84% and 92%, and user’s accuracies of 77% and 85%, respectively, much better classification accuracies than the maximum likelihood classifier. This new approach may be used for other study area for extracting Torreya forest distribution. This research provides valuable data sources for better managing existing Torreya forests and planning potential Torreya expansions in this region in the near future. Full article

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24 pages, 8682 KiB

Open AccessArticle

A Novel Spectral Library Pruning Technique for Spectral Unmixing of Urban Land Cover

by Jeroen Degerickx^1,*

, Akpona Okujeni², Marian-Daniel Iordache³, Martin Hermy¹

, Sebastian Van der Linden²

and Ben Somers¹

¹ Division of Forest, Nature and Landscape, KU Leuven, Leuven 3001, Belgium

² Geography Department, Humboldt-Universität zu Berlin, Berlin 10099, Germany

³ Flemish Institute for Technological Research, Center for Remote Sensing and Earth Observation Processes (VITO-TAP), Mol 2400, Belgium

Remote Sens. 2017, 9(6), 565; https://doi.org/10.3390/rs9060565 - 6 Jun 2017

Cited by 38 | Viewed by 7961

Abstract

Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of [...] Read more.

Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size and heterogeneity of such a generic library requires an efficient pruning technique to extract site-specific spectral libraries. We propose the “Automated MUsic and spectral Separability based Endmember Selection technique” (AMUSES), which selects endmember subsets with respect to the image to be processed, while accounting for internal redundancy. Experiments on simulated hyperspectral data from Brussels (Belgium) showed that AMUSES selects more relevant endmembers compared to the conventional Iterative Endmember Selection (IES) approach. This ultimately improved mapping results (kappa increased from 0.71 to 0.83). Experiments on real HyMap data from Berlin (Germany) using a combination of libraries from different cities underlined the potential of AMUSES for handling libraries with increasing levels of generality (RMSE decreased from 0.18 to 0.15, while only using 55% of the number of spectra compared to IES). Our findings contribute to the value of generic spectral databases in the development of efficient urban mapping workflows. Full article

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13 pages, 3158 KiB

Open AccessArticle

An Automated Approach to Map Winter Cropped Area of Smallholder Farms across Large Scales Using MODIS Imagery

by Meha Jain^1,*, Pinki Mondal², Gillian L. Galford³

, Greg Fiske⁴ and Ruth S. DeFries⁵

¹ School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109, USA

² Center for International Earth Science Information Network, Columbia University, Palisades, NY 10964, USA

³ Gund Institute of Environment and Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405, USA

⁴ Woods Hole Research Center, Falmouth, MA 02540, USA

⁵ Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, USA

Remote Sens. 2017, 9(6), 566; https://doi.org/10.3390/rs9060566 - 6 Jun 2017

Cited by 24 | Viewed by 11236

Abstract

Fine-scale agricultural statistics are an important tool for understanding trends in food production and their associated drivers, yet these data are rarely collected in smallholder systems. These statistics are particularly important for smallholder systems given the large amount of fine-scale heterogeneity in production [...] Read more.

Fine-scale agricultural statistics are an important tool for understanding trends in food production and their associated drivers, yet these data are rarely collected in smallholder systems. These statistics are particularly important for smallholder systems given the large amount of fine-scale heterogeneity in production that occurs in these regions. To overcome the lack of ground data, satellite data are often used to map fine-scale agricultural statistics. However, doing so is challenging for smallholder systems because of (1) complex sub-pixel heterogeneity; (2) little to no available calibration data; and (3) high amounts of cloud cover as most smallholder systems occur in the tropics. We develop an automated method termed the MODIS Scaling Approach (MSA) to map smallholder cropped area across large spatial and temporal scales using MODIS Enhanced Vegetation Index (EVI) satellite data. We use this method to map winter cropped area, a key measure of cropping intensity, across the Indian subcontinent annually from 2000–2001 to 2015–2016. The MSA defines a pixel as cropped based on winter growing season phenology and scales the percent of cropped area within a single MODIS pixel based on observed EVI values at peak phenology. We validated the result with eleven high-resolution scenes (spatial scale of 5 × 5 m² or finer) that we classified into cropped versus non-cropped maps using training data collected by visual inspection of the high-resolution imagery. The MSA had moderate to high accuracies when validated using these eleven scenes across India (R² ranging between 0.19 and 0.89 with an overall R² of 0.71 across all sites). This method requires no calibration data, making it easy to implement across large spatial and temporal scales, with 100% spatial coverage due to the compositing of EVI to generate cloud-free data sets. The accuracies found in this study are similar to those of other studies that map crop production using automated methods and use no calibration data. To aid research on agricultural production at fine spatial scales in India, we make our annual winter crop maps from 2000–2001 to 2015–2016 at 1 × 1 km² produced in this study publically available through the NASA Socioeconomic Data and Applications Center (SEDAC) hosted by the Center for International Earth Science Information Network (CIESIN) at Columbia University. We also make our R script available since it is likely that this method can be used to map smallholder agriculture in other regions across the globe given that our method performed well in disparate agro-ecologies across India. Full article

(This article belongs to the Special Issue Remote Sensing of Smallholder Subsistence Agriculture Using Satellites and UAVs)

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19 pages, 9202 KiB

Open AccessFeature PaperArticle

Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR)

by Oscar Garcia-Pineda^1,*, Jamie Holmes², Matt Rissing², Russell Jones², Cameron Wobus², Jan Svejkovsky³ and Mark Hess³

¹ Water Mapping, LLC, Gulf Breeze, FL 32563, USA

² Abt Associates Inc., Boulder, CO 80302, USA

³ Ocean Imaging Corp., Littleton, CO 80127, USA

Remote Sens. 2017, 9(6), 567; https://doi.org/10.3390/rs9060567 - 6 Jun 2017

Cited by 39 | Viewed by 9088

Abstract

During any marine oil spill, floating oil slicks that reach shorelines threaten a wide array of coastal habitats. To assess the presence of oil near shorelines during the Deepwater Horizon (DWH) oil spill, we scanned the library of Synthetic Aperture Radar (SAR) imagery [...] Read more.

During any marine oil spill, floating oil slicks that reach shorelines threaten a wide array of coastal habitats. To assess the presence of oil near shorelines during the Deepwater Horizon (DWH) oil spill, we scanned the library of Synthetic Aperture Radar (SAR) imagery collected during the event to determine which images intersected shorelines and appeared to contain oil. In total, 715 SAR images taken during the DWH spill were analyzed and processed, with 188 of the images clearly showing oil. Of these, 156 SAR images showed oil within 10 km of the shoreline with appropriate weather conditions for the detection of oil on SAR data. We found detectable oil in SAR images within 10 km of the shoreline from west Louisiana to west Florida, including near beaches, marshes, and islands. The high number of SAR images collected in Barataria Bay, Louisiana in 2010 allowed for the creation of a nearshore oiling persistence map. This analysis shows that, in some areas inside Barataria Bay, floating oil was detected on as many as 29 different days in 2010. The nearshore areas with persistent floating oil corresponded well with areas where ground survey crews discovered heavy shoreline oiling. We conclude that satellite-based SAR imagery can detect oil slicks near shorelines, even in sheltered areas. These data can help assess potential shoreline oil exposure without requiring boats or aircraft. This method can be particularly helpful when shoreline assessment crews are hampered by difficult access or, in the case of DWH, a particularly large spatial and temporal spill extent. Full article

(This article belongs to the Special Issue Remote Sensing and Oil Spill Response: Leveraging New Technologies to Safeguard the Environment)

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14 pages, 5166 KiB

Open AccessArticle

Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record

by Karl-Göran Karlsson^1,*, Nina Håkansson¹, Jonathan P. D. Mittaz², Timo Hanschmann³ and Abhay Devasthale¹

¹ Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, SE-601 76 Norrköping, Sweden

² Department of Meteorology, University of Reading, Whiteknights, P.O. Box 217, RG6 6AH Reading, Berkshire, UK

³ Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany

Remote Sens. 2017, 9(6), 568; https://doi.org/10.3390/rs9060568 - 6 Jun 2017

Cited by 2 | Viewed by 3966

Abstract

A method for reducing the impact of noise in the 3.7 micron spectral channel in climate data records derived from coarse resolution (4 km) global measurements from the Advanced Very High Resolution Radiometer (AVHRR) data is presented. A dynamic size-varying median filter is [...] Read more.

A method for reducing the impact of noise in the 3.7 micron spectral channel in climate data records derived from coarse resolution (4 km) global measurements from the Advanced Very High Resolution Radiometer (AVHRR) data is presented. A dynamic size-varying median filter is applied to measurements guided by measured noise levels and scene temperatures for individual AVHRR sensors on historic National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites in the period 1982–2001. The method was used in the preparation of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data—Second Edition (CLARA-A2), a cloud climate data record produced by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF), as well as in the preparation of the corresponding AVHRR-based datasets produced by the European Space Agency (ESA) Climate Change Initiative (CCI) project ESA-CLOUD-CCI. The impact of the noise filter was equivalent to removing an artificial decreasing trend in global cloud cover of 1–2% per decade in the studied period, mainly explained by the very high noise levels experienced in data from the first satellites in the series (NOAA-7 and NOAA-9). Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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21 pages, 5631 KiB

Open AccessArticle

Development and Implementation of an Electronic Crosstalk Correction for Bands 27–30 in Terra MODIS Collection 6

by Truman Wilson^1,*, Aisheng Wu¹, Ashish Shrestha¹

, Xu Geng¹, Zhipeng Wang¹, Chris Moeller², Richard Frey² and Xiaoxiong Xiong³

¹ Science Systems and Applications Inc., Lanham, MD 20706, USA

² Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI 53706, USA

³ Sciences and Exploration Directorate, NASA/GSFC, Greenbelt, MD 20771, USA

Remote Sens. 2017, 9(6), 569; https://doi.org/10.3390/rs9060569 - 6 Jun 2017

Cited by 75 | Viewed by 5197

Abstract

The photovoltaic bands on the long-wave infrared focal plane assembly of Terra MODIS, bands 27–30, have suffered from steadily increasing contamination from electronic crosstalk as the mission has progressed. This contamination has a great impact on MODIS data products, including image striping and [...] Read more.

The photovoltaic bands on the long-wave infrared focal plane assembly of Terra MODIS, bands 27–30, have suffered from steadily increasing contamination from electronic crosstalk as the mission has progressed. This contamination has a great impact on MODIS data products, including image striping and radiometric bias in the Level-1B calibrated radiance product, and incorrect retrieval in atmospheric products that rely on data from bands 27–30, such as the cloud mask and cloud particle phase products. In this work, we describe the development of an electronic crosstalk correction for bands 27–30 of Terra MODIS using observations of the Moon. In this approach, the derived correction coefficients account for both the “in-band” and “out-of-band” contribution to the signal contamination, which is not considered in previous implementations of the lunar-based correction. The correction coefficients are applied to both the on-board calibrator data and the Earth-view data, resulting in a significant reduction in the image striping and radiometric bias in the Level-1B data, as well as a better performance in the Level-2 cloud mask and cloud particle phase products. This approach will be implemented for Terra MODIS Collection 6 in 2017. Full article

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26 pages, 16807 KiB

Open AccessArticle

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

by Qihao Chen

, Linlin Li, Qiao Xu, Shuai Yang, Xuguo Shi

and Xiuguo Liu^*

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

Remote Sens. 2017, 9(6), 570; https://doi.org/10.3390/rs9060570 - 6 Jun 2017

Cited by 22 | Viewed by 5847

Abstract

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of [...] Read more.

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G⁰ distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G⁰ statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G⁰ statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images. Full article

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

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17 pages, 4774 KiB

Open AccessArticle

Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data

by Tao Jia^1,2, Kai Chen¹ and Jiye Wang^3,*

¹ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China

² Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China

³ Department of Information and Communication, State Grid Corporation of China, Beijing 100031, China

Remote Sens. 2017, 9(6), 571; https://doi.org/10.3390/rs9060571 - 7 Jun 2017

Cited by 16 | Viewed by 4636

Abstract

Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of [...] Read more.

Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of intensification and expansion. We start by calibrating the DMSP/OLS data and selecting 45 major metropolitans. On intensification, results suggest that aggregately, metropolitans displayed cyclical pattern over time and large metropolitans tended to have higher levels of intensification than moderate or small ones. Individually, metropolitans with similar intensification over time could be clustered together using Dendrogram, and evolution pattern of the clusters exhibited similarity to the aggregated one. On expansion, results show that aggregately metropolitans displayed a decreasing trend over time, and moderate or small metropolitans tended to have higher levels of expansion than large ones. Particularly, moderate metropolitans were more likely to expand adjacently, and small ones were more likely to experience scatter or corridor expansion. Each metropolitan can be represented by a mixed expansion model over time, which might tell where and how much expansion occurred in the current year. Furthermore, intensification is highly correlated with expansion over time for small metropolitans, but they are poorly correlated for large or moderate ones. Lastly, the high correlation of intensification and expansion with the change of GDP in each year indicates the reliability of our work. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

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18 pages, 6558 KiB

Open AccessArticle

Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data

by Yoshio Awaya^1,* and Tomoaki Takahashi^2,*

¹ River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan

² Kyushu Research Center, Forestry and Forest Products Research Institute, 4-11-16 Kurokami, Chuo-ku, Kumamoto 860-0862, Japan

Remote Sens. 2017, 9(6), 572; https://doi.org/10.3390/rs9060572 - 7 Jun 2017

Cited by 8 | Viewed by 4848

Abstract

Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total [...] Read more.

Airborne light detection and ranging (LiDAR) has been used for forest biomass estimation for the past three decades. The performance of estimation, in particular, has been of great interest. However, the difference in the performance of estimation between stem volume (SV) and total dry biomass (TDB) estimations has been a priority topic. We compared the performances between SV and TDB estimations for evergreen conifer and deciduous broadleaved forests by correlation and regression analyses and by combining height and no-height variables to identify statistically useful variables. Thirty-eight canopy variables, such as average and standard deviation of the canopy height, as well as the mid-canopy height of the stands, were computed using LiDAR point data. For the case of conifer forests, TDB showed greater correlation than SV; however, the opposite was the case for deciduous broadleaved forests. The average- and mid-canopy height showed the greatest correlation with TDB and SV for conifer and deciduous broadleaved forests, respectively. Setting the best variable as the first and no-height variables as the second variable, a stepwise multiple regression analysis was performed. Predictions by selected equations slightly underestimated the field data used for validation, and their correlation was very high, exceeding 0.9 for coniferous forests. The coefficient of determination of the two-variable equations was smaller than that of the one-variable equation for broadleaved forests. It is suggested that canopy structure variables were not effective for broadleaved forests. The SV and TDB maps showed quite different frequency distributions. The ratio of the stem part of the broadleaved forest is smaller than that of the coniferous forest. This suggests that SV was relatively smaller than TDB for the case of broadleaved forests compared with coniferous forests, resulting in a more even spatial distribution of TDB than that of SV. Full article

(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)

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29 pages, 4566 KiB

Open AccessArticle

Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands

by Lori White^1,*, Koreen Millard^2,3, Sarah Banks¹, Murray Richardson², Jon Pasher¹ and Jason Duffe¹

¹ Environment and Climate Change Canada, National Wildlife Research Centre, 1125 Colonel by Drive, Ottawa, ON K1S 5B6, Canada

² Department of Geography and Environmental Studies, Carleton University, 1125 colonel By Drive, Ottawa, ON K1S 5B6, Canada

³ Defence Research and Development Canada (DRDC), Ottawa Research Center, 3701 Carling Ave., Ottawa, ON K2K 2Y7, Canada

Remote Sens. 2017, 9(6), 573; https://doi.org/10.3390/rs9060573 - 7 Jun 2017

Cited by 46 | Viewed by 6776

Abstract

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal [...] Read more.

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (−25 dB and −19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, but the inclusion of SAR variables did increase overall accuracy, indicating that a multi-sensor approach is preferred. There was no significant difference between the RF classifications which included RADARSAT-2 and simulated RCM data. Both medium- and high-resolution compact polarimetry and dual polarimetric RCM data appear to be suitable for mapping landcover within peatlands when combined with optical data and a DEM. Full article

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22 pages, 13813 KiB

Open AccessArticle

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

by Zheng Lu¹

, Linna Chai^1,*

, Shaomin Liu¹, Huizhen Cui², Yanghua Zhang³, Lingmei Jiang²

, Rui Jin⁴ and Ziwei Xu¹

¹ State Key Laboratory of Earth Surface Processes and Resource Ecology and School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

² State Key Laboratory of Remote Sensing Science and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

³ Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

⁴ Heihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

Remote Sens. 2017, 9(6), 574; https://doi.org/10.3390/rs9060574 - 8 Jun 2017

Cited by 21 | Viewed by 6436

Abstract

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input [...] Read more.

A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China. Full article

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

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18 pages, 4920 KiB

Open AccessArticle

A New Radiometric Correction Method for Side-Scan Sonar Images in Consideration of Seabed Sediment Variation

by Jianhu Zhao^1,2

, Jun Yan^1,2,*

, Hongmei Zhang³

and Junxia Meng^1,2

¹ Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China

² School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

³ School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China

Remote Sens. 2017, 9(6), 575; https://doi.org/10.3390/rs9060575 - 8 Jun 2017

Cited by 28 | Viewed by 7343

Abstract

Affected by the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations, radiometric distortion degrades the quality of side-scan sonar images and seriously affects the application of these images. However, existing methods cannot correct distortion effectively, especially in the [...] Read more.

Affected by the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations, radiometric distortion degrades the quality of side-scan sonar images and seriously affects the application of these images. However, existing methods cannot correct distortion effectively, especially in the presence of seabed sediment variation. This study proposes a new radiometric correction method for side-scan sonar images that considers seabed sediment variation. First, the different effects on backscatter strength (BS) are analyzed, and along-track distortion is removed by establishing a linear relationship between distortion and sonar altitude. Second, because the angle-related effects on BSs with the same incident angle are the same, a novel method of unsupervised sediment classification is proposed for side-scan sonar images. Finally, the angle–BS curves of different sediments are obtained, and angle-related radiometric distortion is corrected. Experiments prove the validity of the proposed method. Full article

(This article belongs to the Section Ocean Remote Sensing)

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15 pages, 859 KiB

Open AccessArticle

Slope-Restricted Multi-Scale Feature Matching for Geostationary Satellite Remote Sensing Images

by Dan Zeng¹, Lidan Wu¹, Boyang Chen^2,* and Wei Shen¹

¹ Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200070, China

² National Satellite Meteorological Center, No. 46, Zhongguancun South Street, Haidian District, Beijing 100081, China

Remote Sens. 2017, 9(6), 576; https://doi.org/10.3390/rs9060576 - 8 Jun 2017

Cited by 8 | Viewed by 5219

Abstract

For geostationary meteorological satellite (GSMS) remote sensing image registration, high computational cost and matching error are the two main challenging problems. To address these issues, this paper proposes a novel algorithm named slope-restricted multi-scale feature matching. In multi-scale feature matching, images are subsampled [...] Read more.

For geostationary meteorological satellite (GSMS) remote sensing image registration, high computational cost and matching error are the two main challenging problems. To address these issues, this paper proposes a novel algorithm named slope-restricted multi-scale feature matching. In multi-scale feature matching, images are subsampled to different scales. From a small scale to a large scale, the offsets between the matched pairs are used to narrow the searching area of feature matching for the next larger scale. Thus, the feature matching is accomplished from coarse to fine, which will make the matching process more accurate and reduce errors. To enhance the matching performance, the outliers in the matched pairs are rectified by using slope-restricted rectification, which is based on local geometric similarity. Compared with other algorithms, the experimental results show that our proposed method is more accurate and efficient. Full article

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10 pages, 12623 KiB

Open AccessArticle

Mapping the Twilight Zone—What We Are Missing between Clouds and Aerosols

by Katharina Schwarz^1,2,*

, Jan Cermak^3,4

, Julia Fuchs^1,3,4 and Hendrik Andersen^1,3,4

¹ Department of Geography, Ruhr-Universität Bochum, Universitätsstraße 150, 44801 Bochum, Germany

² Department of Geography, Bergische Universität Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany

³ Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), 76128 Karlsruhe, Germany

⁴ Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76128 Karlsruhe, Germany

Remote Sens. 2017, 9(6), 577; https://doi.org/10.3390/rs9060577 - 9 Jun 2017

Cited by 18 | Viewed by 5623

Abstract

Scientific understanding of aerosol-cloud interactions can profit from an analysis of the transition regions between pure aerosol and pure clouds as detected in satellite data. This study identifies and evaluates pixels in this region by analysing the residual areas of aerosol and cloud [...] Read more.

Scientific understanding of aerosol-cloud interactions can profit from an analysis of the transition regions between pure aerosol and pure clouds as detected in satellite data. This study identifies and evaluates pixels in this region by analysing the residual areas of aerosol and cloud products from the Moderate Resolution Imaging Radiometer (MODIS) satellite sensor. These pixels are expected to represent the “twilight zone” or transition zone between aerosols and clouds. In the analysis period (February and August, 2007–2011), about 20% of all pixels are discarded by both MODIS aerosol and cloud retrievals (“Lost Pixels”). The reflective properties and spatial distribution of Lost Pixels are predominantly in between pure aerosol and cloud. The high amount of discarded pixels underlines the relevance of analyzing the transition zone as a relevant part of the Earth’s radiation budget and the importance of considering them in research on aerosol-cloud interactions. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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16 pages, 14833 KiB

Open AccessArticle

High-Rise Building Layover Exploitation with Non-Local Frequency Estimation in SAR Interferograms

by Jun Zhu¹, Xiaoli Ding², Zhiwei Li^1,*

, Jianjun Zhu¹ and Bing Xu¹

¹ School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

² Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong, China

Remote Sens. 2017, 9(6), 579; https://doi.org/10.3390/rs9060579 - 10 Jun 2017

Cited by 8 | Viewed by 4817

Abstract

The wide application of high-resolution SAR data, such as TanDEM-X and TerraSAR-X, has resulted in an increase of the data processing difficulty of interferograms, especially in urban areas with serious layovers caused by high-rise buildings. In this paper, a new method based on [...] Read more.

The wide application of high-resolution SAR data, such as TanDEM-X and TerraSAR-X, has resulted in an increase of the data processing difficulty of interferograms, especially in urban areas with serious layovers caused by high-rise buildings. In this paper, a new method based on frequency estimation is proposed to extract and compensate the building layover phase without considering the building structure. We use a non-local algorithm to estimate the high-accuracy frequency in the range direction, which is utilized to extract the layover areas of a building. Then, a two-step method for estimating local frequencies is used for layover phase removal. Efficient frequency estimation and building extraction is demonstrated on real data in comparison with traditional methods. The results of the removal approach with both simulated and real TanDEM-X and TerraSAR-X images are presented to prove the potential of the method. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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14 pages, 4037 KiB

Open AccessArticle

In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR

by Christian N. Koyama^1,2,*, Hai Liu³, Kazunori Takahashi², Masanobu Shimada^1,4, Manabu Watanabe¹

, Tseedulam Khuut⁵

and Motoyuki Sato²

¹ School of Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama-machi, Hiki-gun, Saitama 350-0394, Japan

² Center for North East Asian Studies, Tohoku University, 41 Kawauchi, Sendai, Miyagi 850-8576, Japan

³ Institute of Electromagnetics and Acoustics, Department of Electronic Science, Xiamen University, Xiamen 361005, China

⁴ Japan Aerospace Exploration Agency, Earth Observation Research Center, 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan

⁵ School of Geology and Mining Engineering, Mongolian University of Science and Technology, 8th Khoroo, Baga Toiruu, Ulaanbaatar-46/520, Mongolia

Remote Sens. 2017, 9(6), 580; https://doi.org/10.3390/rs9060580 - 9 Jun 2017

Cited by 37 | Viewed by 9100

Abstract

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of [...] Read more.

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I²EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm. Full article

(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)

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21 pages, 20093 KiB

Open AccessArticle

Remote Sensing Image Registration Using Multiple Image Features

by Kun Yang^1,2,†, Anning Pan^1,2,†, Yang Yang^1,2,*, Su Zhang^1,3,*, Sim Heng Ong⁴ and Haolin Tang^1,3

¹ School of Information Science and Technology, Yunnan Normal University, Kunming 650092, China

² The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650092, China

³ Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650092, China

⁴ Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore

^† These authors contributed equally to this work.

Remote Sens. 2017, 9(6), 581; https://doi.org/10.3390/rs9060581 - 12 Jun 2017

Cited by 137 | Viewed by 12090

Abstract

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between [...] Read more.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases. Full article

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15 pages, 4727 KiB

Open AccessArticle

Seasonal and Interannual Variability of Satellite-Derived Chlorophyll-a (2000–2012) in the Bohai Sea, China

by Hailong Zhang^1,2

, Zhongfeng Qiu^1,2,*

, Deyong Sun^1,2, Shengqiang Wang^1,2 and Yijun He^1,2

¹ School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

² Jiangsu Research Centre for Ocean Survey Technology, NUIST, Nanjing 210044, China

Remote Sens. 2017, 9(6), 582; https://doi.org/10.3390/rs9060582 - 10 Jun 2017

Cited by 61 | Viewed by 7436

Abstract

Knowledge of the chlorophyll-a dynamics and their long-term changes is important for assessing marine ecosystems, especially for coastal waters. In this study, the spatial and temporal variability of sea surface chlorophyll-a concentration (Chl-a) in the Bohai Sea were investigated using 13-year (2000–2012) satellite-derived [...] Read more.

Knowledge of the chlorophyll-a dynamics and their long-term changes is important for assessing marine ecosystems, especially for coastal waters. In this study, the spatial and temporal variability of sea surface chlorophyll-a concentration (Chl-a) in the Bohai Sea were investigated using 13-year (2000–2012) satellite-derived products from MODIS and SeaWiFS observations. Based on linear regression analysis, the results showed that the entire Bohai Sea experienced an increase in Chl-a on a long-term scale, with the largest increase in the central Bohai Sea and the smallest increase in the Bohai strait. Distinct seasonal patterns of Chl-a existed in different sub-regions of the Bohai Sea. A long-lasting Chl-a peak was observed from May to September in coastal waters (Liaodong bay, Qinhuangdao coast, and Bohai bay) and the central Bohai Sea, whereas Laizhou bay had relatively low Chl-a in early summer. In the Bohai strait, two pronounced Chl-a peaks occurred in March and September, but the lowest Chl-a was in summer. This pattern was quite different from those in other regions of the Bohai Sea. The water column condition (stratified or mixed) was likely an important physical factor that affects the seasonal pattern of Chl-a in the Bohai Sea. Meanwhile, increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in both coastal waters and the central Bohai Sea, especially in summer. The increasing trend of Chl-a in the Bohai Sea might be attributed to the increase in nutrient contents from riverine inputs. The Chl-a dynamics documented in this study provide basic knowledge for the future exploration of marine biogeochemical processes and ecosystem evolution in the Bohai Sea. Full article

(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)

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19 pages, 8144 KiB

Open AccessArticle

Spatial and Spectral Hybrid Image Classification for Rice Lodging Assessment through UAV Imagery

by Ming-Der Yang¹, Kai-Siang Huang¹, Yi-Hsuan Kuo¹, Hui Ping Tsai^1,*

and Liang-Mao Lin²

¹ Department of Civil Engineering, National Chung Hsing University, 145 Xingda Rd., Taichung 402, Taiwan

² Agriculture Department, Chiayi County Government, NO.1, Sianghe 1st Rd, Taibao City 61249, Chiayi County, Taiwan

Remote Sens. 2017, 9(6), 583; https://doi.org/10.3390/rs9060583 - 10 Jun 2017

Cited by 162 | Viewed by 13878

Abstract

Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition [...] Read more.

Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment. Full article

(This article belongs to the Special Issue Remote Sensing of Smallholder Subsistence Agriculture Using Satellites and UAVs)

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81 pages, 30167 KiB

Open AccessArticle

Copernicus Sentinel-2A Calibration and Products Validation Status

by Ferran Gascon^1,*, Catherine Bouzinac^2,*, Olivier Thépaut², Mathieu Jung³, Benjamin Francesconi⁴, Jérôme Louis⁵

, Vincent Lonjou⁶, Bruno Lafrance², Stéphane Massera⁷, Angélique Gaudel-Vacaresse⁶, Florie Languille⁶, Bahjat Alhammoud⁸

, Françoise Viallefont⁹, Bringfried Pflug¹⁰

, Jakub Bieniarz¹⁰, Sébastien Clerc^8,*

, Laëtitia Pessiot², Thierry Trémas⁶, Enrico Cadau¹, Roberto De Bonis¹, Claudia Isola¹, Philippe Martimort¹ and Valérie Fernandez¹ Show full author list Hide full author list

¹ ESA (European Space Agency), Paris 75015, France

² CS-SI (Communication & Systèmes—Systèmes d'Information), Toulouse 31506, France

³ Airbus Defence and Space, Toulouse 31402, France

⁴ Thales Alenia Space, Cannes La Bocca 06156, France

⁵ Telespazio, Toulouse 31023, France

⁶ CNES (Centre National d'Etudes Spatiales), Toulouse 31401, France

⁷ IGN (Institut Géographique National) Espace, Ramonville-Saint-Agne 31520, France

⁸ ARGANS, Plymouth PL6 8BX, the United Kingdom

⁹ ONERA (Office National d'Etudes et Recherches Aérospatiales), Toulouse 31055, France

¹⁰ DLR (Deutschen Zentrums für Luft- und Raumfahrt), Berlin 12489, Germany

Remote Sens. 2017, 9(6), 584; https://doi.org/10.3390/rs9060584 - 10 Jun 2017

Cited by 469 | Viewed by 40386

Abstract

As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and [...] Read more.

As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects. Full article

(This article belongs to the Special Issue First Experiences with European Sentinel-2 Multi-Spectral Imager (MSI))

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15 pages, 4992 KiB

Open AccessArticle

Assessment of the Daily Cloud-Free MODIS Snow-Cover Product for Monitoring the Snow-Cover Phenology over the Qinghai-Tibetan Plateau

by Wenfang Xu^1,2, Hanqing Ma^2,3, Donghai Wu⁴

and Wenping Yuan^1,*

¹ State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China

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

³ Laboratory of Remote Sensing and Geospatial Science, Lanzhou Library, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China

⁴ College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

Remote Sens. 2017, 9(6), 585; https://doi.org/10.3390/rs9060585 - 10 Jun 2017

Cited by 33 | Viewed by 5370

Abstract

Snow cover plays a crucial role in surface hydrology and energy balance, especially in the Qinghai-Tibetan Plateau (QTP). This study used 12 years (2000–2011) of ground-observed snow depth at 87 meteorological stations to assess and verify the accuracy of the daily cloud-free snow-cover [...] Read more.

Snow cover plays a crucial role in surface hydrology and energy balance, especially in the Qinghai-Tibetan Plateau (QTP). This study used 12 years (2000–2011) of ground-observed snow depth at 87 meteorological stations to assess and verify the accuracy of the daily cloud-free snow-cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the QTP. On average, the daily cloud-free MODIS snow-cover product correctly identified the occurrence of snow cover with an accuracy of 90.74%, ranging from 54.39% to 99.07% among the 87 sites. The MODIS-derived data have large uncertainties in identifying the snow-cover phenology on the threshold of FSC >0 and FSC >50% (FSC, fractional snow cover). However, the MODIS-derived data can capture the interannual variability of the snow-cover phenology as compared with in situ observations. This study highlights the uncertainties in the daily snow-free MODIS snow-cover product to reflect snow-cover phenology over the QTP. Full article

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18 pages, 6795 KiB

Open AccessArticle

Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images

by Nina Merkle^1,*

, Wenjie Luo², Stefan Auer¹, Rupert Müller¹ and Raquel Urtasun²

¹ German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany

² Department of Computer Science University of Toronto, Toronto, ON M5S 3G, Canada

Remote Sens. 2017, 9(6), 586; https://doi.org/10.3390/rs9060586 - 10 Jun 2017

Cited by 120 | Viewed by 12595

Abstract

Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high [...] Read more.

Improving the geo-localization of optical satellite images is an important pre-processing step for many remote sensing tasks like monitoring by image time series or scene analysis after sudden events. These tasks require geo-referenced and precisely co-registered multi-sensor data. Images captured by the high resolution synthetic aperture radar (SAR) satellite TerraSAR-X exhibit an absolute geo-location accuracy within a few decimeters. These images represent therefore a reliable source to improve the geo-location accuracy of optical images, which is in the order of tens of meters. In this paper, a deep learning-based approach for the geo-localization accuracy improvement of optical satellite images through SAR reference data is investigated. Image registration between SAR and optical images requires few, but accurate and reliable matching points. These are derived from a Siamese neural network. The network is trained using TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe, in order to learn the two-dimensional spatial shifts between optical and SAR image patches. Results confirm that accurate and reliable matching points can be generated with higher matching accuracy and precision with respect to state-of-the-art approaches. Full article

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

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14 pages, 1069 KiB

Open AccessArticle

Geometry-Based Global Alignment for GSMS Remote Sensing Images

by Dan Zeng¹, Rui Fang¹, Shiming Ge^2,*, Shuying Li³ and Zhijiang Zhang¹

¹ Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200070, China

² Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100195, China

³ The 16th Institute, China Aerospace Science and Technology Corporation, Shaanxi 710100, China

Remote Sens. 2017, 9(6), 587; https://doi.org/10.3390/rs9060587 - 10 Jun 2017

Cited by 4 | Viewed by 5430

Abstract

Alignment of latitude and longitude for all pixels is important for geo-stationary meteorological satellite (GSMS) images. To align landmarks and non-landmarks in the GSMS images, we propose a geometry-based global alignment method. Firstly, the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database and GSMS [...] Read more.

Alignment of latitude and longitude for all pixels is important for geo-stationary meteorological satellite (GSMS) images. To align landmarks and non-landmarks in the GSMS images, we propose a geometry-based global alignment method. Firstly, the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database and GSMS images are expressed as feature maps by geometric coding. According to the geometric and gradient similarity of feature maps, initial feature matching is obtained. Then, neighborhood spatial consistency based local geometric refinement algorithm is utilized to remove outliers. Since the earth is not a standard sphere, polynomial fitting models are used to describe the global relationship between latitude, longitude and coordinates for all pixels in the GSMS images. Finally, with registered landmarks and polynomial fitting models, the latitude and longitude of each pixel in the GSMS images can be calculated. Experimental results show that the proposed method globally align the GSMS images with high accuracy, recall and significantly low computation complexity. Full article

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

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16 pages, 2437 KiB

Open AccessArticle

Quantifying Streamflow Variations in Ungauged Lake Basins by Integrating Remote Sensing and Water Balance Modelling: A Case Study of the Erdos Larus relictus National Nature Reserve, China

by Kang Liang

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2017, 9(6), 588; https://doi.org/10.3390/rs9060588 - 10 Jun 2017

Cited by 12 | Viewed by 5706

Abstract

Hydrological predictions in ungauged lakes are one of the most important issues in hydrological sciences. The habitat of the Relict Gull (Larus relictus) in the Erdos Larus relictus National Nature Reserve (ELRNNR) has been seriously endangered by lake shrinkage, yet the hydrological processes [...] Read more.

Hydrological predictions in ungauged lakes are one of the most important issues in hydrological sciences. The habitat of the Relict Gull (Larus relictus) in the Erdos Larus relictus National Nature Reserve (ELRNNR) has been seriously endangered by lake shrinkage, yet the hydrological processes in the catchment are poorly understood due to the lack of in-situ observations. Therefore, it is necessary to assess the variation in lake streamflow and its drivers. In this study, we employed the remote sensing technique and empirical equation to quantify the time series of lake water budgets, and integrated a water balance model and climate elasticity method to further examine ELRNNR basin streamflow variations from1974 to 2013. The results show that lake variations went through three phases with significant differences: The rapidly expanding sub-period (1974–1979), the relatively stable sub-period (1980–1999), and the dramatically shrinking sub-period (2000–2013). Both climate variation (expressed by precipitation and evapotranspiration) and human activities were quantified as drivers of streamflow variation, and the driving forces in the three phases had different contributions. As human activities gradually intensified, the contributions of human disturbances on streamflow variation obviously increased, accounting for 22.3% during 1980–1999 and up to 59.2% during 2000–2013. Intensified human interferences and climate warming have jointly led to the lake shrinkage since 1999. This study provides a useful reference to quantify lake streamflow and its drivers in ungauged basins. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)

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14 pages, 3584 KiB

Open AccessArticle

Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

by Jing Wang^1,*, Zhengjun Liu^2,*

, Haiying Yu³ and Fangfang Li²

¹ School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

² Chinese Academy of Surveying and Mapping, Beijing 100830, China

³ The Fourth Institute of Anhui Surveying and Mapping, Hefei 230031, China

Remote Sens. 2017, 9(6), 589; https://doi.org/10.3390/rs9060589 - 10 Jun 2017

Cited by 32 | Viewed by 6880

Abstract

Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and [...] Read more.

Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and hyperspectral data. In this study, the applicability of LiDAR and hypersectral data for estimating S. alterniflora biomass and mapping its distribution in coastal regions of China was explored to attempt problems of wetland vegetation biomass estimation caused by different vegetation types and different canopy height. Results showed that the highest correlation coefficient with S. alterniflora biomass was vegetation canopy height (0.817), followed by Normalized Difference Vegetation Index (NDVI) (0.635), Atmospherically Resistant Vegetation Index (ARVI) (0.631), Visible Atmospherically Resistant Index (VARI) (0.599), and Ratio Vegetation Index (RVI) (0.520). A multivariate linear estimation model of S. alterniflora biomass using a variable backward elimination method was developed with R squared coefficient of 0.902 and the residual predictive deviation (RPD) of 2.62. The model accuracy of S. alterniflora biomass was higher than that of wetland vegetation for mixed vegetation types because it improved the estimation accuracy caused by differences in spectral features and canopy heights of different kinds of wetland vegetation. The result indicated that estimated S. alterniflora biomass was in agreement with the field survey result. Owing to its basis in the fusion of LiDAR data and hyperspectral data, the proposed method provides an advantage for S. alterniflora mapping. The integration of high spatial resolution hyperspectral imagery and LiDAR data derived canopy height had significantly improved the accuracy of mapping S. alterniflora biomass. Full article

(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)

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13 pages, 6024 KiB

Open AccessArticle

Road Detection by Using a Generalized Hough Transform

by Weifeng Liu^1,*

, Zhenqing Zhang¹, Shuying Li² and Dapeng Tao^3,*

¹ College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China

² The 16th Institute, China Aerospace Science and Technology Corporation, Xi’an 710100, China

³ School of Information Science and Engineering, Yunnan University, Kunming 650091, China

Remote Sens. 2017, 9(6), 590; https://doi.org/10.3390/rs9060590 - 10 Jun 2017

Cited by 46 | Viewed by 8169

Abstract

Road detection plays key roles for remote sensing image analytics. Hough transform (HT) is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to [...] Read more.

Road detection plays key roles for remote sensing image analytics. Hough transform (HT) is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to develop a low computational complexity and time-saving Hough transform algorithm. In this paper, we propose a generalized Hough transform (i.e., Radon transform) implementation for road detection in remote sensing images. Specifically, we present a dictionary learning method to approximate the Radon transform. The proposed approximation method treats a Radon transform as a linear transform, which then facilitates parallel implementation of the Radon transform for multiple images. To evaluate the proposed algorithm, we conduct extensive experiments on the popular RSSCN7 database for straight road detection. The experimental results demonstrate that our method is superior to the traditional algorithms in terms of accuracy and computing complexity. Full article

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

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22 pages, 5379 KiB

Open AccessArticle

Compact Polarimetric Response of Rape (Brassica napus L.) at C-Band: Analysis and Growth Parameters Inversion

by Wangfei Zhang^1,2

, Zengyuan Li¹, Erxue Chen^1,*, Yahong Zhang², Hao Yang³, Lei Zhao¹ and Yongjie Ji²

¹ Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

² College of Forestry, Southwest Forestry University, Kunming 650224, China

³ Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Remote Sens. 2017, 9(6), 591; https://doi.org/10.3390/rs9060591 - 11 Jun 2017

Cited by 32 | Viewed by 5459

Abstract

Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but [...] Read more.

Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but its potential in growth parameters inversion has not been fully explored. In this paper, a time series of images of CP SAR was simulated from five FP SAR data gathered during the entire growth season of rape. CP response of 27 parameters, relying on Stokes parameters and their child parameters, decomposition parameters and backscattering coefficients, were extracted and investigated as a function of days after sowing (DAS) during the whole rape growth cycle to interpret their sensitivity to each growth parameter. Then, random forest (RF) was chosen as an automatic approach for the growth parameters inversion method, and its results were compared with traditional single-parameter regression models. Most of the CP parameters showed high sensitivity with growth parameters and great potential for growth parameters inversion. Among all of the regression models, the quadratic regression model showed the best performance for all of the growth parameters inversion, the best result for biomass inversion was the third component of the Stokes parameters (g3) with R² of 0.765 and RMSE of 73.20 g/m². For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (Uc), with R² of 0.857 and 0.923 and RMSE of 0.66 and 18.71 cm, respectively. RF showed the highest accuracy and smallest RMSE for all of three growth parameters inversion; R² for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m², 0.25 and 13.5 cm, respectively. However, there are also some CP parameters, which showed low sensitivity to growth parameters, that had high importance for RF inversion. The results confirmed the potential of CP data and the RF method in growth parameters inversion, but they also confirmed that it was difficult to give a physical interpretation for the RF inversion model. Full article

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23 pages, 9185 KiB

Open AccessArticle

Gravitation-Based Edge Detection in Hyperspectral Images

by Genyun Sun^1,2

, Aizhu Zhang^1,2,*, Jinchang Ren³, Jingsheng Ma⁴, Peng Wang^1,2, Yuanzhi Zhang⁵ and Xiuping Jia⁶

¹ School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China

² Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China

³ Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK

⁴ Institute of Petroleum, Heriot-Watt University, Edinburgh EH14 4AS, UK

⁵ Key Lab of Lunar Science and Deep-Exploration, Chinese Academy of Sciences, Beijing 100012, China

⁶ School of Engineering and Information Technology, The University of New South Wales at Canberra, Canberra ACT 2600, Australia

Remote Sens. 2017, 9(6), 592; https://doi.org/10.3390/rs9060592 - 11 Jun 2017

Cited by 27 | Viewed by 8213

Abstract

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral [...] Read more.

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitational theory, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of four state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method. Full article

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12 pages, 846 KiB

Open AccessArticle

Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

by Jörg Burdanowitz^1,2,*, Christian Klepp¹, Stephan Bakan² and Stefan A. Buehler¹

¹ Department of Earth Sciences, Meteorological Institute, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany

² Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany

Remote Sens. 2017, 9(6), 593; https://doi.org/10.3390/rs9060593 - 11 Jun 2017

Cited by 10 | Viewed by 5658

Abstract

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based [...] Read more.

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h

^{- 1}

(55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h

^{- 1}

depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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10 pages, 3646 KiB

Open AccessArticle

Classification of Personnel Targets with Baggage Using Dual-band Radar

by Le Yang¹

, Gao Chen¹ and Gang Li^1,2,*

¹ Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

² The Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China

Remote Sens. 2017, 9(6), 594; https://doi.org/10.3390/rs9060594 - 12 Jun 2017

Cited by 20 | Viewed by 5494

Abstract

In this paper, we aim to identify passengers with different baggage by analyzing the micro-Doppler radar signatures corresponding to different kinds of gaits, which is helpful to improve the efficiency of security check in airports. After performing time-frequency analysis on the X-band and [...] Read more.

In this paper, we aim to identify passengers with different baggage by analyzing the micro-Doppler radar signatures corresponding to different kinds of gaits, which is helpful to improve the efficiency of security check in airports. After performing time-frequency analysis on the X-band and K-band radar data, three kinds of micro-Doppler features, i.e., the period, the Doppler offset, and the bandwidth, are extracted from the time-frequency domain. By combining the features extracted by dual-band radar with the one-versus-one support vector machine (SVM) classifier, three kinds of gaits, i.e., walking with no bag, walking with only one carry-on baggage by one hand, and walking with one carry-on baggage by one hand and one handbag by another hand, can be accurately classified. The experimental results based on the measured data demonstrate that the classification accuracy using dual-band radar is higher than that using only a single-band radar sensor. Full article

(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

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13 pages, 8772 KiB

Open AccessArticle

MODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water

by Yi Wang^1,2,3

, Jun Wang^1,2,3,*

, Robert C. Levy⁴, Xiaoguang Xu^1,2

and Jeffrey S. Reid⁵

¹ Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA 52242, USA

² Center of Global and Regional Environmental Research, The University of Iowa, Iowa City, IA 52242, USA

³ Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USA

⁴ NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

⁵ Marine Meteorology Division, Naval Research Laboratory, Monterey, CA 93943, USA

Remote Sens. 2017, 9(6), 595; https://doi.org/10.3390/rs9060595 - 12 Jun 2017

Cited by 30 | Viewed by 7549

Abstract

We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow [...] Read more.

We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow waters that have visible sediments or sea-floor (i.e., Class 2 waters). Over the global coastal water regions in cloud-free conditions, coastal screening leads to ~20% unavailability of AOD retrievals. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm to be negligible regardless of water turbidity, and therefore the 2.1 μm reflectance at the top of the atmosphere is sensitive to both change of fine-mode and coarse-mode AODs. By assuming that the aerosol single scattering properties over coastal turbid water are similar to those over the adjacent open-ocean pixels, the new algorithm can derive AOD over these shallow waters. The test algorithm yields ~18% more MODIS-AERONET collocated pairs for six AERONET stations in the coastal water regions. Furthermore, comparison of the new retrieval with these AERONET observations show that the new AOD retrievals have equivalent or better accuracy than those retrieved by the MODIS operational algorithm’s over coastal land and non-turbid coastal water product. Combining the new retrievals with the existing MODIS operational retrievals yields an overall improvement of AOD over those coastal water regions. Most importantly, this refinement extends the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides. This expanded coverage is crucial for better understanding of impact of anthropogenic aerosol particles on coastal air quality and climate. Full article

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

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19 pages, 20243 KiB

Open AccessArticle

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

by Xiucheng Yang¹, Shanshan Zhao², Xuebin Qin³, Na Zhao^4,* and Ligang Liang⁵

¹ ICube Laboratory, University of Strasbourg, 67081 Strasbourg, France

² School of Earth & Space Sciences, Peking University, Beijing 100080, China

³ Depart of Computing Science, University of Alberta, Edmonton, T6G 2R3, Canada

⁴ School of Software, Yunnan University, Kunming 650091, China

⁵ School of Software & Microelectronucs, Peking University, Beijing 100080, China

Remote Sens. 2017, 9(6), 596; https://doi.org/10.3390/rs9060596 - 12 Jun 2017

Cited by 275 | Viewed by 20993

Abstract

This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands [...] Read more.

This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band- (between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches. Full article

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22 pages, 33394 KiB

Open AccessArticle

Region-of-Interest Extraction Based on Local–Global Contrast Analysis and Intra-Spectrum Information Distribution Estimation for Remote Sensing Images

by Libao Zhang^1,2,*

and Shiyi Wang¹

¹ The College of Information Science and Technology, Beijing Normal University, Beijing 100875, China

² The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

Remote Sens. 2017, 9(6), 597; https://doi.org/10.3390/rs9060597 - 12 Jun 2017

Cited by 11 | Viewed by 5819

Abstract

Traditional saliency analysis models have made great advances in region of interest (ROI) extraction in natural scene images and videos. However, due to different imaging mechanisms and image features, those approaches are not quite appropriate for remote sensing images. Thus, we propose a [...] Read more.

Traditional saliency analysis models have made great advances in region of interest (ROI) extraction in natural scene images and videos. However, due to different imaging mechanisms and image features, those approaches are not quite appropriate for remote sensing images. Thus, we propose a novel saliency analysis and ROI extraction method for remote sensing images, which is composed of local–global contrast analysis for panchromatic images and intra-spectrum information distribution estimation (LI) for multi-spectral images. The panchromatic image is first segmented into superpixels via level set methods to reduce the subsequent computation complexity and keep region boundaries. Then, the spatially weighted superpixel intensity contrast is calculated globally to highlight superpixels unique to others and obtain the intensity saliency map. In multi-spectral images, ROIs are often included in informative superpixels; therefore, the information theory is introduced to each spectrum independently to acquire the spectrum saliency map. The final result is obtained by fusing the intensity saliency map and the spectrum saliency map and enhancing pixel-level saliency. To improve the anti-noise properties, we employ the Gaussian Pyramid for multi-scale analysis, which removes noise points by the blurring operation and the down-sampling operation. Experiments were conducted aiming at comparing the LI model with nine competing models qualitatively and quantitatively. The results show that the LI model performs better in maintaining intact ROIs with well-defined boundaries and less outside interference, and it tends to be stable when faced with images contaminated by noise. Full article

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23 pages, 5862 KiB

Open AccessArticle

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

by Ram K. Deo^1,*, Matthew B. Russell¹

, Grant M. Domke², Hans-Erik Andersen³, Warren B. Cohen⁴ and Christopher W. Woodall⁵

¹ Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. North, St. Paul, MN 55108, USA

² Northern Research Station, Forest Inventory and Analysis, USDA Forest Service, 1992 Folwell Ave., St. Paul, MN 55108, USA

³ Pacific Northwest Research Station, USDA Forest Service, University of Washington, Seattle, WA 98195, USA

⁴ USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97731, USA

⁵ Northern Research Station, Center for Research on Ecosystem Change, USDA Forest Service, 271 Mast Road, Durham, NH 03824, USA

Remote Sens. 2017, 9(6), 598; https://doi.org/10.3390/rs9060598 - 13 Jun 2017

Cited by 40 | Viewed by 6218

Abstract

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory [...] Read more.

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)—in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R²) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R² = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA. Full article

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17 pages, 2602 KiB

Open AccessArticle

Estimating Chlorophyll Fluorescence Parameters Using the Joint Fraunhofer Line Depth and Laser-Induced Saturation Pulse (FLD-LISP) Method in Different Plant Species

by Parinaz Rahimzadeh-Bajgiran^1,2, Bayaer Tubuxin¹ and Kenji Omasa^1,*

¹ Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan

² School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME 04469, USA

Remote Sens. 2017, 9(6), 599; https://doi.org/10.3390/rs9060599 - 13 Jun 2017

Cited by 9 | Viewed by 8785

Abstract

A comprehensive evaluation of the recently developed Fraunhofer line depth (FLD) and laser-induced saturation pulse (FLD-LISP) method was conducted to measure chlorophyll ﬂuorescence (ChlF) parameters of the quantum yield of photosystem II (Φ_PSII), non-photochemical quenching (NPQ), and the photosystem II-based electron [...] Read more.

A comprehensive evaluation of the recently developed Fraunhofer line depth (FLD) and laser-induced saturation pulse (FLD-LISP) method was conducted to measure chlorophyll ﬂuorescence (ChlF) parameters of the quantum yield of photosystem II (Φ_PSII), non-photochemical quenching (NPQ), and the photosystem II-based electron transport rate (ETR) in three plant species including paprika (C3 plant), maize (C4 plant), and pachira (C3 plant). First, the relationships between photosynthetic photon flux density (PPFD) and ChlF parameters retrieved using FLD-LISP and the pulse amplitude-modulated (PAM) methods were analyzed for all three species. Then the relationships between ChlF parameters measured using FLD-LISP and PAM were evaluated for the plants in different growth stages of leaves from mature to aging conditions. The relationships of ChlF parameters/PPFD were similar in both FLD-LISP and PAM methods in all plant species. Φ_PSII showed a linear relationship with PPFD in all three species whereas NPQ was found to be linearly related to PPFD in paprika and maize, but not for pachira. The ETR/PPFD relationship was nonlinear with increasing values observed for PPFDs lower than about 800 μmol m⁻² s⁻¹ for paprika, lower than about 1200 μmol m⁻² s⁻¹ for maize, and lower than about 800 μmol m⁻² s⁻¹ for pachira. The Φ_PSII, NPQ, and ETR of both the FLD-LISP and PAM methods were very well correlated (R² = 0.89, RMSE = 0.05), (R² = 0.86, RMSE = 0.44), and (R² = 0.88, RMSE = 24.69), respectively, for all plants. Therefore, the FLD-LISP method can be recommended as a robust technique for the estimation of ChlF parameters. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Fluorescence and Photosynthetic Efficiency)

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17 pages, 5851 KiB

Open AccessArticle

A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series

by Beatriz Bellón^1,*, Agnès Bégué¹

, Danny Lo Seen¹, Claudio Aparecido De Almeida²

and Margareth Simões^3,4

¹ Cirad, UMR TETIS (Land, Environment, Remote Sensing and Spatial Information), Maison de la Télédétection, Rue Jean-François Breton, 34090 Montpellier, France

² National Institute for Space Research (INPE), Av. dos Astronautas, 1758, São José dos Campos, SP 12227-010, Brazil

³ Embrapa Solos, Rua Jardim Botânico, 1024, Rio de Janeiro, RJ 22460-000, Brazil

⁴ Department of Computer Engineering, Rio de Janeiro State University (UERJ/FEN/DESC/PPGMA), Rua São Francisco Xavier, 524, 5031 D, Maracanã, Rio de Janeiro, RJ 20550-900, Brazil

Remote Sens. 2017, 9(6), 600; https://doi.org/10.3390/rs9060600 - 13 Jun 2017

Cited by 97 | Viewed by 12061

Abstract

In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of [...] Read more.

In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013–2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis. Full article

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17 pages, 5347 KiB

Open AccessArticle

On-Board Detection and Matching of Feature Points

by Jingjin Huang^1,2,3 and Guoqing Zhou^1,2,3,*

¹ School of Precision Instrument & Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China

² GuangXi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology Guilin, Guangxi 541004, China

³ The Center for Remote Sensing, Tianjin University, Tianjin 300072, China

Remote Sens. 2017, 9(6), 601; https://doi.org/10.3390/rs9060601 - 13 Jun 2017

Cited by 19 | Viewed by 5006

Abstract

This paper presents a FPGA-based method for on-board detection and matching of the feature points. With the proposed method, a parallel processing model and a pipeline structure are presented to ensure a high frame rate at processing speed, but with a low power [...] Read more.

This paper presents a FPGA-based method for on-board detection and matching of the feature points. With the proposed method, a parallel processing model and a pipeline structure are presented to ensure a high frame rate at processing speed, but with a low power consumption. To save the FPGA resources and increase the processing speed, a model which combines the modified SURF detector and a BRIEF descriptor, is presented as well. Three pairs of images with different land coverages are used to evaluate the performance of FPGA-based implementation. The experiment results demonstrate that (1) when the image pairs with artificial features (such as buildings and roads), the performance of FPGA-based implementation is better than those image pairs with natural features (such as woods); (2) the proposed FPGA-based method is capable of ensuring the processing speed at a high frame rate, such as the speed of can achieve 304 fps under a 100 MHz clock frequency. The speedup of the proposed implementation is about 27 times higher than that when using the PC-based implementation. Full article

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20 pages, 20920 KiB

Open AccessArticle

Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model

by Hanfa Xing, Yuan Meng^*, Dongyang Hou, Jie Song and Haibin Xu

College of Geography and Environment, Shandong Normal University, Jinan 250300, China

Remote Sens. 2017, 9(6), 602; https://doi.org/10.3390/rs9060602 - 13 Jun 2017

Cited by 21 | Viewed by 5226

Abstract

Land cover classification is the most important element of land cover mapping and is a key input to many societal benefits. Traditional classification methods require a large amount of remotely sensed images, which are time consuming and labour intensive. Recently, crowdsourcing geographic information [...] Read more.

Land cover classification is the most important element of land cover mapping and is a key input to many societal benefits. Traditional classification methods require a large amount of remotely sensed images, which are time consuming and labour intensive. Recently, crowdsourcing geographic information (CGI), including geo-tagged photos and other sources, has been widely used with lower costs, but still requires extensive labour for data classification. Alternatively, CGI textual information is available from online sources containing land cover information, and it provides a useful source for land cover classification. However, the major challenge of utilising CGI is its uneven spatial distributions in land cover regions, leading to less reliability of regions for land cover classification with sparsely distributed CGI. Moreover, classifying various unorganised CGI texts automatically in each land cover region is another challenge. This paper investigates a faster and more automated method that does not require remotely sensed images for land cover classification. Spatial clustering is employed for CGI to reduce the effect of uneven spatial distributions by extracting land cover regions with high density of CGI. To classify unorganised various CGI texts in each extracted region, land cover topics are calculated using topic model. As a case study, we applied this method using points of interest (POIs) as CGI to classify land cover in Shandong province. The classification result using our proposed method achieved an overall accuracy of approximately 80%, providing evidence that CGI with textual information has a great potential for land cover classification. Full article

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22 pages, 2036 KiB

Open AccessArticle

CAWRES: A Waveform Retracking Fuzzy Expert System for Optimizing Coastal Sea Levels from Jason-1 and Jason-2 Satellite Altimetry Data

by Nurul Hazrina Idris^1,2,*

, Xiaoli Deng³, Ami Hassan Md Din^2,4,5 and Nurul Hawani Idris¹

¹ Tropical Map Research Group, Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 Skudai, Malaysia

² Geoscience and Digital Earth Centre, Research Institute for Sustainability and Environment, Universiti Teknologi Malaysia, 81310 Skudai, Malaysia

³ School of Engineering, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia

⁴ Geomatic Innovation Research Group, Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, 81310 Skudai, Malaysia

⁵ Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Malaysia

Remote Sens. 2017, 9(6), 603; https://doi.org/10.3390/rs9060603 - 14 Jun 2017

Cited by 24 | Viewed by 4985

Abstract

This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES’ aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of [...] Read more.

This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES’ aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of radar altimetry data closer to the coast. The principles of CAWRES are twofold. The first is to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system. The second is to minimise the relative offset in the retrieved sea levels caused by switching from one retracker to another using a neural network. The innovative system is validated against geoid height and tide gauges in the Great Barrier Reef, Australia for Jason-1 and Jason-2 satellite missions. The regional investigations have demonstrated that the CAWRES can effectively enhance the quality of 20 Hz sea level data and recover up to 16% more data than the standard MLE4 retracker over the tested region. Comparison against tide gauge indicates that the CAWRES sea levels are more reliable than those of Sensor Geophysical Data Records (SGDR) products, because the former has a higher (≥0.77) temporal correlation and smaller (≤19 cm) root mean square errors. The results demonstrate that the CAWRES can be applied to coastal regions elsewhere as well as other satellite altimeter missions. Full article

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19 pages, 2679 KiB

Open AccessArticle

Do Daily and Seasonal Trends in Leaf Solar Induced Fluorescence Reflect Changes in Photosynthesis, Growth or Light Exposure?

by Rhys Wyber^1,*

, Zbyněk Malenovský^1,2

, Michael B. Ashcroft¹, Barry Osmond¹ and Sharon A. Robinson¹

¹ Centre for Sustainable Ecosystem Solutions, School of Biological Sciences, University of Wollongong, Wollongong 2522, Australia

² Department of Remote Sensing, Global Change Research Institute CAS, Bělidla 986/4a, CZ-60300 Brno, Czech Republic

Remote Sens. 2017, 9(6), 604; https://doi.org/10.3390/rs9060604 - 14 Jun 2017

Cited by 25 | Viewed by 7232

Abstract

Solar induced chlorophyll fluorescence (SIF) emissions of photosynthetically active plants retrieved from space-borne observations have been used to improve models of global primary productivity. However, the relationship between SIF and photosynthesis in diurnal and seasonal cycles is still not fully understood, especially at [...] Read more.

Solar induced chlorophyll fluorescence (SIF) emissions of photosynthetically active plants retrieved from space-borne observations have been used to improve models of global primary productivity. However, the relationship between SIF and photosynthesis in diurnal and seasonal cycles is still not fully understood, especially at large spatial scales, where direct measurements of photosynthesis are unfeasible. Motivated by up-scaling potential, this study examined the diurnal and seasonal relationship between SIF and photosynthetic parameters measured at the level of individual leaves. We monitored SIF in two plant species, avocado (Persea Americana) and orange jasmine (Murraya paniculatta), throughout 18 diurnal cycles during the Southern Hemisphere spring, summer and autumn, and compared them with simultaneous measurements of photosynthetic yields, and leaf and global irradiances. Results showed that at seasonal time scales SIF is principally correlated with changes in leaf irradiance, electron transport rates (ETR) and constitutive heat dissipation (YNO; p < 0.001). Multiple regression models of correlations between photosynthetic parameters and SIF at diurnal time scales identified leaf irradiance as the principle predictor of SIF (p < 0.001). Previous studies have identified correlations between photosynthetic yields, ETR and SIF at larger spatial scales, where heterogeneous canopy architecture and landscape spatial patterns influence the spectral and photosynthetic measurements. Although this study found a significant correlation between leaf-measured YNO and SIF, future dedicated up-scaling experiments are required to elucidate if these observations are also found at larger spatial scales. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Fluorescence and Photosynthetic Efficiency)

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23 pages, 14828 KiB

Open AccessFeature PaperArticle

Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water Altimetry Potentialities

by Ron Abileah^1,†, Andrea Scozzari^2,*,† and Stefano Vignudelli^3,†

¹ Jomegak, San Carlos, CA 94070, USA

² CNR Institute of Information Science and Technologies (CNR-ISTI), Via Moruzzi 1, 56024 Pisa, Italy

³ CNR Institute of Biophysics (CNR-IBF), Via Moruzzi 1, 56024 Pisa, Italy

^† These authors contributed equally to this work.

Remote Sens. 2017, 9(6), 605; https://doi.org/10.3390/rs9060605 - 14 Jun 2017

Cited by 12 | Viewed by 6373

Abstract

The exploitation of synthetic aperture properties in nadir-looking radars is opening new scenarios in the framework of satellite radar altimetry. Both recent and upcoming missions including Cryosat-2, Sentinel-3, Sentinel-6 and SWOT take benefit from the coherent processing of radar data, aimed at improving [...] Read more.

The exploitation of synthetic aperture properties in nadir-looking radars is opening new scenarios in the framework of satellite radar altimetry. Both recent and upcoming missions including Cryosat-2, Sentinel-3, Sentinel-6 and SWOT take benefit from the coherent processing of radar data, aimed at improving range measurements in particular contexts, such as ice, open ocean, coastal zone, and even inland waters. This work investigates the possibilities offered by current and future satellite radar altimetry missions for the study of inland water bodies, probing into the peculiarities of the expected radar returns and their potential usage. In this regard, signals collected by the RA-2 instrument (Radar Altimeter 2) onboard the Envisat mission offer an unprecedented possibility, even with a relatively low pulse repetition frequency, to analyze the peculiarities of actual signals for detecting and ranging small water surfaces. In particular, the RA-2 instrument offers a global archive of Individual Echoes (IEs), collected at the native sampling rate of 1795 Hz, in addition to the 18 Hz data obtained by incoherent averaging, which are typically delivered to the users as standard products. RA-2 shares with future radar platforms such as Sentinel-6 a continuous and interleaved working modality, as was recommended by the scientific community in designing next missions’ requirements. This is a further reason to consider the usage of RA-2 IEs as particularly attractive. Whilst only available for a small percentage of the earth’s surface, sufficient IE data exist to study the height retrieval capability of these echoes, in particular for what concerns small water bodies, where we show that enough coherence is exhibited for focusing relatively narrow surfaces and range them correctly. A peculiar aspect of this work lies in the assumption that most of the returned echoes (in RA-2 IEs) are specular. A theoretical framework is developed according to this assumption, which is validated by investigating real RA-2 data and observing their related specular features. In particular, we discuss how specular echoes are expected to be very common in inland altimetry, and are most often associated with small to medium size lakes and rivers. This paper illustrates the expected electromagnetic behavior of specular water targets by exploiting the classical radar cross-section (RCS) theory for specular surfaces. Results from the model are compared with real IE data in three selected case studies, regarding two rivers of variable width and one flood plain, in order to check different hydrological regimes. The model very closely matches the data in all cases, making the results of this validation activity very promising. In particular, we demonstrate the feasibility of using satellite radar altimetry in rivers much smaller than what was considered possible until now. Full article

(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

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18 pages, 7613 KiB

Open AccessArticle

Atmospheric Effect Analysis and Correction of the Microwave Vegetation Index

by Da-Bin Ji

, Jian-Cheng Shi^*

, Husi Letu

, Tian-Xing Wang

and Tian-Jie Zhao

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China

Remote Sens. 2017, 9(6), 606; https://doi.org/10.3390/rs9060606 - 14 Jun 2017

Cited by 3 | Viewed by 4628

Abstract

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated [...] Read more.

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass. Full article

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

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16 pages, 4646 KiB

Open AccessArticle

Sentinel-1A/B Combined Product Geolocation Accuracy

by Adrian Schubert^1,*

, Nuno Miranda², Dirk Geudtner³ and David Small¹

¹ Remote Sensing Laboratories, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

² ESA-ESRIN, Via Galileo Galilei, 00044 Frascati, Italy

³ ESA-ESTEC, Keplerlaan 1, 2200 AG Noordwijk, The Netherlands

Remote Sens. 2017, 9(6), 607; https://doi.org/10.3390/rs9060607 - 14 Jun 2017

Cited by 91 | Viewed by 9634

Abstract

Sentinel-1A and -1B are twin spaceborne synthetic aperture radar (SAR) sensors developed and operated by the European Space Agency under the auspices of the Copernicus Earth observation programme. Launched in April 2014 and April 2016, Sentinel-1A and -1B are currently operating in tandem, [...] Read more.

Sentinel-1A and -1B are twin spaceborne synthetic aperture radar (SAR) sensors developed and operated by the European Space Agency under the auspices of the Copernicus Earth observation programme. Launched in April 2014 and April 2016, Sentinel-1A and -1B are currently operating in tandem, in a common orbital configuration to provide an increased revisit frequency. In-orbit commissioning was completed for each unit within months of their respective launches, and level-1 SAR products generated by the operational SAR processor have been geometrically calibrated. In order to compare and monitor the geometric characteristics of the level-1 products from both units, as well as to investigate potential improvements, products from both satellites have been monitored since their respective commissioning phases. In this study, we present geolocation accuracy estimates for both Sentinel-1 units based on the time series of level-1 products collected thus far. While both units were demonstrated to be performing consistently, and providing SAR data products according to the nominal product specifications, a subtle beam- and mode-dependent azimuth bias common to the data from both units was identified. A method for removing the bias is proposed, and the corresponding improvement to the geometric accuracies is demonstrated and quantified. Full article

(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)

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25 pages, 9193 KiB

Open AccessArticle

Spatio-Temporal Relationships between Optical Information and Carbon Fluxes in a Mediterranean Tree-Grass Ecosystem

by Javier Pacheco-Labrador^1,*

, Tarek S. El-Madany¹

, M. Pilar Martín²

, Mirco Migliavacca¹

, Micol Rossini³, Arnaud Carrara⁴ and Pablo J. Zarco-Tejada⁵

¹ Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Jena D-07745, Germany

² Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Institute of Economic, Geography and Demography (IEGD-CCHS), Spanish National Research Council (CSIC), C/Albasanz 26-28, 28037 Madrid, Spain

³ Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy

⁴ Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Charles Darwin 14, Parc Tecnològic, 46980 Paterna, Spain

⁵ European Commission, Joint Research Centre (JRC), Directorate D—Sustainable Resources, Via E. Fermi 2749, I-21027 Ispra, Italy

Remote Sens. 2017, 9(6), 608; https://doi.org/10.3390/rs9060608 - 14 Jun 2017

Cited by 17 | Viewed by 6951

Abstract

Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models [...] Read more.

Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPP_hh) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems. Full article

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13 pages, 3831 KiB

Open AccessArticle

Analyzing the Potential Risk of Climate Change on Lyme Disease in Eastern Ontario, Canada Using Time Series Remotely Sensed Temperature Data and Tick Population Modelling

by Angela Cheng¹, Dongmei Chen^1,*

, Katherine Woodstock¹, Nicholas H. Ogden², Xiaotian Wu³ and Jianhong Wu⁴

¹ Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada

² Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, 3200 rue Sicotte, CP 5000, Saint-Hyacinthe, QC J2S 7C6, Canada

³ Department of Mathematics, Shanghai Maritime University, Shanghai 201306, China

⁴ Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada

Remote Sens. 2017, 9(6), 609; https://doi.org/10.3390/rs9060609 - 15 Jun 2017

Cited by 20 | Viewed by 14937

Abstract

The number of Lyme disease cases (Lyme borreliosis) in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern [...] Read more.

The number of Lyme disease cases (Lyme borreliosis) in Ontario, Canada has increased over the last decade, and that figure is projected to continue to increase. The northern limit of Lyme disease cases has also been progressing northward from the northeastern United States into southeastern Ontario. Several factors such as climate change, changes in host abundance, host and vector migration, or possibly a combination of these factors likely contribute to the emergence of Lyme disease cases in eastern Ontario. This study first determined areas of warming using time series remotely sensed temperature data within Ontario, then analyzed possible spatial-temporal changes in Lyme disease risk in eastern Ontario from 2000 to 2013 due to climate change using tick population modeling. The outputs of the model were validated by using tick surveillance data from 2002 to 2012. Our results indicated areas in Ontario where Lyme disease risk changed from unsustainable to sustainable for sustaining Ixodes scapularis (black-legged tick) populations. This study provides evidence that climate change has facilitated the northward expansion of black-legged tick populations’ geographic range over the past decade. The results demonstrate that remote sensing data can be used to increase the spatial detail for Lyme disease risk mapping and provide risk maps for better awareness of possible Lyme disease cases. Further studies are required to determine the contribution of host migration and abundance on changes in eastern Ontario’s Lyme disease risk. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Human Health)

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15 pages, 2152 KiB

Open AccessArticle

Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System

by Daud Jones Kachamba^1,2

, Hans Ole Ørka¹

, Erik Næsset¹, Tron Eid¹ and Terje Gobakken^1,*

¹ Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway

² Department of Forestry, Lilongwe University of Agriculture and Natural Resources, P.O. Box 219, Lilongwe, Malawi

Remote Sens. 2017, 9(6), 610; https://doi.org/10.3390/rs9060610 - 15 Jun 2017

Cited by 34 | Viewed by 6328

Abstract

Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and [...] Read more.

Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and precision of estimates are influenced by the size of field sample plots used to obtain reference values for biomass. The objective of this case study was to assess the influence of sample plot size on efficiency of UAS-assisted biomass estimates in the dry tropical miombo woodlands of Malawi. The results of a design-based field sample inventory assisted by three-dimensional point clouds obtained from aerial imagery acquired with a UAS showed that the root mean square errors as well as the standard error estimates of mean biomass decreased as sample plot sizes increased. Furthermore, relative efficiency values over different sample plot sizes were above 1.0 in a design-based and model-assisted inferential framework, indicating that UAS-assisted inventories were more efficient than purely field-based inventories. The results on relative costs for UAS-assisted and pure field-based sample plot inventories revealed that there is a trade-off between inventory costs and required precision. For example, in our study if a standard error of less than approximately 3 Mg ha⁻¹ was targeted, then a UAS-assisted forest inventory should be applied to ensure more cost effective and precise estimates. Future studies should therefore focus on finding optimum plot sizes for particular applications, like for example in projects under the Reducing Emissions from Deforestation and Forest Degradation, plus forest conservation, sustainable management of forest and enhancement of carbon stocks (REDD+) mechanism with different geographical scales. Full article

(This article belongs to the Section Forest Remote Sensing)

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25 pages, 14378 KiB

Open AccessArticle

Satellite-Based Method for Estimating the Spatial Distribution of Crop Evapotranspiration: Sensitivity to the Priestley-Taylor Coefficient

by José Ángel Martínez Pérez¹, Sandra G. García-Galiano^1,*

, Bernardo Martin-Gorriz² and Alain Baille²

¹ Department of Civil Engineering, Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 52, 30203 Cartagena, Spain

² Department of Food Engineering and Agricultural Equipment, Universidad Politécnica de Cartagena, P. Alfonso XIII, 48, 30203 Cartagena, Spain

Remote Sens. 2017, 9(6), 611; https://doi.org/10.3390/rs9060611 - 16 Jun 2017

Cited by 14 | Viewed by 5444

Abstract

This work discusses an operational method for actual evapotranspiration (ET) retrieval from remote sensing, considering a minimum quantity of ancillary data. The method consists in a graphical approach based on the Priestley-Taylor (PT) equation, where the dry soil and non-limiting water conditions are [...] Read more.

This work discusses an operational method for actual evapotranspiration (ET) retrieval from remote sensing, considering a minimum quantity of ancillary data. The method consists in a graphical approach based on the Priestley-Taylor (PT) equation, where the dry soil and non-limiting water conditions are defined by land surface temperature (LST) and vegetation index (VI) space, both retrieved from remote sensing. Using ET tower flux measurements and Landsat 5 TM images of an irrigation scheme in southeast Spain, a sensitivity analysis of ET spatial distribution was performed for the period 2009–2011 with respect to: (i) the shape (trapezoidal or rectangular) of the LST-VI space; and (ii) the value of the PT coefficient, α. The results from ground truth validation were satisfactory, both shapes providing similar performances in estimating ET, with root mean square error ~30 W/m² and relative difference ~10% with respect to tower-based measurements. Importantly, the best fit with ground data was found for α close to 1, a somewhat different value from the commonly used value of 1.27, indicating that substantial error might arise when using the latter value. Overall, our study underlines the importance of a more precise knowledge of the actual value of α coefficient when using ET retrieval methods based on the LST-VI space. Full article

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31 pages, 10748 KiB

Open AccessArticle

The 2013 FLEX—US Airborne Campaign at the Parker Tract Loblolly Pine Plantation in North Carolina, USA

by Elizabeth M. Middleton¹, Uwe Rascher²

, Lawrence A. Corp³, K. Fred Huemmrich⁴, Bruce D. Cook¹, Asko Noormets⁵, Anke Schickling², Francisco Pinto^2,†, Luis Alonso⁶

, Alexander Damm⁷, Luis Guanter⁸, Roberto Colombo⁹, Petya K. E. Campbell⁴, David R. Landis^10,*, Qingyuan Zhang¹¹, Micol Rossini⁹

, Dirk Schuettemeyer¹² and Remo Bianchi^13,‡

¹ Biospheric Sciences Laboratory, NASA/GSFC, Greenbelt, MD 20771, USA

² IBG-2: Plant Sciences, Institute of Bio- and Geoscience, Forschungszentrum Jülich, 52428 Jülich, Germany

³ Science Systems & Applications Inc., Lanham, MD 20706, USA

⁴ Joint Center for Earth Systems Technology, UMBC, Baltimore, MD 21250, USA

⁵ Department Forestry & Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA

⁶ Image Processing Laboratory (IPL), Parc Cientific, Universitat Valencia, 46010 Valencia, Spain

⁷ Remote Sensing Laboratories, University of Zurich, 8057 Zurich, Switzerland

⁸ Helmholtz Centre Potsdam, German Research Center for Geosciences (GFZ), 14473 Potsdam, Germany

⁹ Department of Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy

¹⁰ Global Science & Technology, Inc., Greenbelt, MD 20770, USA

¹¹ Universities Space Research Associates, Columbia, MD 21046, USA

¹² Earth Observations, ESTEC, 2201 AZ Noordwijk, The Netherlands

¹³ Earth Observations, ESA-ESRIN, 00044 Frascati, Italy

^† Current Adress: Global Wheat Program, International Maize & Wheat Improvement Center (CIMMYT), 06600 México, D.F., Mexico.

^‡ Retired.

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Remote Sens. 2017, 9(6), 612; https://doi.org/10.3390/rs9060612 - 16 Jun 2017

Cited by 32 | Viewed by 8701

Abstract

The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA’s FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September–October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This [...] Read more.

The first European Space Agency (ESA) and NASA collaboration in an airborne campaign to support ESA’s FLuorescence EXplorer (FLEX) mission was conducted in North Carolina, USA during September–October 2013 (FLEX-US 2013) at the Parker Tract Loblolly Pine (LP) Plantation (Plymouth, NC, USA). This campaign combined two unique airborne instrument packages to obtain simultaneous observations of solar-induced fluorescence (SIF), LiDAR-based canopy structural information, visible through shortwave infrared (VSWIR) reflectance spectra, and surface temperature, to advance vegetation studies of carbon cycle dynamics and ecosystem health. We obtained statistically significant results for fluorescence, canopy temperature, and tower fluxes from data collected at four times of day over two consecutive autumn days across an age class chronosequence. Both the red fluorescence (F685) and far-red fluorescence (F740) radiances had highest values at mid-day, but their fluorescence yields exhibited different diurnal responses across LP age classes. The diurnal trends for F685 varied with forest canopy temperature difference (canopy minus air), having a stronger daily amplitude change for young vs. old canopies. The Photochemical Reflectance Index (PRI) was positively correlated with this temperature variable over the diurnal cycle. Tower measurements from mature loblolly stand showed the red/far-red fluorescence ratio was linearly related to canopy light use efficiency (LUE) over the diurnal cycle, but performed even better for the combined morning/afternoon (without midday) observations. This study demonstrates the importance of diurnal observations for interpretation of fluorescence dynamics, the need for red fluorescence to understand canopy physiological processes, and the benefits of combining fluorescence, reflectance, and structure information to clarify canopy function versus structure characteristics for a coniferous forest. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Fluorescence and Photosynthetic Efficiency)

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20 pages, 7888 KiB

Open AccessFeature PaperArticle

Comparing Passive Microwave with Visible-To-Near-Infrared Phenometrics in Croplands of Northern Eurasia

by Woubet G. Alemu¹ and Geoffrey M. Henebry^1,2,*

¹ Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007-3510, USA

² Department of Natural Resource Management, South Dakota State University, Brookings, SD 57007, USA

Remote Sens. 2017, 9(6), 613; https://doi.org/10.3390/rs9060613 - 15 Jun 2017

Cited by 9 | Viewed by 6796

Abstract

Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and [...] Read more.

Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003–2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R² < 0.99). The 10.65 GHz VOD (VOD_1065GHz) achieved the best linear regression fit (R² = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD_10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R² of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD_10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world. Full article

(This article belongs to the Special Issue Land Surface Phenology and Seasonality: Novel Approaches and Applications)

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25 pages, 3379 KiB

Open AccessArticle

Satellite-Based Inversion and Field Validation of Autotrophic and Heterotrophic Respiration in an Alpine Meadow on the Tibetan Plateau

by Ben Niu^1,2, Yongtao He^1,5, Xianzhou Zhang^1,5,*, Ning Zong¹, Gang Fu¹

, Peili Shi^1,5, Yangjian Zhang^1,5, Mingyuan Du³

and Jing Zhang⁴

¹ Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, 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 for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan

⁴ College of Global Change and Earth System Sciences, Beijing Normal University, Beijing 100875, China

⁵ College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

Remote Sens. 2017, 9(6), 615; https://doi.org/10.3390/rs9060615 - 15 Jun 2017

Cited by 8 | Viewed by 5651

Abstract

Alpine meadow ecosystem is among the highest soil carbon density and the most sensitive ecosystem to climate change. Partitioning autotrophic (Ra) and heterotrophic components (Rm) of ecosystem respiration (Re) is critical to evaluating climate change effects on ecosystem carbon cycling. Here we introduce [...] Read more.

Alpine meadow ecosystem is among the highest soil carbon density and the most sensitive ecosystem to climate change. Partitioning autotrophic (Ra) and heterotrophic components (Rm) of ecosystem respiration (Re) is critical to evaluating climate change effects on ecosystem carbon cycling. Here we introduce a satellite-based method, combining MODerate resolution Imaging Spectroradiometer (MODIS) products, eddy covariance (EC) and chamber-based Re components measurements, for estimating carbon dynamics and partitioning of Re from 2009 to 2011 in a typical alpine meadow on the Tibetan Plateau. Six satellite-based gross primary production (GPP) models were employed and compared with GPP_EC, all of which appeared to well explain the temporal GPP_EC trends. However, MODIS versions 6 GPP product (GPP_MOD) and GPP estimation from vegetation photosynthesis model (GPP_VPM) provided the most reliable GPP estimation magnitudes with less than 10% of relative predictive error (RPE) compared to GPP_EC. Thus, they together with MODIS products and GPP_EC were used to estimate Re using the satellite-based method. All satellite-based Re estimations generated an alternative estimation of Re_EC with negligible root mean square errors (RMSEs, g C m⁻² day⁻¹) either in the growing season (0.12) or not (0.08). Moreover, chamber-based Re measurements showed that autotrophic contributions to Re (Ra/Re) could be effectively reflected by all these three satellite-based Re partitions. Results showed that the Ra contribution of Re were 27% (10–48%), 43% (22–59%) and 56% (33–76%) from 2009 to 2011, respectively, of which inter-annual variation is mainly attributed to soil water dynamics. This study showed annual temperature sensitivity of Ra (Q_10,Ra) with an average of 5.20 was significantly higher than that of Q_10,Rm (1.50), and also the inter-annual variation of Q_10,Ra (4.14–7.31) was larger than Q_10,Rm (1.42–1.60). Therefore, our results suggest that the response of Ra to temperature change is stronger than that of Rm in this alpine meadow. Full article

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17 pages, 3531 KiB

Open AccessArticle

Remotely Monitoring Ecosystem Water Use Efficiency of Grassland and Cropland in China’s Arid and Semi-Arid Regions with MODIS Data

by Xuguang Tang^1,2,*, Mingguo Ma¹

, Zhi Ding², Xibao Xu³, Li Yao¹, Xiaojuan Huang¹, Qing Gu¹ and Lisheng Song^1,*

¹ Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing 400715, China

² Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

³ Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

Remote Sens. 2017, 9(6), 616; https://doi.org/10.3390/rs9060616 - 16 Jun 2017

Cited by 42 | Viewed by 8123

Abstract

Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental [...] Read more.

Scarce water resources are available in the arid and semi-arid areas of Northwest China, where significant water-related challenges will be faced in the coming decades. Quantitative evaluations of the spatio-temporal dynamics in ecosystem water use efficiency (WUE), as well as the underlying environmental controls, are crucial for predicting future climate change impacts on ecosystem carbon-water interactions and agricultural production. However, these questions remain poorly understood in this typical region. By means of continuous eddy covariance (EC) measurements and time-series MODIS data, this study revealed the distinct seasonal cycles in gross primary productivity (GPP), evapotranspiration (ET), and WUE for both grassland and cropland ecosystems, and the dominant climate factors performed jointly by temperature and precipitation. The MODIS WUE estimates from GPP and ET products can capture the broad trend in WUE variability of grassland, but with large biases for maize cropland, which was mainly ascribed to large uncertainties resulting from both GPP and ET algorithms. Given the excellent biophysical performance of the MODIS-derived enhanced vegetation index (EVI), a new greenness model (GR) was proposed to track the eight-day changes in ecosystem WUE. Seasonal variations and the scatterplots between EC-based WUE and the estimates from time-series EVI data (WUE_GR) also certified its prediction accuracy with R² and RMSE of both grassland and cropland ecosystems over 0.90 and less than 0.30 g kg⁻¹, respectively. The application of the GR model to regional scales in the near future will provide accurate WUE information to support water resource management in dry regions around the world. Full article

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15 pages, 1980 KiB

Open AccessArticle

Convolutional Neural Networks Based Hyperspectral Image Classification Method with Adaptive Kernels

by Chen Ding^*

, Ying Li, Yong Xia, Wei Wei

, 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. 2017, 9(6), 618; https://doi.org/10.3390/rs9060618 - 16 Jun 2017

Cited by 55 | Viewed by 8469

Abstract

Hyperspectral image (HSI) classification aims at assigning each pixel a pre-defined class label, which underpins lots of vision related applications, such as remote sensing, mineral exploration and ground object identification, etc. Lots of classification methods thus have been proposed for better hyperspectral imagery [...] Read more.

Hyperspectral image (HSI) classification aims at assigning each pixel a pre-defined class label, which underpins lots of vision related applications, such as remote sensing, mineral exploration and ground object identification, etc. Lots of classification methods thus have been proposed for better hyperspectral imagery interpretation. Witnessing the success of convolutional neural networks (CNNs) in the traditional images based classification tasks, plenty of efforts have been made to leverage CNNs to improve HSI classification. An advanced CNNs architecture uses the kernels generated from the clustering method, such as a K-means network uses K-means to generate the kernels. However, the above methods are often obtained heuristically (e.g., the number of kernels should be assigned manually), and how to data-adaptively determine the number of convolutional kernels (i.e., filters), and thus generate the kernels that better represent the data, are seldom studied in existing CNNs based HSI classification methods. In this study, we propose a new CNNs based HSI classification method where the convolutional kernels can be automatically learned from the data through clustering without knowing the cluster number. With those data-adaptive kernels, the proposed CNNs method achieves better classification results. Experimental results from the datasets demonstrate the effectiveness of the proposed method. Full article

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

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16 pages, 2104 KiB

Open AccessArticle

2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

by Gongxin Li^1,2

, Jia Yang^1,2, Wenguang Yang^1,2

, Yuechao Wang¹, Wenxue Wang^1,* and Lianqing Liu^1,*

¹ State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China

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

Remote Sens. 2017, 9(6), 619; https://doi.org/10.3390/rs9060619 - 16 Jun 2017

Cited by 7 | Viewed by 5198

Abstract

Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a [...] Read more.

Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery. Full article

(This article belongs to the Special Issue Radar Systems for the Societal Challenges)

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16 pages, 3127 KiB

Open AccessArticle

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM_2.5 Concentrations in the Northeastern United States

by Xueke Li¹, Chuanrong Zhang^1,*

, Weidong Li¹

and Kai Liu²

¹ Department of Geography, University of Connecticut, Storrs, CT 06269, USA

² Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China

Remote Sens. 2017, 9(6), 620; https://doi.org/10.3390/rs9060620 - 16 Jun 2017

Cited by 42 | Viewed by 7153

Abstract

Degraded air quality by PM_2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM_2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM [...] Read more.

Degraded air quality by PM_2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM_2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM_2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM_2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM_2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM_2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM_2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM_2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM_2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM_2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM_2.5 monitoring and prediction. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

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18 pages, 5983 KiB

Open AccessArticle

Four-Component Model-Based Decomposition for Ship Targets Using PolSAR Data

by Yuyang Xi, Haitao Lang^*, Yunhong Tao, Lin Huang and Zijun Pei

Department of Physics & Electronics, Beijing University of Chemical Technology, Beijing 100029, China

Remote Sens. 2017, 9(6), 621; https://doi.org/10.3390/rs9060621 - 16 Jun 2017

Cited by 32 | Viewed by 5961

Abstract

Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel [...] Read more.

Scattering mechanism (SM) analysis is a promising technique for ship detection and classification in polarimetric SAR (PolSAR) images. In this paper, a four-component model-based decomposition method incorporating surface, double-bounce, volume and cross-polarized components is proposed for analyzing the SMs of ships. A novel cross-polarized scattering component capable of discriminating between the HV scattering power generated by the oriented scatterers on ships from that by volume scatterers is proposed as a means to address the problem of volume scattering power overestimation. In the decomposition stage, by taking into account both the real and imaginary parts of the elements

{〈 [T] 〉}^{H V} (1, 3)

and

{〈 [T] 〉}^{H V} (2, 3)

of the observed coherency matrix, the proposed cross-polarized component can preserve the reflection asymmetry information completely, which is an essential property of man-made targets, such as ships. Based on the proposed decomposition method and an analysis of the different SMs between ships and sea clutter, a novel ship detection metric defined as

M = ln \frac{P_{d} + P_{c}}{P_{s}}

is proposed. Experimental results conducted on RadarSat-2 quad-polarimetric data validate the proposed four-component decomposition method as being more suitable for analyzing the SMs of ship targets than the existing helix matrix-based decomposition methods. Additionally, we find that the proposed ship detection metric can effectively enhance the signal-to-clutter ratio (SCR) and improve ship detection performance. Full article

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20 pages, 5364 KiB

Open AccessArticle

Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator

by Neus Sabater^1,*

, Jorge Vicent¹

, Luis Alonso¹

, Sergio Cogliati²

, Jochem Verrelst¹

and José Moreno¹

¹ Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain

² Remote Sensing of Environmental Dynamics Lab., DISAT, Università di Milano-Bicocca, P.zza della Scienza 1, 20126 Milan, Italy

Remote Sens. 2017, 9(6), 622; https://doi.org/10.3390/rs9060622 - 16 Jun 2017

Cited by 21 | Viewed by 7561

Abstract

In the last decade, signiﬁcant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O₂–B and the deep O₂–A absorption bands present a high [...] Read more.

In the last decade, signiﬁcant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O₂–B and the deep O₂–A absorption bands present a high sensitivity to detect SIF, these regions are also largely inﬂuenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., ﬁrst an AC and second a Spectral Fitting technique to disentangle SIF from reﬂected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reﬂectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reﬂectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reﬂectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefﬁcient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reﬂectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation. Full article

(This article belongs to the Special Issue Remote Sensing of Vegetation Fluorescence and Photosynthetic Efficiency)

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25 pages, 8844 KiB

Open AccessArticle

Land Cover, Land Use, and Climate Change Impacts on Endemic Cichlid Habitats in Northern Tanzania

by Margaret Kalacska^1,*

, J. Pablo Arroyo-Mora²

, Oliver Lucanus³ and Mary A. Kishe-Machumu⁴

¹ Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada

² Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A 0R6, Canada

³ Below Water Pictures, Vaudreuil-Dorion, QC J7V 0K4, Canada

⁴ Tanzania Fisheries Research Institute, P.O. Box 9750, Dar es Salaam, Tanzania

Remote Sens. 2017, 9(6), 623; https://doi.org/10.3390/rs9060623 - 17 Jun 2017

Cited by 19 | Viewed by 12805

Abstract

Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for [...] Read more.

Freshwater ecosystems are among the most threatened on Earth, facing environmental and anthropogenic pressures often surpassing their terrestrial counterparts. Land use and land cover change (LUCC) such as degradation and fragmentation of the terrestrial landscape negatively impacts aquatic ecosystems. Satellite imagery allows for an impartial assessment of the past to determine habitat alterations. It can also be used as a forecasting tool in the development of species conservation strategies through models based on ecological factors extracted from imagery. In this study, we analyze Landsat time sequences (1984–2015) to quantify LUCC around three freshwater ecosystems with endemic cichlids in Tanzania. In addition, we examine population growth, agricultural expansion, and climate change as stressors that impact the habitats. We found that the natural vegetation cover surrounding Lake Chala decreased from 15.5% (1984) to 3.5% (2015). At Chemka Springs, we observed a decrease from 7.4% to 3.5% over the same period. While Lake Natron had minimal LUCC, severe climate change impacts have been forecasted for the region. Subsurface water data from the Gravity Recovery and Climate Experiment (GRACE) satellite observations further show a decrease in water resources for the study areas, which could be exacerbated by increased need from a growing population and an increase in agricultural land use. Full article

(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)

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19 pages, 7360 KiB

Open AccessArticle

Spatiotemporal Variation in Particulate Organic Carbon Based on Long-Term MODIS Observations in Taihu Lake, China

by Changchun Huang^1,2, Quanliang Jiang¹, Ling Yao^3,4,*

, Yunmei Li^1,5,6, Hao Yang^1,6, Tao Huang^1,6 and Mingli Zhang¹

¹ School of geography science, Nanjing Normal University, Nanjing 210023, China

² State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210023, China

³ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

⁴ Guangdong Institute of Geography, Guangdong academy of sciences, Guangzhou 510070, China

⁵ Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China

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

Remote Sens. 2017, 9(6), 624; https://doi.org/10.3390/rs9060624 - 17 Jun 2017

Cited by 17 | Viewed by 7100

Abstract

In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer [...] Read more.

In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) satellite image. The performance of the POC algorithm is highly consistent with the in situ measured POC, with root mean square error percentage (RMSPs) of 38.9% and 31.5% for two independent validations, respectively. The MODIS-derived POC also shows an acceptable result, with RMSPs of 53.6% and 61.0% for two periods of match-up data. POC from 2005 to 2007 is much higher than it is from 2002 to 2004 and 2008 to 2013, due to a large area of algal bloom. Riverine flux is an important source of POC in Taihu Lake, especially in the lake’s bank and bays. The influence of a terrigenous source of POC can reach the center lake during periods of heavy precipitation. Sediment resuspension is also a source of POC in the lake due to the area’s high dynamic ratio (25.4) and wind speed. The source of POC in an inland shallow lake is particularly complex, and additional research on POC is needed to more clearly reveal its variation in inland water. Full article

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

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19 pages, 4578 KiB

Open AccessArticle

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

by Xiaobo Zhu¹, Mingguo Ma^1,*

, Hong Yang^1,2,3 and Wei Ge¹

¹ Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Beibei, Chongqing 400715, China

² Norwegian Institute of Bioeconomy Research (NIBIO), 1431 Ås, Norway

³ CEES, Department of Biosciences, University of Oslo, Blindern, 0316 Oslo, Norway

Remote Sens. 2017, 9(6), 626; https://doi.org/10.3390/rs9060626 - 18 Jun 2017

Cited by 67 | Viewed by 9062

Abstract

Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. [...] Read more.

Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R² = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

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16 pages, 4303 KiB

Open AccessArticle

Estimation of Surface NO₂ Volume Mixing Ratio in Four Metropolitan Cities in Korea Using Multiple Regression Models with OMI and AIRS Data

by Daewon Kim¹, Hanlim Lee^1,*, Hyunkee Hong¹, Wonei Choi¹, Yun Gon Lee² and Junsung Park¹

¹ Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University, Busan 608-737, Korea

² Department of Atmospheric Sciences, Chungnam National University, Daejeon 34134, Korea

Remote Sens. 2017, 9(6), 627; https://doi.org/10.3390/rs9060627 - 18 Jun 2017

Cited by 17 | Viewed by 6481

Abstract

Surface NO₂ volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO₂ VMR_ST) and monthly mean surface NO₂ VMR (NO₂ VMR_M) are estimated for the first time using three regression models with Ozone [...] Read more.

Surface NO₂ volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO₂ VMR_ST) and monthly mean surface NO₂ VMR (NO₂ VMR_M) are estimated for the first time using three regression models with Ozone Monitoring Instrument (OMI) data in four metropolitan cities in South Korea: Seoul, Gyeonggi, Daejeon, and Gwangju. Relationships between the surface NO₂ VMR obtained from in situ measurements (NO₂ VMR_In-situ) and tropospheric NO₂ vertical column density obtained from OMI from 2007 to 2013 were developed using regression models that also include boundary layer height (BLH) from Atmospheric Infrared Sounder (AIRS) and surface pressure, temperature, dew point, and wind speed and direction. The performance of the regression models is evaluated via comparison with the NO₂ VMR_In-situ for two validation years (2006 and 2014). Of the three regression models, a multiple regression model shows the best performance in estimating NO₂ VMR_ST and NO₂ VMR_M. In the validation period, the average correlation coefficient (R), slope, mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), and percent difference between NO₂ VMR_In-situ and NO₂ VMR_ST estimated by the multiple regression model are 0.66, 0.41, −1.36 ppbv, 6.89 ppbv, 8.98 ppbv, and 31.50%, respectively, while the average corresponding values for the other two models are 0.75, 0.41, −1.40 ppbv, 3.59 ppbv, 4.72 ppbv, and 16.59%, respectively. All three models have similar performance for NO₂ VMR_M, with average R, slope, MB, MAE, RMSE, and percent difference between NO₂ VMR_In-situ and NO₂ VMR_M of 0.74, 0.49, −1.90 ppbv, 3.93 ppbv, 5.05 ppbv, and 18.76%, respectively. Full article

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

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25 pages, 20385 KiB

Open AccessFeature PaperArticle

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

by Daniel Guidici¹ and Matthew L. Clark^2,*

¹ Department of Engineering Science, Sonoma State University, 1801 E Cotati Ave, Rohnert Park, CA 94928, USA

² Center for Interdisciplinary Geospatial Analysis (CIGA), Department of Geography, Environment and Planning, Sonoma State University, 1801 E Cotati Ave, Rohnert Park, CA 94928, USA

Remote Sens. 2017, 9(6), 629; https://doi.org/10.3390/rs9060629 - 20 Jun 2017

Cited by 100 | Viewed by 12551

Abstract

In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random [...] Read more.

In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI) imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM), 1.9% (CNN) to 3.5% (RF) over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products. Full article

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

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15 pages, 3972 KiB

Open AccessArticle

Linking Spaceborne and Ground Observations of Autumn Foliage Senescence in Southern Québec, Canada

by Offer Rozenstein^1,* and Jan Adamowski²

¹ Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel

² Department of Bioresource Engineering, McGill University, Macdonald Campus 21, 111 Lakeshore Road, Ste-Anne-de-Bellevue, Montreal, QC H9X 3V9, Canada

Remote Sens. 2017, 9(6), 630; https://doi.org/10.3390/rs9060630 - 21 Jun 2017

Cited by 12 | Viewed by 5763

Abstract

Autumn senescence progresses over several weeks during which leaves change their colors. The onset of leaf coloring and its progression have environmental and economic consequences, however, very few efforts have been devoted to monitoring regional foliage color change in autumn using remote sensing [...] Read more.

Autumn senescence progresses over several weeks during which leaves change their colors. The onset of leaf coloring and its progression have environmental and economic consequences, however, very few efforts have been devoted to monitoring regional foliage color change in autumn using remote sensing imagery. This study aimed to monitor the progression of autumn phenology using satellite remote sensing across a region in Southern Québec, Canada, where phenological observations are frequently performed in autumn across a large number of sites, and to evaluate the satellite retrievals against these in-situ observations. We used a temporally-normalized time-series of Normalized Difference Vegetation Index (NDVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to monitor the different phases of autumn foliage during 2011–2015, and compared the results with ground observations from 38 locations. Since the NDVI time-series is separately normalized per pixel, the outcome is a time-series of foliage coloration status that is independent of the land cover. The results show a significant correlation between the timing of peak autumn coloration to elevation and latitude, but not to longitude, and suggest that temperature is likely a main driver of variation in autumn foliage progression. The interannual coloration phase differences for MODIS retrievals are larger than for ground observations, but most ground site observations correlate significantly with MODIS retrievals. The mean absolute error for the timing of all foliage phases is smaller than the frequency of both ground observation reports and the frequency of the MODIS NDVI time-series, and therefore considered acceptable. Despite this, the observations at four of the ground sites did not correspond well with the MODIS retrievals, and therefore we conclude that further methodological refinements to improve the quality of the time series are required for MODIS spatial monitoring of autumn phenology over Québec to be operationally employed in a reliable manner. Full article

(This article belongs to the Section Forest Remote Sensing)

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19 pages, 8378 KiB

Open AccessArticle

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

by Luis López-Fernández^1,*

, Susana Lagüela^1,2

, Jesús Fernández¹ and Diego González-Aguilera¹

¹ Department of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, Spain

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

Remote Sens. 2017, 9(6), 631; https://doi.org/10.3390/rs9060631 - 20 Jun 2017

Cited by 32 | Viewed by 7693

Abstract

A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the [...] Read more.

A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m² for the area of each solar panel. Full article

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19 pages, 14901 KiB

Open AccessArticle

Exploring the Potential of Spectral Classification in Estimation of Soil Contaminant Elements

by Weichao Sun^1,2

, Xia Zhang^1,*

, Bin Zou³ and Taixia Wu¹

¹ State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 100101, China

² University of Chinese Academy of Sciences, Yuquan Street, Shijingshan District, Beijing 100049, China;

³ School of Geoscience and Info-Physics, Central South University, Changsha, Hunan 410083, China

Remote Sens. 2017, 9(6), 632; https://doi.org/10.3390/rs9060632 - 20 Jun 2017

Cited by 27 | Viewed by 5570

Abstract

Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil [...] Read more.

Soil contamination by arsenic and heavy metals is an increasingly severe environmental problem. Efficiently investigation of soil contamination is the premise of soil protection and further the foundation of food security. Visible and near-infrared reflectance spectroscopy (VNIRS) has been widely used in soil science, due to its rapidity and convenience. With different spectrally active soil characteristics, soil reflectance spectra exhibit distinctive curve forms, which may limit the application of VNIRS in estimating contaminant elements in soil. Consequently, spectral clustering was applied to explore the potential of classification in estimating soil contaminant elements. Spectral clustering based on different distance measure methods and elements with different contamination levels were exploited. In this study, soil samples were collected from Hunan Province, China and 74 reflectance spectra of air-dried soil samples over 350–2500 nm were used to predict nickel (Ni) and zinc (Zn) concentrations. Spectral clustering was achieved by K-means clustering based on squared Euclidean distance and Cosine of spectral angle, respectively. The prediction model was calibrated with the combination of Genetic algorithm and partial least squares regression (GA-PLSR). The prediction accuracy shows that the prediction of Ni and Zn concentrations in soil was improved to different extents by the two clustering methods and the clustering based on squared Euclidean distance had better performance over the clustering relied on Cosine of the spectral angle. The result reveals the potential of spectral classification in predicting soil Ni and Zn concentrations. A selected subset of the 74 soil spectra was used to further explore the potential of spectral classification in estimating Zn concentrations. The prediction was dramatically improved by clustering based on squared Euclidean distance. Additionally, analysis on distance measure methods indicates that Euclidean distance is more suitable to describe the difference between the collected soil reflectance spectra, which brought the better performance of the clustering based on squared Euclidean distance. Full article

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22 pages, 11770 KiB

Open AccessArticle

A New Stereo Pair Disparity Index (SPDI) for Detecting Built-Up Areas from High-Resolution Stereo Imagery

by Feifei Peng^1,2,*, Jianya Gong^3,4, Le Wang⁵, Huayi Wu⁴ and Pengcheng Liu^1,2

¹ Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China

² College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

³ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

⁴ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China

⁵ Department of Geography, The State University of New York, Buffalo, NY 14261, USA

Remote Sens. 2017, 9(6), 633; https://doi.org/10.3390/rs9060633 - 20 Jun 2017

Cited by 16 | Viewed by 7749

Abstract

Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slopes and building heights extracted from auxiliary data, such as Digital Surface Models (DSMs) however, can [...] Read more.

Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slopes and building heights extracted from auxiliary data, such as Digital Surface Models (DSMs) however, can improve the results. Stereo imagery incorporates height information unlike single remotely sensed images. In this study, a new Stereo Pair Disparity Index (SPDI) for indicating built-up areas is calculated from stereo-extracted disparity information. Further, a new method of detecting built-up areas from stereo pairs is proposed based on the SPDI, using disparity information to establish the relationship between two images of a stereo pair. As shown in the experimental results for two stereo pairs covering different scenes with diverse urban settings, the SPDI effectively differentiates between built-up and non-built-up areas. Our proposed method achieves higher accuracy built-up area results from stereo images than the traditional method for single images, and two other widely-applied DSM-based methods for stereo images. Our approach is suitable for spaceborne and airborne stereo pairs and triplets. Our research introduces a new effective height feature (SPDI) for detecting built-up areas from stereo imagery with no need for DSMs. Full article

(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)

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18 pages, 8778 KiB

Open AccessArticle

Seasonal and Interannual Variability of Columbia Glacier, Alaska (2011–2016): Ice Velocity, Mass Flux, Surface Elevation and Front Position

by Saurabh Vijay^*

and Matthias Braun

Institut für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058 Erlangen, Germany

Remote Sens. 2017, 9(6), 635; https://doi.org/10.3390/rs9060635 - 20 Jun 2017

Cited by 23 | Viewed by 8855

Abstract

Alaskan glaciers are among the largest contributors to sea-level rise outside the polar ice sheets. The contributions include dynamic discharge from marine-terminating glaciers which depends on the seasonally variable ice velocity. Columbia Glacier is a large marine-terminating glacier located in Southcentral Alaska that [...] Read more.

Alaskan glaciers are among the largest contributors to sea-level rise outside the polar ice sheets. The contributions include dynamic discharge from marine-terminating glaciers which depends on the seasonally variable ice velocity. Columbia Glacier is a large marine-terminating glacier located in Southcentral Alaska that has been exhibiting pronounced retreat since the early 1980s. Since 2010, the glacier has split into two branches, the main branch and the west branch. We derived a 5-year record of surface velocity, mass flux (ice discharge), surface elevation and changes in front position using a dense time series of TanDEM-X synthetic aperture radar data (2011–2016). We observed distinct seasonal velocity patterns at both branches. At the main branch, the surface velocity peaked during late winter to midsummer but reached a minimum between late summer and fall. Its near-front velocity reached up to 14 m day⁻¹ in May 2015 and was at its lowest speed of ~1 m day⁻¹ in October 2012. Mass flux via the main branch was strongly controlled by the seasonal and interannual fluctuations of its velocity. The west branch also exhibited seasonal velocity variations with comparably lower magnitudes. The role of subglacial hydrology on the ice velocities of Columbia Glacier is already known from the published field measurements during summers of 1987. Our observed variability in its ice velocities on a seasonal basis also suggest that they are primarily controlled by the seasonal transition of the subglacial drainage system from an inefficient to an efficient and channelized drainage networks. However, abrupt velocity increase events for short periods (2014–2015 and 2015–2016 at the main branch, and 2013–2014 at the west branch) appear to be associated with strong near-front thinning and frontal retreat. This needs further investigation on the role of other potential controlling mechanisms. On the technological side, this study demonstrates the potential of high-resolution X-band SAR missions with a short revisit interval to examine glaciological variables and controlling processes. Full article

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

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19 pages, 1515 KiB

Open AccessArticle

Saliency Analysis via Hyperparameter Sparse Representation and Energy Distribution Optimization for Remote Sensing Images

by Libao Zhang^1,2,*

, Xinran Lv¹ and Xu Liang¹

¹ The College of Information Science and Technology, Beijing Normal University, Beijing 100875, China

² The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

Remote Sens. 2017, 9(6), 636; https://doi.org/10.3390/rs9060636 - 21 Jun 2017

Cited by 6 | Viewed by 5655

Abstract

In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distributions, sparse representation based on dictionary learning has been utilized, and has proved able to process high dimensional data adaptively and efficiently. In this paper, a visual attention [...] Read more.

In an effort to detect the region-of-interest (ROI) of remote sensing images with complex data distributions, sparse representation based on dictionary learning has been utilized, and has proved able to process high dimensional data adaptively and efficiently. In this paper, a visual attention model uniting hyperparameter sparse representation with energy distribution optimization is proposed for analyzing saliency and detecting ROIs in remote sensing images. A dictionary learning algorithm based on biological plausibility is adopted to generate the sparse feature space. This method only focuses on finite features, instead of various considerations of feature complexity and massive parameter tuning in other dictionary learning algorithms. In another portion of the model, aimed at obtaining the saliency map, the contribution of each feature is evaluated in a sparse feature space and the coding length of each feature is accumulated. Finally, we calculate the segmentation threshold using the saliency map and obtain the binary mask to separate the ROI from the original images. Experimental results show that the proposed model achieves better performance in saliency analysis and ROI detection for remote sensing images. Full article

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

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14 pages, 4755 KiB

Open AccessFeature PaperArticle

A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013)

by Xuecao Li

and Yuyu Zhou^*

Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA

Remote Sens. 2017, 9(6), 637; https://doi.org/10.3390/rs9060637 - 21 Jun 2017

Cited by 101 | Viewed by 14809

Abstract

The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires [...] Read more.

The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and under- estimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)

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19 pages, 12415 KiB

Open AccessArticle

The Application of ALOS/PALSAR InSAR to Measure Subsurface Penetration Depths in Deserts

by Siting Xiong^1,*

, Jan-Peter Muller¹

and Gang Li²

¹ Imaging Group, Mullard Space Science Laboratory (MSSL), Department of Space & Climate Physics, University College London, Holmbury St Mary, Dorking, Surrey RH5 6NT, UK

² Institute of Space and Earth Information Science, The Chinese University of Hong Kong, New Territories, Hong Kong, China

Remote Sens. 2017, 9(6), 638; https://doi.org/10.3390/rs9060638 - 21 Jun 2017

Cited by 19 | Viewed by 11060

Abstract

Spaceborne Synthetic Aperture Radar (SAR) interferometry has been utilised to acquire high-resolution Digital Elevation Models (DEMs) with wide coverage, particularly for persistently cloud-covered regions where stereophotogrammetry is hard to apply. Since the discovery of sand buried drainage systems by the Shuttle Imaging Radar-A [...] Read more.

Spaceborne Synthetic Aperture Radar (SAR) interferometry has been utilised to acquire high-resolution Digital Elevation Models (DEMs) with wide coverage, particularly for persistently cloud-covered regions where stereophotogrammetry is hard to apply. Since the discovery of sand buried drainage systems by the Shuttle Imaging Radar-A (SIR-A) L-band mission in 1982, radar images have been exploited to map subsurface features beneath a sandy cover of extremely low loss and low bulk humidity in some hyper-arid regions such as from the Japanese Earth Resources Satellite 1 (JERS-1) and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR). Therefore, we hypothesise that a Digital Elevation Model (DEM) derived by InSAR in hyper-arid regions is likely to represent a subsurface elevation model, especially for lower frequency radar systems, such as the L-band system (1.25 GHz). In this paper, we compare the surface appearance of radar images (L-band and C-band) with that of optical images to demonstrate their different abilities to show subsurface features. Moreover, we present an application of L-band InSAR to measure penetration depths in the eastern Sahara Desert. We demonstrate how the retrieved L-band InSAR DEM appears to be of a consistently 1–2 m lower elevation than the C-band Shuttle Radar Topography Mission (SRTM) DEM over sandy covered areas, which indicates the occurrence of penetration and confirms previous studies. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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21 pages, 5669 KiB

Open AccessArticle

A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion

by Aleksandra Grochala^* and Michal Kedzierski

Department of Remote Sensing, Photogrammetry and Imagery Intelligence, Institute of Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 01-476 Warszawa, Poland

Remote Sens. 2017, 9(6), 639; https://doi.org/10.3390/rs9060639 - 21 Jun 2017

Cited by 33 | Viewed by 8291

Abstract

The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There [...] Read more.

The standard ratio of spatial resolution between bands for high resolution satellites is 1:4, which is typical when combining images obtained from the same sensor. However, the cost of simultaneously purchasing a set of panchromatic and multispectral images is still relatively high. There is therefore a need to develop methods of data fusion of very high resolution panchromatic imagery with low-cost multispectral data (e.g., Landsat). Combining high resolution images with low resolution images broadens the scope of use of satellite data, however, it is also accompanied by the problem of a large ratio between spatial resolutions, which results in large spectral distortions in the merged images. The authors propose a modification of the panchromatic image in such a way that it includes the spectral and spatial information from both the panchromatic and multispectral images to improve the quality of spectral data integration. This fusion is done based on a weighted average. The weight is determined using a coefficient, which determines the ratio of the amount of information contained in the corresponding pixels of the integrated images. The effectiveness of the author’s algorithm had been tested for six of the most popular fusion methods. The proposed methodology is ideal mainly for statistical and numerical methods, especially Principal Component Analysis and Gram-Schmidt. The author’s algorithm makes it possible to lower the root mean square error by up to 20% for the Principal Component Analysis. The spectral quality was also increased, especially for the spectral bands extending beyond the panchromatic image, where the correlation rose by 18% for the Gram-Schmidt orthogonalization. Full article

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11 pages, 1303 KiB

Open AccessComment

The Difficulty of Measuring the Absorption of Scattered Sunlight by H₂O and CO₂ in Volcanic Plumes: A Comment on Pering et al. “A Novel and Inexpensive Method for Measuring Volcanic Plume Water Fluxes at High Temporal Resolution,” Remote Sens. 2017, 9, 146

by Christoph Kern

U.S. Geological Survey Cascades Volcano Observatory, 1300 SE Cardinal Ct, Vancouver, WA 98683, USA

Remote Sens. 2017, 9(6), 534; https://doi.org/10.3390/rs9060534 - 27 May 2017

Cited by 2 | Viewed by 4451

Abstract

In their recent study, Pering et al. (2017) presented a novel method for measuring volcanic water vapor fluxes. Their method is based on imaging volcanic gas and aerosol plumes using a camera sensitive to the near-infrared (NIR) absorption of water vapor. The imaging [...] Read more.

In their recent study, Pering et al. (2017) presented a novel method for measuring volcanic water vapor fluxes. Their method is based on imaging volcanic gas and aerosol plumes using a camera sensitive to the near-infrared (NIR) absorption of water vapor. The imaging data are empirically calibrated by comparison with in situ water measurements made within the plumes. Though the presented method may give reasonable results over short time scales, the authors fail to recognize the sensitivity of the technique to light scattering on aerosols within the plume. In fact, the signals measured by Pering et al. are not related to the absorption of NIR radiation by water vapor within the plume. Instead, the measured signals are most likely caused by a change in the effective light path of the detected radiation through the atmospheric background water vapor column. Therefore, their method is actually based on establishing an empirical relationship between in-plume scattering efficiency and plume water content. Since this relationship is sensitive to plume aerosol abundance and numerous environmental factors, the method will only yield accurate results if it is calibrated very frequently using other measurement techniques. Full article

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13 pages, 2396 KiB

Open AccessTechnical Note

The Use of Unmanned Aerial Systems in Marine Mammal Research

by Lorenzo Fiori^1,*, Ashray Doshi², Emmanuelle Martinez^3,4,5, Mark B. Orams⁶ and Barbara Bollard-Breen²

¹ New Zealand Institute of Applied Ecology, School of Applied Science, Auckland University of Technology, 46 Wakefield, WU Building, 1010 Auckland, New Zealand

² New Zealand Conservation and Spatial Innovation Lab, School of Science, Auckland University of Technology, 46 Wakefield, WU Building, 1010 Auckland, New Zealand

³ TriOceans, Marine Research and Technology Institute, Bay of Islands, New Zealand

⁴ Applied and Environmental Sciences Department, NorthTec, 0148 Whangarei, New Zealand

⁵ Coastal Marine Research Group, Institute of Natural and Mathematical Sciences, Massey University, 0632 Albany, New Zealand

⁶ School of Sport and Recreation, Auckland University of Technology, 0627 Northcote, New Zealand

Remote Sens. 2017, 9(6), 543; https://doi.org/10.3390/rs9060543 - 30 May 2017

Cited by 96 | Viewed by 15525

Abstract

Unmanned aerial systems (UAS), commonly referred to as drones, are finding applications in several ecological research areas since remotely piloted aircraft (RPA) technology has ceased to be a military prerogative. Fixed-wing RPA have been tested for line transect aerial surveys of geographically dispersed [...] Read more.

Unmanned aerial systems (UAS), commonly referred to as drones, are finding applications in several ecological research areas since remotely piloted aircraft (RPA) technology has ceased to be a military prerogative. Fixed-wing RPA have been tested for line transect aerial surveys of geographically dispersed marine mammal species. Despite many advantages, their systematic use is far from a reality. Low altitude, long endurance systems are still highly priced. Regulatory bodies also impose limitations while struggling to cope with UAS rapid technological evolution. In contrast, small vertical take-off and landing (VTOL) UAS have become increasingly affordable but lack the flight endurance required for long-range aerial surveys. Although this issue and civil aviation regulations prevent the use of VTOL UAS for marine mammal abundance estimation on a large scale, recent studies have highlighted other potential applications. The present note represents a general overview on the use of UAS as a survey tool for marine mammal studies. The literature pertaining to UAS marine mammal research applications is considered with special concern for advantages and limitations of the survey design. The use of lightweight VTOL UAS to collect marine mammal behavioral data is also discussed. Full article

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12 pages, 3810 KiB

Open AccessLetter

Retrieval of Aerosol Optical Depth Using the Empirical Orthogonal Functions (EOFs) Based on PARASOL Multi-Angle Intensity Data

by Yang Zhang^1,2

, Zhengqiang Li^1,*, Lili Qie¹, Weizhen Hou¹

, Zhihong Liu³, Ying Zhang¹

, Yisong Xie¹, Xingfeng Chen¹

and Hua Xu¹

¹ Environment Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

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

³ College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China

Remote Sens. 2017, 9(6), 578; https://doi.org/10.3390/rs9060578 - 9 Jun 2017

Cited by 16 | Viewed by 5080

Abstract

Aerosol optical depth (AOD) is a widely used aerosol optical parameter in atmospheric physics. To obtain this parameter precisely, many institutions plan to launch satellites with multi-angle measurement sensors, but one important step in aerosol retrieval, the estimation of surface reflectance, is still [...] Read more.

Aerosol optical depth (AOD) is a widely used aerosol optical parameter in atmospheric physics. To obtain this parameter precisely, many institutions plan to launch satellites with multi-angle measurement sensors, but one important step in aerosol retrieval, the estimation of surface reflectance, is still a pressing issue. This paper presents an AOD retrieval method based on the multi-angle intensity data from the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) platform using empirical orthogonal functions (EOFs), which can be universally applied to multi-angle observations. The function of EOFs in this study is to estimate surface intensity contributions, associated with aerosol lookup tables (LUTs), so that the retrieval of AOD can be implemented. A comparison of the retrieved AODs for the Beijing, Xianghe, Taihu, and Hongkong_PolyU sites with those from the Aerosol Robotic Network (AERONET) ground-based observations produced high correlation coefficients (r) of 0.892, 0.915, 0.831, and 0.897, respectively, while the corresponding root mean square errors (RMSEs) are 0.095, 0.093, 0.099, and 0.076, respectively. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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11 pages, 5950 KiB

Open AccessTechnical Note

Tree Stem Diameter Estimation From Volumetric TLS Image Data

by Johannes Heinzel^* and Markus O. Huber

Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland

Remote Sens. 2017, 9(6), 614; https://doi.org/10.3390/rs9060614 - 15 Jun 2017

Cited by 20 | Viewed by 5180

Abstract

Recently, a new method on tree stem isolation using volumetric image data from terrestrial laser scans (TLS) has been introduced by the same authors. The method transfers TLS data into a voxel grid data structure and isolates the tree stems from the overall [...] Read more.

Recently, a new method on tree stem isolation using volumetric image data from terrestrial laser scans (TLS) has been introduced by the same authors. The method transfers TLS data into a voxel grid data structure and isolates the tree stems from the overall forest vegetation. While the stem detection method yields on a three dimensional localisation of the tree stems, the present study introduces a supplemental technique, which accurately estimates the diameter at breast height (DBH) from the stem objects. Often, large pieces of the stems are occluded by other vegetation and are only partially represented in the laser scanning data, not covering the complete circumference. Therefore, it was not possible to measure the diameter at 130 cm height directly on the stem imagery. Instead, a method has been developed, which estimated the diameter from the fragmented stem information at the specific cross sections. The stem information was processed in a way, which allowed applying a Hough transform to the image for fitting circles to the cross sections. In contrast to other studies, Hough transform was applied to single stem images with information from other vegetation parts already being removed. Even in cases where only a single and very small fragment of a stem is available, the diameter could be estimated from the curvature. It also has been demonstrated that the image resolution for DBH measurement can be significantly higher than the resolution used for stem isolation in order to increase the precision. Verification of the computed DBH on nine spatially independent test sites showed that applying the Hough transform to single stem cross section images produced accurate results. When excluding the five strongest individual outliers a bias of −0.02 cm, a root mean square error (RMSE) of 2.9 cm and a

R^{2}

of 0.98 were achieved. Full article

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24 pages, 22900 KiB

Open AccessTechnical Note

Flood Inundation Mapping from Optical Satellite Images Using Spatiotemporal Context Learning and Modest AdaBoost

by Xiaoyi Liu^1,2, Hichem Sahli^3,4, Yu Meng^1,*

, Qingqing Huang¹ and Lei Lin^1,2

¹ Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

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

³ Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium

⁴ Interuniversity Microelectronics Centre (IMEC), 3001 Heverlee, Belgium

Remote Sens. 2017, 9(6), 617; https://doi.org/10.3390/rs9060617 - 16 Jun 2017

Cited by 21 | Viewed by 9824

Abstract

Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention [...] Read more.

Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important for disaster response. In this work, we propose a novel procedure combining spatiotemporal context learning method and Modest AdaBoost classifier, which aims to extract inundation in an automatic and accurate way. First, the context model was built with images to calculate the confidence value of each pixel, which represents the probability of the pixel remaining unchanged. Then, the pixels with the highest probabilities, which we define as ‘permanent pixels’, were used as samples to train the Modest AdaBoost classifier. By applying the strong classifier to the target scene, an inundation map can be obtained. The proposed procedure is validated using two flood cases with different sensors, HJ-1A CCD and GF-4 PMS. Qualitative and quantitative evaluation results showed that the proposed procedure can achieve accurate and robust mapping results. Full article

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

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8 pages, 1294 KiB

Open AccessLetter

A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection

by Wenzhuo Li¹, Kaimin Sun^2,*

, Deren Li^2,3, Ting Bai² and Haigang Sui^2,3

¹ School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

² State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

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

Remote Sens. 2017, 9(6), 625; https://doi.org/10.3390/rs9060625 - 17 Jun 2017

Cited by 23 | Viewed by 6697

Abstract

Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs). This letter reports on [...] Read more.

Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs). This letter reports on a new approach to perform bundle adjustment—named united bundle adjustment (UBA)—to solve this co-registration problem for change detection in multi-temporal UAV images. In UBA, multi-temporal UAV images are matched with each other to construct a unified tie point net. One single bundle adjustment process is performed on the unified tie point net, placing every image into the same coordinate system and thus automatically accomplishing spatial co-registration. We then perform change detection using both orthophotos and three-dimensional height information derived from dense image matching techniques. Experimental results show that UBA co-registration accuracy is higher than the accuracy of commonly-used approaches for multi-temporal UAV images. Our proposed preprocessing method extends the capacities of consumer-level UAVs so they can eventually meet the growing need for automatic building change detection and dynamic monitoring using only RGB band images. Full article

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