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Remote Sens., Volume 6, Issue 5 (May 2014) – 52 articles , Pages 3533-4646

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1261 KiB  
Article
Multi-Polarization ASAR Backscattering from Herbaceous Wetlands in Poyang Lake Region, China
by Huiyong Sang, Jixian Zhang, Hui Lin and Liang Zhai
Remote Sens. 2014, 6(5), 4621-4646; https://doi.org/10.3390/rs6054621 - 22 May 2014
Cited by 30 | Viewed by 7632
Abstract
Wetlands are one of the most important ecosystems on Earth. There is an urgent need to quantify the biophysical parameters (e.g., plant height, aboveground biomass) and map total remaining areas of wetlands in order to evaluate the ecological status of wetlands. In this [...] Read more.
Wetlands are one of the most important ecosystems on Earth. There is an urgent need to quantify the biophysical parameters (e.g., plant height, aboveground biomass) and map total remaining areas of wetlands in order to evaluate the ecological status of wetlands. In this study, Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) dual-polarization C-band data acquired in 2005 is tested to investigate radar backscattering mechanisms with the variation of hydrological conditions during the growing cycle of two types of herbaceous wetland species, which colonize lake borders with different elevation in Poyang Lake region, China. Phragmites communis (L.) Trin. is semi-aquatic emergent vegetation with vertical stem and blade-like leaves, and the emergent Carex spp. has rhizome and long leaves. In this study, the potential of ASAR data in HH-, HV-, and VV-polarization in mapping different wetland types is examined, by observing their dynamic variations throughout the whole flooding cycle. The sensitivity of ASAR backscattering coefficients to vegetation parameters of plant height, fresh and dry biomass, and vegetation water content is also analyzed for Phragmites communis (L.) Trin. and Carex spp. The research for Phragmites communis (L.) Trin. shows that HH polarization is more sensitive to plant height and dry biomass than HV polarization. ASAR backscattering coefficients are relatively less sensitive to fresh biomass, especially in HV polarization. However, both are highly dependent on canopy water content. In contrast, the dependence of HH- and HV- backscattering from Carex community on vegetation parameters is poor, and the radar backscattering mechanism is controlled by ground water level. Full article
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<p>(<b>a</b>) is a phase color synthesis image of the subset HH-polarized data acquired on 25 April (red), 25 May (green), and 18 September (blue) of 2005; the red areas colonized by <span class="html-italic">Carex</span> spp. are exposed substrate during low water stage, the yellow areas colonized by <span class="html-italic">Carex</span> spp. and emergent <span class="html-italic">macrophytes</span> such as <span class="html-italic">Phragmites communis</span> are partly flooded during high water stage, and the white areas colonized by emergent macrophytes and trees are exposed during peak flood stage; (<b>b</b>) is the geo-location of Poyang Lake Nature Reserve and the acquired images in Poyang Lake region; and (<b>c</b>) is the geo-location of Jiangxi Province and Poyang Lake in China.</p> Full article ">
<p>The cumulative monthly precipitation and average water level of Poyang Lake collected in 2004 and 2005 at Duchang hydrology station and the water level data of Xiushui River collected at Wucheng hydrology station on the dates when ENVISAT/ASAR data were acquired in 2005.</p> Full article ">
<p><span class="html-italic">Phragmites communis</span> collected on 20 April (<b>a</b>); 25 May (<b>b</b>); and 18 October (<b>c</b>); and <span class="html-italic">Carex</span> spp. on 28 March (<b>d</b>); 15 April (<b>e</b>); 25 May (<b>f</b>); 18 October (<b>g</b>); 26 November (<b>h</b>); and 29 December (<b>i</b>) in 2005 from the study area.</p> Full article ">
<p>Temporal variations of (<b>a</b>) above-ground/water height; (<b>b</b>) fresh/dry biomass; (<b>c</b>) vegetation water content; and (<b>d</b>) ASAR backscattering coefficients in HH&amp;VV; and (<b>e</b>) HV; polarization mode of <span class="html-italic">Phragmites communis</span> in 2005.</p> Full article ">
<p>Temporal variations of (<b>a</b>) above-ground/water height; (<b>b</b>) fresh/dry biomass; (<b>c</b>) vegetation water content; and (<b>d</b>) ASAR backscattering coefficients in HH&amp;VV; and (<b>e</b>) HV; polarization mode of <span class="html-italic">Phragmites communis</span> in 2005.</p> Full article ">
<p>Temporal variations of (<b>a</b>) above-ground/water height; (<b>b</b>) fresh/dry biomass; (<b>c</b>) vegetation water content; and (<b>d</b>) ASAR backscattering coefficients in HH&amp;VV and (<b>e</b>) HV polarization mode of <span class="html-italic">Carex</span> spp. in 2005.</p> Full article ">
<p>Temporal variations of (<b>a</b>) above-ground/water height; (<b>b</b>) fresh/dry biomass; (<b>c</b>) vegetation water content; and (<b>d</b>) ASAR backscattering coefficients in HH&amp;VV and (<b>e</b>) HV polarization mode of <span class="html-italic">Carex</span> spp. in 2005.</p> Full article ">
<p>The relationships between ASAR backscattering coefficients in HH polarization and (<b>left panel</b>) height, fresh biomass, dry biomass, and vegetation water content; and in HV polarization and (<b>right panel</b>) height, fresh biomass, dry biomass, and vegetation water content of <span class="html-italic">Phragmites communis</span> wetland. (<b>a</b>,<b>b</b>) height, (<b>c</b>,<b>d</b>) fresh biomass, (<b>e</b>,<b>f</b>) dry biomass, and (<b>g</b>,<b>h</b>) vegetation water content.</p> Full article ">
<p>The relationships between ASAR backscattering coefficients in HH polarization and (<b>left panel</b>) height, fresh biomass, dry biomass, and vegetation water content; and in HV polarization and (<b>right panel</b>) height, fresh biomass, dry biomass, and vegetation water content of <span class="html-italic">Phragmites communis</span> wetland. (<b>a</b>,<b>b</b>) height, (<b>c</b>,<b>d</b>) fresh biomass, (<b>e</b>,<b>f</b>) dry biomass, and (<b>g</b>,<b>h</b>) vegetation water content.</p> Full article ">
<p>Scatter plots of ASAR backscatters in HH polarization against (<b>left panel</b>) plant height, fresh biomass, dry biomass, and vegetation water content; and HV polarization against (<b>right panel</b>) plant height, fresh biomass, dry biomass, and vegetation water content of <span class="html-italic">Carex</span> spp. (<b>a</b>,<b>b</b>) plant height, (<b>c</b>,<b>d</b>) fresh biomass, (<b>e</b>,<b>f</b>) dry biomass, and (<b>g</b>,<b>h</b>) vegetation water content.</p> Full article ">
<p>Scatter plots of ASAR backscatters in HH polarization against (<b>left panel</b>) plant height, fresh biomass, dry biomass, and vegetation water content; and HV polarization against (<b>right panel</b>) plant height, fresh biomass, dry biomass, and vegetation water content of <span class="html-italic">Carex</span> spp. (<b>a</b>,<b>b</b>) plant height, (<b>c</b>,<b>d</b>) fresh biomass, (<b>e</b>,<b>f</b>) dry biomass, and (<b>g</b>,<b>h</b>) vegetation water content.</p> Full article ">
2147 KiB  
Article
External Validation of the ASTER GDEM2, GMTED2010 and CGIAR-CSI- SRTM v4.1 Free Access Digital Elevation Models (DEMs) in Tunisia and Algeria
by Djamel Athmania and Hammadi Achour
Remote Sens. 2014, 6(5), 4600-4620; https://doi.org/10.3390/rs6054600 - 21 May 2014
Cited by 96 | Viewed by 12638
Abstract
Digital Elevation Models (DEMs) including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available for nearly the entire earth’s surface. DEMs that are usually [...] Read more.
Digital Elevation Models (DEMs) including Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available for nearly the entire earth’s surface. DEMs that are usually subject to errors need to be evaluated using reference elevation data of higher accuracy. This work was performed to assess the vertical accuracy of the ASTER GDEM version 2, (ASTER GDEM2), the Consultative Group on International Agriculture Research-Consortium for Spatial Information (CGIAR-CSI) SRTM version 4.1 (SRTM v4.1) and the systematic subsample GMTED2010, at their original spatial resolution, using Global Navigation Satellite Systems (GNSS) validation points. Two test sites, the Anaguid Saharan platform in southern Tunisia and the Tebessa basin in north eastern Algeria, were chosen for accuracy assessment of the above mentioned DEMs, based on geostatistical and statistical measurements. Within the geostatistical approach, empirical variograms of each DEM were compared with those of the GPS validation points. Statistical measures were computed from the elevation differences between the DEM pixel value and the corresponding GPS point. For each DEM, a Root Mean Square Error (RMSE) was determined for model validation. In addition, statistical tools such as frequency histograms and Q-Q plots were used to evaluate error distributions in each DEM. The results indicate that the vertical accuracy of SRTM model is much higher than ASTER GDEM2 and GMTED2010 for both sites. In Anaguid test site, the vertical accuracy of SRTM is estimated 3.6 m (in terms of RMSE) 5.3 m and 4.5 m for the ASTERGDEM2 and GMTED2010 DEMs, respectively. In Tebessa test site, the overall vertical accuracy shows a RMSE of 9.8 m, 8.3 m and 9.6 m for ASTER GDEM 2, SRTM and GMTED2010 DEM, respectively. This work is the first study to report the lower accuracy of ASTER GDEM2 compared to the GMTED2010 data. Full article
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<p>Location of the test sites plotted on a shaded relief map obtained from SRTM DEMs.</p> Full article ">
<p>Landscape characteristics of the (<b>a</b>) Anaguid Saharan platform and (<b>b</b>) Tebessa Basin. Note the GPS base station photo in (a).</p> Full article ">
<p>Data used for Anaguid test site. (<b>a</b>) Location of the Anaguid Saharan platform, (<b>b</b>) RTK-DGPS reference data plotted on ASTER GDEM2, (<b>c</b>) SRTM v.4.1 and (<b>d</b>) systematic subsample GMTED 2010.</p> Full article ">
<p>Data used for Tebessa test site: (<b>a</b>) location of Tebessa basin, (<b>b</b>) GNSS data plotted on ASTER GDEM2, (<b>c</b>) SRTM v4.1 and (<b>d</b>) systematic subsample GMTED 2010.</p> Full article ">
<p>Scatter plots of satellites in view values <span class="html-italic">vs.</span> PDOP for (<b>a</b>) Anaguid and (<b>b</b>) Tebessa test sites.</p> Full article ">
<p>Empirical variograms of the three DEMs compared with that of GNSS elevations points for each test site: (<b>a</b>) Anaguid and (<b>b</b>) Tebessa. The red, black, blue and green variograms represent GNSS, SRTM v4.1, GMTED2010 and ASTER GDEM2, respectively.</p> Full article ">
<p>Summary statistics for Anaguid test site. Scatter plots of GNSS elevations <span class="html-italic">vs.</span> (<b>a</b>) ASTER GDEM2, (<b>b</b>) SRTM v4.1 and (<b>c</b>) GMTED2010. The fine red line stands for the line of perfect fit. Histograms of elevation errors and relevant descriptive statistics. (<b>d</b>) ASTER GDEM2 minus GNSS elevations, (<b>e</b>) SRTM v4.1 minus GNSS elevations and (<b>f</b>) GMTED2010 minus GNSS elevations. The solid red line represents the fitted density curve.</p> Full article ">
<p>Summary statistics for Tebessa tezst site. Scatter plots of GNSS elevations <span class="html-italic">vs.</span> (<b>a</b>) ASTER GDEM2, (<b>b</b>), SRTM v4.1 and (<b>c</b>) GMTED2010. The red line stands for the line of perfect fit. Histograms of elevation errors and relevant descriptive statistics: (<b>d</b>) ASTER GDEM2 minus GNSS elevations, (<b>e</b>) SRTM v4.1 minus GNSS elevations and (<b>f</b>) GMTED2010 minus GNSS elevations. The solid red line represents the fitted density curve.</p> Full article ">
<p>Q-Q plots for the Anaguid test site (a, b and c) showing the error distribution for (<b>a</b>) ASTER GDEM2, (<b>b</b>) SRTM v4.1 and (<b>c</b>) GMTED2010. Q-Q plots for the Tebessa test site showing the error distribution for (<b>d</b>) ASTER GDEM2, (<b>e</b>) SRTM v4.1 and (<b>f</b>) GMTED2010. The solid and dashed red lines represent theoretical normal distribution and 95% confidence intervals, respectively.</p> Full article ">
584 KiB  
Article
Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures
by Nadja Stumberg, Marius Hauglin, Ole Martin Bollandsås, Terje Gobakken and Erik Næsset
Remote Sens. 2014, 6(5), 4582-4599; https://doi.org/10.3390/rs6054582 - 21 May 2014
Cited by 7 | Viewed by 7133
Abstract
The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using [...] Read more.
The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using laser height and intensity data. The main objective of the present study was to assess a possible improvement in the performance of classifying tree and nontree laser echoes from high-density ALS data. The data were collected along a 1000 km long transect stretching from southern to northern Norway. Different geostatistical and statistical measures derived from laser height and intensity values were used to extent and potentially improve more simple models ignoring the spatial context. Generalised linear models (GLM) and support vector machines (SVM) were employed as classification methods. Total accuracies and Cohen’s kappa coefficients were calculated and compared to those of simpler models from a previous study. For both classification methods, all models revealed total accuracies similar to the results of the simpler models. Concerning classification performance, however, the comparison of the kappa coefficients indicated a significant improvement for some models both using GLM and SVM, with classification accuracies >94%. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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<p>Overview of the study area with the 25 specific field sites (black points). The 1000 km long transect (black line) stretches from to 66°19′N 14°9′E to 58°3′N 9°0′E.</p> Full article ">
<p>Illustration of a PCQ sample plot (<b>left</b>, further described in the text, Section 2.2.) and a detailed demonstration of the computation of the geostatistical and statistical measures (<b>right</b>). Tree locations and the respective crown areas are represented in the three tree height classes: &lt;1 m (black ellipses), 1–2 m (dark grey ellipses), and &gt;2 m (light grey ellipses). Using a circular 3 m radius moving window (black dashed circle), laser echoes (black points) were selected for the computation of the geostatistical and statistical measures for each grid point (white points). The geostatistical measure was estimated using different lags (light grey dashed circles).</p> Full article ">
3416 KiB  
Article
The Improved NRL Tropical Cyclone Monitoring System with a Unified Microwave Brightness Temperature Calibration Scheme
by Song Yang, Jeffrey Hawkins and Kim Richardson
Remote Sens. 2014, 6(5), 4563-4581; https://doi.org/10.3390/rs6054563 - 19 May 2014
Cited by 17 | Viewed by 7692
Abstract
The near real-time NRL global tropical cyclone (TC) monitoring system based on multiple satellite passive microwave (PMW) sensors is improved with a new inter-sensor calibration scheme to correct the biases caused by differences in these sensor’s high frequency channels. Since the PMW sensor [...] Read more.
The near real-time NRL global tropical cyclone (TC) monitoring system based on multiple satellite passive microwave (PMW) sensors is improved with a new inter-sensor calibration scheme to correct the biases caused by differences in these sensor’s high frequency channels. Since the PMW sensor 89 GHz channel is used in multiple current and near future operational and research satellites, a unified scheme to calibrate all satellite PMW sensor’s ice scattering channels to a common 89 GHz is created so that their brightness temperatures (TBs) will be consistent and permit more accurate manual and automated analyses. In order to develop a physically consistent calibration scheme, cloud resolving model simulations of a squall line system over the west Pacific coast and hurricane Bonnie in the Atlantic Ocean are applied to simulate the views from different PMW sensors. To clarify the complicated TB biases due to the competing nature of scattering and emission effects, a four-cloud based calibration scheme is developed (rain, non-rain, light rain, and cloudy). This new physically consistent inter-sensor calibration scheme is then evaluated with the synthetic TBs of hurricane Bonnie and a squall line as well as observed TCs. Results demonstrate the large TB biases up to 13 K for heavy rain situations before calibration between TMI and AMSR-E are reduced to less than 3 K after calibration. The comparison stats show that the overall bias and RMSE are reduced by 74% and 66% for hurricane Bonnie, and 98% and 85% for squall lines, respectively. For the observed hurricane Igor, the bias and RMSE decrease 41% and 25% respectively. This study demonstrates the importance of TB calibrations between PMW sensors in order to systematically monitor the global TC life cycles in terms of intensity, inner core structure and convective organization. A physics-based calibration scheme on TC’s TB corrections developed in this study is able to significantly reduce the biases between different PMW sensors. Full article
(This article belongs to the Special Issue Calibration and Verification of Remote Sensing Instruments and Observations)
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Graphical abstract

Graphical abstract
Full article ">
<p>H-polarization high frequency image comparison of Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager Sounder (SSMIS) for tropical cyclone Jasmine on 9 February 2012 as an example showing impact of the frequency shift on satellite passive microwave (PMW) T<sub>B</sub> s.</p> Full article ">
<p>Comparison of TMI and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) T<sub>B</sub>s (K) for hurricane Igor: (<b>a</b>) TMI 85 GHz-H pol; (<b>b</b>) AMSR-E 89 GHz-H pol; (<b>c</b>) T<sub>BTMI85h</sub>−T<sub>BAMSR-E89h.</sub></p> Full article ">
<p>Surface rainrate (mm·hr<sup>−1</sup>) and simulated T<sub>B</sub> (K) for TMI 85 GHz-H pol from MM5 simulation for hurricane Bonnie (top panel) and Goddard Cumulus Ensemble (GCE) simulation for squall line (bottom panel).</p> Full article ">
<p>Horizontal distributions of the simulated TMI 85 GHz-H pol (top-left), AMSR-E 89 GHz-H pol (bottom left), TMI 85 GHz PCT (top right) and their T<sub>B</sub> difference (bottom-right) for (<b>a</b>) hurricane Bonnie; and (<b>b</b>) squall line.</p> Full article ">
<p>Same as <a href="#f4-remotesensing-06-04563" class="html-fig">Figure 4</a>, except for SSMIS 91 GHz-H pol and AMSR-E 89 GHz-H pol.</p> Full article ">
<p>(<b>Left panel</b>) Comparison of T<sub>B</sub> differences between the simulated TMI 85 and AMSR-E 89 GHz-H for hurricane Bonnie and squall line. The black, yellow, blue and green color points are for the classified cloud conditions of rain, light rain, non-rain, and cloudy, respectively. The heavy dash lines are their related polynomial fitting lines. (<b>Right panel</b>) Same as (left panel) except for SSMIS 91 and AMSR-E 89 GHz.</p> Full article ">
<p>Flowchart of the physically-based T<sub>B</sub> frequency calibration scheme.</p> Full article ">
<p>T<sub>B</sub> difference between TMI 85-H and AMSR-E 89-H GHz before calibration (<b>top panels</b>) and after calibrated TMI (<b>bottom panels</b>) for Hurricane Bonnie (<b>left panels</b>) and squall line (<b>right panels</b>).</p> Full article ">
<p>Same as <a href="#f8-remotesensing-06-04563" class="html-fig">Figure 8</a>, except for SSMIS 91 and AMSR-E 89 GHz-H pol.</p> Full article ">
1305 KiB  
Article
Scattering Mechanisms for the “Ear” Feature of Lop Nur Lake Basin
by Huaze Gong, Yun Shao, Tingting Zhang, Long Liu and Zhihong Gao
Remote Sens. 2014, 6(5), 4546-4562; https://doi.org/10.3390/rs6054546 - 16 May 2014
Cited by 11 | Viewed by 6907
Abstract
Lop Nur is a famous dry lake in the arid region of China. It was an important section of the ancient “Silk Road”, famous in history as the prosperous communication channel between Eastern and Western cultures. At present, there is no surface water [...] Read more.
Lop Nur is a famous dry lake in the arid region of China. It was an important section of the ancient “Silk Road”, famous in history as the prosperous communication channel between Eastern and Western cultures. At present, there is no surface water in Lop Nur Lake basin, and on SAR (Synthetic Aperture Radar) images, it looks like an “Ear”. The objective of this paper is to interpret the Lop Nur phenomenon from the perspective of scattering mechanisms. Based on field investigation and analysis of sample properties, a two-layer scattering structure is proposed with detailed explanations of scattering mechanisms. In view of the rough surface, the MIEM (Modified Integral Equation Model) was introduced to represent air-surface scattering in Lop Nur. Then, a two-layer scattering model was developed which can describe surface scattering contribution. Using polarimetric decomposition, validations were carried out, and the RMSE (root mean square error) values for the HH and VV polarizations were found to be 1.67 dB and 1.06 dB, respectively. Furthermore, according to model parametric analysis, surface roughness was identified as an apparent reason for the “Ear” feature. In addition, the polarimetric decomposition result also showed that the volume scattering part had rich texture information and could portray the “Ear” feature exactly compared with the other two parts. It is maintained that subsurface properties, mainly generating volume scattering, can determine the surface roughness under the certain climate conditions, according to geomorphological dynamics, which can help to develop an inversion technology for Lop Nur. Full article
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Graphical abstract

Graphical abstract
Full article ">
<p>Field investigation routes and sampling site locations. A, B and C are field routes in 2006, and I, II and III are field routes in 2008. The dashed rectangle is the coverage of full-polarimetric SAR data used in the last part of this paper. There is a large salt pond in the middle of the image as a black square. ALOS-PALSAR image (HH polarization, ScanSAR mode) obtained on 15 January 2011 were used as base map.</p> Full article ">
<p>Moisture variation from surface to base of the lacustrine deposits at selected sampling sites. It can be seen that the moisture increased abruptly rather than gradually from the fourth sample (average depth of 40 cm) to the fifth sample (average depth of 50 cm) counting from surface to base in each sampling pit. The fourth and fifth samples were from different depths at different sites. The average moisture of the first four samples was approximately 2%, but the moisture of the fifth sample increased to 10% at least. The dash line in the figure stands for the significant difference.</p> Full article ">
<p>Subsurface scattering mechanism illustration. The subsurface structure of Lop Nur can be regarded as consisting of two layers with different dielectric properties, an upper dry layer and a moist saline subsurface layer. The GPR detected a boundary between the dry upper layer and the wet lower layer at a depth of approximately 50–55 cm ranging from the center to the edge of Lop Nur Lake basin.</p> Full article ">
<p>Two-layer structure and scattering mechanisms, where <span class="html-italic">ɛ</span> stands for complex dielectric constants, <span class="html-italic">D</span> is the thickness of layer 1, <span class="html-italic">W</span> represents Fourier transformation of surface correlation function, <span class="html-italic">s</span> and <span class="html-italic">l</span> are RMS height and correlation length respectively, <span class="html-italic">a</span> is albedo, <span class="html-italic">κe</span> means extinction coefficient, <span class="html-italic">θ</span> and <span class="html-italic">θ′</span> accounts for incidence and transmission angles.</p> Full article ">
<p>Importance of multiple scattering part based on MIEM (VV polarization, incidence angle = 30°, RMS height = 2.5 cm, correlation length = 6.0 cm), where square line is EMSL measured result, and circle line and triangle line represent single scattering part and simulated result by MIEM including multiple scattering part.</p> Full article ">
<p>Comparison between <span class="html-italic">σ°</span> from the surface scattering contribution using eigenvector-eigenvalue based decomposition and simulated results using the two-layer scattering model. Two scenes of ALOS-PALSAR polarimetric data (L-band, quad-polarization, 23.9° and 25.6° incidence angles for 19 April and 6 May 2009) were used. A and B are for HH and VV polarization, respectively, with RMSE, R square and linear fitting formula shown. The solid line in each diagram accounts for the 1:1 line, and the dashed line represents the linear fitting line.</p> Full article ">
<p>The variation patterns of simulated <span class="html-italic">σ°<sub>VV</sub></span> from the two-layer scattering model (L-band, VV polarization, 25.6° incidence angle, <span class="html-italic">ɛ</span><sub>1</sub> = 4.43 − j0.01, <span class="html-italic">l</span><sub>1</sub> = 40.0 cm, <span class="html-italic">ɛ′</span><sub>2</sub> = 19.94, <span class="html-italic">s</span><sub>2</sub> = 0.4 cm, <span class="html-italic">l</span><sub>2</sub> = 6.0 cm, <span class="html-italic">thickness of layer 1</span> = 35 cm) with RMS height of top surface and <span class="html-italic">ɛ″</span> of layer 2. Different colors represent different levels, and corresponding color bar is shown.</p> Full article ">
<p>The variation patterns of simulated <span class="html-italic">σ°<sub>VV</sub></span> from the two-layer scattering model (L-band, VV polarization, 25.6° incidence angle, <span class="html-italic">ɛ</span><sub>1</sub> = 4.43 − j0.01, <span class="html-italic">l</span><sub>1</sub> = 40.0 cm, <span class="html-italic">ɛ</span><sub>2</sub> = 19.94 − j71.58, <span class="html-italic">s</span><sub>2</sub> = 0.4 cm, <span class="html-italic">l</span><sub>2</sub> = 6.0 cm) with RMS height of surface and thickness of layer 1. Different colors represent different levels, and corresponding color bar is shown.</p> Full article ">
<p>Polarimetric decomposition results in surface scattering, dihedral scattering and volume scattering with model-based decomposition (Yamaguchi four-component decomposition), based on ALOS-PALSAR data (25.6° incidence angle, 6 May 2009). The coverage of this scene image is shown in <a href="#f1-remotesensing-06-04546" class="html-fig">Figure 1</a> as a dashed rectangle.</p> Full article ">
1686 KiB  
Article
Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality
by Lars T. Waser, Meinrad Küchler, Kai Jütte and Theresia Stampfer
Remote Sens. 2014, 6(5), 4515-4545; https://doi.org/10.3390/rs6054515 - 16 May 2014
Cited by 140 | Viewed by 10848
Abstract
Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg–Vorpommern is [...] Read more.
Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg–Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions. Full article
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<p>Location (<b>left</b>) and WorldView-2 scene (<b>right</b>) of the study area Schuenhagen in the German state Mecklenburg–Vorpommern.</p> Full article ">
<p>Examples of the four different levels: (<b>a</b>) (&lt;25%); (<b>b</b>) (25%–&lt;50%); (<b>c</b>) (50%–&lt;75%): and (<b>d</b>) (&gt;75%) (clockwise) of damaged ash. Parts of the crane used for the training is visible in the right upper image.</p> Full article ">
<p>Example of delineated tree damage (levels 1–4) for ash using the CIR aerial images. Slightly damaged crowns of ash appear pink (1) and heavily damaged ones gray (4).</p> Full article ">
<p>Methodological workflow of the ash damage and tree species classification approach.</p> Full article ">
<p>WV-2 CIR orthoimage (histogram-equalized) with reference crown segments of four different damage levels and three tree species. Shadows are masked out. The color of ash crowns (in the middle of the orthoimage) varies mostly due to different levels of ash mortality.</p> Full article ">
<p>Subset of the corresponding tree species map with the selected reference crown segments. Shadows are masked out.</p> Full article ">
<p>Subset of correspondingly damaged ash. Reference polygons are given for all four damage levels. The two falsely predicted segments are marked in blue. Shadows are masked out.</p> Full article ">
1851 KiB  
Article
On the Atmospheric Correction of Antarctic Airborne Hyperspectral Data
by Martin Black, Andrew Fleming, Teal Riley, Graham Ferrier, Peter Fretwell, John McFee, Stephen Achal and Alejandra Umana Diaz
Remote Sens. 2014, 6(5), 4498-4514; https://doi.org/10.3390/rs6054498 - 16 May 2014
Cited by 14 | Viewed by 8205
Abstract
The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing [...] Read more.
The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing airborne hyperspectral data in this unique coastal Antarctic environment. Results from the Atmospheric and Topographic Correction version 4 (ATCOR-4) package reveal absolute reflectance values somewhat in line with laboratory measured spectra, with Root Mean Square Error (RMSE) values of 5% in the visible near infrared (0.4–1 µm) and 8% in the shortwave infrared (1–2.5 µm). Residual noise remains present due to the absorption by atmospheric gases and aerosols, but certain parts of the spectrum match laboratory measured features very well. This study demonstrates that commercially available packages for carrying out atmospheric correction are capable of correcting airborne hyperspectral data in the challenging environment present in Antarctica. However, it is anticipated that future results from atmospheric correction could be improved by measuring in situ atmospheric data to generate atmospheric profiles and aerosol models, or with the use of multiple ground targets for calibration and validation. Full article
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<p>Location maps showing the context within Antarctica (<b>A</b>); the location of Adelaide Island within the Antarctic Peninsula (<b>B</b>) and the location of Rothera Point in the context of Adelaide Island (<b>C</b>; black dot).</p> Full article ">
<p>CASI colour composite image mosaic of Rothera Point following radiometric and geometric correction, with inset showing the three calibration targets. Bands shown: Red: 650.2 nm, Green: 554.6 nm, Blue: 439.6 nm.</p> Full article ">
<p>Schematic of the three solar radiation components in flat terrain and the pixel under consideration (<span class="html-italic">ρ</span>). Scattered or path radiance L<sub>1</sub>, reflected radiance L<sub>2</sub>, and radiation reflected from the local neighbourhood (adjacency effect) L<sub>3</sub>.</p> Full article ">
<p>Radiance simulations using MODTRAN-5 with constant water vapour values and different atmospheric profiles.</p> Full article ">
<p>Overview of the major processing phases of ATCOR-4.</p> Full article ">
<p>Radiosonde data from 7 February 2011, launched at 11:38 UTC.</p> Full article ">
<p>Atmospheric correction results for the VNIR (CASI; blue) and SWIR (SASI; red) data (<span class="html-italic">±</span>2% error estimates are shaded grey) and laboratory spectra (LAB; black), for the three calibrated targets; white (<b>1</b>); grey (<b>2</b>); and black (<b>3</b>). Labels are discussed in the text. Root Mean Square Error (RMSE) values are shown for each target.</p> Full article ">
1579 KiB  
Article
Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses
by Peter Waylen, Jane Southworth, Cerian Gibbes and Huiping Tsai
Remote Sens. 2014, 6(5), 4473-4497; https://doi.org/10.3390/rs6054473 - 14 May 2014
Cited by 35 | Viewed by 9503
Abstract
Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to [...] Read more.
Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of “benchmarks”; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques’ abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change. Full article
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<p>Study area map of the state of Florida with the climate divisions labeled in bold.</p> Full article ">
<p>Observed monthly distributions of NDVI values of pixels in (<b>a</b>) climatic division four, classified as Scrubland by C-CAP in 1996, 2002 and 2006; (<b>b</b>) division five, Agricultural Land; (<b>c</b>) division six, Developed Land; (<b>d</b>) division three, Palustrine Wetland.</p> Full article ">
<p>Comparison of Monte Carlo simulations of the distribution of the relative directional persistence variable, R, following various numbers of transitions (<b>a</b>) 5 transitions, (<b>b</b>) 10 transitions, (<b>c</b>) 15 transitions, (<b>d</b>) 20 transitions, (<b>e</b>) 25 transitions, and (<b>f</b>) 28 transitions and the anticipated results from a random walk (<span class="html-italic">p =</span> 0.5) process.</p> Full article ">
<p>Hypothetical normally distributed NDVI values (lower panel) and the changing probabilities of success and failure in the following step depending upon the value of the current value of NDVI, as used in defining the relative persistence variable, R.</p> Full article ">
<p>Monthly results from the application of the <b>s</b>patially explicit persistence analysis of NDVI from a 25-year time series for (<b>a</b>) Directional persistence relative to 1982, (Green represents increases in the variable, yellow areas of little to no change and red decreases); (<b>b</b>) regions of statistical significance at 0.005 significance level relating to &gt;±14 years; and (<b>c</b>) those significant at 0.05 level relating to &gt;±10 years</p> Full article ">
<p>Monthly results from the application of the <b>s</b>patially explicit persistence analysis of NDVI from a 25-year time series for (<b>a</b>) Directional persistence relative to 1982, (Green represents increases in the variable, yellow areas of little to no change and red decreases); (<b>b</b>) regions of statistical significance at 0.005 significance level relating to &gt;±14 years; and (<b>c</b>) those significant at 0.05 level relating to &gt;±10 years</p> Full article ">
<p>The NDVI relative directional persistence scores for the state of Florida for (<b>a</b>) January and (<b>b</b>) August showing all scores and only the statistically significant ones for January (<b>c</b>) and August (<b>d</b>).</p> Full article ">
<p>The spatial distribution of statistically significant massive persistence scores across the state of Florida, for areas of palustrine wetland for (<b>a</b>) August, (<b>b</b>) September, (<b>c</b>) October and (<b>d</b>) November.</p> Full article ">
<p>Land cover change across Florida as determined from the C-CAP analysis for 1996–2001–2006 only, highlighting some dominant land cover conversions across the state.</p> Full article ">
1555 KiB  
Article
Shallow-Water Benthic Identification Using Multispectral Satellite Imagery: Investigation on the Effects of Improving Noise Correction Method and Spectral Cover
by Masita Dwi Mandini Manessa, Ariyo Kanno, Masahiko Sekine, Eghbert Elvan Ampou, Nuryani Widagti and Abd. Rahman As-syakur
Remote Sens. 2014, 6(5), 4454-4472; https://doi.org/10.3390/rs6054454 - 14 May 2014
Cited by 35 | Viewed by 8338
Abstract
Lyzenga’s method is used widely for radiative transfer analysis because of its simplicity of application to cases of shallow-water coral reef ecosystems with limited information of water properties. WorldView-2 imagery has been used previously to study bottom-type identification in shallow-water coral reef habitats. [...] Read more.
Lyzenga’s method is used widely for radiative transfer analysis because of its simplicity of application to cases of shallow-water coral reef ecosystems with limited information of water properties. WorldView-2 imagery has been used previously to study bottom-type identification in shallow-water coral reef habitats. However, this is the first time WorldView-2 imagery has been applied to bottom-type identification using Lyzenga’s method. This research applied both of Lyzenga’s methods: the original from 1981 and the one from 2006 with improved noise correction that uses the near-infrared (NIR) band. The objectives of this study are to examine whether the utilization of NIR bands in the correction of atmospheric and sea-surface scattering improves the accuracy of bottom classification, and whether increasing the number of visible bands also improves accuracy. Firstly, it has been determined that the improved 2006 correction method, which uses NIR bands, is only more accurate than the original 1981 correction method in the case of three visible bands. When applying six bands, the accuracy of the 1981 correction method is better than that of the 2006 correction method. Secondly, the increased number of visible bands, when applied to Lyzenga’s empirical radiative transfer model, improves the accuracy of bottom classification significantly. Full article
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<p>Scatter plot of transformed radiance value of several bottom types at various depths.</p> Full article ">
<p>(<b>a</b>) Indonesia; (<b>b</b>) Lombok Island; (<b>c</b>) Gili Islands: Gili Trawangan, Gili Air, Gili Meno (from right to left).</p> Full article ">
<p>Illustration representing the different habitat categories used in the classification: (<b>a</b>) Living Coral; (<b>b</b>) Sand; (<b>c</b>) Mixed Objects (rock, sand, coral, rubble); (<b>d</b>) Rubble; and (<b>e</b>) Seagrass. Courtesy of Climate Change Team of Indonesia Research Institute for Marine Research and Observation.</p> Full article ">
<p>(<b>a</b>) Visualization of WorldView-2 spectral radiance; (<b>b</b>) transformed radiance of band blue/band 2 (X1) in Lyzenga81 correction; (<b>c</b>) depth-invariance (Y25) in Lyzenga81; (<b>d</b>) transformed radiance of band 2 (X1) in Lyzenga06; and (<b>e</b>) depth invariance (Y25) in Lyzenga06 correction. Images are represented using the band blue, and ratio of band blue/band red, respectively.</p> Full article ">
<p>Scatter plot matrix of transformed radiance of Lyzenga81 and Lyzenga06 correction methods.</p> Full article ">
<p>Image classification: (<b>a</b>) Lyzenga81 correction method for three visible bands; (<b>b</b>) Lyzenga06 correction method for three visible bands; (<b>c</b>) Lyzenga81 correction method for six visible bands; (<b>d</b>) Lyzenga06 correction method for six visible bands.</p> Full article ">
2392 KiB  
Article
Estimating Canopy Nitrogen Content in a Heterogeneous Grassland with Varying Fire and Grazing Treatments: Konza Prairie, Kansas, USA
by Bohua Ling, Douglas G. Goodin, Rhett L. Mohler, Angela N. Laws and Anthony Joern
Remote Sens. 2014, 6(5), 4430-4453; https://doi.org/10.3390/rs6054430 - 14 May 2014
Cited by 22 | Viewed by 6867
Abstract
Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted [...] Read more.
Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to assess the feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures have been less extensive. In this study, we use in situ and aircraft hyperspectral data to assess several empirical methods for extracting canopy nitrogen from a tallgrass prairie with varying fire and grazing treatments. The remote sensing data were collected four times between May and September in 2011, and were then coupled with the field-measured leaf nitrogen levels for empirical modeling of canopy nitrogen content based on first derivatives, continuum-removed reflectance and ratio-based indices in the 562–600 nm range. Results indicated that the best-performing model type varied between in situ and aircraft data in different months. However, models from the pooled samples over the growing season with acceptable accuracy suggested that these methods are robust with respect to canopy heterogeneity across spatial and temporal scales. Full article
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<p>Study sites at Konza Prairie Biological Station (KPBS). KPBS include more than 50 watersheds with different fire frequencies. Samples for this study were collected in three watersheds, N1B, N4D and N20B, with fire frequencies of one year, four years and 20 years respectively. The total area of the three watersheds is ∼3.40 km<sup>2</sup>.</p> Full article ">
<p>Plot design. Fifteen 10 m × 10 m plots with six subplots spirally arranged around the plot center were set up for data collection in each of the three watersheds. Hyperspectral measurements for each subplot were taken from a 0.4 m<sup>2</sup> circular field of view with a diameter of 0.7 m. Five foliar samples were clipped in each field of view for chemical analysis of leaf nitrogen concentration.</p> Full article ">
<p>(<b>a</b>) The absorption feature related to leaf chlorophyll concentration in the spectral region of 562–600 nm. (<b>b</b>) Continuum removal applied to the absorption feature.</p> Full article ">
<p>Correlations between canopy nitrogen and the first derivative spectral data at the wavelengths of 562–600 nm for three watersheds N1B, N4D and N20B respectively and as a whole in (<b>a</b>) May, (<b>b</b>) July, (<b>c</b>) August, (<b>d</b>) September, and for (<b>e</b>) four months respectively and the whole growing seasons in May–September across various watersheds.</p> Full article ">
<p>Correlations between canopy nitrogen and the continuum removed spectra at the wavelengths of 562–600 nm for three watersheds N1B, N4D and N20B respectively and as a whole in (<b>a</b>) May, (<b>b</b>) July, (<b>c</b>) August, (<b>d</b>) September, and for (<b>e</b>) four months respectively and the whole growing seasons in May–September across various watersheds.</p> Full article ">
<p>Maps of N<sub>can</sub> distribution in selected watersheds at KPBS in (<b>a</b>) May, (<b>b</b>) July, (<b>c</b>) August, and (<b>d</b>) September. White areas are where gallery forest pixels were masked prior to analysis.</p> Full article ">
<p>Comparison between N<sub>can</sub> stratification and grass density in selected watersheds at KPBS in (<b>a</b>) May, (<b>b</b>) July, (<b>c</b>) August, and (<b>d</b>) September. Overlaps of high N<sub>can</sub> level and dense grasses are in the dark shade; low N<sub>can</sub> level and sparse grasses are in the light.</p> Full article ">
<p>Comparison between N<sub>can</sub> stratification and (<b>a</b>) DEM in selected watersheds at KPBS in (<b>b</b>) May, (<b>c</b>) July, (<b>d</b>) August, and (<b>e</b>) September. Hillslopes are highlighted in the light shade, and flat lowlands are in the dark shade. Coincidence between N<sub>can</sub> distribution patterns and topographic features is most noticeable in May.</p> Full article ">
1519 KiB  
Article
Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data
by Lin Li, Heng Luo and Haihong Zhu
Remote Sens. 2014, 6(5), 4409-4429; https://doi.org/10.3390/rs6054409 - 14 May 2014
Cited by 15 | Viewed by 9454
Abstract
Image quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3 [...] Read more.
Image quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3 (Ziyuan-3) satellite is the first civil high resolution mapping satellite in China, which was established in 2012. So far, there has been no reports on adopting NIIRS as the common indicator for the quality assessment of that satellite image data. This lack of a common quality indicator results in a gap between satellite data users around the world and those in China regarding the understanding of the quality and usability of ZY-3 data. To overcome the gap, using the general image-quality equation (GIQE), this study evaluates the ZY-3 sensor-corrected (SC) panchromatic nadir (NAD) data in terms of the NIIRS. In order to solve the uncertainty resulting from the exceeding of the ground sample distance (GSD) of ZY-3 data (2.1 m) in GIQE (less than 2.03 m), eight images are used to establish the relationship between the manually obtained NIIRS and the GIQE predicted NIIRS. An adjusted GIQE is based on the relationship and verified by another five images. Our study demonstrates that the method of using adjusted GIQE for calculating NIIRS can be used for the quality assessment of ZY-3 satellite images and reveals that the NIIRS value of ZY-3 SC NAD data is about 2.79. Full article
(This article belongs to the Special Issue Satellite Mapping Technology and Application)
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<p>The distribution of ZY-3 NAD data used in this study.</p> Full article ">
<p>Two samples in the image of the agriculture area of Wuhan, HB, for edge extraction. The two edges are the common borders between different fields. (<b>a</b>) Edge extracted for edge response (ER)x. (<b>b</b>) Edge extracted for ERy.</p> Full article ">
<p>(<b>a</b>) The measurement of RER. (<b>b</b>) The measurement of H. The edge in Case 1 is monotonically increasing, the H is defined as the value at 1.25 pixels from the edge. Case 2 is not monotonically increasing, the H is the maximum value in the pixel range of +1 to +3 [<a href="#b12-remotesensing-06-04409" class="html-bibr">12</a>].</p> Full article ">
<p>Results of the GIQE parameters. (<b>a</b>) The calculation results of GSD. (<b>b</b>) The chart of the results of RER. (<b>c</b>) The values of H. (<b>d</b>) The results of SNR.</p> Full article ">
<p>Linear regression analysis. The expression is <span class="html-italic">y</span> = 2.146 × <span class="html-italic">x</span> − 1.479, and a high correlation can be found in the R-squared value of 0.641; the adjusted R-squared is 0.582, and the <span class="html-italic">p</span>-value is 0.017.</p> Full article ">
<p>Results of the ONIIRS, PNIIRS and NIIRS<sub>ZY3</sub>.</p> Full article ">
<p>ZY-3 SC nadir (NAD) images in different areas. (<b>a</b>) A railway station of Wuhan, HB. In the image, the large buildings of the railway station and other houses can be found, and eight tracks can be detected in the rail yard. (<b>b</b>) The mountainous area of Wuhu, AH. Some forest clearings can be detected in the image, and two utility towers in the forest are obvious and easily found. (<b>c</b>) A field in Baotou, IM, where we can find windbreaks between fields. (<b>d</b>) The airport of Wuhan, HB. The taxiway and runway is clearly identifiable, and some medium-sized airplanes can be detected.</p> Full article ">
<p>Comparison of the GSD and NIIRS of different satellite imagery.</p> Full article ">
757 KiB  
Article
On-Orbit Geometric Calibration Model and Its Applications for High-Resolution Optical Satellite Imagery
by Mi Wang, Bo Yang, Fen Hu and Xi Zang
Remote Sens. 2014, 6(5), 4391-4408; https://doi.org/10.3390/rs6054391 - 14 May 2014
Cited by 114 | Viewed by 10238
Abstract
On-orbit geometric calibration is a key technology to guarantee the geometric quality of high-resolution optical satellite imagery. In this paper, we present an approach for the on-orbit geometric calibration of high-resolution optical satellite imagery, focusing on two core problems: constructing an on-orbit geometric [...] Read more.
On-orbit geometric calibration is a key technology to guarantee the geometric quality of high-resolution optical satellite imagery. In this paper, we present an approach for the on-orbit geometric calibration of high-resolution optical satellite imagery, focusing on two core problems: constructing an on-orbit geometric calibration model and proposing a robust calculation method. First, a rigorous geometric imaging model is constructed based on the analysis of the major error sources. Second, we construct an on-orbit geometric calibration model through performing reasonable optimizing and parameter selection of the rigorous geometric imaging model. On this basis, the calibration parameters are partially calculated with a stepwise iterative method by dividing them into two groups: external and internal calibration parameters. Furthermore, to verify the effectiveness of the proposed calibration model and methodology, on-orbit geometric calibration experiments for ZY1-02C panchromatic camera and ZY-3 three-line array camera are conducted using the reference data of the Songshan calibration test site located in the Henan Province, China. The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters. Therefore, on-orbit geometric calibration is necessary for optical satellite imagery. On the other hand, by choosing multiple images, which cover different areas and are acquired at different points in time to verify their geometric accuracy before and after calibration, we find that after on-orbit geometric calibration, the geometric accuracy of these images without ground control points is significantly improved. Additionally, due to the effective elimination of the internal distortion of the camera, greater geometric accuracy was achieved with less ground control points than before calibration. Full article
(This article belongs to the Special Issue Satellite Mapping Technology and Application)
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<p>Camera’s coordinate system and satellite’s body-fixed coordinate system.</p> Full article ">
<p>Relationship between GPS antenna center and projection center of camera.</p> Full article ">
<p>Directional angle of charge coupled device (CCD) detector.</p> Full article ">
<p>Matching result for ZY1-02C’s panchromatic image and the reference orthophoto: (<b>left</b>) ZY1-02C’s panchromatic image; (<b>right</b>) orthophoto.</p> Full article ">
<p>CCD distortion curve of the ZY1-02C panchromatic camera.</p> Full article ">
<p>CCD distortion curve of ZY-3 three-line array Camera: (<b>left</b>) Nadir (NAD); (<b>middle</b>) forward (FWD); (<b>right</b>) backward (BWD).</p> Full article ">
1595 KiB  
Article
Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation
by Jing Lu, Ronglin Tang, Huajun Tang, Zhao-Liang Li, Guoqing Zhou, Kun Shao, Yuyun Bi and Jelila Labed
Remote Sens. 2014, 6(5), 4369-4390; https://doi.org/10.3390/rs6054369 - 12 May 2014
Cited by 8 | Viewed by 6910
Abstract
In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with [...] Read more.
In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with surface net radiation, this study simplified the daily EF parameterization scheme using incoming solar radiation as an input. Daily EF estimates from the simplified scheme were nearly equivalent to the results from the original scheme. In situ measurements from six Ameriflux sites with different land covers were used to validate the new simplified EF parameterization scheme. Results showed that daily EF estimates for clear skies were consistent with the in situ EF corrected by the residual energy method, showing a coefficient of determination of 0.586 and a root mean square error of 0.152. Similar results were also obtained for partly clear sky conditions. The non-closure of the measured energy and heat fluxes and the uncertainty in determining fractional vegetation cover were likely to cause discrepancies in estimated daily EF and measured counterparts. The daily EF estimates of different land covers indicate that the constant coefficients in the simplified EF parameterization scheme are not strongly site-specific. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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<p>Relationships between surface net radiation (<span class="html-italic">R</span><sub>n</sub>) and incoming solar radiation (<span class="html-italic">R</span><sub>g</sub>) based on ALEX-simulated data with different surface characteristics for three representative clear days and <span class="html-italic">in situ</span> measurements at the US-SRC station for clear skies in 2011 (see Section 3). Atmosphere forcing driven the ALEX model was measured at the Yucheng station on 24 April, 29 May, and 21 July 2010, and more details can be found in the Appendix of [<a href="#b38-remotesensing-06-04369" class="html-bibr">38</a>].</p> Full article ">
<p>Comparisons of daily evaporative fraction (EF) estimates from <a href="#FD8" class="html-disp-formula">Equation (8)</a> with (<b>a</b>) ALEX-simulated values and (<b>b</b>) the results using Δ<span class="html-italic">R</span><sub>n</sub>.</p> Full article ">
<p>Distribution of sites for validation of EF parameterization scheme (The background is the land use type of 2012 classified according to the University of Maryland scheme, which is extracted from the Land Cover Type Climate Modeling Grid product MCD12C1 with 0.05 degree spatial resolution.).</p> Full article ">
<p>Distribution of clear days and partly clear days in one year.</p> Full article ">
<p>Comparisons of daily average <span class="html-italic">H</span> + <span class="html-italic">LE</span> from EC-based measurements with daily average <span class="html-italic">R</span><sub>n</sub> − <span class="html-italic">G</span> for clear and partly clear skies at six Ameriflux sites.</p> Full article ">
<p>Comparisons of daily EF estimates with values from EC measurements, the Bowen ratio (BR) correction method, and the residual energy (RE) correction method for <b>(a)</b> clear skies and <b>(b)</b> partly clear skies.</p> Full article ">
<p>Comparisons of (<b>a</b>) <span class="html-italic">f</span><sub>c</sub> from different schemes and (<b>b</b>) daily EF estimates corresponding to different <span class="html-italic">f</span><sub>c</sub> (<span class="html-italic">f</span><sub>c_non-linear</sub> represents the results from a non-linear model; <span class="html-italic">f</span><sub>c_0.2/0.66</sub> denotes the results for NDVI<sub>min</sub> = 0.2 and NDVI<sub>max</sub> = 0.66).</p> Full article ">
<p>Comparisons of daily EF estimates with the EF corrected by the residual energy (RE) method for different underlying surfaces, (<b>a</b>) croplands (CRO), (<b>b</b>) grasslands (GRA), (<b>c</b>) open shrublands (OSH), (<b>d</b>) evergreen needleleaf forest (ENF), (<b>e</b>) deciduous broadleaf forest (DBF), and (<b>f</b>) evergreen broadleaf forest (EBF).</p> Full article ">
<p>Error in EF estimates, daily averaged incoming solar radiation (<span class="html-italic">R</span><sub>g</sub>), fractional vegetation cover (<span class="html-italic">f</span><sub>c</sub>), and Δ<span class="html-italic">T</span><sub>s</sub> − Δ<span class="html-italic">T</span><sub>a</sub> at the US-Ne2 station for all selected clear days and partly clear days, and daily precipitation (P) for all days in one year.</p> Full article ">
1006 KiB  
Article
Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling
by Lidia Vlassova, Fernando Perez-Cabello, Hector Nieto, Pilar Martín, David Riaño and Juan De la Riva
Remote Sens. 2014, 6(5), 4345-4368; https://doi.org/10.3390/rs6054345 - 12 May 2014
Cited by 94 | Viewed by 11039
Abstract
Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC [...] Read more.
Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components. Full article
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<p>Study area: (<b>a</b>) Location of the study site (<b>b</b>) orthophoto of the study area corresponding to MODIS pixel.</p> Full article ">
<p>Relationship between <span class="html-italic">w</span> and LST<sub>Landsat</sub>–LST<sub>ref</sub>.</p> Full article ">
<p>Relationship between near surface air temperature <span class="html-italic">T<sub>air</sub></span> and LST<sub>Landsat</sub>–LST<sub>ref</sub>.</p> Full article ">
<p>Box plot showing variability of Landsat LST estimated from Landsat-5 TM images using MW (in red) and SC (in blue) within MODIS pixel.</p> Full article ">
745 KiB  
Article
Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm
by Kenneth Olofsson, Johan Holmgren and Håkan Olsson
Remote Sens. 2014, 6(5), 4323-4344; https://doi.org/10.3390/rs6054323 - 12 May 2014
Cited by 189 | Viewed by 13976
Abstract
Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and [...] Read more.
Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and the RANSAC algorithm and (2) assess the influence of distance to the scanner on the measurement accuracy. Tree detection and stem diameter estimates were validated for 16 circular plots with 20 m radius. The three dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). The proportion of detected trees decreased as the distance to the scanner increased and followed the trend of decreasing visible area. Within 10 m from the scanner, the proportion of detected trees was 87% on average for the plots and the diameter at breast height was estimated with a relative root-mean-square-error (RMSE) of 14%. The most accurate diameter measurements were obtained for pine, which had a RMSE of 7% for all the full 20 m radius plots. The RANSAC algorithm reduced noise and made it possible to obtain reliable estimates. Full article
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<p>A raster of the stem probability factor in grey scale. The final tree stem positions are marked in red.</p> Full article ">
<p>TLS data points cut out at 1.0–1.5 m above ground: To the left part of a tree stem and to the right part of a small tree with dense branches.</p> Full article ">
<p>TLS data points projected to a raster in the x, y-plane. A high density of laser data points give a bright pixel. The coloured circles are the results from the stem finding algorithm. The red circle is the final chosen stem diameter by the algorithm.</p> Full article ">
<p>The outline of the tree crown in red. The TLS data points are projected onto a radially symmetric image. The x-axis is the radial distance from the tree stem, from left to right and the y-axis is the height from ground. The algorithm start the search from the top until the first canopy pixel is found.</p> Full article ">
<p>Laser data points of a spruce classified into stem (BLUE) and canopy (RED).</p> Full article ">
<p>In each iteration in the RANSAC algorithm inliers are found within a given tolerance of the chosen circle. This model is saved for further calculations if the circle is of a valid size, there are few points inside the trunk, and the number of inliers is larger than the previous chosen circle.</p> Full article ">
<p>The correlation for different rotation angles used to match TLS detected trees with manually measured tree positions for one example field plot.</p> Full article ">
<p>The proportion of detected trees (solid line) and proportion of non-shaded area for all plots (dotted line) plotted against distance to the scanner.</p> Full article ">
<p>Scatterplot for TLS estimated versus field measured diameter at breast height (DBH). All points outside the dotted lines were denoted as outliers. For the points at DBH = 200 mm, inside the ellipse, the RANSAC algorithm was not able to give any results and therefore the values were set to the diameter class obtained by the Hough transform.</p> Full article ">
1135 KiB  
Article
Transferability of a Visible and Near-Infrared Model for Soil Organic Matter Estimation in Riparian Landscapes
by Yaolin Liu, Qinghu Jiang, Teng Fei, Junjie Wang, Tiezhu Shi, Kai Guo, Xiran Li and Yiyun Chen
Remote Sens. 2014, 6(5), 4305-4322; https://doi.org/10.3390/rs6054305 - 9 May 2014
Cited by 45 | Viewed by 8024
Abstract
The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set [...] Read more.
The transferability of a visible and near-infrared (VNIR) model for soil organic matter (SOM) estimation in riparian landscapes is explored. The results indicate that for the soil samples with air-drying, grinding and 2-mm sieving pretreatment, the model calibrated from the soil sample set with mixed land-use types can be applied in the SOM prediction of cropland soil samples (r2Pre = 0.66, RMSE = 2.78 g∙kg−1, residual prediction deviation (RPD) = 1.45). The models calibrated from cropland soil samples, however, cannot be transferred to the SOM prediction of soil samples with diverse land-use types and different SOM ranges. Wavelengths in the region of 350–800 nm and around 1900 nm are important for SOM estimation. The correlation analysis reveals that the spectral wavelengths from the soil samples with and without the air-drying, grinding and 2-mm sieving pretreatment are not linearly correlated at each wavelength in the region of 350–1000 nm, which is an important spectral region for SOM estimation in riparian landscapes. This result explains why the models calibrated from samples without pretreatment fail in the SOM estimation. The Kennard–Stone algorithm performed well in the selection of a representative subset for SOM estimation using the spectra of soil samples with pretreatment, but failed in soil samples without the pretreatment. Our study also demonstrates that a widely applicable SOM prediction model for riparian landscapes should be based on a wide range of SOM content. Full article
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<p>Maps and images showing the geographical location of the study area, the distribution of sampling sites and the landscape, as indicated by a LANDSAT-7 ETM+ image with a composition of Bands 5 (red), 4 (green) and 3 (blue). The three photographs show the landscape and land use of the study area: (<b>A</b>) artificial forest, irrigated cropland and meadow on the dam; (<b>B</b>) pond and irrigated cropland; (<b>C</b>) artificial forest, canal and irrigated cropland.</p> Full article ">
<p>Box plots and histograms of the soil organic matter (SOM) content for the total sample set (Dataset 0), subsets by location (Dataset 1, Dataset 2 and Dataset 3), subsets divided by the SOM content (Dataset D and Dataset S), and subsets divided by the Kennard–Stone algorithm with soil reflectance (Dataset KSc (AP) and Dataset KSp (AP) for soil samples with pretreatment, Dataset KSc (BP) and Dataset KSp (BP) for soil samples without pretreatment). “sk” denotes skewness; “n” denotes the number of the samples; and ‘std’ denotes standard derivation.</p> Full article ">
<p>Pearson correlations between SOM and reflectance from soil samples with pretreatment (<b>a</b>) and without pretreatment (<b>b</b>).</p> Full article ">
<p>Correlation coefficient maps of reflectance wavelength pairs from different soil sample sets: Dataset 1 (reflectance from soil samples without pretreatment (<b>a</b>) and with pretreatment (<b>d</b>)), Dataset 2 (reflectance from soil samples without pretreatment (<b>b</b>) and with pretreatment (<b>e</b>)), and Dataset 3 (reflectance from soil samples without pretreatment (<b>c</b>) and with pretreatment (<b>f</b>)).</p> Full article ">
<p>Pearson’s r of the spectral wavelengths from soil samples with different pretreatments (BP denotes spectra from soil samples without pretreatment; AP denotes spectra from soil samples with the air-drying, grinding and 2-mm sieving pretreatment).</p> Full article ">
<p>Scatter plots of laboratory-measured <span class="html-italic">versus</span> visible and near-infrared (VNIR) reflectance-predicted SOM, using PLSR with selective modeling schemes to examine the model transferability: (<b>a</b>) Scheme 3; (<b>b</b>) Scheme 6; (<b>c</b>) Scheme 7; and (<b>d</b>) Scheme 8.</p> Full article ">
<p>Variable importance in the projection (VIP) scores for the PLSR model calibrated from reflectance of pretreated soil samples using Scheme 8.</p> Full article ">
724 KiB  
Article
Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed Grasslands
by Xiaohui Yang and Xulin Guo
Remote Sens. 2014, 6(5), 4289-4304; https://doi.org/10.3390/rs6054289 - 8 May 2014
Cited by 17 | Viewed by 6264
Abstract
Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead [...] Read more.
Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead materials, and shadowing of vegetation canopies. Dead materials refer to the dead component of vegetation, including fallen litter and standing dead grasses accumulated from previous years. The abundant dead materials have been presenting challenges to accurately estimate green vegetation using spectral vegetation indices (VIs) derived from remote sensing data in mixed grasslands. Therefore, a close investigation of the relationship between VIs and dead materials is needed. The identified relationships could provide better insight into not only using remote sensing data for quantitative estimation of dead materials, but also the improvement of green vegetation estimation in the mixed grassland that has a high proportion of dead materials. In this article, the spectral reflectance of dead materials and green vegetation mixtures and dead material cover were measured in mixed grasslands located in Grassland National Park (GNP), Saskatchewan, Canada. Nine VIs were derived from the measured spectral reflectance. The relationship between dead material cover and VIs was quantified using the regression model and sensitivity analysis. Results indicated that the relationship between dead material cover and VIs is a function of the amount of dead material cover. Weak positive relationship was found between VIs and dead materials where the cover was less than 50%, and a significant high negative relationship was evident when cover was greater than 50%. When the combined exponential and linear model was applied to fit the negative relationships, more than 90% variation in dead material cover could be explained by VIs. Sensitivity analysis was further applied to the developed models, indicating that sensitivities of all VIs were significant over the entire range of dead material cover except for the triangular vegetation index (TVI), which has insignificant sensitivity when dead material cover was greater than 94%. Among all VIs, the weighted difference vegetation index (WDVI) had the highest sensitivity to changes in dead material cover higher than 50%. The results from this study indicated that vegetation indices based on combination of reflectance in red and NIR bands can be used to estimate dead material cover that is greater than 50%. Full article
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<p>West Block of Grasslands National Park and Dixon Community Pasture.</p> Full article ">
<p>Spectral responses curve of the grasslands in GNP. Three primary water absorption (noisy) regions (1361–1395, 1811–1925, and 2475–2500 nm) from the field measurements were deleted.</p> Full article ">
<p>Scatter plot of <span class="html-italic">Y</span> (VIs) <span class="html-italic">vs. X</span> (dead material cover) and the curvilinear model fit (thick line) (<b>a</b>–<b>i</b>).</p> Full article ">
<p>Relationships between VIs and dead material cover with cover less than 50% of total land cover (<b>a</b>–<b>i</b>).</p> Full article ">
<p>Relationships between VIs and dead material cover with cover higher than 50% of total land cover (<b>a</b>–<b>i</b>).</p> Full article ">
<p>Sensitivity analysis for VIs with different level of dead material cover. <span class="html-italic">S</span> is the sensitivity parameter and <span class="html-italic">X</span> is the dead material cover. The sensitivity of a VI to dead material is significant when <span class="html-italic">s</span> &gt; 1.98.</p> Full article ">
1661 KiB  
Article
Global Ecosystem Response Types Derived from the Standardized Precipitation Evapotranspiration Index and FPAR3g Series
by Eva Ivits, Stephanie Horion, Rasmus Fensholt and Michael Cherlet
Remote Sens. 2014, 6(5), 4266-4288; https://doi.org/10.3390/rs6054266 - 8 May 2014
Cited by 12 | Viewed by 8135
Abstract
Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns [...] Read more.
Observing trends in global ecosystem dynamics is an important first step, but attributing these trends to climate variability represents a further step in understanding Earth system changes. In the present study, we classified global Ecosystem Response Types (ERTs) based on common spatio-temporal patterns in time-series of Standardized Precipitation Evapotranspiration Index (SPEI) and FPAR3g anomalies (1982–2011) by using an extended Principal Component Analysis. The ERTs represent region specific spatio-temporal patterns of ecosystems responding to drought or ecosystems with decreasing severity in drought events as well as ecosystems where drought was not a dominant factor in a 30-year period. Highest explanatory values in the SPEI12-FPAR3g anomalies and strongest SPEI12-FPAR3g correlations were seen in the ERTs of Australia and South America whereas lowest explanatory value and lowest correlations were observed in Asia and North America. These ERTs complement traditional pixel based methods by enabling the combined assessment of the location, timing, duration, frequency and severity of climatic and vegetation anomalies with the joint assessment of wetting and drying climatic conditions. The ERTs produced here thus have potential in supporting global change studies by mapping reference conditions of long term ecosystem changes. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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<p>Ecosystem Response Types (ERTs) derived by spatio-temporal analysis of co-varying SPEI12 and FPAR3g anomalies for (<b>A</b>) Africa, (<b>B</b>) Asia, (<b>C</b>) Australia, (<b>D</b>) Europe, (<b>E</b>) North America and (<b>F</b>) South America.</p> Full article ">
<p>RGB color composite of SPEI12-FPAR3g correlation (<b>red</b>), SPEI12 trend (<b>green</b>) and negative SPEI12 anomalies (<b>blue</b>) of the ERTs. Similar tones indicate ERTs with similar vegetation response to SPEI12 anomalies between 1982 and 2011.</p> Full article ">
<p>Redundancy analysis triplots. ERTs are represented with crosses, FPAR3g (F) variables with black arrows and the SPEI12 (S) variables with red arrows. LTA = Long Term Average; GT0 = positive anomalies; LT0 = negative anomalies; Trd = Thiel-Sen slopes; R = correlation between the FPAR3g and the ERTs average SPEI12 profile. (<b>A</b>) Africa, (<b>B</b>) Asia, (<b>C</b>) Australia, (<b>D</b>) Europe, (<b>E</b>) North America and (<b>F</b>) South America.</p> Full article ">
<p>Pearson’s r-values between the FPAR3g anomalies and the ERTs average SPEI12 profiles. (<b>A</b>) Africa, (<b>B</b>) Asia, (<b>C</b>) Australia, (<b>D</b>) Europe, (<b>E</b>) North America and (<b>F</b>) South America.</p> Full article ">
<p>SPEI12 (<b>left</b>) and FPAR3g (<b>right</b>) anomalies between 1982 and 2011 averaged within the ERTs.</p> Full article ">
<p>SPEI12 (<b>left</b>) and FPAR3g (<b>right</b>) anomalies between 1982 and 2011 averaged within the ERTs.</p> Full article ">
<p>Temporal profiles of SPEI12 and FPAR3g anomalies in the ERTs with highest correlations.</p> Full article ">
<p>Non-parametric (Theil-Sen) slopes for the FPAR3g (<b>top</b>) and SPEI12 (<b>bottom</b>) anomalies for the ERTs for the years 1982–2011.</p> Full article ">
4928 KiB  
Article
Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment
by Antonio Luis Montealegre, María Teresa Lamelas, Mihai A. Tanase and Juan De la Riva
Remote Sens. 2014, 6(5), 4240-4265; https://doi.org/10.3390/rs6054240 - 8 May 2014
Cited by 51 | Viewed by 8950
Abstract
Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually [...] Read more.
Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels. Full article
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
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<p>Study area and Composite Burn Index (CBI) field plots location. MODIS vegetation fractional cover (VFC) images and high spatial resolution ortophotography (PNOA-2012) are used as backdrop.</p> Full article ">
<p>Frequency histogram of the CBI values.</p> Full article ">
<p>Flow diagram of the ALS data processing.</p> Full article ">
<p>Partial views of different Composite Burn Index (CBI) plots depending on its burn severity value. Each photograph corresponds to the point cloud showed in the <a href="#f5-remotesensing-06-04240" class="html-fig">Figure 5</a>.</p> Full article ">
<p>Examples of ALS point clouds at plot level and their correspondence with the CBI values. (<b>a</b>) CBI = 0.0; (<b>b</b>) CBI = 1.0; (<b>c</b>) CBI = 1.5; (<b>d</b>) CBI = 2.0; (<b>e</b>) CBI = 2.5; (<b>f</b>) CBI = 3.0.</p> Full article ">
<p>ROC curves and AUC values of the regression model. Fitted ROC curve in green. Dashed line for an uninformative test (sensitivity + specificity = 1). (<b>a</b>) ROC Training Dataset; (<b>b</b>) ROC Validation Dataset.</p> Full article ">
<p>Fire severity maps obtained with the logistic method for pine cover in the four study locations. High spatial resolution ortophotography (2012) is used as backdrop.</p> Full article ">
<p>Scatterplots depicting the relationship between three remotely sensed severity metrics (RdNBR, dNBR, and logistic model) and CBI. The black lines represent the regression trend. The coefficient of determination (R<sup>2</sup>) and the equation of the line are shown for each fit. (<b>a</b>) CBI <span class="html-italic">vs.</span> dNBR; (<b>b</b>) Logistic model <span class="html-italic">vs.</span> dNBR; (<b>c</b>) CBI <span class="html-italic">vs.</span> RdNBR; (<b>c</b>) CBI <span class="html-italic">vs.</span> RdNBR; (<b>e</b>) CBI <span class="html-italic">vs.</span> Logistic model.</p> Full article ">
3230 KiB  
Article
Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011
by Donghai Wu, Hao Wu, Xiang Zhao, Tao Zhou, Bijian Tang, Wenqian Zhao and Kun Jia
Remote Sens. 2014, 6(5), 4217-4239; https://doi.org/10.3390/rs6054217 - 5 May 2014
Cited by 153 | Viewed by 10356
Abstract
Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, [...] Read more.
Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVCmax and FVCmean datasets in the last 30 years were reported by the Mann–Kendall method and Sen’s slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVCmax and FVCmean, which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student’s t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVCmax and FVCmean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVCmax pixels on a global scale (excluding permanent snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVCmean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVCmax significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change. Full article
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<p>Time series of monthly GIMMS and MODIS fractional vegetation cover (FVC) (2000–2011) for selected regions. GIMMS time series FVC is depicted in black and MODIS FVC in grey.</p> Full article ">
<p>The corresponding scatter plots, including the regression slope, intercept and correlation coefficient between monthly GIMMS FVC and MODIS FVC 2000–2011.</p> Full article ">
<p>The corresponding scatter plots, including the regression slope, intercept and correlation coefficient between GIMMS FVC and VALERI FVC.</p> Full article ">
<p>Monthly FVC changes of the six continents in the world from 1982 to 2011. (<b>A</b>–<b>G</b>) Global, Africa, Asia, Europe, North America, Oceania and South America monthly FVC changes, respectively.</p> Full article ">
<p>The annual maximum and mean FVC of the seven continents of the world from 1982 to 2011. (<b>A</b>–<b>G</b>) Global, Africa, Asia, Europe, North America, Oceania and South America yearly FVC (%) change, respectively. The black lines represent the annual maximum value and grey the annual mean value.</p> Full article ">
<p>(<b>A</b>) Annual maximum mean FVC image computed from 1982 to 2011. (<b>B</b>) Annual mean FVC image computed from 1982 to 2011.</p> Full article ">
<p>(<b>A</b>) The GIMMS FVC trend for the 0.05 significance level (1982–2011 annual maximum observations for pixels with positive and negative k). (<b>B</b>) The GIMMS FVC trend for the 0.05 significance level (1982–2011 annual mean observations for pixels with positive and negative k).</p> Full article ">
<p>(<b>A</b>) The GIMMS FVC trend for the 0.05 significance level (1982–2011 annual maximum observations for pixels with positive and negative k). (<b>B</b>) The GIMMS FVC trend for the 0.05 significance level (1982–2011 annual mean observations for pixels with positive and negative k).</p> Full article ">
1566 KiB  
Article
On Line Validation Exercise (OLIVE): A Web Based Service for the Validation of Medium Resolution Land Products. Application to FAPAR Products
by Marie Weiss, Frédéric Baret, Tom Block, Benjamin Koetz, Alessandro Burini, Bettina Scholze, Patrice Lecharpentier, Carsten Brockmann, Richard Fernandes, Stephen Plummer, Ranga Myneni, Nadine Gobron, Joanne Nightingale, Gabriela Schaepman-Strub, Fernando Camacho and Arturo Sanchez-Azofeifa
Remote Sens. 2014, 6(5), 4190-4216; https://doi.org/10.3390/rs6054190 - 5 May 2014
Cited by 57 | Viewed by 10080
Abstract
The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth [...] Read more.
The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts. Full article
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<p>BELMANIP2.1 site (white stars) and DIRECT site (black triangles) locations. The GLOBCOVER map (plate carrée projection) was aggregated into 5 main biomes and is presented as background.</p> Full article ">
<p>Distribution of the occurrence of QA values for each product (GEOV1, MGVIo, MODC5). QA = 0 is the best estimate while the highest QA value for each product corresponds to invalid product. Computation is performed over the BELMANIP2.1 sites (49 × 49 km<sup>2</sup>), during the periods defined in <a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>, and for the original temporal sampling.</p> Full article ">
<p>Spatio-temporal distribution of the fraction of invalid products for GEO, MGVIo and MOD.C5 products. For MODIS, the backup algorithm (QA = 2) is included. Computation is performed over the BELMANIP2.1 sites (49 × 49 km<sup>2</sup>), during the periods defined in <a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>, and for the original temporal sampling.</p> Full article ">
<p>Fraction of valid data for the GEOV1/VGT FAPAR product. Colored dots correspond to the 445 BELMANIP2.1 sites, with color corresponding to the fraction (%) of valid data over 3 × 3 km<sup>2</sup>, during the period defined in <a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>, and for the original temporal sampling. Six continental domains are identified by the dashed green lines (North America, South America, Europe, Africa, Asia and Oceania).</p> Full article ">
<p>Typical temporal profiles over a selection of BELMANIP2.1 sites for GEOV1 (red), MGVIo (blue) and MODC5 (green): (<b>a</b>) Non forest (site #5, 34.02°S, 65.63°W); (<b>b</b>) Needleleaf forest (site #65, 30.28°N, 83.85°W); (<b>c</b>) Deciduous broadleaf forest (site #165, 5.98°N,31.18°E); (<b>d</b>) Evergreen broadleaf forest (site #320, 24.54°N, 121.2°E). Values are computed as the median of 3 × 3 pixels centered on each site at their initial temporal resolution.</p> Full article ">
<p>Distribution of the smoothness metrics (δFAPAR) computed for GEOV1 (red), MGVIo (blue) and MOD.C5 (green). Computation is performed over the BELMANIP2.1 sites (49 × 49 km<sup>2</sup>), during the periods defined in <a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>, and for the original temporal sampling.</p> Full article ">
<p>Global average value dynamics of the three products: GEOV1 (red), MGVIo (blue) and MODC5 (green). Products are displayed at their original temporal resolution (<a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>). The 49 × 49 pixel values are averaged over all the BELMANIP2 sites for the period defined in the validation exercise.</p> Full article ">
<p>Average global seasonality computed for the three products: GEOV1 (red), MGVIo (blue) and MODC5 (green). Products are displayed at their original temporal resolution (<a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>). The 49 × 49 pixel values are averaged over all the BELMANIP2 sites, and then averaged over years for the period defined in the validation exercise.</p> Full article ">
<p>Distribution of FAPAR product value for the four main biome types: Non-Forest, Deciduous Broadleaf Forest (DBF), Evergreen Needleleaf Forest (ENF), Evergreen Broadleaf Forest (EBF). GEOV1 (red), MGVIo (blue) and MODC5 (green). For each product, N is the number of observations that were used to compute the distribution. Computation is performed over the BELMANIP2.1 sites (49 × 49 km<sup>2</sup>), during the periods defined in <a href="#t2-remotesensing-06-04190" class="html-table">Table 2</a>, and for the original temporal sampling.</p> Full article ">
1167 KiB  
Article
Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery
by Komeil Rokni, Anuar Ahmad, Ali Selamat and Sharifeh Hazini
Remote Sens. 2014, 6(5), 4173-4189; https://doi.org/10.3390/rs6054173 - 5 May 2014
Cited by 538 | Viewed by 25456
Abstract
Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation [...] Read more.
Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously. Full article
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<p>Flowchart showing the overall methods adopted in the study.</p> Full article ">
<p>The dataset after pre-processing (RGB-543 false color composite).</p> Full article ">
<p>Lake Urmia surface area changes map in the period 2000–2013.</p> Full article ">
<p>Lake Urmia surface area change maps, between (<b>a</b>) five, (<b>b</b>) four, (<b>c</b>) three, and (<b>d</b>) two different times, generated using the NDWI-PCs approach.</p> Full article ">
4474 KiB  
Article
Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery
by Nasrullah Zaini, Freek Van der Meer and Harald Van der Werff
Remote Sens. 2014, 6(5), 4149-4172; https://doi.org/10.3390/rs6054149 - 5 May 2014
Cited by 70 | Viewed by 9448
Abstract
The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring [...] Read more.
The development of advanced laboratory-based imaging hyperspectral sensors, such as SisuCHEMA, has created an opportunity to extract compositional information of mineral mixtures from spectral images. Determining proportions of minerals on rock surfaces based on spectral signature is a challenging approach due to naturally-occurring minerals that exist in the form of intimate mixtures, and grain size variations. This study demonstrates the application of SisuCHEMA hyperspectral data to determine mineral components in hand specimens of carbonate rocks. Here, we applied wavelength position, spectral angle mapper (SAM) and linear spectral unmixing (LSU) approaches to estimate the chemical composition and the relative abundance of carbonate minerals on the rock surfaces. The accuracy of these classification methods and correlation between mineral chemistry and mineral spectral characteristics in determining mineral constituents of rocks are also analyzed. Results showed that chemical composition (Ca-Mg ratio) of carbonate minerals at a pixel (e.g., sub-grain) level can be extracted from the image pixel spectra using these spectral analysis methods. The results also indicated that the spatial distribution and the proportions of calcite-dolomite mixtures on the rock surfaces vary between the spectral methods. For the image shortwave infrared (SWIR) spectra, the wavelength position approach was found to be sensitive to all compositional variations of carbonate mineral mixtures when compared to the SAM and LSU approaches. The correlation between geochemical elements and spectroscopic parameters also revealed the presence of these carbonate mixtures with various chemical compositions in the rock samples. This study concludes that the wavelength position approach is a stable and reproducible technique for estimating carbonate mineral chemistry on the rock surfaces using laboratory-based hyperspectral data. Full article
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<p>An integrated system of SisuCHEMA hyperspectral scanner [<a href="#b41-remotesensing-06-04149" class="html-bibr">41</a>].</p> Full article ">
<p>Fresh surfaces of carbonate rock samples, with red rectangles pointing out areas of selected SisuCHEMA images.</p> Full article ">
<p>Selected SisuCHEMA images A, B, C, and D of carbonate rocks (bands 200, 205, and 210) with locations of two portable X-ray fluorescence (PXRF) spot measurements (red circles).</p> Full article ">
<p>Example of SisuCHEMA spectra (<b>right</b>) of calcite (red curve), dolomite (blue curve) and calcite-dolomite mixtures (black curve) derived from different pixel locations of image D (<b>left</b>). The curves show shift in wavelength position of carbonate absorption feature.</p> Full article ">
<p>Laboratory spectral endmembers of (<b>a</b>) pure and (<b>b</b>) mixed calcite and dolomite synthetic samples (C = calcite and D = dolomite, prefix numbers showing the mineral contents in percent) (Modified after Zaini <span class="html-italic">et al.</span> [<a href="#b38-remotesensing-06-04149" class="html-bibr">38</a>]).</p> Full article ">
<p>(<b>a</b>) Wavelength position images, showing the variability carbonate mineral mixtures. (<b>b</b>) Estimated proportion of classified minerals derived from the images. (C = calcite and D = dolomite, prefix numbers showing the mineral contents in percent).</p> Full article ">
<p>SAM classification results: (<b>a</b>) SAM images using a 0.1 radians threshold angle, (<b>b</b>) estimated proportion of classified minerals derived from (a) images, (<b>c</b>) SAM images using a 0.2 radians threshold angle, (<b>d</b>) estimated proportion of classified minerals derived from (c) images. The classification images illustrate roughly similar carbonate mineral abundances in different threshold angles. (C = calcite and D = dolomite, prefix numbers showing the mineral contents in percent).</p> Full article ">
<p>LSU classification results: (<b>a</b>) unconstrained dolomite endmember images, (<b>b</b>) estimated proportion of classified minerals derived from the unconstrained images, (<b>c</b>) constrained dolomite endmember images using a 1.0 default value of weight, and (<b>d</b>) estimated proportion of classified minerals derived from the constrained images. The classification images present a slight variability of carbonate fractional abundances in different unmixing approaches. (C = calcite and D = dolomite, prefix numbers showing the mineral contents in percent).</p> Full article ">
<p>Histograms comparing proportion estimation of carbonate mineral mixtures of three spectral analysis techniques applied to the selected SisuCHEMA images. Samples (<b>a</b>) A, (<b>b</b>) B, (<b>c</b>) C, and (<b>d</b>) D. Classification results of carbonate mineral abundances differ slightly from one approach to another for a given image.</p> Full article ">
1398 KiB  
Article
A Non-MLE Approach for Satellite Scatterometer Wind Vector Retrievals in Tropical Cyclones
by Suleiman Alsweiss, Rafik Hanna, Peth Laupattarakasem, W. Linwood Jones, Christopher C. Hennon and Ruiyao Chen
Remote Sens. 2014, 6(5), 4133-4148; https://doi.org/10.3390/rs6054133 - 5 May 2014
Cited by 8 | Viewed by 8665
Abstract
Satellite microwave scatterometers are the principal source of global synoptic-scale ocean vector wind (OVW) measurements for a number of scientific and operational oceanic wind applications. However, for extreme wind events such as tropical cyclones, their performance is significantly degraded. This paper presents a [...] Read more.
Satellite microwave scatterometers are the principal source of global synoptic-scale ocean vector wind (OVW) measurements for a number of scientific and operational oceanic wind applications. However, for extreme wind events such as tropical cyclones, their performance is significantly degraded. This paper presents a novel OVW retrieval algorithm for tropical cyclones which improves the accuracy of scatterometer based ocean surface winds when compared to low-flying aircraft with in-situ and remotely sensed observations. Unlike the traditional maximum likelihood estimation (MLE) wind vector retrieval technique, this new approach sequentially estimates scalar wind directions and wind speeds. A detailed description of the algorithm is provided along with results for ten QuikSCAT hurricane overpasses (from 2003–2008) to evaluate the performance of the new algorithm. Results are compared with independent surface wind analyses from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s H*Wind surface analyses and with the corresponding SeaWinds Project’s L2B-12.5 km OVW products. They demonstrate that the proposed algorithm extends the SeaWinds capability to retrieve wind speeds beyond the current range of approximately 35 m/s (minimal hurricane category-1) with improved wind direction accuracy, making this new approach a potential candidate for current and future conically scanning scatterometer wind retrieval algorithms. Full article
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<p>QuikSCAT conically scanning measurement geometry.</p> Full article ">
<p>Time sequence of QuikSCAT measuring 4-flavor radar backscatter at one wind vector cell location. Each arc represents a portion of conical scan series of measurements.</p> Full article ">
<p>Observed ocean backscatter difference ( <math display="inline"> <mrow> <mo>Δ</mo> <msubsup> <mrow> <mi>σ</mi></mrow> <mrow> <mi mathvariant="italic">Meas</mi></mrow> <mn>0</mn></msubsup></mrow></math>, (forward-aft looks)) from Hurricane Fabian (Rev. # 21898) for: (<b>a</b>) horizontal polarization, (<b>b</b>) vertical polarization, and (<b>c</b>) average of horizontal and vertical polarizations.</p> Full article ">
<p>The coefficients (<b>a</b>) <span class="html-italic">c</span><sub>1</sub> and (<b>b</b>) <span class="html-italic">c</span><sub>2</sub> used to retrieve wind direction.</p> Full article ">
<p>X-Winds wind direction retrievals for Hurricane Fabian (Rev. # 21898): (<b>a</b>) is initial wind direction solutions, (<b>b</b>) is wind direction mirror image, and (<b>c</b>) is the complete hurricane wind direction silhouette after de-aliasing and interpolation.</p> Full article ">
<p>Wind speeds retrieval for a window of a 3 × 3 wind vector cells. X-axes are retrieved wind speeds (m/s), and y-axes are the absolute difference between measured and modeled radar backscatter ( <math display="inline"> <mrow> <msubsup> <mrow> <mi>σ</mi></mrow> <mrow> <mi mathvariant="italic">Meas</mi></mrow> <mn>0</mn></msubsup></mrow></math>– <math display="inline"> <mrow> <msubsup> <mrow> <mi>σ</mi></mrow> <mrow> <mi mathvariant="italic">GMF</mi></mrow> <mn>0</mn></msubsup></mrow></math>). Color indicates different <span class="html-italic">σ</span><sup>0</sup> flavors.</p> Full article ">
<p>Surface wind speeds for Hurricane Fabian (upper panels) and Hurricane Ivan (lower panels). Color indicates wind speeds from 0–50 m/s. Left panels are X-Winds retrievals, center panels are JPL L2B-12.5 km, and right panels are H*Wind surface analyses.</p> Full article ">
<p>Wind speed comparisons with H*Wind for composite of 10 QuikSCAT hurricane revolutions: (<b>a</b>) is X-Winds and (<b>b</b>) is L2B-12.5 km. Color scale denotes the QRad H-pol brightness temperature (warm colors indicate rain).</p> Full article ">
<p>Composite wind direction comparisons for ten hurricane cases: (<b>a</b>) L2B-12.5 km wind directions comparison with H*Wind, and (<b>b</b>) X-Winds’ wind directions comparison with H*Wind. Color scale denotes the QRad TbH (warm colors indicate rain).</p> Full article ">
8380 KiB  
Article
Calibrated Full-Waveform Airborne Laser Scanning for 3D Object Segmentation
by Fanar M. Abed, Jon P. Mills and Pauline E. Miller
Remote Sens. 2014, 6(5), 4109-4132; https://doi.org/10.3390/rs6054109 - 2 May 2014
Cited by 5 | Viewed by 6803
Abstract
Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation. Such potential is further augmented by [...] Read more.
Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS) offer increased potential for 3D object segmentation. Such potential is further augmented by the availability of full-waveform (FWF) ALS data. FWF ALS has demonstrated enhanced performance in segmentation and classification through the additional physical observables which can be provided alongside standard geometric information. However, use of FWF information is not recommended without prior radiometric calibration, taking into account all parameters affecting the backscatter energy. This paper reports the implementation of a radiometric calibration workflow for FWF ALS data, and demonstrates how the resultant FWF information can be used to improve segmentation of an urban area. The developed segmentation algorithm presents a novel approach which uses the calibrated backscatter cross-section as a weighting function to estimate the segmentation similarity measure. The normal vector and the local Euclidian distance are used as criteria to segment the point clouds through a region growing approach. The paper demonstrates the potential to enhance 3D object segmentation in urban areas by integrating the FWF physical backscattered energy alongside geometric information. The method is demonstrated through application to an interest area sampled from a relatively dense FWF ALS dataset. The results are assessed through comparison to those delivered from utilising only geometric information. Validation against a manual segmentation demonstrates a successful automatic implementation, achieving a segmentation accuracy of 82%, and out-performs a purely geometric approach. Full article
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<p>Study site, with red polygon defining the ground coverage and trajectory depicted in grey. Sample land-cover features are detailed in orthophotos A to E, and described further in Section 4.</p> Full article ">
<p>Flowchart illustrating the developed radiometric calibration routine.</p> Full article ">
<p>Histograms and statistics for the backscatter signals before and after calibration of a selected road target from overlapping flightlines: (<b>a</b>) the original amplitude signals; (<b>b</b>–<b>e</b>) the four backscatter parameters (σ, γ, σ<sub>α</sub>, γ<sub>α</sub>) respectively after calibration through the developed routine. (Mean and StDev values in the second box refer to flightline 1 and flightline 2, respectively).</p> Full article ">
<p>Segmentation of a highway bridge: (<b>a</b>) orthophoto (<b>b</b>) segmented point cloud.(A) refers to car; (B) refers to bridge barriers; (C) refers to central road reservation; (D) refers to road marking; (E) refers to grass regions.</p> Full article ">
<p>Segmentation results of a mown grass target: (<b>a</b>) orthophoto (<b>b</b>) segmented point cloud.</p> Full article ">
<p>Segmentation of natural terrain target: (<b>a</b>) orthophoto (<b>b</b>) segmented point cloud.</p> Full article ">
<p>Interest area illustrated by: (<b>a</b>) orthophoto (<b>b</b>) digital surface model.</p> Full article ">
<p>3D perspective view of segmentation results forthe interest area: (<b>a</b>) with FWF physical backscattering information (<b>b</b>) without FWF physical backscattering information.</p> Full article ">
<p>Validation results for house roof segments: (<b>a</b>) manual segmentation (<b>b</b>) automatic segmentation.</p> Full article ">
3823 KiB  
Article
Monitoring Changes in Rice Cultivated Area from SAR and Optical Satellite Images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam
by Kirsi Karila, Olli Nevalainen, Anssi Krooks, Mika Karjalainen and Sanna Kaasalainen
Remote Sens. 2014, 6(5), 4090-4108; https://doi.org/10.3390/rs6054090 - 2 May 2014
Cited by 35 | Viewed by 10167
Abstract
The objective of this study was to obtain up-to-date information on land use and to identify long term changes in land use, especially rice, aquaculture and other crops in Ben Tre and Tra Vinh provinces in Vietnam’s Mekong Delta. Long-term changes in land-use [...] Read more.
The objective of this study was to obtain up-to-date information on land use and to identify long term changes in land use, especially rice, aquaculture and other crops in Ben Tre and Tra Vinh provinces in Vietnam’s Mekong Delta. Long-term changes in land-use of the study area have not been studied using long time series of SAR and optical Earth observation (EO) data before. EO data from 1979–2012 was used: ENVISAT ASAR Wide Swath Mode, SPOT and Landsat imagery. An unsupervised ISODATA classification was performed on multitemporal SAR images. The results were validated using ground truth data. Using the Synthetic Aperture Radar (SAR) imagery maps for 2005, 2009 and 2011 were obtained. Different rice crops, aquaculture and fruit trees could be distinguished with an overall accuracy of 80%. Using available optical imagery the time series was extended from 2005 to 1979. Long-term decrease in the rice acreage and increase in the aquaculture acreage could be detected. Full article
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Graphical abstract

Graphical abstract
Full article ">
<p>A district map of the study area with the ground truth point (GTP) locations.</p> Full article ">
<p>The land-use classifications from ENVISAT ASAR imagery</p> Full article ">
<p>The land-use classifications from the SPOT imagery.</p> Full article ">
<p>The land-use classifications from the Landsat imagery.</p> Full article ">
<p>The relative change (%) in acreage of rice cultivation (<b>Left</b>) and aquaculture (<b>Right</b>) based on the SAR classification results. The red color denotes the decrease in acreage and green denotes the increase in acreage.</p> Full article ">
<p>The total acreages of land-use classes in Ben Tre and Tra Vinh provinces based SAR images for 2005, 2009 and 2011. “All rice” is the sum of 2 × irrigated, 2 × rainfed and 3 × irrigated rice crop.</p> Full article ">
<p>The overall land use in Tra Vinh province based on optical images for years 1979, 1987, 2002 and 2005.</p> Full article ">
<p>The SPOT results (1987, 1998, 2005) for four districts in Tra Vinh.</p> Full article ">
5888 KiB  
Article
A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic
by Patrick H. Freeborn, Mark A. Cochrane and Martin J. Wooster
Remote Sens. 2014, 6(5), 4061-4089; https://doi.org/10.3390/rs6054061 - 2 May 2014
Cited by 18 | Viewed by 7502
Abstract
Although it is assumed that satellite-derived descriptions of fire activity will differ depending on the dataset selected for analysis, as of yet, the effects of failed and false detections at the pixel level and on an instantaneous basis have not been propagated through [...] Read more.
Although it is assumed that satellite-derived descriptions of fire activity will differ depending on the dataset selected for analysis, as of yet, the effects of failed and false detections at the pixel level and on an instantaneous basis have not been propagated through space and time to determine their cumulative impact on the characterization of individual fire regime parameters. Here we perform the first ever decade long, multi-scale map comparison of fire chronologies and fire seasonality derived from three publicly available satellite-based fire products: the MODIS active fire product (MCD14ML), the ATSR nighttime World Fire Atlas (WFA), and the MODIS burned area product (MCD45A1). Results indicate that: (i) the agreement between fire chronologies derived from two dissimilar satellite products improves as fire pixels are aggregated into coarser grid cells, but diminishes as the number of years included in the time series increases; and (ii) all three datasets provide distinctly different portraits of the onset, peak, and duration of the fire season regardless of the map resolution. Differences in regional, long-term fire regime parameters derived from the three datasets are attributed to the unique capability of each sensor and detection algorithm to recognize geographical gradients, seasonal oscillations, decadal trends, and interannual variability in active fire characteristics and burned area patterns. Since different satellite sensors and detection algorithm strategies are sensitive to different types of fires, we demonstrate that disagreements in fire regime maps derived from dissimilar satellite-based fire products can be used as an advantage to highlight spatial and temporal transitions in landscape fire activity. Given access to multiple, publically available datasets, we caution against describing fire regimes using a single satellite-based active fire or burned area product. Full article
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<p>Map of the Central African Republic (CAR) showing: (i) major road networks; (ii) locations of the western and eastern grid cells (labeled WGC and EGC) used to demonstrate two example fire seasons in <a href="#f2-remotesensing-06-04061" class="html-fig">Figure 2</a>; and (iii) percent tree cover characterized according to the 500 m Global Land Cover Facility (GLCF) Version 3 of the Collection 4 Vegetation Continuous Field (VCF) product.</p> Full article ">
<p>Normalized seasonal profiles of (<b>a</b>) MODIS active fire (AF) pixel counts and (<b>b</b>) cumulative distributions of MODIS AF pixel counts for two 0.05° grid cells at 16-day temporal resolution. Seasonal profiles are generated from 10 years of aggregated observations, and the locations of the example grid cells, referred to as the western and eastern grid cells (WGC and EGC), are shown in <a href="#f1-remotesensing-06-04061" class="html-fig">Figure 1</a>. The 10th and 90th percentiles of the cumulative AF pixel counts are shown in (b) to demonstrate the “fire season duration” as determined in this work.</p> Full article ">
<p>Comparison of annual fire occurrence maps in the CAR for individual years during the study period. Comparisons between maps derived from the MCD14ML and WFA active fire products (<b>a</b>), and between the MCD14ML active fire product and MCD45A1 burned area products (<b>b</b>), are based on the proportion of grid cells in the CAR that contained: (i) at least one fire pixel recorded in both datasets; or (ii) no fire pixels recorded in either dataset. Each temporal profile (solid line) coincides with a particular grid cell resolution, ranging from 0.05° to 0.5° in size.</p> Full article ">
<p>Decadal trends in (<b>a</b>) the mean FRP and proportion of total active fire pixels detected at night; and (<b>b</b>) mean burned area cluster size measured by MODIS Terra and Aqua across the CAR between 2002 and 2012.</p> Full article ">
<p>Decadal trends in annual fire occurrence characterized according to the proportion of 0.05° grid cells in the CAR that contained at least one fire pixel recorded in (<b>a</b>) the MODIS active fire product; (<b>b</b>) the (A)ATSR nighttime world fire atlas; and (<b>c</b>) the MODIS burned area product. Trends in annual fire occurrence coincide with a transition in measured active fire characteristics and burned area patterns (<a href="#f4-remotesensing-06-04061" class="html-fig">Figure 4</a>), and differences in these trends contribute to the reduced agreement between the datasets towards the end of the study period (<a href="#f3-remotesensing-06-04061" class="html-fig">Figure 3</a>).</p> Full article ">
<p>Locations where the MODIS active fire (MCD14ML), the (A)ATSR nighttime world fire atlas (WFA), and the MODIS burned area (MCD45A1) datasets agreed in terms of annual fire occurrence. Shown in (<b>a</b>) are the locations where the datasets agreed only for the 2002/03 fire season, and shown in (<b>b</b>) are the locations where all three datasets agreed in any year during the study period.</p> Full article ">
<p>Comparisons between fire chronologies derived from (<b>a</b>) the active fire (AF) datasets (MCD14ML and WFA); and (<b>b</b>) the MODIS AF dataset (MCD14ML) and the MODIS burned area (BA) dataset (MCD45A1). Comparisons are based on the proportion of grid cells in the CAR where both fire products recorded an identical 10 yr time-series of annual fire occurrence (e.g., see <a href="#t1-remotesensing-06-04061" class="html-table">Table 1</a> for an example of a fire chronology in a single grid cell). Results demonstrate that the agreement between fire chronologies improves for coarser grid cell resolutions, but diminishes as the number of years included in the time-series increases.</p> Full article ">
<p>Maps of the agreement (in years; at 0.05° grid cell resolution) between fire chronologies derived from (<b>a</b>) the MODIS active fire product (MCD14ML) and the (A)ATSR nighttime world fire atlas (WFA); and (<b>b</b>) the MCD14ML active fire product and the MODIS burned area product (MCD45A1). A value of “10” (red) indicates that both satellite products generated an identical 10 year sequence of annual fire occurrence. A value of “0” (dark blue) indicates that annual fire occurrence derived from the dissimilar products disagreed in every year.</p> Full article ">
<p>Comparisons of fire chronologies at 0.05° grid cell resolution derived from ten years of the MODIS active fire (MCD14ML) and MODIS burned area (MCD45A1) datasets, expressed as a function of median tree canopy cover characterized using the Vegetation Continuous Field (VCF) product [<a href="#b59-remotesensing-06-04061" class="html-bibr">59</a>].</p> Full article ">
3070 KiB  
Article
Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR
by Jason Sherba, Leonhard Blesius and Jerry Davis
Remote Sens. 2014, 6(5), 4043-4060; https://doi.org/10.3390/rs6054043 - 2 May 2014
Cited by 23 | Viewed by 9278
Abstract
LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created [...] Read more.
LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads. Full article
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<p>Bolinas ridge study site shown within the context of California and Marin County.</p> Full article ">
<p>A typical abandoned logging road within the study site.</p> Full article ">
<p>A 1 m × 1 m slope model of the study site is shown on the left with sample points and transects overlaid. On the right the results of applying an Edge Extraction Lee Sigma filter on the slope model. White areas on the left are LiDAR artifacts.</p> Full article ">
<p>(<b>a</b>) Road seed objects, (<b>b</b>) Straightened road seed objects, (<b>c</b>) Candidate road objects, (<b>d</b>) Road objects after road seed objects grown into candidate objects.</p> Full article ">
<p>A ridge road seen on the plan curvature raster (scaling factor of 100 applied to curvature values for display purposes).</p> Full article ">
<p>Classified roads from the object-based classification with post processing.</p> Full article ">
<p>Classified roads from the pixel-based classification without post-processing.</p> Full article ">
<p>Slope model and classification results without post-processing created from the LiDAR point cloud with ground point spacings of (<b>A</b>) 0.91, (<b>B</b>) 1.3, (<b>C</b>) 1.89, (<b>D</b>) 2.73, (<b>E</b>) 3.93, and (<b>F</b>) 5.7 m.</p> Full article ">
<p>The relationship between LiDAR point cloud spacing and object-based classification accuracy.</p> Full article ">
1110 KiB  
Article
A Three-Dimensional Index for Characterizing Crop Water Stress
by Jessica A. Torrion, Stephan J. Maas, Wenxuan Guo, James P. Bordovsky and Andy M. Cranmer
Remote Sens. 2014, 6(5), 4025-4042; https://doi.org/10.3390/rs6054025 - 2 May 2014
Cited by 10 | Viewed by 9131
Abstract
The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict [...] Read more.
The application of remotely sensed estimates of canopy minus air temperature (Tc-Ta) for detecting crop water stress can be limited in semi-arid regions, because of the lack of full ground cover (GC) at water-critical crop stages. Thus, soil background may restrict water stress interpretation by thermal remote sensing. For partial GC, the combination of plant canopy temperature and surrounding soil temperature in an image pixel is expressed as surface temperature (Ts). Soil brightness (SB) for an image scene varies with surface soil moisture. This study evaluates SB, GC and Ts-Ta and determines a fusion approach to assess crop water stress. The study was conducted (2007 and 2008) on a commercial scale, center pivot irrigated research site in the Texas High Plains. High-resolution aircraft-based imagery (red, near-infrared and thermal) was acquired on clear days. The GC and SB were derived using the Perpendicular Vegetation Index approach. The Ts-Ta was derived using an array of ground Ts sensors, thermal imagery and weather station air temperature. The Ts-Ta, GC and SB were fused using the hue, saturation, intensity method, respectively. Results showed that this method can be used to assess water stress in reference to the differential irrigation plots and corresponding yield without the use of additional energy balance calculation for water stress in partial GC conditions. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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<p>(<b>Top</b>) Cumulative crop evapotranspiration (ET), rainfall and applied total water (irrigation plus rainfall) for the high irrigated (HighIrr), low irrigated (LowIrr) and non-irrigated (dryland) treatments plotted <span class="html-italic">vs.</span> the day of year (DOY); (<b>Bottom</b>) the amount and timing of daily precipitation events. The results are presented for the 2007 and 2008 growing seasons from planting (vertical line with date) to crop maturity. Arrows indicate the dates on which airborne remote sensing imagery was acquired.</p> Full article ">
<p>Array of sensors used to monitor surface temperature (<span class="html-italic">Ts</span>): (<b>left</b>) high irrigation treatment; (<b>right</b>) dryland treatment.</p> Full article ">
<p>Typical scatter plot of image pixel reflectances in the red and near-infrared bands.</p> Full article ">
<p>Red <span class="html-italic">vs.</span> NIR scatter plot of pixel reflectance from an image scene taken on 8 August 2007 (<b>A</b>), and the soil line evaluated from multi-temporal image data for georeferenced bare soil surfaces (near the pivot pump and adjacent to the plots) with varying degrees of moisture (<b>B</b>).</p> Full article ">
<p>Percent of ground cover (GC) of cotton (C) and sorghum, <span class="html-italic">Sorghum bicolor</span> (S), percent soil brightness (SB) and canopy minus air temperature (<span class="html-italic">Tc-Ta</span>) (°C), for two dates in 2007 (<b>A</b>,<b>B</b>) and 2008 (<b>C</b>) along with the respective irrigation plots for the highly irrigated (HighIrr), low irrigated (LowIrr) and dryland treatment.Note: sorghum was part of a 6-year (2003–2008) crop rotation and irrigation project and is out of the scope of this 2-year (2007–2008) study.</p> Full article ">
<p>Estimated <span class="html-italic">vs.</span> measured ground cove (GC) for the various irrigation plots in the research site (2007–2008).</p> Full article ">
<p>Color composites produced by hue, saturation and intensity (HIS) fusion of <span class="html-italic">Ts-Ta</span> (−30 °C to 30 °C), GC (0–100%) and SB (0–100%) for 2007 (<b>A</b>,<b>B</b>) and 2008 (<b>C</b>).</p> Full article ">
<p>Lint yield for the three irrigation treatments in each year [<a href="#b29-remotesensing-06-04025" class="html-bibr">29</a>]. The standard error of estimates was calculated using the Procmixed model in SAS [<a href="#b30-remotesensing-06-04025" class="html-bibr">30</a>] with the irrigation treatment as the fixed effect and the year as a random effect. The yield difference between treatments greater than the least significant difference (LSD<sub>0.05</sub>) is significant.</p> Full article ">
<p>GC, SB and <span class="html-italic">Ts-Ta</span> plotted on three orthogonal axes. GC is associated with crop growth, SB with apparent soil moistness and <span class="html-italic">Ts-Ta</span> with the surface energy balance. Plotted data are for the 8 August 2007, image acquisition date.</p> Full article ">
2308 KiB  
Article
Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds
by Darren Turner, Arko Lucieer, Zbyněk Malenovský, Diana H. King and Sharon A. Robinson
Remote Sens. 2014, 6(5), 4003-4024; https://doi.org/10.3390/rs6054003 - 2 May 2014
Cited by 182 | Viewed by 19578
Abstract
In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For [...] Read more.
In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i) a visible camera (1 cm/pixel), (ii) a 6 band multispectral camera (3 cm/pixel), and (iii) a thermal infrared camera (10 cm/pixel). Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT). The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE) of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2), and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops)
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<p>Site location map for the three Antarctic test sites.</p> Full article ">
<p>Oktokopter fitted with FLIR Photon 320 Thermal Infrared camera with Ethernet module mounted below.</p> Full article ">
<p>Comparison of two consecutive TIR frames; (<b>a</b>) blurry image with blur index of 0.32, and (<b>b</b>) sharp image with blur index of 0.22.</p> Full article ">
<p>Robinson Ridge study site: (<b>a</b>) visible mosaic of entire area, (<b>b</b>) RGB image subset, (<b>c</b>) multispectral image subset, (<b>d</b>) thermal infrared image subset, and (<b>e</b>) typical multi-spectral reflectance function of a healthy Antarctic moss turf.</p> Full article ">
<p>Statistical relationship between the ground-measured moss health and the MTVI2 index computed from mosaic of multispectral mini-MCA images obtained at Robinson Ridge test site.</p> Full article ">
<p>Spatially co-registered thematic maps; (<b>a</b>) Overview; (<b>b</b>) Moss health derived from MTVI2 vegetation index and (<b>c</b>) Moss surface temperature at ultra-high spatial resolution (a red circle highlights thermal shadow cast by tall boulder).</p> Full article ">
<p>Statistical relationship between thermal infrared DN values from UAV imagery and ground measured surface temperature for 19 sample points at the Robinson Ridge test site.</p> Full article ">
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