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Open AccessArticle Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation

by Hector A. Orengo and Cameron A. Petrie

Remote Sens. 2017, 9(7), 735; doi:10.3390/rs9070735 (registering DOI)

Received: 6 June 2017 / Revised: 6 July 2017 / Accepted: 12 July 2017 / Published: 16 July 2017

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Abstract

Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied

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Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine^© Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated. Full article

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Open AccessArticle A Burned Area Mapping Algorithm for Chinese FengYun-3 MERSI Satellite Data

by Tianchan Shan, Changlin Wang, Fang Chen, Qinchun Wu, Bin Li, Bo Yu, Zeeshan Shirazi, Zhengyang Lin and Wei Wu

Remote Sens. 2017, 9(7), 736; doi:10.3390/rs9070736 (registering DOI)

Received: 12 June 2017 / Revised: 8 July 2017 / Accepted: 12 July 2017 / Published: 16 July 2017

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Abstract

Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager

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Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites. Full article

Open AccessArticle Continued Reforestation and Urban Expansion in the New Century of a Tropical Island in the Caribbean

by Chao Wang, Mei Yu and Qiong Gao

Remote Sens. 2017, 9(7), 731; doi:10.3390/rs9070731 (registering DOI)

Received: 22 April 2017 / Revised: 22 June 2017 / Accepted: 12 July 2017 / Published: 15 July 2017

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Abstract

Accurate and timely monitoring of tropical land cover/use (LCLU) changes is urgent due to the rapid deforestation/reforestation and its impact on global land-atmosphere interaction. However, persistent cloud cover in the tropics imposes the greatest challenge and retards LCLU mapping in mountainous areas such

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Accurate and timely monitoring of tropical land cover/use (LCLU) changes is urgent due to the rapid deforestation/reforestation and its impact on global land-atmosphere interaction. However, persistent cloud cover in the tropics imposes the greatest challenge and retards LCLU mapping in mountainous areas such as the tropic island of Puerto Rico, where forest transition changed from deforestation to reforestation due to the economy shift from agriculture to industry and service after the 1940s. To improve the LCLU mapping in the tropics and to evaluate the trend of forest transition of Puerto Rico in the new century, we integrated the optical Landsat images with the L-band SAR to map LC in 2010 by taking advantage of the cloud-penetrating ability of the SAR signals. The results showed that the incorporation of SAR data with the Landsat data significantly, although not substantially, enhanced the accuracy of LCLU mapping of Puerto Rico, and the Kappa statistic reached 90.5% from 88.4% without SAR data. The enhancement of mapping by SAR is important for urban and forest, as well as locations with limited optical data caused by cloud cover. We found both forests and urban lands continued expanding in the new century despite the declining population. However, the forest cover change slowed down in 2000–2010 compared to that in 1991–2000. The deforestation rate reduced by 42.1% in 2000–2010, and the reforestation was mostly located in the east and southeast of the island where Hurricane Georges landed and caused severe vegetation damage in 1998. We also found that reforestation increased, but deforestation decreased along the topography slope. Reforestation was much higher within the protected area compared to that in the surroundings in the wet and moist forest zones. Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessArticle Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning

by Lin Pan, Xingxing Li, Xiaohong Zhang, Xin Li, Cuixian Lu, Qile Zhao and Jingnan Liu

Remote Sens. 2017, 9(7), 734; doi:10.3390/rs9070734 (registering DOI)

Received: 2 June 2017 / Revised: 2 July 2017 / Accepted: 12 July 2017 / Published: 15 July 2017

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Abstract

The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free

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The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free carrier phase combinations, was noticed. Consequently, the B1/B3 precise point positioning (PPP) cannot directly use the current B1/B2 clock products. Datasets from 35 globally distributed stations are employed to investigate the IFCB. For new generation BeiDou Navigation Satellite System (BDS) satellites, namely BDS-3 satellites, the IFCB between B1/B2a and B1/B3 satellite clocks, between B1/B2b and B1/B3 satellite clocks, between B1C/B2a and B1C/B3 satellite clocks, and between B1C/B2b and B1C/B3 satellite clocks is analyzed, and no significant IFCB variations can be observed. The IFCB between B1/B2 and B1/B3 satellite clocks for BDS-2 satellites varies with time, and the IFCB variations are generally confined to peak amplitudes of about 5 cm. The IFCB of BDS-2 satellites exhibits periodic signal, and the accuracy of prediction for IFCB, namely the root mean square (RMS) statistic of the difference between predicted and estimated IFCB values, is 1.2 cm. A triple-frequency PPP model with consideration of IFCB is developed. Compared with B1/B2-based PPP, the positioning accuracy of triple-frequency PPP with BDS-2 satellites can be improved by 12%, 25% and 10% in east, north and vertical directions, respectively. Full article

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

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Open AccessArticle New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves

by Chang-Hwan Park, Andreas Behrendt, Ellsworth LeDrew and Volker Wulfmeyer

Remote Sens. 2017, 9(7), 732; doi:10.3390/rs9070732 (registering DOI)

Received: 7 May 2017 / Revised: 9 July 2017 / Accepted: 11 July 2017 / Published: 15 July 2017

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Abstract

Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil

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Microwave remote sensing techniques are used, among others, for temporally and spatially highly-resolved observations of land-surface properties, e.g., for the management of agricultural productivity and water resource, as well as to improve the performances of numerical weather prediction and climate simulations with soil moisture data. In this context, the effective dielectric constant of the soil is a key variable to quantify the land surface properties. We propose a new approach for the effective dielectric constant of the multiphase soil that is based on an arithmetic average of the dielectric constants of the land-surface components with damping. The results show, on average, better agreement with experimental data than previous approaches. Furthermore, the proposed new model overcomes the theoretical limitation of previous models in the incorporation of non-physical parameters to simulate measured data experimentally with satisfactory accuracy. For microwave remote sensing such as SMAP (Soil Moisture Active Passive), SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), the physical-based model in our study showed a 23–35% RMSE (root-mean-square error) reduction compared to the most prevalent refractive mixing model [1] in the prediction of the dielectric constant for the real and imaginary part, respectively. Furthermore, in radiowave bands used in portable soil sensors such as TDR (time-domain reflectometer) and GPR (ground-penetrating radar) the new dielectric mixing model reduced RMSE by up to 53% in the prediction of the dielectric constant. We found that the permittivity over the saturation point (porosity of dry soil) has a very different and varying pattern compared to that measured in the unsaturated condition. However, in our study, this pattern was mathematically derived from the same mixing rule applied for the unsaturated condition. It is expected that the new dielectric mixing model might help to improve the accuracy of flood monitoring by satellite. Full article

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

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Open AccessArticle Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery

by Daniel Kępski, Bartłomiej Luks, Krzysztof Migała, Tomasz Wawrzyniak, Sebastian Westermann and Bronisław Wojtuń

Remote Sens. 2017, 9(7), 733; doi:10.3390/rs9070733 (registering DOI)

Received: 24 April 2017 / Revised: 27 June 2017 / Accepted: 12 July 2017 / Published: 15 July 2017

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Abstract

Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation

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Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km², representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials. Full article

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

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Open AccessArticle Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping

by Ting Zhang, Yi Du and Fangfang Lu

Remote Sens. 2017, 9(7), 724; doi:10.3390/rs9070724 (registering DOI)

Received: 13 April 2017 / Revised: 6 July 2017 / Accepted: 10 July 2017 / Published: 15 July 2017

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Abstract

When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of

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When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of spatial structures that encode the expected features at the fine (target) resolution are helpful to constrain the spatial patterns of remote sensing images to be generated at that resolution. These prior models can be used properly by multiple-point statistics (MPS), capable of extracting the intrinsic features of patterns from prior models such as training images, and copying them to the simulated regions using hard and soft conditional data, or even without any conditional data. However, because traditional MPS methods based on linear dimensionality reduction are not suitable to deal with nonlinear data, and isometric mapping (ISOMAP) can reduce the dimensionality of nonlinear data effectively, this paper presents a sequential simulation framework for generating super-resolution remote sensing images using ISOMAP and MPS. Using four different examples, it is demonstrated that the structural characteristics of super-resolution reconstruction of remote sensing images using this method, are similar to those of training images. Full article

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

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Open AccessArticle Estimating Tropical Cyclone Size in the Northwestern Pacific from Geostationary Satellite Infrared Images

by Xiaoqin Lu, Hui Yu, Xiaoming Yang and Xiaofeng Li

Remote Sens. 2017, 9(7), 728; doi:10.3390/rs9070728 (registering DOI)

Received: 27 June 2017 / Revised: 27 June 2017 / Accepted: 11 July 2017 / Published: 14 July 2017

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Abstract

Thirty-year (1980–2009) tropical cyclone (TC) images from geostationary satellite (GOES, Meteosat, GMS, MTSAT and FY2) infrared sensors covering the Northwestern Pacific were used to build a TC size dataset based on objective models. The models are based on a correlation between the size

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Thirty-year (1980–2009) tropical cyclone (TC) images from geostationary satellite (GOES, Meteosat, GMS, MTSAT and FY2) infrared sensors covering the Northwestern Pacific were used to build a TC size dataset based on objective models. The models are based on a correlation between the size of TCs, defined as the mean azimuth radius of 34 kt surface winds (R34) and the brightness temperature radial profiles derived from satellite imagery. Using satellite images between 2001 and 2009, we obtained 16,548 matchup samples and found the correlation to be positive in the TC’s inner core region (in the annulus field 64 km from the TC center) and negative in its outer region (in the annulus field 100–250 km from the TC center). Then, we performed a stepwise regression to select the dominant variables and derived the associated coefficients for the objective models. Independent validation against best track archives shows the median estimation error to be between 27 and 65 km, which are not significantly different to other satellite series data. Finally, we applied the models to 721 TCs and made 13,726 measurements of TC size. The difference of mean TC size derived from our models, and also that from the US Joint Typhoon Warning Center (JTWC) best track archives is 19 km. The developed database is valuable in the research fields of TC structure, climatology, and the initialization of forecasting models. Full article

(This article belongs to the Section Ocean Remote Sensing)

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Open AccessArticle 100 Years of Competition between Reduction in Channel Capacity and Streamflow during Floods in the Guadalquivir River (Southern Spain)

by Patricio Bohorquez and José David del Moral-Erencia

Remote Sens. 2017, 9(7), 727; doi:10.3390/rs9070727 (registering DOI)

Received: 30 May 2017 / Revised: 8 July 2017 / Accepted: 11 July 2017 / Published: 14 July 2017

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Abstract

Reduction in channel capacity can trigger an increase in flood hazard over time. It represents a geomorphic driver that competes against its hydrologic counterpart where streamflow decreases. We show that this situation arose in the Guadalquivir River (Southern Spain) after impoundment. We identify

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Reduction in channel capacity can trigger an increase in flood hazard over time. It represents a geomorphic driver that competes against its hydrologic counterpart where streamflow decreases. We show that this situation arose in the Guadalquivir River (Southern Spain) after impoundment. We identify the physical parameters that raised flood hazard in the period 1997–2013 with respect to past years 1910–1996 and quantify their effects by accounting for temporal trends in both streamflow and channel capacity. First, we collect historical hydrological data to lengthen records of extreme flooding events since 1910. Next, inundated areas and grade lines across a 70 km stretch of up to 2 km wide floodplain are delimited from Landsat and TerraSAR-X satellite images of the most recent floods (2009–2013). Flooded areas are also computed using standard two-dimensional Saint-Venant equations. Simulated stages are verified locally and across the whole domain with collected hydrological data and satellite images, respectively. The thoughtful analysis of flooding and geomorphic dynamics over multi-decadal timescales illustrates that non-stationary channel adaptation to river impoundment decreased channel capacity and increased flood hazard. Previous to channel squeezing and pre-vegetation encroachment, river discharges as high as 1450 m³·s⁻¹ (the year 1924) were required to inundate the same areas as the 790 m³·s⁻¹ streamflow for recent floods (the year 2010). We conclude that future projections of one-in-a-century river floods need to include geomorphic drivers as they compete with the reduction of peak discharges under the current climate change scenario. Full article

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

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Open AccessTechnical Note Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign

by Davide Giudici, Andrea Monti Guarnieri and Juan Pablo Cuesta Gonzalez

Remote Sens. 2017, 9(7), 729; doi:10.3390/rs9070729 (registering DOI)

Received: 23 May 2017 / Revised: 7 July 2017 / Accepted: 12 July 2017 / Published: 14 July 2017

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Abstract

In the present paper, we describe the design, execution, and the results of an outdoor experimental campaign involving the Engineering Model of the first of the two Argentinean L-band Synthetic Aperture Radars (SARs) of the Satélite Argentino de Observación con Microondas (SAOCOM) mission,

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In the present paper, we describe the design, execution, and the results of an outdoor experimental campaign involving the Engineering Model of the first of the two Argentinean L-band Synthetic Aperture Radars (SARs) of the Satélite Argentino de Observación con Microondas (SAOCOM) mission, SAOCOM-1A. The experiment’s main objectives were to test the end-to-end SAR operation and to assess the instrument amplitude and phase stability as well as the far-field antenna pattern, through the illumination of a moving target placed several kilometers away from the SAR. The campaign was carried out in Bariloche, Argentina, during June 2016. The experiment was successful, demonstrating an end-to-end readiness of the SAOCOM-SAR functionality in realistic conditions. The results showed an excellent SAR signal quality in terms of amplitude and phase stability. Full article

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

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Open AccessArticle Urban Land-Cover Dynamics in Arid China Based on High-Resolution Urban Land Mapping Products

by Tao Pan, Dengsheng Lu, Chi Zhang, Xi Chen, Hua Shao, Wenhui Kuang, Wenfeng Chi, Zhengjia Liu, Guoming Du and Liangzhong Cao

Remote Sens. 2017, 9(7), 730; doi:10.3390/rs9070730 (registering DOI)

Received: 24 May 2017 / Revised: 1 July 2017 / Accepted: 12 July 2017 / Published: 14 July 2017

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Abstract

Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in

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Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities. Full article

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

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Open AccessArticle High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective

by Zhifeng Xiao, Yang Long, Deren Li, Chunshan Wei, Gefu Tang and Junyi Liu

Remote Sens. 2017, 9(7), 725; doi:10.3390/rs9070725 (registering DOI)

Received: 26 May 2017 / Revised: 4 July 2017 / Accepted: 9 July 2017 / Published: 14 July 2017

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Abstract

Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned

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Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned using only several tens of codes; this approach can improve the retrieval accuracy and decrease the time and storage requirements. To accomplish this goal, we first investigate the learning of a series of features with different dimensions using a few tens to thousands of codes via our improved CNN frameworks. Then, a Principal Component Analysis (PCA) is introduced to compress the high-dimensional remote sensing image feature codes learned by traditional CNNs. Comprehensive comparisons are conducted to evaluate the retrieval performance based on feature codes of different dimensions learned by the improved CNNs as well as the PCA compression. To further demonstrate the powerful ability of the low-dimensional feature representation learned by the improved CNN frameworks, a Feature Weighted Map (FWM), which can perform feature visualization and provides a better understanding of the nature of Deep Convolutional Neural Networks (DCNNs) frameworks, is explored. All the CNN models are trained from scratch using a large-scale and high-resolution remote sensing image archive, which will be published and made available to the public. The experimental results show that our method outperforms state-of-the-art CNN frameworks in terms of accuracy and storage. Full article

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Open AccessArticle Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy

by Martin Danner, Katja Berger, Matthias Wocher, Wolfram Mauser and Tobias Hank

Remote Sens. 2017, 9(7), 726; doi:10.3390/rs9070726 (registering DOI)

Received: 13 June 2017 / Revised: 4 July 2017 / Accepted: 12 July 2017 / Published: 14 July 2017

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Abstract

The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30°

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The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30° across-track pointing capabilities will allow for the collection of hyperspectral time-series of homogeneous quality. Various studies have shown the possibility to retrieve geo-biophysical plant variables, like leaf area index (LAI) or leaf chlorophyll content (LCC), from narrowband observations with fixed viewing geometry by inversion of radiative transfer models (RTM). In this study we assess the capability of the well-known PROSPECT 5B + 4SAIL (Scattering by Arbitrarily Inclined Leaves) RTM to estimate these variables from off-nadir observations obtained during a field campaign with respect to EnMAP-like sun–target–sensor-geometries. A novel approach for multiple inquiries of a large look-up-table (LUT) in hierarchical steps is introduced that accounts for the varying instances of all variables of interest. Results show that anisotropic effects are strongest for early growth stages of the winter wheat canopy which influences also the retrieval of the variables. RTM inversions from off-nadir spectra lead to a decreased accuracy for the retrieval of LAI with a relative root mean squared error (rRMSE) of 18% at nadir vs. 25% (backscatter) and 24% (forward scatter) at off-nadir. For LCC estimations, however, off-nadir observations yield improvements, i.e., rRMSE (nadir) = 24% vs. rRMSE (forward scatter) = 20%. It follows that for a variable retrieval through RTM inversion, the final user will benefit from EnMAP time-series for biophysical studies regardless of the acquisition angle and will thus be able to exploit the maximum revisit capability of the mission. Full article

(This article belongs to the Special Issue Widespread Applications Based on Hyperspectral Technologies from Space)

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Open AccessArticle Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

by Hao Jiang, Shuisen Chen, Dan Li, Chongyang Wang and Ji Yang

Remote Sens. 2017, 9(7), 721; doi:10.3390/rs9070721

Received: 17 May 2017 / Revised: 29 June 2017 / Accepted: 8 July 2017 / Published: 13 July 2017

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Abstract

The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made

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The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application. Full article

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

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Open AccessArticle Evaluation of the Multi-Scale Ultra-High Resolution (MUR) Analysis of Lake Surface Temperature

by Erik Crosman, Jorge Vazquez-Cuervo and Toshio Michael Chin

Remote Sens. 2017, 9(7), 723; doi:10.3390/rs9070723

Received: 25 May 2017 / Revised: 30 June 2017 / Accepted: 7 July 2017 / Published: 13 July 2017

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Abstract

Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input

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Obtaining accurate and timely lake surface water temperature (LSWT) analyses from satellite remains difficult. Data gaps, cloud contamination, variations in atmospheric profiles of temperature and moisture, and a lack of in situ observations provide challenges for satellite-derived LSWT for climatological analysis or input into geophysical models. In this study, the Multi-scale Ultra-high Resolution (MUR) analysis of LSWT is evaluated between 2007 and 2015 over a small (Lake Oneida), medium (Lake Okeechobee), and large (Lake Michigan) lake. The advantages of the MUR LSWT analyses include daily consistency, high-resolution (~1 km), near-real time production, and multi-platform data synthesis. The MUR LSWT versus in situ measurements for Lake Michigan (Lake Okeechobee) have an overall bias (MUR LSWT-in situ) of −0.20 °C (0.31 °C) and a RMSE of 0.86 °C (0.91 °C). The MUR LSWT versus in situ measurements for Lake Oneida have overall large biases (−1.74 °C) and RMSE (3.42°C) due to a lack of available satellite imagery over the lake, but performs better during the less cloudy 15 July–30 September period. The results of this study highlight the importance of calculating validation statistics on a seasonal and annual basis for evaluating satellite-derived LSWT. Full article

(This article belongs to the Special Issue Sea Surface Temperature Retrievals from Remote Sensing)

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Open AccessArticle Estimation of SOS and EOS for Midwestern US Corn and Soybean Crops

by Jie Ren, James B. Campbell and Yang Shao

Remote Sens. 2017, 9(7), 722; doi:10.3390/rs9070722

Received: 9 June 2017 / Revised: 6 July 2017 / Accepted: 6 July 2017 / Published: 13 July 2017

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Abstract

Understanding crop phenology is fundamental to agricultural production, management, planning, and decision-making. This study used 250 m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series data to detect crop phenology across the Midwestern United States, 2007–2015. Key crop phenology metrics,

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Understanding crop phenology is fundamental to agricultural production, management, planning, and decision-making. This study used 250 m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series data to detect crop phenology across the Midwestern United States, 2007–2015. Key crop phenology metrics, start of season (SOS) and end of season (EOS), were estimated for corn and soybean. For such a large study region, we found that MODIS-estimated SOS and EOS values were highly dependent on the nature of input time-series data, analytical methods, and threshold values chosen for crop phenology detection. With the entire sequence of MODIS EVI time-series data as input, SOS values were inconsistent compared to crop emergent dates from the United States Department of Agriculture (USDA) Crop Progress Reports (CPR). However, when we removed winter EVI images from the time-series data to reduce impacts of snow cover, we obtained much more consistent SOS estimation. Various threshold values (10 to 50% of seasonal EVI amplitude) were applied to derive SOS values. For both corn’s and soybean’s SOS estimation, a threshold value of 25% generated the best overall agreement with the CPR crop emergent dates. Root-mean-square error (RMSE) values were 4.81 and 5.30 days for corn and soybean, respectively. For corn’s EOS estimation, a threshold value of 40% led to a high R² value of 0.82 and RMSE value of 5.16 days. We further examined spatial patterns of SOS and EOS for both crops—SOS for corn displayed a clear south-north gradient; the southern portion of the Midwest US has earlier SOS and EOS dates. Full article

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

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Open AccessArticle Assessment of GPM and TRMM Precipitation Products over Singapore

by Mou Leong Tan and Zheng Duan

Remote Sens. 2017, 9(7), 720; doi:10.3390/rs9070720

Received: 2 May 2017 / Revised: 30 June 2017 / Accepted: 10 July 2017 / Published: 13 July 2017

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Abstract

The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has

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The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has made significant contributions to the development of various SPPs since its launch in 1997. The Global Precipitation Measurement (GPM) mission launched in 2014 and is expected to continue the success of TRMM. During the transition from the TRMM era to the GPM era, it is necessary to assess GPM products and make comparisons with TRMM products in different regions to achieve a global view of the performance of GPM products. To this end, this study aims to assess the capability of the latest Integrated Multi-satellite Retrievals for GPM (IMERG) and two TRMM Multisatellite Precipitation Analysis (TMPA) products (TMPA 3B42 and TMPA 3B42RT) in estimating precipitation over Singapore that represents a typical tropical region. The evaluation was conducted at daily, monthly, seasonal and annual scales from 1 April 2014 to 31 January 2016. The capability of SPPs in detecting rainy/non-rainy days and different precipitation classes was also evaluated. The findings showed that: (1) all SPPs correlated well with measurements from gauges at the monthly scale, but moderately at the daily scale; (2) SPPs performed better in the northeast monsoon season (1 December–15 March) than in the inter-monsoon 1 (16 March–31 May), southwest monsoon (1 June–30 September) and inter-monsoon 2 (1 October–30 November) seasons; (3) IMERG had better performance in the characterization of spatial precipitation variability and precipitation detection capability compared to the TMPA products; (4) for the daily precipitation estimates, IMERG had the lowest systematic bias, followed by 3B42 and 3B42RT; and (5) most of the SPPs overestimated moderate precipitation events (1–20 mm/day), while underestimating light (0.1–1 mm/day) and heavy (>20 mm/day) precipitation events. Overall, IMERG is superior but with only slight improvement compared to the TMPA products over Singapore. This study is one of the earliest assessments of IMERG and a comparison of it with TMPA products in Singapore. Our findings were compared with existing studies conducted in other regions, and some limitations of the IMERG and TMPA products in this tropical region were identified and discussed. This study provides an added value to the understanding of the global performance of the IMERG product. Full article

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

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Open AccessArticle Modelling Shadow Using 3D Tree Models in High Spatial and Temporal Resolution

by Elena Rosskopf, Christopher Morhart and Michael Nahm

Remote Sens. 2017, 9(7), 719; doi:10.3390/rs9070719

Received: 24 May 2017 / Revised: 29 June 2017 / Accepted: 9 July 2017 / Published: 13 July 2017

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Abstract

Information about the availability of solar irradiance for crops is of high importance for improving management practices of agricultural ecosystems such as agroforestry systems (AFS). Hence, the development of a high-resolution model that allows for the quantification of tree shading on a diurnal

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Information about the availability of solar irradiance for crops is of high importance for improving management practices of agricultural ecosystems such as agroforestry systems (AFS). Hence, the development of a high-resolution model that allows for the quantification of tree shading on a diurnal and annual time scale is highly demanded to generate realistic estimations of the shading dynamics in a given AFS. We describe an approach using 3D data derived from a terrestrial laser scanner and the steps undertaken to develop a vector-based model that quantifies and visualizes the shadow cast by single trees at daily, monthly, seasonal or annual levels with the input of cylinder-based tree models. It is able to compute the shadow of given tree models in time intervals of 10 min. To simulate seasonal growth and shedding of leaves, ellipsoids as replacement for leaves can be added to the tips of the tree model’s branches. The shadow model is flexible in its input of location (latitude, longitude), tree architecture and temporal resolution. Due to the possibility to feed this model with factual climate data such as cloud covers, it represents the first 3D tree model that enables the user to retrospectively analyze the shadow regime below a given tree, and to quantify shadow-related developments in AFS. Full article

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Open AccessArticle Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

by Rong Fang and Bogdan M. Strimbu

Remote Sens. 2017, 9(7), 716; doi:10.3390/rs9070716

Received: 3 May 2017 / Revised: 7 July 2017 / Accepted: 9 July 2017 / Published: 12 July 2017

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Abstract

The estimation of tree biomass and the products that can be obtained from a tree stem have focused forest research for more than two centuries. Traditionally, measurements of the entire tree bole were expensive or inaccurate, even when sophisticated remote sensing techniques were

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The estimation of tree biomass and the products that can be obtained from a tree stem have focused forest research for more than two centuries. Traditionally, measurements of the entire tree bole were expensive or inaccurate, even when sophisticated remote sensing techniques were used. We propose a fast and accurate procedure for measuring diameters along the merchantable portion of the stem at any given height. The procedure uses unreferenced photos captured with a consumer grade camera. A photogrammetric point cloud (PPC) is produced from the acquired images using structure from motion, which is a computer vision range imaging technique. A set of 18 loblolly pines (Pinus taeda Lindl.) from east Louisiana, USA, were photographed, subsequently cut, and the diameter measured every meter. The same diameters were measured on the point cloud with AutoCAD Civil3D. The ground point cloud reconstruction provided useful information for at most 13 m along the stem. The PPC measurements are biased, overestimating real diameters by 17.2 mm, but with a reduced standard deviation (8.2%). A linear equation with parameters of the error at a diameter at breast height (d_1.3) and the error of photogrammetric rendering reduced the bias to 1.4 mm. The usability of the PPC measurements in taper modeling was assessed with four models: Max and Burkhart [¹], Baldwin and Feduccia [²], Lenhart et al. [³], and Kozak [⁴]. The evaluation revealed that the data fit well with all the models (R² ≥ 0.97), with the Kozak and the Baldwin and Feduccia performing the best. The results support the replacement of taper with PPC, as faster, and more accurate and precise product estimations are expected. Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessArticle Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands

by Julie Lovitt, Mir Mustafizur Rahman and Gregory J. McDermid

Remote Sens. 2017, 9(7), 715; doi:10.3390/rs9070715

Received: 26 April 2017 / Revised: 13 June 2017 / Accepted: 6 July 2017 / Published: 12 July 2017

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Abstract

Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle

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Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14–42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers. Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessArticle First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau

by Xiaojing Bai, Binbin He, Xing Li, Jiangyuan Zeng, Xin Wang, Zuoliang Wang, Yijian Zeng and Zhongbo Su

Remote Sens. 2017, 9(7), 714; doi:10.3390/rs9070714

Received: 14 May 2017 / Revised: 20 June 2017 / Accepted: 6 July 2017 / Published: 12 July 2017

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Abstract

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the

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The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m³/m³ for the ascending data, and are 0.012, 0.026, and 0.055 m³/m³ for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites. Full article

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

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Open AccessArticle Monitoring of Subsidence along Jingjin Inter-City Railway with High-Resolution TerraSAR-X MT-InSAR Analysis

by Qingli Luo, Guoqing Zhou and Daniele Perissin

Remote Sens. 2017, 9(7), 717; doi:10.3390/rs9070717

Received: 9 May 2017 / Revised: 10 July 2017 / Accepted: 10 July 2017 / Published: 12 July 2017

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Abstract

Synthetic Aperture Radar Interferometry (InSAR), widely applied for the monitoring of land subsidence, has the advantage of high accuracy and wide coverage. High-resolution SAR data offers a chance to reveal impressive details of large-scale man-made linear features (LMLFs) with Multi-temporal InSAR (MT-InSAR) analysis.

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Synthetic Aperture Radar Interferometry (InSAR), widely applied for the monitoring of land subsidence, has the advantage of high accuracy and wide coverage. High-resolution SAR data offers a chance to reveal impressive details of large-scale man-made linear features (LMLFs) with Multi-temporal InSAR (MT-InSAR) analysis. Despite these advantages, research validating high-resolution MT-InSAR results along high-speed railways with high spatial and temporal density leveling data is limited. This paper explored the monitoring ability of high-resolution MT-InSAR in an experiment along Jingjin Inter-City Railway, located in Tianjin, China. Validation between these MT-InSAR results and a high spatial/temporal density leveling measurement was conducted. A total of 37 TSX images spanning half a year were processed for MT-InSAR analysis. The distance between two consecutive leveling points is 60 m along Jingjin Inter-City railway and the time interval of the study was about one month. The Root Mean Square Error (RMSE) index of average subsidence rate comparison between MT-InSAR results and leveling data was 3.28 mm/yr, with 34 points, and that of the displacement comparison was 2.90 mm with 464 valid observations. The experimental results along Jingjin Inter-City railway showed a high correlation between these two distinct measurements. These analyses show that millimeter accuracy can be achieved with MT-InSAR analysis when monitoring subsidence along a high-speed railway. We discuss the possible reason for the subsiding center, and the characteristics of both leveling and MT-InSAR results. We propose further planning for the monitoring of subsidence over LMLFs. Full article

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

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Open AccessArticle A Method to Improve High-Resolution Sea Ice Drift Retrievals in the Presence of Deformation Zones

by Jakob Griebel and Wolfgang Dierking

Remote Sens. 2017, 9(7), 718; doi:10.3390/rs9070718

Received: 5 May 2017 / Revised: 6 July 2017 / Accepted: 9 July 2017 / Published: 12 July 2017

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Abstract

Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR

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Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions. Full article

(This article belongs to the Section Ocean Remote Sensing)

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Open AccessFeature PaperArticle An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery

by Weizeng Shao, Jing Wang, Xiaofeng Li and Jian Sun

Remote Sens. 2017, 9(7), 711; doi:10.3390/rs9070711

Received: 17 April 2017 / Revised: 28 June 2017 / Accepted: 6 July 2017 / Published: 11 July 2017

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Abstract

In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval

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In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval from HH-polarization TS-X/TD-X image, polarization ratio (PR) has to be used for inverting wind speed, which is treated as an input in XWAVE. Wind speed encounters saturation in tropical cyclone. In our work, wind speed is replaced by normalized radar cross section (NRCS) to avoiding using SAR-derived wind speed, which does not work in high winds, and the empirical algorithm can be conveniently implemented without converting NRCS in HH-polarization to NRCS in VV-polarization by using X-band PR. A total of 120 TS-X/TD-X images, 60 in VV-polarization and 60 in HH-polarization, with homogenous wave patterns, and the coincide significant wave height data from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field at a 0.125° grid were collected as a dataset for tuning the algorithm. The range of SWH is from 0 to 7 m. We then applied the algorithm to 24 VV and 21 HH additional SAR images to extract SWH at locations of 30 National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) buoys. It is found that the algorithm performs well with a SWH stander deviation (STD) of about 0.5 m for both VV and HH polarization TS-X/TD-X images. For large wave validation (SWH 6–7 m), we applied the empirical algorithm to a tropical cyclone Sandy TD-X image acquired in 2012, and obtained good result with a SWH STD of 0.3 m. We concluded that the proposed empirical algorithm works for wave retrieval from TS-X/TD-X image in co-polarization without external sea surface wind information. Full article

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

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Open AccessArticle Intercalibration and Gaussian Process Modeling of Nighttime Lights Imagery for Measuring Urbanization Trends in Africa 2000–2013

by David J. Savory, Ricardo Andrade-Pacheco, Peter W. Gething, Alemayehu Midekisa, Adam Bennett and Hugh J. W. Sturrock

Remote Sens. 2017, 9(7), 713; doi:10.3390/rs9070713

Received: 7 February 2017 / Revised: 3 May 2017 / Accepted: 5 July 2017 / Published: 11 July 2017

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Abstract

Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental

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Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental sciences. Nighttime lights imagery (NTL), maintained by the National Oceanic and Atmospheric Administration, offers a unique vantage point for studying trends in urbanization. A well-documented deficiency of this dataset is the lack of intra- and inter-annual calibration between satellites, which makes the imagery unsuitable for temporal analysis in their raw format. Here we have generated an ‘intercalibrated’ time series of annual NTL images for Africa (2000–2013) by building on the widely used invariant region and quadratic regression method (IRQR). Gaussian process methods (GP) were used to identify NTL latent functions independent from the temporal noise signals in the annual datasets. The corrected time series was used to explore the positive association of NTL with Gross Domestic Product (GDP) and urban population (UP). Additionally, the proportion of change in ‘lit area’ occurring in urban areas was measured by defining urban agglomerations as contiguously lit pixels of >250 km², with all other pixels being rural. For validation, the IRQR and GP time series were compared as predictors of the invariant region dataset. Root mean square error values for the GP smoothed dataset were substantially lower. Correlation of NTL with GDP and UP using GP smoothing showed significant increases in R² over the IRQR method on both continental and national scales. Urban growth results suggested that the majority of growth in lit pixels between 2000 and 2013 occurred in rural areas. With this study, we demonstrated the effectiveness of GP to improve conventional intercalibration, used NTL to describe temporal patterns of urbanization in Africa, and detected NTL responses to environmental and humanitarian crises. The smoothed datasets are freely available for further use. Full article

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

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Open AccessArticle Assessment of Land Use-Cover Changes and Successional Stages of Vegetation in the Natural Protected Area Altas Cumbres, Northeastern Mexico, Using Landsat Satellite Imagery

by Uriel Jeshua Sánchez-Reyes, Santiago Niño-Maldonado, Ludivina Barrientos-Lozano and Jacinto Treviño-Carreón

Remote Sens. 2017, 9(7), 712; doi:10.3390/rs9070712

Received: 20 May 2017 / Revised: 30 June 2017 / Accepted: 8 July 2017 / Published: 11 July 2017

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Abstract

Loss of vegetation cover is a major factor that endangers biodiversity. Therefore, the use of geographic information systems and the analysis of satellite images are important for monitoring these changes in Natural Protected Areas (NPAs). In northeastern Mexico, the Natural Protected Area Altas

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Loss of vegetation cover is a major factor that endangers biodiversity. Therefore, the use of geographic information systems and the analysis of satellite images are important for monitoring these changes in Natural Protected Areas (NPAs). In northeastern Mexico, the Natural Protected Area Altas Cumbres (NPAAC) represents a relevant floristic and faunistic patch on which the impact of loss of vegetation cover has not been assessed. This work aimed to analyze changes of land use and coverage (LULCC) over the last 42 years on the interior and around the exterior of the area, and also to propose the time of succession for the most important types of vegetation. For the analysis, LANDSAT satellite images from 1973, 1986, 2000, 2005 and 2015 were used, they were classified in seven categories through a segmentation and maximum likelihood analysis. A cross-tabulation analysis was performed to determine the succession gradient. Towards the interior of the area, a significant reduction of tropical vegetation and, to a lesser extent, temperate forests was found, as well as an increase in scrub cover from 1973 to 2015. In addition, urban and vegetation-free areas, as well as modified vegetation, increased to the exterior. Towards the interior of the NPA, the processes of perturbation and recovery were mostly not linear, while in the exterior adjacent area, the presence of secondary vegetation with distinct definite time of succession was evident. The analysis carried out is the first contribution that evaluates LULCC in this important NPA of northeastern Mexico. Results suggest the need to evaluate the effects of these modifications on species. Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessArticle Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

by Jibo Yue, Guijun Yang, Changchun Li, Zhenhai Li, Yanjie Wang, Haikuan Feng and Bo Xu

Remote Sens. 2017, 9(7), 708; doi:10.3390/rs9070708

Received: 12 May 2017 / Revised: 5 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017

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Abstract

Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV).

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Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R² = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R₆₇₀ only: R² = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R² = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R² = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R² = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R² = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management. Full article

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

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Open AccessArticle Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery

by Yong Xu, Lin Lin and Deyu Meng

Remote Sens. 2017, 9(7), 709; doi:10.3390/rs9070709

Received: 11 May 2017 / Revised: 16 June 2017 / Accepted: 5 July 2017 / Published: 10 July 2017

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Abstract

Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in

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Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in detecting subtle spatial variations within a coarse pixel—especially for a fast-growing city. Given that the historical land use/cover products and satellite data at finer resolution are valuable to reflect the urban dynamics with more spatial details, finer spatial resolution images, as well as land cover products at previous times, are exploited in this study to improve the change detection capability of coarse resolution satellite data. The proposed approach involves two main steps. First, pairs of coarse and finer resolution satellite data at previous times are learned and then applied to generate synthetic satellite data with finer spatial resolution from coarse resolution satellite data. Second, a land cover map was produced at a finer spatial resolution and adjusted with the obtained synthetic satellite data and prior land cover maps. The approach was tested for generating finer resolution synthetic Landsat images using MODIS data from the Guangzhou study area. The finer resolution Landsat-like data were then applied to detect land cover changes with more spatial details. Test results show that the change detection accuracy using the proposed approach with the synthetic Landsat data is much better than the results using the original MODIS data or conventional spatial and temporal fusion-based approaches. The proposed approach is beneficial for detecting subtle urban land cover changes with more spatial details when multitemporal coarse satellite data are available. Full article

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

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Open AccessArticle Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems

by Jianhu Zhao, Xinglei Zhao, Hongmei Zhang and Fengnian Zhou

Remote Sens. 2017, 9(7), 710; doi:10.3390/rs9070710

Received: 24 May 2017 / Revised: 5 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017

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Abstract

Airborne LiDAR bathymetry (ALB) is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused

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Airborne LiDAR bathymetry (ALB) is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model. Full article

(This article belongs to the Section Ocean Remote Sensing)

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Open AccessArticle Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

by Hong Chi, Guoqing Sun, Jinliang Huang, Rendong Li, Xianyou Ren, Wenjian Ni and Anmin Fu

Remote Sens. 2017, 9(7), 707; doi:10.3390/rs9070707

Received: 20 June 2017 / Revised: 4 July 2017 / Accepted: 4 July 2017 / Published: 9 July 2017

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Abstract

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha⁻¹) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were

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Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha⁻¹) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg·ha⁻¹, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha⁻¹. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R² of 0.72 and an RMSE of 25.24 Mg·ha⁻¹ in validation at stand level (size varied from ~0.3 ha to ~3 ha). Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessArticle Dry Season Evapotranspiration Dynamics over Human-Impacted Landscapes in the Southern Amazon Using the Landsat-Based METRIC Model

by Kul Khand, Izaya Numata, Jeppe Kjaersgaard and George L. Vourlitis

Remote Sens. 2017, 9(7), 706; doi:10.3390/rs9070706

Received: 24 May 2017 / Revised: 26 June 2017 / Accepted: 5 July 2017 / Published: 9 July 2017

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Abstract

Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land

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Although seasonal and temporal variations in evapotranspiration (ET) in Amazonia have been studied based upon flux-tower data and coarse resolution satellite-based models, ET dynamics over human-impacted landscapes are highly uncertain in this region. In this study, we estimate ET rates from critical land cover types over highly fragmented landscapes in the southern Amazon and characterize the ET dynamics during the dry season using the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC, a Landsat-based ET model, that generates spatially continuous ET estimates at a 30 m spatial resolution widely used for agricultural applications, was adapted to the southern Amazon by using the NDVI indexed reference ET fraction (ET_rF) approach. Compared to flux tower-based ET rates, this approach showed an improved performance on the forest ET estimation over the standard METRIC approach, with R² = 0.73 from R² = 0.70 and RMSE reduced from 0.77 mm/day to 0.35 mm/day. We used this approach integrated into the METRIC procedure to estimate ET rates from primary, regenerated, and degraded forests and pasture in Acre, Rondônia, and Mato Grosso, all located in the southern Amazon, during the dry season in 2009. The lowest ET rates occurred in Mato Grosso, the driest region. Acre and Rondônia, both located in the southwestern Amazon, had similar ET rates for all land cover types. Dry season ET rates between primary forest and regenerated forest were similar (p > 0.05) in all sites, ranging between 2.5 and 3.4 mm/day for both forest cover types in the three sites. ET rates from degraded forest in Mato Grosso were significantly lower (p < 0.05) compared to the other forest cover types, with a value of 2.03 mm/day on average. Pasture showed the lowest ET rates during the dry season at all study sites, with the dry season average ET varying from 1.7 mm/day in Mato Grosso to 2.8 mm/day in Acre. Full article

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Open AccessArticle High Resolution Orthomosaics of African Coral Reefs: A Tool for Wide-Scale Benthic Monitoring

by Marco Palma, Monica Rivas Casado, Ubaldo Pantaleo and Carlo Cerrano

Remote Sens. 2017, 9(7), 705; doi:10.3390/rs9070705

Received: 27 March 2017 / Revised: 23 June 2017 / Accepted: 4 July 2017 / Published: 8 July 2017

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Abstract

Coral reefs play a key role in coastal protection and habitat provision. They are also well known for their recreational value. Attempts to protect these ecosystems have not successfully stopped large-scale degradation. Significant efforts have been made by government and research organizations to

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Coral reefs play a key role in coastal protection and habitat provision. They are also well known for their recreational value. Attempts to protect these ecosystems have not successfully stopped large-scale degradation. Significant efforts have been made by government and research organizations to ensure that coral reefs are monitored systematically to gain a deeper understanding of the causes, the effects and the extent of threats affecting coral reefs. However, further research is needed to fully understand the importance that sampling design has on coral reef characterization and assessment. This study examines the effect that sampling design has on the estimation of seascape metrics when coupling semi-autonomous underwater vehicles, structure-from-motion photogrammetry techniques and high resolution (0.4 cm) underwater imagery. For this purpose, we use FRAGSTATS v4 to estimate key seascape metrics that enable quantification of the area, density, edge, shape, contagion, interspersion and diversity of sessile organisms for a range of sampling scales (0.5 m × 0.5 m, 2 m × 2 m, 5 m × 5 m, 7 m × 7 m), quadrat densities (from 1–100 quadrats) and sampling strategies (nested vs. random) within a 1655 m² case study area in Ponta do Ouro Partial Marine Reserve (Mozambique). Results show that the benthic community is rather disaggregated within a rocky matrix; the embedded patches frequently have a small size and a regular shape; and the population is highly represented by soft corals. The genus Acropora is the more frequent and shows bigger colonies in the group of hard corals. Each of the seascape metrics has specific requirements of the sampling scale and quadrat density for robust estimation. Overall, the majority of the metrics were accurately identified by sampling scales equal to or coarser than 5 m × 5 m and quadrat densities equal to or larger than 30. The study indicates that special attention needs to be dedicated to the design of coral reef monitoring programmes, with decisions being based on the seascape metrics and statistics being determined. The results presented here are representative of the eastern South Africa coral reefs and are expected to be transferable to coral reefs with similar characteristics. The work presented here is limited to one study site and further research is required to confirm the findings. Full article

(This article belongs to the Section Ocean Remote Sensing)

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Open AccessArticle MMASTER: Improved ASTER DEMs for Elevation Change Monitoring

by Luc Girod, Christopher Nuth, Andreas Kääb, Robert McNabb and Olivier Galland

Remote Sens. 2017, 9(7), 704; doi:10.3390/rs9070704

Received: 6 June 2017 / Revised: 4 July 2017 / Accepted: 4 July 2017 / Published: 8 July 2017

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Abstract

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system on board the Terra (EOS AM-1) satellite has been a source of stereoscopic images covering the whole globe at 15-m resolution with consistent quality for over 16 years. The potential of these data

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The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system on board the Terra (EOS AM-1) satellite has been a source of stereoscopic images covering the whole globe at 15-m resolution with consistent quality for over 16 years. The potential of these data in terms of geomorphological analysis and change detection in three dimensions is unrivaled and should be exploited more. Due to uncorrected errors in the image geometry due to sensor motion (“jitter”), however, the quality of the DEMs and orthoimages currently available is often insufficient for a number of applications, including surface change detection. We have therefore developed a series of algorithms packaged under the name MicMac ASTER (MMASTER). It is composed of a tool to compute Rational Polynomial Coefficient (RPC) models from the ASTER metadata, a method that improves the quality of the matching by identifying and correcting jitter-induced cross-track parallax errors and a correction for along-track jitter when computing differences between DEMs (either with another MMASTER DEM or with another data source). Our method outputs more precise DEMs with less unmatched areas and reduced overall noise compared to NASA’s standard AST14DMO product. The algorithms were implemented in the open source photogrammetric library and software suite MicMac. Here, we briefly examine the potential of MMASTER-produced DEMs to investigate a variety of geomorphological changes, including river erosion, seismic deformation, changes in biomass, volcanic deformation and glacier mass balance. Full article

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

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Open AccessArticle Mapping Typical Urban LULC from Landsat Imagery without Training Samples or Self-Defined Parameters

by Hui Li, Cuizhen Wang, Cheng Zhong, Zhi Zhang and Qingbin Liu

Remote Sens. 2017, 9(7), 700; doi:10.3390/rs9070700

Received: 14 June 2017 / Revised: 26 June 2017 / Accepted: 28 June 2017 / Published: 7 July 2017

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Abstract

Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have

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Land use/land cover (LULC) change is one of the most important indicators in understanding the interactions between humans and the environment. Traditionally, when LULC maps are produced yearly, most existing remote-sensing methods have to collect ground reference data annually, as the classifiers have to be trained individually in each corresponding year. This study presented a novel strategy to map LULC classes without training samples or assigning parameters. First of all, several novel indices were carefully selected from the index pool, which were able to highlight certain LULC very well. Following this, a common unsupervised classifier was employed to extract the LULC from the associated index image without assigning thresholds. Finally, a supervised classification was implemented with samples automatically collected from the unsupervised classification outputs. Results illustrated that the proposed method could achieve satisfactory performance, reaching similar accuracies to traditional approaches. Findings of this study demonstrate that the proposed strategy is a simple and effective alternative to mapping urban LULC. With the proposed strategy, the budget and time required for remote-sensing data processing could be reduced dramatically. Full article

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Open AccessArticle Multi-Channel Deconvolution for Forward-Looking Phase Array Radar Imaging

by Jie Xia, Xinfei Lu and Weidong Chen

Remote Sens. 2017, 9(7), 703; doi:10.3390/rs9070703

Received: 14 February 2017 / Revised: 28 June 2017 / Accepted: 5 July 2017 / Published: 7 July 2017

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Abstract

The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets’ backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited

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The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets’ backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited antenna beamwidth. However, as a typical inverse problem, deconvolution is essentially a highly ill-posed problem which is sensitive to noise and cannot ensure a reliable and robust estimation. In this paper, multi-channel deconvolution is proposed for improving the performance of deconvolution, which intends to considerably alleviate the ill-posed problem of single-channel deconvolution. To depict the performance improvement obtained by multi-channel more effectively, evaluation parameters are generalized to characterize the angular spectrum of antenna pattern or singular value distribution of observation matrix, which are conducted to compare different deconvolution systems. Here we present two multi-channel deconvolution algorithms which improve upon the traditional deconvolution algorithms via combining with multi-channel technique. Extensive simulations and experimental results based on real data are presented to verify the effectiveness of the proposed imaging methods. Full article

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Open AccessArticle Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

by Li Li, Jian Yao, Yahui Liu, Wei Yuan, Shuzhu Shi and Shenggu Yuan

Remote Sens. 2017, 9(7), 701; doi:10.3390/rs9070701

Received: 21 April 2017 / Revised: 13 June 2017 / Accepted: 5 July 2017 / Published: 7 July 2017

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Abstract

When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new

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When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index. Full article

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Open AccessArticle Estimating Mangrove Canopy Height and Above-Ground Biomass in the Everglades National Park with Airborne LiDAR and TanDEM-X Data

by Emanuelle A. Feliciano, Shimon Wdowinski, Matthew D. Potts, Seung-Kuk Lee and Temilola E. Fatoyinbo

Remote Sens. 2017, 9(7), 702; doi:10.3390/rs9070702

Received: 1 June 2017 / Revised: 29 June 2017 / Accepted: 4 July 2017 / Published: 7 July 2017

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Abstract

Mangrove forests are important natural ecosystems due to their ability to capture and store large amounts of carbon. Forest structural parameters, such as canopy height and above-ground biomass (AGB), provide a good measure for monitoring temporal changes in carbon content. The protected coastal

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Mangrove forests are important natural ecosystems due to their ability to capture and store large amounts of carbon. Forest structural parameters, such as canopy height and above-ground biomass (AGB), provide a good measure for monitoring temporal changes in carbon content. The protected coastal mangrove forest of the Everglades National Park (ENP) provides an ideal location for studying these processes, as harmful human activities are minimal. We estimated mangrove canopy height and AGB in the ENP using Airborne LiDAR/Laser (ALS) and TanDEM-X (TDX) datasets acquired between 2011 and 2013. Analysis of both datasets revealed that mangrove canopy height can reach up to ~25 m and AGB can reach up to ~250 Mg•ha−1. In general, mangroves ranging from 9 m to 12 m in stature dominate the forest canopy. The comparison of ALS and TDX canopy height observations yielded an R2 = 0.85 and Root Mean Square Error (RMSE) = 1.96 m. Compared to a previous study based on data acquired during 2000–2004, our analysis shows an increase in mangrove stature and AGB, suggesting that ENP mangrove forests are continuing to accumulate biomass. Our results suggest that ENP mangrove forests have managed to recover from natural disturbances, such as Hurricane Wilma. Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessArticle Detecting Wind Farm Impacts on Local Vegetation Growth in Texas and Illinois Using MODIS Vegetation Greenness Measurements

by Geng Xia and Liming Zhou

Remote Sens. 2017, 9(7), 698; doi:10.3390/rs9070698

Received: 19 May 2017 / Revised: 3 July 2017 / Accepted: 4 July 2017 / Published: 6 July 2017

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Abstract

This study examines the possible impacts of real-world wind farms (WFs) on vegetation growth using two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), at a ~250 m resolution from the MODerate resolution Imaging Spectroradimeter (MODIS) for

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This study examines the possible impacts of real-world wind farms (WFs) on vegetation growth using two vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), at a ~250 m resolution from the MODerate resolution Imaging Spectroradimeter (MODIS) for the period 2003–2014. We focus on two well-studied large WF regions, one in western Texas and the other in northern Illinois. These two regions differ distinctively in terms of land cover, topography, and background climate, allowing us to examine whether the WF impacts on vegetation, if any, vary due to the differences in atmospheric and boundary conditions. We use three methods (spatial coupling analysis, time series analysis, and seasonal cycle analysis) and consider two groups of pixels, wind farm pixels (WFPs) and non-wind-farm pixels (NWFPs), to quantify and attribute such impacts during the pre- and post-turbine periods. Our results indicate that the WFs have insignificant or no detectible impacts on local vegetation growth. At the pixel level, the VI changes demonstrate a random nature and have no spatial coupling with the WF layout. At the regional level, there is no systematic shift in vegetation greenness between the pre- and post-turbine periods. At interannual and seasonal time scales, there are no confident vegetation changes over WFPs relative to NWFPs. These results remain robust when the pre- and post-turbine periods and NWFPs are defined differently. Most importantly, the majority of the VI changes are within the MODIS data uncertainty, suggesting that the WF impacts on vegetation, if any, cannot be separated confidently from the data uncertainty and noise. Overall, there are some small decreases in vegetation greenness over WF regions, but no convincing observational evidence is found for the impacts of operating WFs on vegetation growth. Full article

(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)

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Open AccessArticle Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing

by Chuiqing Zeng, Douglas J. King, Murray Richardson and Bo Shan

Remote Sens. 2017, 9(7), 696; doi:10.3390/rs9070696

Received: 24 April 2017 / Revised: 13 June 2017 / Accepted: 2 July 2017 / Published: 6 July 2017

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Abstract

Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and

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Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors have been developed for UAVs, but the sensors, associated hardware, and data processing software are still cost prohibitive for use by individual farmers or small remote sensing firms. This study simulated hyperspectral image data by fusing multispectral camera imagery and spectrometer data. We mounted a multispectral camera and spectrometer, both being low cost and low weight, on a standard UAV and developed procedures for their precise data alignment, followed by fusion of the spectrometer data with the image data to produce estimated spectra for all the multispectral camera image pixels. To align the data collected from the two sensors in both the time and space domains, a post-acquisition correlation-based global optimization method was used. Data fusion, to estimate hyperspectral reflectance, was implemented using several methods for comparison. Flight data from two crop sites, one being tomatoes, and the other corn and soybeans, were used to evaluate the alignment procedure and the data fusion results. The data alignment procedure resulted in a peak R² between the spectrometer and camera data of 0.95 and 0.72, respectively, for the two test sites. The corresponding multispectral camera data for these space and time offsets were taken as the best match to a given spectrometer reading, and used in modelling to estimate hyperspectral imagery from the multispectral camera pixel data. Of the fusion approaches evaluated, principal component analysis (PCA) based models and Bayesian imputation reached a similar accuracy, and outperformed simple spline interpolation. Mean absolute error (MAE) between predicted and observed spectra was 17% relative to the mean of the observed spectra, and root mean squared error (RMSE) was 0.028. This approach to deriving estimated hyperspectral image data can be applied in a simple fashion at very low cost for crop assessment and monitoring within individual fields. Full article

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Open AccessArticle Wavelet-Based Topographic Effect Compensation in Accurate Mountain Glacier Velocity Extraction: A Case Study of the Muztagh Ata Region, Eastern Pamir

by Shiyong Yan, Yi Li, Zhixing Ruan, Mingyang Lv, Guang Liu and Kazhong Deng

Remote Sens. 2017, 9(7), 697; doi:10.3390/rs9070697

Received: 14 March 2017 / Revised: 26 June 2017 / Accepted: 2 July 2017 / Published: 6 July 2017

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Abstract

Glaciers in high mountain regions play an important role in global climate research. Glacier motion, which is the main characteristic of glacier activity, has attracted much interest and has been widely studied, because an accurate ice motion field is crucial for both glacier

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Glaciers in high mountain regions play an important role in global climate research. Glacier motion, which is the main characteristic of glacier activity, has attracted much interest and has been widely studied, because an accurate ice motion field is crucial for both glacier activity analysis and ice avalanche prediction. Unfortunately, the serious topographic effects associated with the complex terrain in high mountain regions can result in errors in ice movement estimation. Thus, according to the different characteristics of the results of pixel tracking in the wavelet domain after random sample consensus (RANSAC)-based global deformation removal, a wavelet-based topographic effect compensation operation is presented in this paper. The proposed method is then used for ice motion estimation in the Muztagh Ata region, without the use of synthetic-aperture radar (SAR) imaging geometry parameters. The results show that the proposed method can effectively improve the accuracy of glacier motion estimation by reducing the mean and standard deviation values from 0.32 m and 0.4 m to 0.16 m and 0.23 m, respectively, in non-glacial regions, after precisely compensating the topographic effect with Advanced Land Observing Satellite–Phased Array-type L-band Synthetic Aperture Radar (ALOS–PALSAR) imagery. Therefore, the presented wavelet-based topographic effect compensation method is also effective without requiring the SAR imaging geometry parameters and has the potential to be widely used in the accurate estimation of mountain glacier velocity. Full article

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

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Open AccessArticle Micro-Doppler Estimation and Analysis of Slow Moving Objects in Forward Scattering Radar System

by Raja Syamsul Azmir Raja Abdullah, Ali Alnaeb, Asem Ahmad Salah, Nur Emileen Abdul Rashid, Aduwati Sali and Idnin Pasya

Remote Sens. 2017, 9(7), 699; doi:10.3390/rs9070699

Received: 27 April 2017 / Revised: 23 June 2017 / Accepted: 5 July 2017 / Published: 6 July 2017

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Abstract

Micro-Doppler signature can convey information of detected targets and has been used for target recognition in many Radar systems. Nevertheless, micro-Doppler for the specific Forward Scattering Radar (FSR) system has yet to be analyzed and investigated in detail; consequently, information carried by the

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Micro-Doppler signature can convey information of detected targets and has been used for target recognition in many Radar systems. Nevertheless, micro-Doppler for the specific Forward Scattering Radar (FSR) system has yet to be analyzed and investigated in detail; consequently, information carried by the micro-Doppler in FSR is not fully understood. This paper demonstrates the feasibility and effectiveness of FSR in detecting and extracting micro-Doppler signature generated from a target’s micro-motions. Comprehensive theoretical analyses and simulation results followed by experimental investigations into the feasibility of using the FSR for detecting micro-Doppler signatures are presented in this paper. The obtained results verified that the FSR system is capable of detecting micro-Doppler signature of a swinging pendulum placed on a moving trolley and discriminating different swinging speeds. Furthermore, human movement and micro-Doppler from hand motions can be detected and monitored by using the FSR system which resembles a potential application for human gait monitoring and classification. Full article

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

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Open AccessCommunication An NDVI-Based Vegetation Phenology Is Improved to be More Consistent with Photosynthesis Dynamics through Applying a Light Use Efficiency Model over Boreal High-Latitude Forests

by Siheng Wang, Lifu Zhang, Changping Huang and Na Qiao

Remote Sens. 2017, 9(7), 695; doi:10.3390/rs9070695

Received: 7 June 2017 / Revised: 29 June 2017 / Accepted: 4 July 2017 / Published: 6 July 2017

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Abstract

Remote sensing of high-latitude forests phenology is essential for understanding the global carbon cycle and the response of vegetation to climate change. The normalized difference vegetation index (NDVI) has long been used to study boreal evergreen needleleaf forests (ENF) and deciduous broadleaf forests.

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Remote sensing of high-latitude forests phenology is essential for understanding the global carbon cycle and the response of vegetation to climate change. The normalized difference vegetation index (NDVI) has long been used to study boreal evergreen needleleaf forests (ENF) and deciduous broadleaf forests. However, the NDVI-based growing season is generally reported to be longer than that based on gross primary production (GPP), which can be attributed to the difference between greenness and photosynthesis. Instead of introducing environmental factors such as land surface or air temperature like previous studies, this study attempts to make VI-based phenology more consistent with photosynthesis dynamics through applying a light use efficiency model. NDVI (MOD13C2) was used as a proxy for both fractional of absorbed photosynthetically active radiation (APAR) and light use efficiency at seasonal time scale. Results show that VI-based phenology is improved towards tracking seasonal GPP changes more precisely after applying the light use efficiency model compared to raw NDVI or APAR, especially over ENF. Full article

(This article belongs to the Section Forest Remote Sensing)

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Open AccessLetter GF-3 SAR Ocean Wind Retrieval: The First View and Preliminary Assessment

by He Wang, Jingsong Yang, Alexis Mouche, Weizeng Shao, Jianhua Zhu, Lin Ren and Chunhua Xie

Remote Sens. 2017, 9(7), 694; doi:10.3390/rs9070694

Received: 4 June 2017 / Revised: 22 June 2017 / Accepted: 4 July 2017 / Published: 5 July 2017

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Abstract

Gaofen-3 (GF-3) is the first Chinese civil C-band synthetic aperture radar (SAR) launched on 10 August 2016 by the China Academy of Space Technology (CAST), which operates in 12 imaging modes with a fine spatial resolution up to 1 m. As one of

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Gaofen-3 (GF-3) is the first Chinese civil C-band synthetic aperture radar (SAR) launched on 10 August 2016 by the China Academy of Space Technology (CAST), which operates in 12 imaging modes with a fine spatial resolution up to 1 m. As one of the primary users, the State Oceanic Administration (SOA) operationally processes GF-3 SAR Level-1 products into ocean surface wind vector and plans to officially release the near real-time SAR wind products in the near future. In this paper, the methodology of wind retrieval at C-band SAR is introduced and the first results of GF-3 SAR-derived winds are presented. In particular, the case of the coastal katabatic wind off the west coast of the U.S. captured by GF-3 is discussed. The preliminary accuracy assessment of wind speed and direction retrievals from GF-3 SAR is carried out against in situ measurements from National Data Buoy Center (NDBC) buoy measurements of National Oceanic and Atmospheric Administration (NOAA). Only the buoys located inside the GF-3 SAR wind cell (1 km) were considered as co-located in space, while the time interval between observations of SAR and buoy was limited to less the 30 min. These criteria yielded 56 co-locations during the period from January to April 2017, showing the Root Mean Square Error (RMSE) of 2.46 m/s and 22.22° for wind speed and direction, respectively. Different performances due to geophysical model function (GMF) and Polarization Ratio (PR) are discussed. The preliminary results indicate that GF-3 wind retrievals are encouraging for operational implementation. Full article

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

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Open AccessArticle Assessment and Improvement of MISR Angstrom Exponent and Single-Scattering Albedo Products Using AERONET Data in China

by Yidan Si, Shenshen Li, Liangfu Chen, Huazhe Shang, Lei Wang and Husi Letu

Remote Sens. 2017, 9(7), 693; doi:10.3390/rs9070693

Received: 25 April 2017 / Revised: 22 June 2017 / Accepted: 3 July 2017 / Published: 5 July 2017

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Abstract

Mapping the components, size, and absorbing/scattering properties of particle pollution is of great interest in the environmental and public health fields. Although the Multi-angle Imaging SpectroRadiometer (MISR) can detect a greater number of aerosol microphysical properties than most other spaceborne sensors, the Angstrom

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Mapping the components, size, and absorbing/scattering properties of particle pollution is of great interest in the environmental and public health fields. Although the Multi-angle Imaging SpectroRadiometer (MISR) can detect a greater number of aerosol microphysical properties than most other spaceborne sensors, the Angstrom exponent (AE) and single-scattering albedo (SSA) products are not widely utilized or as robust as the aerosol optical depth (AOD) product. This study focused on validating MISR AE and SSA data using AErosol RObotic NETwork (AERONET) data for China from 2004 to 2014. The national mean value of the MISR data (1.08) was 0.095 lower than that of the AERONET data. However, the MISR SSA average (0.99) was significantly higher than that of AERONET (0.89). In this study, we developed a method to improve the AE and SSA by narrowing the selection of MISR mixtures via the introduction of the following group thresholds obtained from an 11-year AERONET dataset: minimum and maximum values (for the method of MISR_Imp_All) and the top 10% and bottom 10% of the averaged values (for MISR_Imp_10%). Overall, our improved AE values were closer to the AERONET AE values, and additional samples (MISR_Imp_All: 28.04% and 64.72%, MISR_Imp_10%: 34.11% and 73.13%) had absolute differences of less than 0.1 and 0.3 (defined by the expected error tests, e.g., EE_0.1) compared with the original MISR product (18.46% and 50.23%). For the SSA product, our method also improved the mean, EE_0.05, and EE_0.1 from 0.99, 16.13%, and 56.45% (MISR original product) to 0.96, 40.32%, and 70.97% (MISR_Imp_All), and 0.94, 54.84%, and 90.32% (MISR_Imp_10%), respectively. Full article

(This article belongs to the Section Atmosphere Remote Sensing)

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Open AccessArticle Aerosol Property Retrieval Algorithm over Northeast Asia from TANSO-CAI Measurements Onboard GOSAT

by Sanghee Lee, Mijin Kim, Myungje Choi, Sujung Go, Jhoon Kim, Jung-Hyun Kim, Hyun-Kwang Lim, Ukkyo Jeong, Tae-Young Goo, Akihiko Kuze, Kei Shiomi and Yokota Tatsuya

Remote Sens. 2017, 9(7), 687; doi:10.3390/rs9070687

Received: 19 April 2017 / Revised: 23 June 2017 / Accepted: 26 June 2017 / Published: 5 July 2017

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Abstract

The presence of aerosol has resulted in serious limitations in the data coverage and large uncertainties in retrieving carbon dioxide (CO₂) amounts from satellite measurements. For this reason, an aerosol retrieval algorithm was developed for the Thermal and Near-infrared Sensor for

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The presence of aerosol has resulted in serious limitations in the data coverage and large uncertainties in retrieving carbon dioxide (CO₂) amounts from satellite measurements. For this reason, an aerosol retrieval algorithm was developed for the Thermal and Near-infrared Sensor for carbon Observation-Cloud and Aerosol Imager (TANSO-CAI) launched in January 2009 on board the Greenhouse Gases Observing Satellite (GOSAT). The algorithm retrieves aerosol optical depth (AOD), aerosol size information, and aerosol type in 0.1° grid resolution by look-up tables constructed using inversion products from Aerosol Robotic NETwork (AERONET) sun-photometer observation over Northeast Asia as a priori information. To improve the accuracy of the TANSO-CAI aerosol algorithm, we consider both seasonal and annual estimated radiometric degradation factors of TANSO-CAI in this study. Surface reflectance is determined by the same 23-path composite method of Rayleigh and gas corrected reflectance to avoid the stripes of each band. To distinguish aerosol absorptivity, reflectance difference test between ultraviolet (band 1) and visible (band 2) wavelengths depending on AODs was used. To remove clouds in aerosol retrieval, the normalized difference vegetation index and ratio of reflectance between band 2 (0.674 μm) and band 3 (0.870 μm) threshold tests have been applied. To mask turbid water over ocean, a threshold test for the estimated surface reflectance at band 2 was also introduced. The TANSO-CAI aerosol algorithm provides aerosol properties such as AOD, size information and aerosol types from June 2009 to December 2013 in this study. Here, we focused on the algorithm improvement for AOD retrievals and their validation in this study. The retrieved AODs were compared with those from AERONET and the Aqua/MODerate resolution Imaging Sensor (MODIS) Collection 6 Level 2 dataset over land and ocean. Comparisons of AODs between AERONET and TANSO-CAI over Northeast Asia showed good agreement with correlation coefficient (R) 0.739 ± 0.046, root mean square error (RMSE) 0.232 ± 0.047, and linear regression line slope 0.960 ± 0.083 for the entire period. Over ocean, the comparisons between Aqua/MODIS and TANSO-CAI for the same period over Northeast Asia showed improved consistency, with correlation coefficient 0.830 ± 0.047, RMSE 0.140 ± 0.019, and linear regression line slope 1.226 ± 0.063 for the entire period. Over land, however, the comparisons between Aqua/MODIS and TANSO-CAI show relatively lower correlation (approximate R = 0.67, RMSE = 0.40, slope = 0.77) than those over ocean. In order to improve accuracy in retrieving CO₂ amounts, the retrieved aerosol properties in this study have been provided as input for CO₂ retrieval with GOSAT TANSO-Fourier Transform Spectrometer measurements. Full article

(This article belongs to the Section Atmosphere Remote Sensing)

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Open AccessArticle Parallel Seasonal Patterns of Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees

by Kyle R. Springer, Ran Wang and John A. Gamon

Remote Sens. 2017, 9(7), 691; doi:10.3390/rs9070691

Received: 19 May 2017 / Revised: 28 June 2017 / Accepted: 30 June 2017 / Published: 5 July 2017

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Abstract

Tree species in the boreal forest cycle between periods of active growth and dormancy alter their photosynthetic processes in response to changing environmental conditions. For deciduous species, these changes are readily visible, while evergreen species have subtler foliar changes during seasonal transitions. In

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Tree species in the boreal forest cycle between periods of active growth and dormancy alter their photosynthetic processes in response to changing environmental conditions. For deciduous species, these changes are readily visible, while evergreen species have subtler foliar changes during seasonal transitions. In this study, we used remotely sensed optical indices to observe seasonal changes in photosynthetic activity, or photosynthetic phenology, of six boreal tree species. We evaluated the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), the chlorophyll/carotenoid index (CCI), and steady-state chlorophyll fluorescence (F_S) as a measure of solar-induced fluorescence (SIF), and compared these optical metrics to gas exchange to determine their efficacy in detecting seasonal changes in plant photosynthetic activity. The NDVI and PRI exhibited complementary responses. The NDVI paralleled photosynthetic phenology in deciduous species, but not in evergreens. The PRI closely paralleled photosynthetic activity in evergreens, but less so in deciduous species. The CCI and F_S tracked photosynthetic phenology in both deciduous and evergreen species. The seasonal patterns of optical metrics and photosynthetic activity revealed subtle differences across and within functional groups. With the CCI and fluorescence becoming available from satellite sensors, they offer new opportunities for assessing photosynthetic phenology, particularly for evergreen species, which have been difficult to assess with previous methods. Full article

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

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Open AccessArticle Effect of Solar-Cloud-Satellite Geometry on Land Surface Shortwave Radiation Derived from Remotely Sensed Data

by Tianxing Wang, Jiancheng Shi, Letu Husi, Tianjie Zhao, Dabin Ji, Chuan Xiong and Bo Gao

Remote Sens. 2017, 9(7), 690; doi:10.3390/rs9070690

Received: 11 May 2017 / Revised: 30 June 2017 / Accepted: 2 July 2017 / Published: 5 July 2017

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Abstract

Clouds and their associated shadows are major obstacles to most land surface remote sensing applications. Meanwhile, solar-cloud-satellite geometry (SCSG) makes the effect of clouds and shadows on derived land surface biophysical parameters more complicated. However, in most existing studies, the SCSG effect has

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Clouds and their associated shadows are major obstacles to most land surface remote sensing applications. Meanwhile, solar-cloud-satellite geometry (SCSG) makes the effect of clouds and shadows on derived land surface biophysical parameters more complicated. However, in most existing studies, the SCSG effect has been frequently neglected although it is pointed out by many works that SCSG effect is a noticeable problem, especially in the field of land surface radiation budget. Taking shortwave downward radiation (SWDR) as a testing variable, this study quantified the SCSG effect on the derived SWDR, and proposed an operational scheme to correct the big effect. The results demonstrate that the proposed correcting scheme is very effective and works very well. It is revealed that a significant under- or overestimation is detected in retrieved SWDR if the SCSG effect is ignored. Typically, the induced error in SWDR can reach up to 80%. The scheme and findings of this study are expected to be inspirational for the land surface remote sensing community, wherein solar-cloud-satellite geometry is an unavoidable issue. Full article

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

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Open AccessErratum Erratum: A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data. Remote Sens. 2017, 9, 505

by Sebastian Lamprecht, Andreas Hill, Johannes Stoffels and Thomas Udelhoven

Remote Sens. 2017, 9(7), 692; doi:10.3390/rs9070692

Received: 29 June 2017 / Revised: 30 June 2017 / Accepted: 30 June 2017 / Published: 5 July 2017

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Abstract Since Equation (2) has been rearranged incorrectly during preparation for this article [1], the authors would like to correct the relevant text of Section 3.4.3 as follows:[...] Full article

Open AccessArticle Remote Sensing of Spatiotemporal Changes in Wetland Geomorphology Based on Type 2 Fuzzy Sets: A Case Study of Beidagang Wetland from 1975 to 2015

by Hongyuan Huo, Jifa Guo, Zhao-Liang Li and Xiaoguang Jiang

Remote Sens. 2017, 9(7), 683; doi:10.3390/rs9070683

Received: 9 May 2017 / Revised: 26 June 2017 / Accepted: 29 June 2017 / Published: 4 July 2017

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Abstract

Few studies have considered the spatiotemporal changes in wetland land cover based on type 2 fuzzy sets using long-term series of remotely sensed data. This paper presents an improved interval type 2 fuzzy c-means (IT2FCM*) approach to analyse the spatial and temporal changes

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Few studies have considered the spatiotemporal changes in wetland land cover based on type 2 fuzzy sets using long-term series of remotely sensed data. This paper presents an improved interval type 2 fuzzy c-means (IT2FCM*) approach to analyse the spatial and temporal changes in the geomorphology of the Beidagang wetland in North China from 1975 to 2015 based on long-term Landsat data. Unlike traditional type 1 fuzzy c-means methods, the IT2FCM* algorithm based on interval type-2 fuzzy set has an ability to better handle the spectral uncertainty. Four indexes were adopted to validate the separability of classes with the IT2FCM* algorithm. These four validity indexes showed that IT2FCM* obtained better results than traditional methods. Additionally, the accuracy of the classification results was assessed based on the confusion matrix and kappa coefficient, which were high for the analysis of wetland landscape changes. Based on the analysis of separability of classes with the IT2FCM* algorithm using four validity indexes, the classification results, and the membership value images, the long-term series of satellite datasets were processed using the IT2FCM* method, and the study area was classified into six classes. Because water resources and vegetation are two key wetland components, the water resource dynamics and vegetation dynamics, based on the normalized difference vegetation index (NDVI), were analysed in detail according to the spatiotemporal classification results. The results show that the changes in vegetation types have historically been associated with water resource variations and that water resources play an important role in the evolution of vegetation types. Full article

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Open AccessArticle Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization

by Fulvio Capodici, Antonino Maltese, Giuseppe Ciraolo, Guido D’Urso and Goffredo La Loggia

Remote Sens. 2017, 9(7), 677; doi:10.3390/rs9070677

Received: 10 April 2017 / Accepted: 28 June 2017 / Published: 4 July 2017

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Abstract

The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture

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The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies. Full article

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

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