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Remote Sens., Volume 7, Issue 8 (August 2015) – 69 articles , Pages 9492-11015

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Open AccessTechnical Note
Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models
Remote Sens. 2015, 7(8), 10996-11015; https://doi.org/10.3390/rs70810996 - 24 Aug 2015
Cited by 19 | Viewed by 2850
Abstract
Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data [...] Read more.
Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Open AccessArticle
Spatial and Temporal Changes in Vegetation Phenology at Middle and High Latitudes of the Northern Hemisphere over the Past Three Decades
Remote Sens. 2015, 7(8), 10973-10995; https://doi.org/10.3390/rs70810973 - 24 Aug 2015
Cited by 42 | Viewed by 3154
Abstract
Vegetation phenology is a key biological indicator for monitoring terrestrial ecosystems and global change, and regions with the most obvious phenological changes in vegetation are primarily located at high latitudes and altitudes. Over the past three decades, investigations of obvious phenological changes in [...] Read more.
Vegetation phenology is a key biological indicator for monitoring terrestrial ecosystems and global change, and regions with the most obvious phenological changes in vegetation are primarily located at high latitudes and altitudes. Over the past three decades, investigations of obvious phenological changes in vegetation at middle and high latitudes in the Northern Hemisphere have provided significant contributions to understanding global climate change. In this study, phenological parameters were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) to analyze the spatial and temporal characteristics of vegetation phenological changes above 40°N in the Northern Hemisphere from 1982–2013. The results showed that the start of season (SOS) was significantly advanced (−2.2 ± 0.6 days·decade−1, p < 0.05) and that the end of season (EOS) was slightly delayed (0.78 ± 0.6 days·decade−1, p = 0.21) over the entire study area in the initial 21 years (1982–2002). When the time scale was extended to 2013, the change rate of the SOS and EOS was significantly reduced; in addition, the SOS was delayed (3.2 ± 1.7 days·decade−1, p < 0.05), and the EOS was advanced (4.5 ± 0.9 days·decade−1, p < 0.05) over the entire study area in the last 11 years (2003–2013). The trends of advanced SOS and delayed EOS over the past three decades were slower than those over the initial two decades on a hemispheric scale. The change trends showed obvious variability with different vegetation types and were greater for woody plants than for herbaceous plants. For broad-leaved forest, the SOS was significantly advanced (2 ± 0.5 days·decade−1, p < 0.05) and the EOS was significantly delayed (2.7 ± 0.6 days·decade−1, p < 0.05) from 1982–2013. The trend of delayed EOS was greater than that of advanced SOS for different vegetation types. With respect to the spatial distribution of phenological trends in the Northern Hemisphere, the trends of advanced SOS and delayed EOS were strongest in Europe followed by North America, and the trends were least significant in Asia. Coniferous forest, shrub forest, grassland, and the entire study area have the same change trends for the two time periods (1982–2002 and 2003–2013), and the increased rate of the phenology parameters has decelerated over the most recent decade. The length of season (LOS) of broad-leaved forest and mixed forest over the past 32 years shows a strong increased trend, and simultaneously, the SOS and EOS show an advanced trend and a delayed trend, respectively Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle
A Method for Retrieving Daily Land Surface Albedo from Space at 30-m Resolution
Remote Sens. 2015, 7(8), 10951-10972; https://doi.org/10.3390/rs70810951 - 24 Aug 2015
Cited by 2 | Viewed by 2175
Abstract
Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and [...] Read more.
Land surface albedo data with high spatio-temporal resolution are increasingly important for scientific studies addressing spatially and/or temporally small-scale phenomena, such as urban heat islands and urban land surface energy balance. Our previous study derived albedo data with 2–4-day and 30-m temporal and spatial resolution that have better spatio-temporal resolution than existing albedo data, but do not completely satisfy the requirements for monitoring high-frequency land surface changes at the small scale. Downscaling technology provides a chance to further improve the albedo data spatio-temporal resolution and accuracy. This paper introduces a method that combines downscaling technology for land surface reflectance with an empirical method of deriving land surface albedo. Firstly, downscaling daily MODIS land surface reflectance data (MOD09GA) from 500 m to 30 m on the basis of HJ-1A/B BRDF data with 2–4-day and 30-m temporal and spatial resolution is performed: this is the key step in the improved method. Subsequently, the daily 30-m land surface albedo data are derived by an empirical method combining prior knowledge of the MODIS BRDF product and the downscaled daily 30-m reflectance. Validation of albedo data obtained using the proposed method shows that the new method has both improved spatio-temporal resolution and good accuracy (a total absolute accuracy of 0.022 and a total root mean squared error at six sites of 0.028). Full article
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Open AccessEditorial
The Challenges of Remote Monitoring of Wetlands
Remote Sens. 2015, 7(8), 10938-10950; https://doi.org/10.3390/rs70810938 - 24 Aug 2015
Cited by 72 | Viewed by 4307
Abstract
Wetlands are highly productive and support a wide variety of ecosystem goods and services. Various forms of global change impose compelling needs for timely and reliable information on the status of wetlands worldwide, but several characteristics of wetlands make them challenging to monitor [...] Read more.
Wetlands are highly productive and support a wide variety of ecosystem goods and services. Various forms of global change impose compelling needs for timely and reliable information on the status of wetlands worldwide, but several characteristics of wetlands make them challenging to monitor remotely: they lack a single, unifying land-cover feature; they tend to be highly dynamic and their energy signatures are constantly changing; and steep environmental gradients in and around wetlands produce narrow ecotones that often are below the resolving capacity of remote sensors. These challenges and needs set the context for a special issue focused on wetland remote sensing. Contributed papers responded to one of three overarching questions aimed at improving remote, large-area monitoring of wetlands: (1) What approaches and data products are being developed specifically to support regional to global long-term monitoring of wetland landscapes? (2) What are the promising new technologies and sensor/multisensor approaches for more accurate and consistent detection of wetlands? (3) Are there studies that demonstrate how remote long-term monitoring of wetland landscapes can reveal changes that correspond with changes in land cover and land use and/or changes in climate? Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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Open AccessArticle
Use of the Standardized Precipitation Evapotranspiration Index (SPEI) to Characterize the Drying Trend in Southwest China from 1982–2012
Remote Sens. 2015, 7(8), 10917-10937; https://doi.org/10.3390/rs70810917 - 24 Aug 2015
Cited by 38 | Viewed by 3228
Abstract
In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982–2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, [...] Read more.
In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982–2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, and the spatio-temporal extent of drought were evaluated, respectively. The results explicitly showed a drying trend of Southwest China. The mean SPEI values at five time scales all decreased significantly. Some moderate and severe droughts were captured after 2005 and the droughts were even getting aggravated. By examining the spatio-temporal extent, the aggravating condition of drought was further revealed. To investigate the performance of SPEI, correlation analysis was conducted between SPEI and two remotely sensed drought indices: Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI). The comparison was also conducted with the Standardized Precipitation Index (SPI). The results showed that for both SMCI and VCI, the SPI and SPEI had approximate correlations with them. The SPEI could better monitor the soil moisture than the SPI in months with significant increase of temperature. The correlations between the VCI and SPI/SPEI were lower; nevertheless, the SPEI was slightly superior to the SPI. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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Open AccessArticle
Accurate Determination of Glacier Surface Velocity Fields with a DEM-Assisted Pixel-Tracking Technique from SAR Imagery
Remote Sens. 2015, 7(8), 10898-10916; https://doi.org/10.3390/rs70810898 - 24 Aug 2015
Cited by 12 | Viewed by 2310
Abstract
We obtained accurate, detailed motion distribution of glaciers in Central Asia by applying digital elevation model (DEM) assisted pixel-tracking method to L-band synthetic aperture radar imagery. The paper firstly introduces and analyzes each component of the offset field briefly, and then describes the [...] Read more.
We obtained accurate, detailed motion distribution of glaciers in Central Asia by applying digital elevation model (DEM) assisted pixel-tracking method to L-band synthetic aperture radar imagery. The paper firstly introduces and analyzes each component of the offset field briefly, and then describes the method used to efficiently and precisely compensate the topography-related offset caused by the large spatial baseline and rugged terrain with the help of DEM. The results indicate that the rugged topography not only forms the complex shapes of glaciers, but also affects the glacier velocity estimation, especially with large spatial baseline. The maximum velocity, 0.85 m∙d−1, was observed in the middle part on the Fedchenko Glacier, which is the world’s longest mountain glacier. The motion fluctuation on its main trunk is apparently influenced by mass flowing in from tributaries, as well as angles between tributaries and the main stream. The approach presented in this paper was proved to be highly appropriate for monitoring glacier motion and will provide valuable sensitive indicators of current and future climate change for environmental analysis. Full article
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Open AccessArticle
Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation
Remote Sens. 2015, 7(8), 10878-10897; https://doi.org/10.3390/rs70810878 - 24 Aug 2015
Cited by 5 | Viewed by 2393
Abstract
The Soil Moisture and Ocean Salinity (SMOS) mission was initiated in 2009 with the goal of acquiring global soil moisture data over land using multi-angular L-band radiometric measurements. Specifically, surface soil moisture was estimated using the L-band Microwave Emission of the Biosphere (L-MEB) [...] Read more.
The Soil Moisture and Ocean Salinity (SMOS) mission was initiated in 2009 with the goal of acquiring global soil moisture data over land using multi-angular L-band radiometric measurements. Specifically, surface soil moisture was estimated using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model. This study evaluated the applicability of this model to the Heihe River Basin in Northern China for specific underlying surfaces by simulating brightness temperature (BT) with the L-MEB model. To analyze the influence of a ground sampling strategy on the simulations, two resampling methods based on ground observations were compared. In the first method, the simulated BT of each point observation was initially acquired. The simulations were then resampled at a 1 km resolution. The other method was based on gridded data with a resolution of 1 km averaged from point observations, such as soil moisture, soil temperature, and soil texture. The simulated BTs at a 1 km resolution were then obtained using the L-MEB model. Because of the large variability in soil moisture, the resampling method based on gridded data was used in the simulation. The simulated BTs based on the calibrated parameters were validated using airborne L-band data from the Polarimetric L-band Multibeam Radiometer (PLMR) acquired during the HiWATER project. The root mean square errors (RMSEs) between the simulated results and the PLMR data were 6 to 7 K for V-polarization and 3 to 5 K for H-polarization at different angles. These results demonstrate that the model effectively represents agricultural land surfaces, and this study will serve as a reference for applying the L-MEB model in arid regions and for selecting a ground sampling strategy. Full article
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Open AccessArticle
Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval
Remote Sens. 2015, 7(8), 10856-10877; https://doi.org/10.3390/rs70810856 - 24 Aug 2015
Cited by 1 | Viewed by 2265
Abstract
With the development of quantitative remote sensing, regional evapotranspiration (ET) modeling based on the feature space has made substantial progress. Among those feature space based evapotranspiration models, accurate determination of the dry/wet lines remains a challenging task. This paper reports the development of [...] Read more.
With the development of quantitative remote sensing, regional evapotranspiration (ET) modeling based on the feature space has made substantial progress. Among those feature space based evapotranspiration models, accurate determination of the dry/wet lines remains a challenging task. This paper reports the development of a new model, named DDTI (Determination of Dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between the thermal inertia and the soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value measured in the laboratory. Three evaluation methods, which are based on the comparison of the locations of the theoretical dry line determined by two models (DDTI model and the heat energy balance model), the comparison of ET results, and the comparison of the evaporative fraction between the estimates from the two models and the in situ measurements, were used to assess the performance of the new model DDTI. The location of the theoretical dry line determined by DDTI is more reasonable than that determined by the heat energy balance model. ET estimated from DDTI has an RMSE (Root Mean Square Error) of 56.77 W/m2 and a bias of 27.17 W/m2; while the heat energy balance model estimated ET with an RMSE of 83.36 W/m2 and a bias of −38.42 W/m2. When comparing the coeffcient of determination for the two models with the observations from Yucheng, DDTI demonstrated ET with an R2 of 0.9065; while the heat energy balance model has an R2 of 0.7729. When compared with the in situ measurements of evaporative fraction (EF) at Yucheng Experimental Station, the ET model based on DDTI reproduces the pixel scale EF with an RMSE of 0.149, much lower than that based on the heat energy balance model which has an RMSE of 0.220. Also, the EF bias between the DDTI model and the in situ measurements is 0.064, lower than the EF bias of the heat energy balance model, which is 0.084. Full article
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Open AccessArticle
Phenological Response of an Arizona Dryland Forest to Short-Term Climatic Extremes
Remote Sens. 2015, 7(8), 10832-10855; https://doi.org/10.3390/rs70810832 - 24 Aug 2015
Cited by 15 | Viewed by 1994
Abstract
Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of [...] Read more.
Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of site characteristics and climatic conditions on the phenological patterns of an Arizona, USA, ponderosa pine (Pinus ponderosa) forest during a five-year period (2005 to 2009) that encompassed extreme wet and dry precipitation regimes. We assembled 80 synthetic Landsat images by applying the spatial and temporal adaptive reflectance fusion method (STARFM) to 500 m MODIS and 30 m Landsat-5 Thematic Mapper (TM) data. We tested relationships between site characteristics and the timing of peak Normalized Difference Vegetation Index (NDVI) to assess the effect of climatic stress on the green-up of individual pixels during or after the summer monsoon. Our results show that drought-induced stress led to a fragmented phenological response that was highly dependent on microsite parameters, as both the spatial autocorrelation of peak timing and the number of significant site variables increased during the drought year. Pixels at lower elevations and with higher proportions of herbaceous vegetation were more likely to exhibit dynamic responses to changes in precipitation conditions. Our study demonstrates the complexity of responses within dryland forest ecosystems and highlights the need for standardized monitoring of phenology trends in these areas. The spatial and temporal variability of phenological signals may provide a quantitative solution to the problem of how to evaluate dryland land surface trends across time. Full article
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Open AccessArticle
The Extraction of Vegetation Points from LiDAR Using 3D Fractal Dimension Analyses
Remote Sens. 2015, 7(8), 10815-10831; https://doi.org/10.3390/rs70810815 - 21 Aug 2015
Cited by 8 | Viewed by 2967
Abstract
Light Detection and Ranging (LiDAR), a high-precision technique used for acquiring three-dimensional (3D) surface information, is widely used to study surface vegetation information. Moreover, the extraction of a vegetation point set from the LiDAR point cloud is a basic starting-point for vegetation information [...] Read more.
Light Detection and Ranging (LiDAR), a high-precision technique used for acquiring three-dimensional (3D) surface information, is widely used to study surface vegetation information. Moreover, the extraction of a vegetation point set from the LiDAR point cloud is a basic starting-point for vegetation information analysis, and an important part of its further processing. To extract the vegetation point set completely and to describe the different spatial morphological characteristics of various features in a LiDAR point cloud, we have used 3D fractal dimensions. We discovered that every feature has its own distinctive 3D fractal dimension interval. Based on the 3D fractal dimensions of tall trees, we propose a new method for the extraction of vegetation using airborne LiDAR. According to this method, target features can be distinguished based on their morphological characteristics. The non-ground points acquired by filtering are processed by region growing segmentation and the morphological characteristics are evaluated by 3D fractal dimensions to determine the features required for the determination of the point set for tall trees. Avon, New York, USA was selected as the study area to test the method and the result proves the method’s efficiency. Thus, this approach is feasible. Additionally, the method uses the 3D coordinate properties of the LiDAR point cloud and does not require additional information, such as return intensity, giving it a larger scope of application. Full article
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Open AccessArticle
Net Surface Shortwave Radiation from GOES Imagery—Product Evaluation Using Ground-Based Measurements from SURFRAD
Remote Sens. 2015, 7(8), 10788-10814; https://doi.org/10.3390/rs70810788 - 21 Aug 2015
Cited by 15 | Viewed by 2895
Abstract
The Earth’s surface net radiation controls the energy and water exchanges between the Earth’s surface and the atmosphere, and can be derived from satellite observations. The ability to monitor the net surface radiation over large areas at high spatial and temporal resolution is [...] Read more.
The Earth’s surface net radiation controls the energy and water exchanges between the Earth’s surface and the atmosphere, and can be derived from satellite observations. The ability to monitor the net surface radiation over large areas at high spatial and temporal resolution is essential for many applications, such as weather forecasting, short-term climate prediction or water resources management. The objective of this paper is to derive the net surface radiation in the shortwave domain at high temporal (half-hourly) and spatial resolution (~1 km) using visible imagery from Geostationary Operational Environmental Satellite (GOES). The retrieval algorithm represents an adaptation to GOES data of a standard algorithm initially developed for the NASA-operated Clouds and Earth’s Radiant Energy System (CERES) scanner. The methodology relies on: (1) the estimation of top of atmosphere shortwave radiation from GOES spectral measurements; and (2) the calculation of net surface shortwave (SW) radiation accounting for atmospheric effects. Comparison of GOES-retrieved net surface shortwave radiation with ground-measurements at the National Oceanic and Atmospheric Administration’s (NOAA) Surface Radiation (SURFRAD) stations yields very good agreement with average bias lower than 5 W·m−2 and root mean square difference around 70 W·m−2. The algorithm performance is usually higher over areas characterized by low spatial variability in term of land cover type and surface biophysical properties. The technique does not involve retrieval and assessment of cloud properties and can be easily adapted to other meteorological satellites around the globe. Full article
(This article belongs to the Special Issue Remote Sensing of Solar Surface Radiation)
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Open AccessArticle
Cross-Calibration of GF-1/WFV over a Desert Site Using Landsat-8/OLI Imagery and ZY-3/TLC Data
Remote Sens. 2015, 7(8), 10763-10787; https://doi.org/10.3390/rs70810763 - 20 Aug 2015
Cited by 18 | Viewed by 2867
Abstract
The wide field of view (WFV) is an optical imaging sensor on-board the Gao Fen 1 (GF-1). The WFV lacks an on-board calibrator, so on-orbit radiometric calibration is required. Zhong et al. proposed a method for cross-calibrating the charge-coupled device on-board the Chinese [...] Read more.
The wide field of view (WFV) is an optical imaging sensor on-board the Gao Fen 1 (GF-1). The WFV lacks an on-board calibrator, so on-orbit radiometric calibration is required. Zhong et al. proposed a method for cross-calibrating the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) that can be applied to the GF-1/WFV. However, the accuracy is limited because of the wider radiometric dynamic range and the higher spatial resolution of the GF-1/WFV. Therefore, Landsat-8 Operational Land Imager (OLI) imagery with a radiometric resolution similar to that of the GF-1/WFV and DEM extracted from ZY-3 three-line array panchromatic camera (TLC) with a higher spatial resolution were used. A calibration site with uniform surface material and a natural topographic variation was selected, and a model of this site’s bidirectional reflectance distribution function (BRDF) was developed. The model has excellent agreement with the real situation, as shown by the comparison of the simulations to the actual OLI surface reflectance. Then, the model was used to calibrate the WFV. Compared with the TOA reflectance from synchronized Landsat-8/OLI images, all errors calculated with the calibration coefficients retrieved in this paper are less than 5%, much less than the errors calculated with the calibration coefficients given by the China Centre for Resource Satellite Data and Application (CRESDA). Full article
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Open AccessArticle
Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data
Remote Sens. 2015, 7(8), 10737-10762; https://doi.org/10.3390/rs70810737 - 20 Aug 2015
Cited by 14 | Viewed by 2755
Abstract
This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and [...] Read more.
This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas. Full article
(This article belongs to the Special Issue Earth Observations for the Sustainable Development)
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Open AccessArticle
Monitoring Ground Subsidence in Hong Kong via Spaceborne Radar: Experiments and Validation
Remote Sens. 2015, 7(8), 10715-10736; https://doi.org/10.3390/rs70810715 - 20 Aug 2015
Cited by 15 | Viewed by 2327
Abstract
The persistent scatterers interferometry (PSI) technique is gradually becoming known for its capability of providing up to millimeter accuracy of measurement on ground displacement. Nevertheless, there is still quite a good amount of doubt regarding its correctness or accuracy. In this paper, we [...] Read more.
The persistent scatterers interferometry (PSI) technique is gradually becoming known for its capability of providing up to millimeter accuracy of measurement on ground displacement. Nevertheless, there is still quite a good amount of doubt regarding its correctness or accuracy. In this paper, we carried out an experiment corroborating the capability of the PSI technique with the help of a traditional survey method in the urban area of Hong Kong, China. Seventy three TerraSAR-X (TSX) and TanDEM-X (TDX) images spanning over four years are used for the data process. There are three aims of this study. The first is to generate a displacement map of urban Hong Kong and to check for spots with possible ground movements. This information will be provided to the local surveyors so that they can check these specific locations. The second is to validate if the accuracy of the PSI technique can indeed reach the millimeter level in this real application scenario. For validating the accuracy of PSI, four corner reflectors (CR) were installed at a construction site on reclaimed land in Hong Kong. They were manually moved up or down by a few to tens of millimeters, and the value derived from the PSI analysis was compared to the true value. The experiment, carried out in unideal conditions, nevertheless proved undoubtedly that millimeter accuracy can be achieved by the PSI technique. The last is to evaluate the advantages and limitations of the PSI technique. Overall, the PSI technique can be extremely useful if used in collaboration with other techniques, so that the advantages can be highlighted and the drawbacks avoided. Full article
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Open AccessArticle
Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices
Remote Sens. 2015, 7(8), 10689-10714; https://doi.org/10.3390/rs70810689 - 19 Aug 2015
Cited by 18 | Viewed by 2776
Abstract
As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for [...] Read more.
As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected. Full article
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Open AccessArticle
Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data
Remote Sens. 2015, 7(8), 10668-10688; https://doi.org/10.3390/rs70810668 - 18 Aug 2015
Cited by 15 | Viewed by 2998
Abstract
Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis [...] Read more.
Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis Program (EnMAP), frequent and area-wide mapping of natural environments by means of high quality hyperspectral data becomes possible. However, the amplified spectral mixing due to the sensor’s ground sampling distance of 30 m on the one hand and the patterns of natural landscapes in the form of gradual transitions between different land cover types on the other require special attention. Based on simulated EnMAP data, this study focuses on mapping shrub vegetation along a landscape gradient of shrub encroachment in a semi-arid, natural environment in Portugal. We demonstrate how probability outputs from a support vector classification (SVC) model can be used to extend a hard classification by information on shrub cover fractions. This results in a more realistic representation of gradual transitions in shrub vegetation maps. We suggest a new, adapted approach for SVC parameter selection: During the grid search, parameter pairs are evaluated with regard to the prediction of synthetically mixed test data, representing shrub to non-shrub transitions, instead of the hard classification of original, discrete test data. Validation with an unbiased, equalized random sampling shows that the resulting shrub-class probabilities from adapted SVC more accurately represent shrub cover fractions (mean absolute error/root mean squared error of 16.3%/23.2%) compared to standard SVC (17.1%/29.5%). Simultaneously, the discrete classification output was considerably improved by incorporating synthetic mixtures into parameter selection (averaged F1 accuracies increased from 72.4% to 81.3%). Based on our findings, the integration of synthetic mixtures into SVC parameterization allows the use of SVC for sub-pixel cover fraction estimation and, this way, can be recommended for deriving improved qualitative and quantitative descriptions of gradual transitions in shrub vegetation. The approach is therefore of high relevance for mapping natural ecosystems from future EnMAP data. Full article
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Open AccessArticle
Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China
Remote Sens. 2015, 7(8), 10646-10667; https://doi.org/10.3390/rs70810646 - 18 Aug 2015
Cited by 36 | Viewed by 3498
Abstract
Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) [...] Read more.
Rice farming in Northeast China is crucially important for China’s food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers’ fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments. Full article
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Open AccessArticle
New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis
Remote Sens. 2015, 7(8), 10626-10645; https://doi.org/10.3390/rs70810626 - 18 Aug 2015
Cited by 25 | Viewed by 3255
Abstract
The full-spectrum Solar-Induced chlorophyll Fluorescence (SIF) within the 650-800 nm spectral region can provide important information regarding physiological and biochemical activities in vegetation. This paper proposes a new Full-spectrum Spectral Fitting Method (F-SFM) for the retrieval of SIF spectra based on Principal Components [...] Read more.
The full-spectrum Solar-Induced chlorophyll Fluorescence (SIF) within the 650-800 nm spectral region can provide important information regarding physiological and biochemical activities in vegetation. This paper proposes a new Full-spectrum Spectral Fitting Method (F-SFM) for the retrieval of SIF spectra based on Principal Components Analysis (PCA). Using F-SFM, both the full-spectrum reflectance and SIF within the 650-800 nm region were modeled by PCA based on a training dataset simulated by the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, and the weighting coefficients of the principal components were estimated by the least-squares fitting method. An iterative process was employed to improve the accuracy of the estimation of the reflectance. In each iteration, the SIF spectra retrieved from the last run were removed from the total upwelling radiance to minimize the small contribution of the SIF to the apparent reflectance outside the absorption bands. Then, the F-SFM algorithm was tested using both simulated and field-measured data with different Spectral Resolutions (SRs) and Signal-to-Noise Ratios (SNRs). For data with an SR of 0.3 nm and without noise, the Relative Root Mean Square Error (RRMSE) was less than 14% within the spectral region that was studied, and the peak-value ratio (SIF735/SIF685) was accurately estimated with an RRMSE of 3.56%. In addition, the F-SFM algorithm proved less sensitive to the SR than the three-band Fraunhofer Line Discrimination (3 FLD) and improved FLD (iFLD) methods. In the case of the field spectral data with SRs of 3 nm and 0.3 nm, the double-peak shape and the diurnal variation trend of the SIF spectra could be reasonably reconstructed by F-SFM, and the retrieved SIF values at the O2-A and O2-B bands were consistent with those retrieved by 3FLD from data with a high SR (0.3 nm) and SNR (1000). Therefore, the F-SFM method can provide full-spectrum SIF information with high accuracy even at relatively low SRs and SNRs, and shows promise for use in applications involving the SIF shape information. Full article
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Open AccessArticle
Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar
Remote Sens. 2015, 7(8), 10607-10625; https://doi.org/10.3390/rs70810607 - 18 Aug 2015
Cited by 16 | Viewed by 2913
Abstract
Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal [...] Read more.
Light Detection and Ranging (Lidar) is a state of the art technology to assess forest aboveground biomass (AGB). To date, methods developed to relate Lidar metrics with forest parameters were built upon the vertical component of the data. In multi-layered tropical forests, signal penetration might be restricted, limiting the efficiency of these methods. A potential way for improving AGB models in such forests would be to combine traditional approaches by descriptors of the horizontal canopy structure. We assessed the capability and complementarity of three recently proposed methods for assessing AGB at the plot level using point distributional approach (DM), canopy volume profile approach (CVP), 2D canopy grain approach (FOTO), and further evaluated the potential of a topographical complexity index (TCI) to explain part of the variability of AGB with slope. This research has been conducted in a mountainous wet evergreen tropical forest of Western Ghats in India. AGB biomass models were developed using a best subset regression approach, and model performance was assessed through cross-validation. Results demonstrated that the variability in AGB could be efficiently captured when variables describing both the vertical (DM or CVP) and horizontal (FOTO) structure were combined. Integrating FOTO metrics with those of either DM or CVP decreased the root mean squared error of the models by 4.42% and 6.01%, respectively. These results are of high interest for AGB mapping in the tropics and could significantly contribute to the REDD+ program. Model quality could be further enhanced by improving the robustness of field-based biomass models and influence of topography on area-based Lidar descriptors of the forest structure. Full article
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Open AccessArticle
Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach
Remote Sens. 2015, 7(8), 10589-10606; https://doi.org/10.3390/rs70810589 - 18 Aug 2015
Cited by 13 | Viewed by 2740
Abstract
This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover [...] Read more.
This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing “Trees” and “Others”, well with “Grassland” and “Cropland”, but problematic with “Water” and “Urban” across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map. Full article
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Open AccessArticle
Building Façade Recognition Using Oblique Aerial Images
Remote Sens. 2015, 7(8), 10562-10588; https://doi.org/10.3390/rs70810562 - 18 Aug 2015
Cited by 6 | Viewed by 2720
Abstract
This study proposes a method to recognize façades from large-scale urban scenes based on multi-level image features utilizing a recently developed oblique aerial photogrammetry technique. The method involves the use of multi-level image features, a bottom-up feature extraction procedure to produce regions of [...] Read more.
This study proposes a method to recognize façades from large-scale urban scenes based on multi-level image features utilizing a recently developed oblique aerial photogrammetry technique. The method involves the use of multi-level image features, a bottom-up feature extraction procedure to produce regions of interest through monoscopic analysis, and then a coarse-to-fine feature matching strategy to characterise and match the regions in a stereoscopic model. Feature extraction from typical urban Manhattan scenes is based on line segments. Windows are re-organised based on the spatial constraints of line segments and the homogeneous structure of the spectrum. Façades as regions of interest are successfully constructed with a remarkable single edge and evidence from windows to get rid of occlusion. Feature matching is hierarchically performed beginning from distinctive facades and regularly distributed windows to the sub-pixel point primitives. The proposed strategy can effectively solve ambiguity and multi-solution problems in the complex urban scene matching process, particularly repetitive and poor-texture façades in oblique view. Full article
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Open AccessArticle
Gravimetric Vegetation Water Content Estimation for Corn Using L-Band Bi-Angular, Dual-Polarized Brightness Temperatures and Leaf Area Index
Remote Sens. 2015, 7(8), 10543-10561; https://doi.org/10.3390/rs70810543 - 17 Aug 2015
Cited by 1 | Viewed by 2228
Abstract
In this study, an algorithm to retrieve the gravimetric vegetation water content (GVWC, %) of corn was developed. First, the method for obtaining the optical depth from L-band (1.4 GHz) bi-angular, dual-polarized brightness temperatures (TB) for short vegetation was investigated. Then, the quantitative [...] Read more.
In this study, an algorithm to retrieve the gravimetric vegetation water content (GVWC, %) of corn was developed. First, the method for obtaining the optical depth from L-band (1.4 GHz) bi-angular, dual-polarized brightness temperatures (TB) for short vegetation was investigated. Then, the quantitative relationship between the corn optical depth, corn GVWC and corn leaf area index (LAI) was constructed. Finally, using the Polarimetric L-band Microwave Radiometer (PLMR) airborne data in the 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, the Global Land Surface Satellite (GLASS) LAI product, the height and areal density of the corn stalks, the corn GVWC was estimated (corn GLASS-GVWC). Both the in situ measured corn GVWC and the corn GVWC retrieved based on the in situ measured corn LAI (corn LAINET-GVWC) were used to validate the accuracy of the corn GLASS-GVWC. The results show that the GVWC retrieval method proposed in this study is feasible for monitoring the corn GVWC. However, the accuracy of the retrieval results is highly sensitive to the accuracy of the LAI input parameters. Full article
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Open AccessArticle
Fourteen-Year Record (2000–2013) of the Spatial and Temporal Dynamics of Floating Algae Blooms in Lake Chaohu, Observed from Time Series of MODIS Images
Remote Sens. 2015, 7(8), 10523-10542; https://doi.org/10.3390/rs70810523 - 17 Aug 2015
Cited by 50 | Viewed by 2550
Abstract
As the fifth largest freshwater lake in China, Lake Chaohu has drawn increasing attention due to the decline in water quality and the occurrence of massive algal blooms. We applied an algae pixel-growing algorithm to MODIS Terra or Aqua data (2100 images) to [...] Read more.
As the fifth largest freshwater lake in China, Lake Chaohu has drawn increasing attention due to the decline in water quality and the occurrence of massive algal blooms. We applied an algae pixel-growing algorithm to MODIS Terra or Aqua data (2100 images) to characterize surface floating algae bloom dynamics from 2000 to 2013 with respect to meteorological and lake nutrient conditions. The results show an increase in surface algal bloom coverage, frequency, and duration with a trend toward earlier bloom formation. Importantly, spatial and temporal patterns in the historically less compromised eastern and middle lake areas show that water quality conditions are deteriorating. This has occurred at the same time as lake management has made a catchment scale effort to reduce impact. Our results show that nutrient concentrations were not the main driver of inter-annual bloom variations. Local meteorological conditions, in particular wind speed and temperature, played an important role in the dynamics of floating algal bloom. This highlights the important challenges for lake management. Full article
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Open AccessArticle
Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands
Remote Sens. 2015, 7(8), 10501-10522; https://doi.org/10.3390/rs70810501 - 17 Aug 2015
Cited by 10 | Viewed by 2254
Abstract
Burn severity metrics and classification have yet to be tested for many eastern U.S. deciduous vegetation types, but, if suitable, would be valuable for documenting and monitoring landscape-scale restoration projects that employ prescribed fire treatments. Here we present a performance analysis of the [...] Read more.
Burn severity metrics and classification have yet to be tested for many eastern U.S. deciduous vegetation types, but, if suitable, would be valuable for documenting and monitoring landscape-scale restoration projects that employ prescribed fire treatments. Here we present a performance analysis of the Composite Burn Index (CBI) and its relationship to spectral data (differenced Normalized Burn Ratio (dNBR) and its relative form (RdNBR)) across an oak woodland - grassland landscape in southwestern Oklahoma, USA. Correlation and regression analyses were used to compare CBI strata, assess models describing burn severity, and determine thresholds for burn severity classes. Confusion matrices were used to assess burn severity classification accuracy. Our findings suggest that dNBR and RdNBR, thresholded using total CBI, can produce an accurate burn severity map in oak woodlands, particularly from an initial assessment period. Lower accuracies occurred for burn severity classifications of grasslands and raises questions related to definitions and detection of burn severity for grasslands, particularly in transition to more densely treed structures such as savannas and woodlands. Full article
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Open AccessArticle
Automatic In Situ Calibration of a Spinning Beam LiDAR System in Static and Kinematic Modes
Remote Sens. 2015, 7(8), 10480-10500; https://doi.org/10.3390/rs70810480 - 17 Aug 2015
Cited by 10 | Viewed by 2910
Abstract
The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development [...] Read more.
The Velodyne LiDAR series is one of the most popular spinning beam LiDAR systems currently available on the market. In this paper, the temporal stability of the range measurements of the Velodyne HDL-32E LiDAR system is first investigated as motivation for the development of a new automatic calibration method that allows quick and frequent recovery of the inherent time-varying errors. The basic principle of the method is that the LiDAR’s internal systematic error parameters are estimated by constraining point clouds of some known and automatically detected cylindrical features such as lamp poles to fit to the 3D cylinder models. This is analogous to the plumb-line calibration method in which the lens distortion parameters are estimated by constraining the image points of straight lines to fit to the 2D line model. The calibration can be performed at every measurement epoch in both static and kinematic modes. Four real datasets were used to verify the method, two of which were captured in static mode and the other two in kinematic mode. The overall results indicate that up to approximately 72% and 41% accuracy improvement were realized as a result of the calibration for the static and kinematic datasets, respectively. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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Open AccessArticle
Consistency between In Situ, Model-Derived and High-Resolution-Image-Based Soil Temperature Endmembers: Towards a Robust Data-Based Model for Multi-Resolution Monitoring of Crop Evapotranspiration
Remote Sens. 2015, 7(8), 10444-10479; https://doi.org/10.3390/rs70810444 - 14 Aug 2015
Cited by 19 | Viewed by 2417
Abstract
Due to their image-based nature, “contextual” approaches are very attractive to estimate evapotranspiration (ET) from remotely-sensed land surface temperature (LST) data. Their application is however limited to highly heterogeneous areas where the soil and vegetation temperature endmembers (Tends) can be observed at the [...] Read more.
Due to their image-based nature, “contextual” approaches are very attractive to estimate evapotranspiration (ET) from remotely-sensed land surface temperature (LST) data. Their application is however limited to highly heterogeneous areas where the soil and vegetation temperature endmembers (Tends) can be observed at the thermal sensor resolution. This paper aims to develop a simple theoretical approach to estimate Tends independently from LST images. Soil Tends are simulated by a soil energy balance model forced by meteorological data. Vegetation Tends are obtained from soil Tends and air temperature. Model-derived soil Tends are first evaluated with in situ measurements made over an irrigated area in Morocco. The root mean square difference (RMSD) between modeled and ground-based soil Tends is estimated as 2.4 ºC. Model-derived soil Tends are next compared with the soil Tends retrieved from 90-m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data collected over two irrigated areas in Mexico and Spain. Such a comparison reveals a strong consistency between model-derived and high-resolution image-based soil Tends. A recent contextual ET model (SEB-1S) is then applied to 90-m resolution and to 1-km resolution (aggregated) ASTER data using the model-derived or image-based Tends as the input. The RMSD between 90-m resolution SEB-1S and in situ ET is estimated as 65 and 82 W·m-2, and the RMSD between 1-km resolution SEB-1S and aggregated SEB-1S ET is estimated as 78 and 56 W·m-2, for the image-based and model-derived Tends, respectively. In light of the above results, Tends should be estimated a priori when contextual models are applied to low resolution images. Moreover, the consistency over highly heterogeneous areas between model-derived and high-resolution image-based Tends provides a meaningful basis for developing mixed modeling observational approaches. Full article
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Open AccessArticle
Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)
Remote Sens. 2015, 7(8), 10425-10443; https://doi.org/10.3390/rs70810425 - 14 Aug 2015
Cited by 32 | Viewed by 3322
Abstract
Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification [...] Read more.
Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d’Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products. Full article
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Open AccessArticle
Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series
Remote Sens. 2015, 7(8), 10400-10424; https://doi.org/10.3390/rs70810400 - 13 Aug 2015
Cited by 35 | Viewed by 4105
Abstract
With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as [...] Read more.
With the ever-increasing number of satellites and the availability of data free of charge, the integration of multi-sensor images in coherent time series offers new opportunities for land cover and crop type classification. This article investigates the potential of structural biophysical variables as common parameters to consistently combine multi-sensor time series and to exploit them for land/crop cover classification. Artificial neural networks were trained based on a radiative transfer model in order to retrieve high resolution LAI, FAPAR and FCOVER from Landsat-8 and SPOT-4. The correlation coefficients between field measurements and the retrieved biophysical variables were 0.83, 0.85 and 0.79 for LAI, FAPAR and FCOVER, respectively. The retrieved biophysical variables’ time series displayed consistent average temporal trajectories, even though the class variability and signal-to-noise ratio increased compared to NDVI. Six random forest classifiers were trained and applied along the season with different inputs: spectral bands, NDVI, as well as FAPAR, LAI and FCOVER, separately and jointly. Classifications with structural biophysical variables reached end-of-season overall accuracies ranging from 73%–76% when used alone and 77% when used jointly. This corresponds to 90% and 95% of the accuracy level achieved with the spectral bands and NDVI. FCOVER appears to be the most promising biophysical variable for classification. When assuming that the cropland extent is known, crop type classification reaches 89% with spectral information, 87% with the NDVI and 81%–84% with biophysical variables. Full article
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Open AccessTechnical Note
A High Performance Remote Sensing Product Generation System Based on a Service Oriented Architecture for the Next Generation of Geostationary Operational Environmental Satellites
Remote Sens. 2015, 7(8), 10385-10399; https://doi.org/10.3390/rs70810385 - 13 Aug 2015
Cited by 10 | Viewed by 2442
Abstract
The Geostationary Operational Environmental Satellite (GOES) series R, S, T, U (GOES-R) will collect remote sensing data at several orders of magnitude compared to legacy missions, 24 × 7, over its 20-year operational lifecycle. A suite of 34 Earth and space weather products [...] Read more.
The Geostationary Operational Environmental Satellite (GOES) series R, S, T, U (GOES-R) will collect remote sensing data at several orders of magnitude compared to legacy missions, 24 × 7, over its 20-year operational lifecycle. A suite of 34 Earth and space weather products must be produced at low latency for timely delivery to forecasters. A ground system (GS) has been developed to meet these challenging requirements, using High Performance Computing (HPC) within a Service Oriented Architecture (SOA). This approach provides a robust, flexible architecture to support the operational GS as it generates remote sensing products by ingesting and combining data from multiple sources. Test results show that the system meets the key latency and availability requirements for all products. Full article
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Open AccessArticle
Temporal-Spatial Evolution Analysis of Lake Size-Distribution in the Middle and Lower Yangtze River Basin Using Landsat Imagery Data
Remote Sens. 2015, 7(8), 10364-10384; https://doi.org/10.3390/rs70810364 - 12 Aug 2015
Cited by 10 | Viewed by 2983
Abstract
Four natural lakes in the middle and lower reaches of the Yangtze River—Dongting Lake, Poyang Lake, Chaohu Lake and Taihu Lake—play a key role in the climate, environment, and ecology of this area. Upstream of these lakes, the Three Gorges Dam Project has [...] Read more.
Four natural lakes in the middle and lower reaches of the Yangtze River—Dongting Lake, Poyang Lake, Chaohu Lake and Taihu Lake—play a key role in the climate, environment, and ecology of this area. Upstream of these lakes, the Three Gorges Dam Project has been storing water for 12 years. Future monitoring and management of rivers and lakes can certainly benefit from research on the patterns of variation of natural lakes downstream of the Three Gorges Project. This research applies Landsat TM/ETM data to evaluate water area changes in the four lakes from 2002 to 2013. The water area is estimated using AWEI (Automated Water Extraction Index) from satellite images. The average areas decreased respectively 452, 11, and 5 km2 (29.6%, 1.4% and 0.2%) from 2002 to 2013 for Dongting, Chaohu, and Taihu Lakes. Meanwhile, it increased 300 km2 (11.0%) for Poyang Lake. Precipitation and changes in river inflow may account for the fluctuation in the surface area to a large degree, especially between 2009 and 2013. The present study was undertaken to characterize the evolution of lakes and to explore the potential driving force of variation in order to assist the management of dams upstream in the river basin. Full article
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