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Remote Sens., Volume 6, Issue 1 (January 2014) – 41 articles , Pages 1-906

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174 KiB  
Editorial
Remote Sensing Best Paper Award for the Year 2014
by Prasad Thenkabail
Remote Sens. 2014, 6(1), 905-906; https://doi.org/10.3390/rs6010905 - 22 Jan 2014
Cited by 1 | Viewed by 10375
Abstract
Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper [...] Read more.
Remote Sensing has started to institute a “Best Paper” award to recognize the most outstanding papers in the area of remote sensing techniques, design and applications published in Remote Sensing. We are pleased to announce the first “Remote Sensing Best Paper Award” for the year 2014. [...] Full article
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807 KiB  
Article
Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration
by Yunjun Yao, Shunlin Liang, Shaohua Zhao, Yuhu Zhang, Qiming Qin, Jie Cheng, Kun Jia, Xianhong Xie, Nannan Zhang and Meng Liu
Remote Sens. 2014, 6(1), 880-904; https://doi.org/10.3390/rs6010880 - 17 Jan 2014
Cited by 30 | Viewed by 11273
Abstract
Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, [...] Read more.
Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m2 to 87.6 W/m2, and with the square of correlation coefficient (R2) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information. Full article
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<p>Schematic diagram of the modified satellite-based Priestley-Taylor model (“Tree” picture source derived from Anderson <span class="html-italic">et al.</span> [<a href="#b45-remotesensing-06-00880" class="html-bibr">45</a>]).</p>
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<p>Location of the 40 flux tower sites used in this study.</p>
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<p>Eight-day time series comparisons of the modeled LE (daily total) estimates based on two PT algorithms and the ground-measured LE using the data collected from the ten flux towers in their respective land cover classes from the validation tower set. DBF: deciduous broadleaf forest; DNF: Deciduous needleleaf forest; EBF: evergreen broadleaf forest; ENF: evergreen needleleaf forest; MF: mixed forest; SHR: shrubland. All <span class="html-italic">r</span> values are significant with 99% confidence.</p>
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<p>Comparison of the predicted and ground-measured annual LE collected at all 40 flux towers sites shown in <a href="#t1-remotesensing-06-00880" class="html-table">Table 1</a>.</p>
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<p>Comparison of the annual anomalies of predicted LE and ground-measured LE collected at the flux towers sites where at least five years of data are available.</p>
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<p>Sensitivity analysis of LE with net radiation, NDVI, DT, and air pressure near surface.</p>
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<p>Map of annual composites of monthly LE over Three-North Shelter Forest Region of China for 1982–2009.</p>
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<p>Spatial pattern of linear trends in annual (<b>a</b>) LE based on MS-PT algorithm driven by GMAO-MERRA data and GIMMIS-NDVI products; (<b>b</b>) Precipitation from GMAO-MERRA data; and (<b>c</b>) air temperature from GMAO-MERRA data during 1982–2009.</p>
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<p>Annual anomalies in global land surface EDI and PDSI for 1984–2007.</p>
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1040 KiB  
Article
Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds
by Zhizhong Kang, Liqiang Zhang, Lei Tuo, Baoqian Wang and Jinlei Chen
Remote Sens. 2014, 6(1), 857-879; https://doi.org/10.3390/rs6010857 - 15 Jan 2014
Cited by 67 | Viewed by 8629
Abstract
An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom [...] Read more.
An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm, and the axis is optimized using a global extraction strategy based on segment-wise fitting. The cross-sectional planes, which are orthogonal to the central axis, are then determined for every interval. The cross-sectional points are extracted by intersecting straight lines that rotate orthogonally around the central axis within the cross-sectional plane with the tunnel point cloud. An interpolation algorithm based on quadric parametric surface fitting, using the BaySAC (Bayesian SAmpling Consensus) algorithm, is proposed to compute the cross-sectional point when it cannot be acquired directly from the tunnel points along the extraction direction of interest. Because the standard shape of the tunnel cross section is a circle, circle fitting is implemented using RANSAC to reduce the noise. The proposed approach is tested on terrestrial point clouds that cover a 150-m-long segment of a Shanghai subway tunnel, which were acquired using a LMS VZ-400 laser scanner. The results indicate that the proposed quadric parametric surface fitting using the optimized BaySAC achieves a higher overall fitting accuracy (0.9 mm) than the accuracy (1.6 mm) obtained by the plain RANSAC. The results also show that the proposed cross section extraction algorithm can achieve high accuracy (millimeter level, which was assessed by comparing the fitted radii with the designed radius of the cross section and comparing corresponding chord lengths in different cross sections) and high efficiency (less than 3 s/section on average). Full article
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<p>Extraction of boundary points using a moving window.</p>
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<p>Histogram of hypothesis model parameters.</p>
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<p>Determination of the central-axis point.</p>
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<p>Segment-wise fitting.</p>
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<p>Adjustment of the pseudo cross-sectional plane [<a href="#b15-remotesensing-06-00857" class="html-bibr">15</a>]. (<b>a</b>) Plan view; (<b>b</b>) Perspective view.</p>
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<p>Cross-sectional point estimation.</p>
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<p>Extraction of the cross section. (<b>a</b>) Extracted cross-sectional points; (<b>b</b>) The real cross-sectional points.</p>
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<p>The experimental dataset.</p>
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<p>2D projection of the tunnel points onto the XOY plane.</p>
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1039 KiB  
Article
The Inylchek Glacier in Kyrgyzstan, Central Asia: Insight on Surface Kinematics from Optical Remote Sensing Imagery
by Mohamad Nobakht, Mahdi Motagh, Hans-Ulrich Wetzel, Sigrid Roessner and Hermann Kaufmann
Remote Sens. 2014, 6(1), 841-856; https://doi.org/10.3390/rs6010841 - 14 Jan 2014
Cited by 17 | Viewed by 9431
Abstract
Mountain chains of Central Asia host a large number of glaciated areas that provide critical water supplies to the semi-arid populated foothills and lowlands of this region. Spatio-temporal variations of glacier flows are a key indicator of the impact of climate change on [...] Read more.
Mountain chains of Central Asia host a large number of glaciated areas that provide critical water supplies to the semi-arid populated foothills and lowlands of this region. Spatio-temporal variations of glacier flows are a key indicator of the impact of climate change on water resources as the glaciers react sensitively to climate. Satellite remote sensing using optical imagery is an efficient method for studying ice-velocity fields on mountain glaciers. In this study, temporal and spatial changes in surface velocity associated with the Inylchek glacier in Kyrgyzstan are investigated. We present a detailed map for the kinematics of the Inylchek glacier obtained by cross-correlation analysis of Landsat images, acquired between 2000 and 2011, and a set of ASTER images covering the time period between 2001 and 2007. Our results indicate a high-velocity region in the elevated part of the glacier, moving up to a rate of about 0.5 m/day. Time series analysis of optical data reveals some annual variations in the mean surface velocity of the Inylchek during 2000–2011. In particular, our findings suggest an opposite trend between periods of the northward glacial flow in Proletarskyi and Zvezdochka glacier, and the rate of westward motion observed for the main stream of the Inylchek. Full article
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<p>Geographic position of the Inylchek glacier and Lake Merzbacher in a Landsat image, acquired on 21 August 2006, bands 4,3,2 &gt; RGB. The red rectangle specifies the area covered by ASTER images used in this study. The inset shows the location of the Inylchek glacier in Kyrgyzstan; the red area covers Kyrgyzstan in Central Asia.</p>
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<p>N/S component of the correlation of Landsat images over the central Inylchek glacier for the period between 21 August 2006 and 24 August 2007 overlaid on a Landsat RGB image. Displacements are positive towards the North. Decorrelation points are depicted in gray.</p>
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<p>Mean surface velocities (solid lines) and standard deviation of measurements (shaded area), derived from repeat Landsat imagery by cross-correlation analysis along the profile <b>P1</b> indicated in <a href="#f1-remotesensing-06-00841" class="html-fig">Figure 1</a>.</p>
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<p>Mean surface velocities in E/W direction along the profile <b>P3</b> shown in <a href="#f1-remotesensing-06-00841" class="html-fig">Figure 1</a>. Velocities are positive towards the east and periods of highest and lowest velocities are indicated by dash lines. The standard deviation of measurements (shaded bar) is presented alongside the time periods for more clarity.</p>
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<p>Mean surface velocities in N/S direction along the profile <b>P4</b> shown in <a href="#f1-remotesensing-06-00841" class="html-fig">Figure 1</a>. Velocities are positive towards the north and periods of highest and lowest velocities are indicated by dash lines. The standard deviation of measurements (shaded bar) is presented alongside the time periods for more clarity.</p>
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<p>Mean velocities averaged across profile <b>P3</b> (red) and <b>P4</b> (blue), utilizing ASTER (solid) and Landsat imagery (dashed). Error bar shows the standard deviation of the measured velocities across the profiles.</p>
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<p>Mean surface velocities (solid lines) and their uncertainties (shaded area), derived from repeated Landsat imagery by cross-correlation analysis along the profiles <b>P2</b> indicated in <a href="#f1-remotesensing-06-00841" class="html-fig">Figure 1</a>.</p>
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<p>Displacement field at the conjunction area between the southern Inylchek and the Proletarskyi glacier. Red arrows represent the displacement pattern of the glacier in the 2009–2010 period and black arrows are for the 2010–2011 period. Medial moraines are depicted by black oval. The underlying image was acquired by the GeoEye sensor on 7 August 2002.</p>
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1679 KiB  
Article
Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001–2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products
by José Andrés Moreno Ruiz, José Rafael García Lázaro, Isabel Del Águila Cano and Pedro Hernández Leal
Remote Sens. 2014, 6(1), 815-840; https://doi.org/10.3390/rs6010815 - 13 Jan 2014
Cited by 35 | Viewed by 9499
Abstract
An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to [...] Read more.
An algorithm based on a Bayesian network classifier was adapted to produce 10-day burned area (BA) maps from the Long Term Data Record Version 3 (LTDR) at a spatial resolution of 0.05° (~5 km) for the North American boreal region from 2001 to 2011. The modified algorithm used the Brightness Temperature channel from the Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 T31 (11.03 μm) instead of the Advanced Very High Resolution Radiometer (AVHRR) band T3 (3.75 μm). The accuracy of the BA-LTDR, the Collection 5.1 MODIS Burned Area (MCD45A1), the MODIS Collection 5.1 Direct Broadcast Monthly Burned Area (MCD64A1) and the Burned Area GEOLAND-2 (BA GEOLAND-2) products was assessed using reference data from the Alaska Fire Service (AFS) and the Canadian Forest Service National Fire Database (CFSNFD). The linear regression analysis of the burned area percentages of the MCD64A1 product using 40 km × 40 km grids versus the reference data for the years from 2001 to 2011 showed an agreement of R2 = 0.84 and a slope = 0.76, while the BA-LTDR showed an agreement of R2 = 0.75 and a slope = 0.69. These results represent an improvement over the MCD45A1 product, which showed an agreement of R2 = 0.67 and a slope = 0.42. The MCD64A1, BA-LTDR and MCD45A1 products underestimated the total burned area in the study region, whereas the BA GEOLAND-2 product overestimated it by approximately five-fold, with an agreement of R2 = 0.05. Despite MCD64A1 showing the best overall results, the BA-LTDR product proved to be an alternative for mapping burned areas in the North American boreal forest region compared with the other global BA products, even those with higher spatial/spectral resolution. Full article
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<p>The North American boreal forest study region (70°N, −168.5°E; 45°N, −50°E), where boreal forest is represented by green color and all the other land covers by brown color.</p>
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<p>Annual distribution of the burned area estimate (million ha) in the study region for the analyzed products and the reference data.</p>
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<p>Graphical comparison of the burned area estimate accuracy by year (slope and R<sup>2</sup>) for the North American boreal region for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The center represents a value of 0.0, and the external circle represents a value of 1.0.</p>
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<p>Pareto boundaries (5 km and 1 km) of burned areas in the North America region, and commission and omission errors of the different burned area products for the period 2001–2011.</p>
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<p>The 200 km × 200 km sub-scenes with large fires for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The red line represents the perimeter of all of the fires registered by the reference data on the sub-scene.</p>
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<p>The 200 km × 200 km sub-scenes with large fires for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The red line represents the perimeter of all of the fires registered by the reference data on the sub-scene.</p>
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<p>The 200 km × 200 km sub-scenes with large fires for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The red line represents the perimeter of all of the fires registered by the reference data on the sub-scene.</p>
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<p>The 200 km × 200 km sub-scenes with large fires for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The red line represents the perimeter of all of the fires registered by the reference data on the sub-scene.</p>
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<p>The 200 km × 200 km sub-scenes with large fires for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The red line represents the perimeter of all of the fires registered by the reference data on the sub-scene.</p>
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<p>The 200 km × 200 km sub-scenes with large fires for the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product (MCD45A1), MODIS Collection 5.1 Direct Broadcast Monthly Burned Area Product (MCD64A1), burned area GEOLAND-2 product (BA GEOLAND-2), and burned area product from Long Term Data Record (BA-LTDR). The red line represents the perimeter of all of the fires registered by the reference data on the sub-scene.</p>
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653 KiB  
Article
A New Spaceborne Burst Synthetic Aperture Radar Imaging Mode for Wide Swath Coverage
by Pingping Huang and Wei Xu
Remote Sens. 2014, 6(1), 801-814; https://doi.org/10.3390/rs6010801 - 13 Jan 2014
Cited by 5 | Viewed by 8745
Abstract
This paper presents a new spaceborne synthetic aperture radar (SAR) burst mode named “Extended Terrain Observation by Progressive Scans (ETOPS)” for wide swath imaged coverage. This scheme extends the imaging performance of the conventional Terrain Observation by Progressive Scans (TOPS) mode with a [...] Read more.
This paper presents a new spaceborne synthetic aperture radar (SAR) burst mode named “Extended Terrain Observation by Progressive Scans (ETOPS)” for wide swath imaged coverage. This scheme extends the imaging performance of the conventional Terrain Observation by Progressive Scans (TOPS) mode with a very limited azimuth beam steering capability. Compared with the TOPS mode with the same azimuth beam steering range for the same swath width, a finer azimuth resolution could be obtained. With the same system parameters, examples of four burst SAR imaging modes named ScanSAR, TOPS, inverse TOPS (ITOPS) and ETOPS are given, and their corresponding system performances are analyzed and compared. Simulation results show that the proposed ETOPS mode could obtain a better high-resolution wide-swath imaging performance under the same conditions. Full article
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<p>Terrain Observation by Progressive Scans (TOPS) mode acquisition geometry.</p>
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<p>Antenna beam steering. (<b>a</b>) Grating lobes; (<b>b</b>) Main lobe reduction.</p>
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<p>Different azimuth burst imaging schemes in ScanSAR and TOPS. (<b>a</b>) ScanSAR; (<b>b</b>) TOPS SAR. (SAR = synthetic aperture radar)</p>
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<p>Azimuth ambiguity to signal ratio (AASR) varying with the pulse repetition frequency (PRF). (<b>a</b>) With different steering angles; (<b>b</b>) With a steering angle of 0.7°.</p>
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<p>The impact of the azimuth antenna pattern (AAP) on the AASR and the azimuth loss <span class="html-italic">L<sub>az</sub></span>. (<b>a</b>) AASR; (<b>b</b>) Azimuth loss <span class="html-italic">L<sub>az</sub></span>.</p>
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<p>The TOPS timeline in the azimuth time-frequency diagram.</p>
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<p>The relationship between cycle time and azimuth resolution.</p>
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<p>The Extended Terrain Observation by Progressive Scans (ETOPS) timeline in the azimuth time-frequency diagram.</p>
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<p>The timing diagram for wide-swath imaging with three-sub-swaths according to <a href="#t1-remotesensing-06-00801" class="html-table">Table 1</a>.</p>
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13620 KiB  
Article
Cloud and Cloud-Shadow Detection in SPOT5 HRG Imagery with Automated Morphological Feature Extraction
by Adrian Fisher
Remote Sens. 2014, 6(1), 776-800; https://doi.org/10.3390/rs6010776 - 10 Jan 2014
Cited by 84 | Viewed by 12425
Abstract
Detecting clouds in satellite imagery is becoming more important with increasing data availability, however many earth observation sensors are not designed for this task. In Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) imagery, the reflectance properties of clouds [...] Read more.
Detecting clouds in satellite imagery is becoming more important with increasing data availability, however many earth observation sensors are not designed for this task. In Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) imagery, the reflectance properties of clouds are very similar to common features on the earth’s surface, in the four available bands (green, red, near-infrared and shortwave-infrared). The method presented here, called SPOTCASM (SPOT cloud and shadow masking), deals with this problem by using a series of novel image processing steps, and is the first cloud masking method to be developed specifically for SPOT5 HRG imagery. It firstly detects marker pixels using image specific threshold values, and secondly grows segments from these markers using the watershed-from-markers transform. The threshold values are defined as lines in a 2-dimensional histogram of the image surface reflectance values, calculated from two bands. Sun and satellite angles, and the similarity between the area of cloud and shadow objects are used to test their validity. SPOTCASM was tested on an archive of 313 cloudy images from across New South Wales (NSW), Australia, with 95% of images having an overall accuracy greater than 85%. Commission errors due to false clouds (such as highly reflective ground), and false shadows (such as a dark water body) can be high, as can omission errors due to thin cloud that is very similar to the underlying ground surface. These errors can be quickly reduced through manual editing, which is the current method being employed in the operational environment in which SPOTCASM is implemented. The method is being used to mask clouds and shadows from an expanding archive of imagery across NSW, facilitating environmental change detection. Full article
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<p>Location of the cloudy Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical (HRG) images over New South Wales, Australia (2004–2012) used to train and validate the SPOT cloud and shadow masking (SPOTCASM) method. The 10 numbered scene boundaries show the location of the training images used to develop the method.</p>
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<p>An example of the reflectance properties of selected pixels from a subset of image 8, showing the similarity of cloud to buildings, and cloud-shadow to water. (<b>A</b>) Image subset displayed with bands 4, 3, and 1 and red, green and blue; (<b>B</b>) Each spectral curve represents the mean ± two standard deviations of 100 manually sampled pixels; (<b>C</b>) A 2-dimensional scatterplot of the same 100 pixels, which were taken from the image subset.</p>
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<p>Two examples of the watershed transform applied to a 1-dimensional signal. (<b>A</b>) When three markers are located at the three local minima, three segments are formed with boundaries (watershed lines) on the local maxima; (<b>B</b>) When only two markers are selected, segment 2 floods over a peak and into the neighboring trough, until a boundary is formed with segment 1.</p>
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<p>Histograms of the amount of cloud and shadow in the 313 SPOT5 HRG reference images used for training and validation.</p>
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<p>The six main steps of the SPOTCASM method.</p>
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<p>(<b>A</b>) An example of creating the water mask for image 9. The subset is shown with bands 4, 3, 1 as red, green and blue; (<b>B</b>) The sum of the morphological gradient of all bands is used to define the edges of the water bodies, shown as greyscale where white is high and black is low; (<b>C</b>) Internal and external marker pixels for water; (<b>D</b>) Water mask grown using the watershed from markers algorithm; (<b>E</b>) Histograms used to define marker pixels. The dotted black line on the lower 2-dimensional histogram defines the area used to create the upper histogram, while the red dashed line defines the right-hand edge of the water peak and the position of <span class="html-italic">a</span><sub>2</sub>. This method allows most water to be masked, while cloud shadows that have very similar spectral properties to water are excluded.</p>
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<p>Examples of thresholds identified in image 1 (<b>Left</b>) and image 7 (<b>Right</b>). Image numbers are from <a href="#f1-remotesensing-06-00776" class="html-fig">Figure 1</a> and subsets of these images are shown in the results section. (<b>A</b>) Threshold lines for the water mask; (<b>B</b>) Threshold lines for the vegetation mask; (<b>C</b>) Threshold lines for the cloud markers.</p>
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<p>An example of how SPOTCASM generates cloud segments. (<b>A</b>) A subset of image 3 shown with bands 4, 3, 1 as red, green and blue; (<b>B</b>) Internal and external markers; (<b>C</b>) An alternating sequential filter (ASF) is applied to the image to smooth within object variation while enhancing object edges; (<b>D</b>) The sum of the morphological gradient of each band after the ASF was applied enhances object edges, shown as greyscale where white is high and black is low; (<b>E</b>) Cloud objects are grown using the watershed from markers algorithm, before a five pixel buffer is applied; (<b>F</b>) Thresholds for marker pixels are identified on the 2-dimensional histogram of bands 1 (green) and band 4 (SWIR). The density values along a line perpendicular to the d-line, going through point <span class="html-italic">d<sub>2</sub></span> are used to define the offset to the e-line.</p>
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<p>The decision tree used to iteratively refine possible cloud and cloud-shadow objects. Any size comparison mentioned in the tree is performed using <a href="#FD3" class="html-disp-formula">Equations (3)</a> and <a href="#FD4" class="html-disp-formula">(4)</a>. The numbers in the red (reject), green (accept) and yellow (no decision) rectangles identify the 19 different end-points to the decision tree. (<b>A</b>) The simplest case; (<b>B</b>) The more complicated case where possible clouds are located in the shadow search area; (<b>C</b>) The case complicated only by null pixels located in the shadow search area.</p>
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<p>The decision tree used to iteratively refine possible cloud and cloud-shadow objects. Any size comparison mentioned in the tree is performed using <a href="#FD3" class="html-disp-formula">Equations (3)</a> and <a href="#FD4" class="html-disp-formula">(4)</a>. The numbers in the red (reject), green (accept) and yellow (no decision) rectangles identify the 19 different end-points to the decision tree. (<b>A</b>) The simplest case; (<b>B</b>) The more complicated case where possible clouds are located in the shadow search area; (<b>C</b>) The case complicated only by null pixels located in the shadow search area.</p>
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11704 KiB  
Article
Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa
by Manuela Hirschmugl, Martin Steinegger, Heinz Gallaun and Mathias Schardt
Remote Sens. 2014, 6(1), 756-775; https://doi.org/10.3390/rs6010756 - 9 Jan 2014
Cited by 44 | Viewed by 11306
Abstract
Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in [...] Read more.
Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods. Full article
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<p>Location of the two study sites in Cameroon and Central African Republic with a Congo Basin vegetation types map as background [<a href="#b40-remotesensing-06-00756" class="html-bibr">40</a>]. The areas marked in red represent the study sites.</p>
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<p>Overview of processing steps.</p>
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<p>(<b>a</b>) Visual interpretation result; (<b>b</b>) 90 × 90 m aggregated visual interpretation result (percentage of gaps for interpretable areas) superimposed on Landsat SLC-off image (bands 432).</p>
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<p>Schematically classification system for the multi-temporal stack of features (e.g., soil fraction). The soil fraction behavior of one example pixel (bottom: black line) is compared to typical behavior of degraded areas (top: colored lines) and classified to the best fitting line.</p>
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<p>Performance of the temporal behavior of mean soil fraction values of degradation areas from different years in the Cameroon study site.</p>
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<p>Final degradation map for CAR study site. The different colors indicate the time of (first) degradation occurrence.</p>
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<p>Detail of CAR result: (<b>left</b>) Landsat image 2001, (<b>right)</b> Landsat image 2001 superimposed with nonforest areas (white) and forest areas affected by degradation (red).</p>
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<p>Final degradation map for the Cameroon study site in comparison with the IFL map [<a href="#b5-remotesensing-06-00756" class="html-bibr">5</a>]. The different colors indicate the time of degradation occurrence mapped by our approach, the black hatched area represent the areas classified as IFL.</p>
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1662 KiB  
Article
Radar-to-Radar Interference Suppression for Distributed Radar Sensor Networks
by Wen-Qin Wang and Huaizong Shao
Remote Sens. 2014, 6(1), 740-755; https://doi.org/10.3390/rs6010740 - 9 Jan 2014
Cited by 21 | Viewed by 8251
Abstract
Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched [...] Read more.
Radar sensor networks, including bi- and multi-static radars, provide several operational advantages, like reduced vulnerability, good system flexibility and an increased radar cross-section. However, radar-to-radar interference suppression is a major problem in distributed radar sensor networks. In this paper, we present a cross-matched filtering-based radar-to-radar interference suppression algorithm. This algorithm first uses an iterative filtering algorithm to suppress the radar-to-radar interferences and, then, separately matched filtering for each radar. Besides the detailed algorithm derivation, extensive numerical simulation examples are performed with the down-chirp and up-chirp waveforms, partially overlapped or inverse chirp rate linearly frequency modulation (LFM) waveforms and orthogonal frequency division multiplexing (ODFM) chirp diverse waveforms. The effectiveness of the algorithm is verified by the simulation results. Full article
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<p>The radar-to-radar interferences in a distributed radar sensor network.</p>
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<p>Block diagram of the interference suppression algorithm.</p>
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<p>Illustration of down-chirp and up-chirp waveforms.</p>
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<p>Comparative pulse compression results for the two radars with down-chirp and up-chirp waveforms. (<b>a</b>) Ideal result and mutual interference; (<b>b</b>) pulse compression using the second waveform as the reference function; (<b>c</b>) after being processed by the iterative suppression algorithm; (<b>d</b>) final matched filtering results for the first radar.</p>
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<p>Result of the interference suppression ratio.</p>
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<p>Illustration of three waveforms with overlapped frequency.</p>
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<p>Comparative pulse compression results for the three radars with partially overlapped frequency or inverse chirp rate waveforms. (<b>a</b>) Ideal result and mutual interference; (<b>b</b>) after being pulse compressed by using the second waveform as the reference function and processed by the specific filter; (<b>c</b>) pulse compression after suppressing the second interference; (<b>d</b>) final matched filtering result for the first radar.</p>
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<p>Result of the interference suppression ratio.</p>
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<p>Illustration of orthogonal frequency division multiplexing (OFDM) chirp diverse waveforms.</p>
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6736 KiB  
Article
Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
by Beth Cole, Julia McMorrow and Martin Evans
Remote Sens. 2014, 6(1), 716-739; https://doi.org/10.3390/rs6010716 - 9 Jan 2014
Cited by 35 | Viewed by 9095
Abstract
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the [...] Read more.
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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<p>Location map showing area covered by the image, the different restoration blocks, and image subsets referred to in the paper.</p>
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<p>Cumulative proportion of variance accounted for by increasing latent factors in Partial Least Squares Regression (PLSR) model.</p>
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<p>Plots of X-scores against Y-scores for each of the latent factors in the PLSR model. <span class="html-italic">R</span><sup>2</sup> values shown in each one.</p>
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<p>Plots of PLSR residual against observed values for each PFT.</p>
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<p>Regression parameter profile and variable importance plots for the PLSR using five latent factors. (<b>a</b>) Variable importance plot for all bands; (<b>b</b>) Variable importance plot with spectral bands removed; (<b>c</b>) Regression parameter profile for all bands; (<b>d</b>) Regression parameter profile with spectral bands removed.</p>
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<p>Regression parameter profile and variable importance plots for the PLSR using five latent factors. (<b>a</b>) Variable importance plot for all bands; (<b>b</b>) Variable importance plot with spectral bands removed; (<b>c</b>) Regression parameter profile for all bands; (<b>d</b>) Regression parameter profile with spectral bands removed.</p>
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<p>Regression parameter profile and variable importance plots for the PLSR using 10 latent factors. (<b>a</b>) Variable importance plot for all bands; (<b>b</b>) Regression parameter profile all bands.</p>
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<p>Scatterplots of the PLSR predicted <span class="html-italic">vs.</span> observed values for the vegetation abundance in the validation dataset.</p>
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<p>Scatterplots of the PLSR predicted <span class="html-italic">vs.</span> observed values for the vegetation abundance in the validation dataset.</p>
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<p>(<b>a</b>) Subset of the abundance of shrub image predicted from PLSR; (<b>b</b>) Eagle sensor true colour image (bands 640 nm, 550 nm and 460 nm), of the same area.</p>
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<p>(<b>a</b>) Subset of the abundance of bare peat image predicted from PLSR; (<b>b</b>) Eagle sensor true colour image (bands 640 nm, 550 nm and 460 nm) of the same area.</p>
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4401 KiB  
Article
Airborne Dual-Wavelength LiDAR Data for Classifying Land Cover
by Cheng-Kai Wang, Yi-Hsing Tseng and Hone-Jay Chu
Remote Sens. 2014, 6(1), 700-715; https://doi.org/10.3390/rs6010700 - 8 Jan 2014
Cited by 45 | Viewed by 8948
Abstract
This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major [...] Read more.
This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths) could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation) when classifying land cover. Full article
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<p>(<b>a</b>) Location of the study area; (<b>b</b>) location of the reference data for classification.</p>
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<p>Flowchart of the approach. DSM, digital surface model; DEM, digital elevation model; SVM, support vector machine; HV, high vegetation; LV, low vegetation; SOIL, bare soil; ROOF, roofs; R&amp;G, road and gravel.</p>
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<p>Frequency distribution of (<b>a</b>) the amplitude from the Riegl system, (<b>b</b>) the amplitude from the Optech system, (<b>c</b>) the surface height from the Riegl system, (<b>d</b>) the surface height from the Optech system, (<b>e</b>) the echo width from the Riegl system and (<b>f</b>) the echo width from the Optech system.</p>
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<p>Results of the classifications using the five feature sets: (<b>a</b>) Riegl surface height, echo width (set <span class="html-italic">ϕ</span><sub>1</sub>); (<b>b</b>) Optech amplitude, Riegl surface height, echo width (set <span class="html-italic">ϕ</span><sub>2</sub>); (<b>c</b>) Riegl amplitude, Riegl surface height, echo width (set <span class="html-italic">ϕ</span><sub>3</sub>); (<b>d</b>) Riegl amplitude, Optech amplitude, Riegl surface height, echo width (set <span class="html-italic">ϕ</span><sub>4</sub>); (<b>e</b>) Riegl amplitude, Optech amplitude, Riegl surface height (set <span class="html-italic">ϕ</span><sub>5</sub>); and (<b>f</b>) the orthoimage.</p>
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1988 KiB  
Article
An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation
by Chuli Hu, Jia Li, Nengcheng Chen and Qingfeng Guan
Remote Sens. 2014, 6(1), 677-699; https://doi.org/10.3390/rs6010677 - 7 Jan 2014
Cited by 8 | Viewed by 8151
Abstract
In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose [...] Read more.
In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations. Full article
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<p>Conceptual level of the sensor object model (blue font are the corresponding instructions).</p>
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<p>Assignment of meta-attribute values for the AQUA_MODIS SRO_rs.</p>
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<p>Sample of AQUA_MODIS SRO_rs representation.</p>
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<p>The assignment of meta-attribute values for RADARSAT-2_SAR SRO_rs.</p>
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<p>The stages of our proposed object model involved in the integration of satellite imagery observation (the marked numbers represent the detailed experimental flows).</p>
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<p>Sensor application based on the proposed model.</p>
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<p>Useful observation information extracted from the corresponding new RS_rs of SRO_rs.</p>
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1202 KiB  
Article
Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990
by Marian Vittek, Andreas Brink, Francois Donnay, Dario Simonetti and Baudouin Desclée
Remote Sens. 2014, 6(1), 658-676; https://doi.org/10.3390/rs6010658 - 7 Jan 2014
Cited by 98 | Viewed by 12353
Abstract
Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data [...] Read more.
Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover changes occurring between 1975 and 1990 in West Africa using a systematic sample of satellite imagery. The primary data sources for the land cover classification were Landsat Multispectral Scanner (MSS) for 1975 and Landsat Thematic Mapper (TM) for the 1990 period. Dedicated selection of the appropriate image data for land cover change monitoring was performed for the year 1975. Based on this selected dataset, the land cover analysis is based on a systematic sample of 220 suitable Landsat image extracts (out of 246) of 20 km × 20 km at each one degree latitude/longitude intersection. Object-based classification, originally dedicated for Landsat TM land cover change monitoring and adapted for MSS, was used to produce land cover change information for four different land cover classes: dense tree cover, tree cover mosaic, other wooded land and other vegetation cover. Our results reveal that in 1975 about 6% of West Africa was covered by dense tree cover complemented with 12% of tree cover mosaic. Almost half of the area was covered by other wooded land and the remaining 32% was represented by other vegetation cover. Over the 1975–1990 period, the net annual change rate of dense tree cover was estimated at −0.95%, at −0.37% for the other wooded land and very low for tree cover mosaic (−0.05%). On the other side, other vegetation cover increased annually by 0.70%, most probably due to the expansion of agricultural areas. This study demonstrates the potential of Landsat MSS and TM data for large scale land cover change assessment in West Africa and highlights the importance of consistent and systematic data processing methods with targeted image acquisition procedures for long-term monitoring. Full article
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<p>Study area and sampling scheme covering three ecoregions in West Africa.</p>
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<p>Availability of good quality Landsat MSS images for the 1975 reference year over sub-Saharan Africa (including our study area over West Africa) overlaid on White’s ecoregions [<a href="#b22-remotesensing-06-00658" class="html-bibr">22</a>].</p>
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<p>Repartition of Landsat MSS image extracts availability depending on the data source comparing sub-Saharan and West Africa (figures correspond to the number of concerned sample sites).</p>
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<p>Comparison of the acquisition years of the best candidates selected for Landsat Multispectral Scanner (MSS) (around 1975) and Thematic Mapper (TM) images (around 1990) for (<b>a</b>) the whole sub-Saharan Africa (<span class="html-italic">n</span> = 1,799), and only for (<b>b</b>) West Africa (<span class="html-italic">n</span> = 220).</p>
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<p>Comparison of acquisition months of the best candidates selected for Landsat MSS (around 1975) and TM images (around 1990) for (<b>a</b>) the whole sub-Saharan Africa (<span class="html-italic">n</span> = 1,799), and only for (<b>b</b>) West Africa (<span class="html-italic">n</span> = 220).</p>
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<p>Scheme of TREES data processing chain with adaptation for Landsat MSS images.</p>
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<p>Area proportions of land cover classes for each sample site (<span class="html-italic">n</span> = 220) for the year 1975 (<b>a</b>) Tree cover (<b>b</b>) Tree cover mosaic (<b>c</b>) Other wooded land (<b>d</b>) Other vegetation cover.</p>
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<p>Net land cover changes in West Africa between 1975 and 1990 focused on (<b>a</b>) forest to all other land cover classes (deforestation) and (<b>b</b>) from other wooded land to other vegetation cover.</p>
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<p>Loss, gain and net land cover changes for each sample site depending of its latitude and for two dynamics (<b>a</b>) forest to all other land cover classes, and (<b>b</b>) other wooded land to other vegetation cover.</p>
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1830 KiB  
Article
Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences
by Hongyan Zhang, Zeyu Yang, Liangpei Zhang and Huanfeng Shen
Remote Sens. 2014, 6(1), 637-657; https://doi.org/10.3390/rs6010637 - 6 Jan 2014
Cited by 74 | Viewed by 10509
Abstract
Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by the super-resolution reconstruction technique. However, different [...] Read more.
Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by the super-resolution reconstruction technique. However, different sensor shooting angles lead to different resolutions for each angle image, which affects the effectiveness of the super-resolution reconstruction of the multi-angle images. In view of this, we propose utilizing adaptive weighted super-resolution reconstruction to alleviate the limitations of the different resolutions. This paper employs two adaptive weighting themes. The first approach uses the angle between the imaging angle of the current image and that of the nadir image. The second is closely related to the residual error of each low-resolution angle image. The experimental results confirm the feasibility of the proposed method and demonstrate the effectiveness of the proposed adaptive weighted super-resolution approach. Full article
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<p>Simulation imaging process for acquiring the digital images, where the desired HR image is at the left side with the observed image at the extreme right.</p>
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<p>Illustration of a multi-angle imaging system.</p>
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<p>Panchromatic image of the multi-angle WorldView-2 imagery. From (<b>a</b>) to (<b>e</b>): 81.4° in the forward direction, 59.8° and 44.6° in the backward direction, and 44.7° and 56.0° in the forward direction.</p>
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<p>Band 1 of the multi-angle WorldView-2 multispectral imagery. From (<b>a</b>) to (<b>e</b>): 81.4° in the forward direction, 59.8° and 44.6° in the backward direction, and 44.7° and 56.0° in the forward direction.</p>
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<p>Two experimental regions cropped from <a href="#f3-remotesensing-06-00637" class="html-fig">Figure 3</a>. (<b>a</b>) Image1; (<b>b</b>) Image2.</p>
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<p>Experimental results of different resolution enhancement methods with Image1. (<b>a</b>) Original HR image; (<b>b</b>) bilinear interpolation; (<b>c</b>) general algorithm (GEN); (<b>d</b>) angle weighted (ANGW); (<b>e</b>) residual error weighted (RESW).</p>
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<p>Experimental results of different resolution enhancement methods with Image2. (<b>a</b>) Original HR image; (<b>b</b>) bilinear interpolation; (<b>c</b>) general algorithm (GEN); (<b>d</b>) angle weighted (ANGW); (<b>e</b>) residual error weighted (RESW).</p>
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<p>Sensitivity analysis of the regularization parameter <span class="html-italic">λ</span> in terms of ISNR, PSNR and SSIM in the simulation image data experiment. (<b>a</b>,<b>c</b>,<b>e</b>) show the ISNR, PSNR, and SSIM values of Image1, respectively; (<b>b</b>,<b>d</b>,<b>f</b>) show the ISNR, PSNR, and SSIM values of Image2, respectively</p>
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<p>Sensitivity analysis of the regularization parameter <span class="html-italic">λ</span> in terms of ISNR, PSNR and SSIM in the simulation image data experiment. (<b>a</b>,<b>c</b>,<b>e</b>) show the ISNR, PSNR, and SSIM values of Image1, respectively; (<b>b</b>,<b>d</b>,<b>f</b>) show the ISNR, PSNR, and SSIM values of Image2, respectively</p>
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<p>SRR results of the real multi-angle remote sensing images. (<b>a</b>) bilinear interpolation; (<b>b</b>) GEN; (<b>c</b>) ANGW; (<b>d</b>) RESW.</p>
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1544 KiB  
Article
Temporal Behavior of Lake Size-Distribution in a Thawing Permafrost Landscape in Northwestern Siberia
by Johanna Mård Karlsson, Steve W. Lyon and Georgia Destouni
Remote Sens. 2014, 6(1), 621-636; https://doi.org/10.3390/rs6010621 - 6 Jan 2014
Cited by 65 | Viewed by 9140
Abstract
Arctic warming alters regional hydrological systems, as permafrost thaw increases active layer thickness and in turn alters the pathways of water flow through the landscape. Further, permafrost thaw may change the connectivity between deeper and shallower groundwater and surface water altering the terrestrial [...] Read more.
Arctic warming alters regional hydrological systems, as permafrost thaw increases active layer thickness and in turn alters the pathways of water flow through the landscape. Further, permafrost thaw may change the connectivity between deeper and shallower groundwater and surface water altering the terrestrial water balance and distribution. Thermokarst lakes and wetlands in the Arctic offer a window into such changes as these landscape elements depend on permafrost and are some of the most dynamic and widespread features in Arctic lowland regions. In this study we used Landsat remotely sensed imagery to investigate potential shifts in thermokarst lake size-distributions, which may be brought about by permafrost thaw, over three distinct time periods (1973, 1987–1988, and 2007–2009) in three hydrological basins in northwestern Siberia. Results revealed fluctuations in total area and number of lakes over time, with both appearing and disappearing lakes alongside stable lakes. On the whole basin scales, there is no indication of any sustained long-term change in thermokarst lake area or lake size abundance over time. This statistical temporal consistency indicates that spatially variable change effects on local permafrost conditions have driven the individual lake changes that have indeed occurred over time. The results highlight the importance of using multi-temporal remote sensing data that can reveal complex spatiotemporal variations distinguishing fluctuations from sustained change trends, for accurate interpretation of thermokarst lake changes and their possible drivers in periods of climate and permafrost change. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
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<p>Location of Nadym and Pur river basins, and 7129 sub-basin, thermokarst lake distribution (as of 1973), permafrost distribution, peatland distribution, and areas excluded from the remote sensing study due to cloud cover.</p>
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<p>Relative number of lakes with different lake areas (ha) for the Nadym and Pur river basins (lakes &gt; 10 ha), and the 7129 sub-basin (lakes &gt; 1 ha) comparing three different time periods (from 1973, through 1987–1988, to 2007–2009).</p>
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<p>Annual rate of lake number change for lakes with the lower lake area threshold of 10 ha, normalized by total lake area for Pur, Nadym river basins, and 7129 sub-basin.</p>
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<p>Examples of newly formed lakes (blue polygons representing the 2009 lake surface area) in the Pur river basin (<b>A</b>,<b>B</b>); and thermokarst lakes that have been drained (red polygons representing the 1973 lake surface area) in the Nadym river basin (<b>C</b>). White features in subset <b>A</b> and <b>B</b> are associated with infrastructure, e.g., road networks.</p>
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<p>Log-abundance log-size plot for lakes in Nadym and Pur river basins with lower (10 ha) resolution, and 7129 sub-basin with higher (1 ha) resolution. For comparison, the figure also shows fitted power law functions (lines on log-log plot with slopes −1.66, −1.60, −0.94 and <span class="html-italic">R</span><sup>2</sup> values 0.95, 0.96, 0.92 for Nadym, Pur and 7129, respectively).</p>
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3021 KiB  
Article
Melt Patterns and Dynamics in Alaska and Patagonia Derived from Passive Microwave Brightness Temperatures
by Kathryn Semmens and Joan Ramage
Remote Sens. 2014, 6(1), 603-620; https://doi.org/10.3390/rs6010603 - 6 Jan 2014
Cited by 4 | Viewed by 6955
Abstract
Glaciers and icefields are critical components of Earth’s cryosphere to study and monitor for understanding the effects of a changing climate. To provide a regional perspective of glacier melt dynamics for the past several decades, brightness temperatures (Tb) from the passive [...] Read more.
Glaciers and icefields are critical components of Earth’s cryosphere to study and monitor for understanding the effects of a changing climate. To provide a regional perspective of glacier melt dynamics for the past several decades, brightness temperatures (Tb) from the passive microwave sensor Special Sensor Microwave Imager (SSM/I) were used to characterize melt regime patterns over large glacierized areas in Alaska and Patagonia. The distinctness of the melt signal at 37V-GHz and the ability to acquire daily data regardless of clouds or darkness make the dataset ideal for studying melt dynamics in both hemispheres. A 24-year (1988–2011) time series of annual Tb histograms was constructed to (1) characterize and assess temporal and spatial trends in melt patterns, (2) determine years of anomalous Tb distribution, and (3) investigate potential contributing factors. Distance from coast and temperature were key factors influencing melt. Years of high percentage of positive Tb anomalies were associated with relatively higher stream discharge (e.g., Copper and Mendenhall Rivers, Alaska, USA and Rio Baker, Chile). The characterization of melt over broad spatial domains and a multi-decadal time period offers a more comprehensive picture of the changing cryosphere and provides a baseline from which to assess future change. Full article
(This article belongs to the Special Issue Cryospheric Remote Sensing)
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<p>Generalization of melt regime pattern determined from the sum of annual brightness temperature histograms. Warm/wet pixels exhibit an asymmetric high distribution skewed to the right (#6; red curve); in contrast, cold/frozen pixels exhibit the asymmetric low distribution skewed left (#2; blue curve). Pixels that are cold/frozen a large portion of the year but also have some melt exhibit bimodal low distribution (#3; green curve) while ones that have more days of melt have a bimodal high distribution (#5; orange curve). Equal time cold/frozen and warm/wet are evenly bimodal (#4; purple curve). Pixels with mixed or water signals usually close to the coast are unimodal (#1; yellow curve). The thin vertical line is the melt threshold for this sensor and wavelength (246 K) determined from previous work [<a href="#b20-remotesensing-06-00603" class="html-bibr">20</a>] and (in conjunction with diurnal amplitude variations, DAV) indicates when the surface starts melting.</p>
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<p>Conceptual diagram showing average histogram (thick black line) for all years compared to an individual year histogram (thin black line) and the positive (red) and negative (blue) anomaly brightness temperature frequencies.</p>
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<p>Digital Elevation Model [<a href="#b26-remotesensing-06-00603" class="html-bibr">26</a>] of southern Alaska with locations of pixels analyzed in the study. Dots are the centroid of the 25 km EASE-grid pixels used for the SSM/I T<sub>b</sub> data analysis. Dot colors depict the characteristic melt regime pattern (<a href="#f1-remotesensing-06-00603" class="html-fig">Figure 1</a> colors are the key) determined from the sum and average T<sub>b</sub> frequencies from 1988 to 2011. Relatively warmer and wetter melt patterns are found closer to the coast.</p>
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<p>Digital elevation map (SRTM, 90 m resolution) for the Northern (NPI) and Southern (SPI) Patagonian Icefields. Dots are the center of the 25 km EASE-grid pixels and colors indicate general melt regime pattern from <a href="#f1-remotesensing-06-00603" class="html-fig">Figure 1</a>.</p>
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<p>Time series of T<sub>b</sub> histogram anomalies (difference from the average of 1988–2011) for a coast to inland transect from Malaspina to Hubbard Glaciers (see the dark blue rectangle in <a href="#f3-remotesensing-06-00603" class="html-fig">Figure 3</a> for location).Red is a positive T<sub>b</sub> deviation, blue is a negative deviation. Vertical black line in each panel is the 246 K melt threshold. For scale, the y-axis distance between each year line is 100 (this scale measures the number of occurrences of the T<sub>b</sub> above or below the average frequency).</p>
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<p>Time series of T<sub>b</sub> anomalies for a coast/west to inland/east transect from the SPI (first four panels) and for the pixel over San Rafael Glacier to the north in NPI (right-hand panel red outline; see <a href="#f4-remotesensing-06-00603" class="html-fig">Figure 4</a> for location).The black oval above the last panel is shows seasonal components (spring is orange; fall is black; winter is blue; summer is red). Vertical black line in each panel is the 246 K melt threshold. For scale, the y-axis distance between each year line is 100.</p>
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<p>Time series (1988–2011) of the percentage of positive (and negative) T<sub>b</sub> anomalies above (and below) the melt threshold (246 K) for each year for the Alaska transect pixels (stars and solid lines) and for the Patagonia Icefield transect (circles and dashed lines) (see <a href="#f3-remotesensing-06-00603" class="html-fig">Figures 3</a> and <a href="#f4-remotesensing-06-00603" class="html-fig">4</a> for locations). Line colors correspond to the pixel’s characteristic melt regime (see key <a href="#f1-remotesensing-06-00603" class="html-fig">Figure 1</a>).</p>
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<p>Discharge for Copper River at Million Dollar Bridge near Cordova, AK (USGS 15214000, see <a href="#f3-remotesensing-06-00603" class="html-fig">Figure 3</a> for location) for two negative T<sub>b</sub> anomaly years in blue (1991 and 1992) and two positive T<sub>b</sub> anomaly years in red (2005 and 2009) compared to the mean 1988 to 2011 (thick black line).The inset at right shows the T<sub>b</sub> anomalies for the nearest pixel (average elevation 841 m) and the average air temperature from Cordova (USAF 702960 WBAN 26410, 60.489°N, 145.451°W, 14.6 m elevation). The inset at left shows the relationship of T<sub>b</sub> (black), diurnal amplitude variation (DAV) (gray), air temperature (red), and discharge (blue). Melt thresholds are dashed horizontal lines, and the period of melt-refreeze or high DAV is denoted. The spring freshet follows the end of the high DAV period. Positive anomaly years tend to have earlier freshet and earlier and higher peak flows compared to negative anomaly years.</p>
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<p>Discharge for the Mendenhall River near Auke Bay, AK (USGS 15052500, see <a href="#f3-remotesensing-06-00603" class="html-fig">Figure 3</a> for location) for two negative T<sub>b</sub> anomaly years in blue (1991 and 1992) and two positive T<sub>b</sub> anomaly years in red (2002 and 2003) compared to the mean 1988 to 2011 (thick black line).The inset at right shows the T<sub>b</sub> anomalies for a nearby glacier pixel (average elevation 1,358 m) and the average air temperature from Juneau (USAF 703810 WBAN 25309, 58.357°N, 134.564°W, 7.3 m elevation). The inset at left shows the relationship of T<sub>b</sub> (black), diurnal amplitude variation (DAV) (gray bottom), air temperature (red), and discharge (blue) for 1992. Melt thresholds are dashed horizontal lines, and the period of melt-refreeze or high DAV is denoted.</p>
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2505 KiB  
Article
High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors
by Hamideh Nouri, Simon Beecham, Sharolyn Anderson and Pamela Nagler
Remote Sens. 2014, 6(1), 580-602; https://doi.org/10.3390/rs6010580 - 6 Jan 2014
Cited by 110 | Viewed by 17569
Abstract
Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The [...] Read more.
Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05) and the lowest one was for turf (r2 = 0.88, p > 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive relationship between ETMODIS and ETWV2 (r2 = 0.9857, p > 0.05). This indicates that the relationship between NDVI using high resolution WorldView-2 imagery and ground-based validation approaches could provide an effective predictive tool for determining ET rates from unstressed mixed urban landscape plantings. Full article
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<p>Veale Gardens in the Adelaide Parklands: (<b>a</b>) satellite image of the Adelaide Parklands, (<b>b</b>) mix of vegetation types in Veale Gardens, (<b>c</b>) satellite image of Veale Gardens.</p>
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<p>Flowchart of the analysis procedure of NDVI mapping with high resolution WV2 images.</p>
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<p>Multi-spectral images of Veale Gardens in March 2012, June 2012, August 2012, November 2012, and January 2013.</p>
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<p>Flow chart of steps to create NDVI image and to calculate zonal statistics.</p>
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<p>Comparing NDVI statistics (<b>a</b>) and aNDVI statistics (<b>b</b>).</p>
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<p>Objects in Veale Gardens.</p>
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<p>Different categories of landcovers in Veale Gardens.</p>
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<p>Comparison of evapotranspiration (ET)<sub>REF</sub> with ET<sub>L</sub> estimated using WUCOLS, PF and IPOS approaches.</p>
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<p>Temporal variation of ET<sub>L</sub>, mean Landscape NDVI, and each vegetation type’s Landscape NDVI.</p>
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1214 KiB  
Article
Impact of Tree-Oriented Growth Order in Marker-Controlled Region Growing for Individual Tree Crown Delineation Using Airborne Laser Scanner (ALS) Data
by Zhen Zhen, Lindi J. Quackenbush and Lianjun Zhang
Remote Sens. 2014, 6(1), 555-579; https://doi.org/10.3390/rs6010555 - 6 Jan 2014
Cited by 55 | Viewed by 8282
Abstract
Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study [...] Read more.
Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study implemented a marker-controlled region growing (MCRG) algorithm that considers homogeneity, crown size, and shape using airborne laser scanning (ALS) data, and investigated the impact of three growth orders (i.e., sequential, independent, and simultaneous) on tree crown delineation. The study also investigated the benefit of combining ALS data and orthoimagery in treetop detection at both plot and individual tree levels. The results showed that complementary data from the orthoimagery reduced omission error associated with small trees in the treetop detection procedure and improved treetop detection percentage on a plot level by 2%–5% compared to ALS alone. For tree crown delineation, the growth order applied in the MCRG algorithm influenced accuracy. Simultaneous growth yielded slightly higher accuracy (about 2% improvement for producer’s and user’s accuracy) than sequential growth. Independent growth provided comparable accuracy to simultaneous growth in this study by dealing with overlapping pixels among trees according to crown shape. This study provides several recommendations for applying region growing in future ITCD research. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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<p>Study area: Plot 1 and Plot 2 within a 1,000 × 1,000 m block of Heiberg Memorial Forest, located in Tully, NY (Canopy height model, generated from 2010 ALS data).</p>
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<p>Flow diagram for this study.</p>
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<p>The relationship between crown size and tree height.</p>
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<p>Threshold of height difference (<span class="html-italic">thres<sub>diff</sub></span>).</p>
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<p>The logic of the six criteria applied in the growing procedure. <span class="html-italic">Note</span>: c. (i) represents the <span class="html-italic">i<sup>th</sup></span> criterion; NSP: new starting pixel.</p>
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<p>Empirical variogram fitting of smoothed CMM for (<b>a</b>) Plot 1: nugget = 0, sill = 22, range = 16, model = exponential; (<b>b</b>) Plot 2: nugget = 0, sill = 16, range = 13, model = exponential.</p>
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<p>(<b>a</b>) an example of a pseudo-waveform used to identify height at the largest crown extension (Tree 3); (<b>b</b>) linear regression between tree height and height at the largest crown extension.</p>
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<p>Pseudo-competition based on circularity: (<b>a</b>) pixels common to overlap area (<b>b</b>) tree boundaries after assignment of overlapping pixels.</p>
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<p>The flow diagram of <span class="html-italic">G_sim</span> in region growing. <span class="html-italic">Note</span>: c.(i) represents the i<sup>th</sup> criterion; NSP: new starting pixel.</p>
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587 KiB  
Article
Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images
by Mar Bisquert, Juan Manuel Sánchez and Vicente Caselles
Remote Sens. 2014, 6(1), 540-554; https://doi.org/10.3390/rs6010540 - 3 Jan 2014
Cited by 32 | Viewed by 8342
Abstract
Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation [...] Read more.
Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency. Full article
(This article belongs to the Special Issue Analysis of Remote Sensing Image Data)
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<p>16-day cumulative fire events in Galicia and Asturias averaged over 2001–2006.</p>
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<p>Linear adjustment between the percentage of fire-affected cells in Galicia and Asturias and the indices variation in the previous two weeks, for the 50% of the total temporal series available and for the indices: (<b>a</b>) EVI (MOD13), (<b>b</b>) EVI (MOD09), (<b>c</b>) GEMI, (<b>d</b>) SAVI.</p>
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<p>Fire probability <span class="html-italic">vs.</span> the variations of the indices obtained from the product MOD09A1: NDWI, GVMI, NDVI, NDII and VARI for: (<b>a</b>) Galicia, (<b>b</b>) Asturias.</p>
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<p>Comparison between the probability of fire occurrence predicted by the logistic regression and the fire occurrence observed.</p>
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<p>Fire risk maps for Galicia (25 May 2006 to 9 June 2006) and Asturias (26 June 2005 to 11 July 2005). Black points represent the number of fires registered in each cell.</p>
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3862 KiB  
Article
Peat Mapping Associations of Airborne Radiometric Survey Data
by David Beamish
Remote Sens. 2014, 6(1), 521-539; https://doi.org/10.3390/rs6010521 - 3 Jan 2014
Cited by 27 | Viewed by 9648
Abstract
This study considers recent airborne radiometric (gamma ray) survey data, obtained at high-resolution, across various regions of the UK. The datasets all display a very evident attenuation of signal in association with peat, and intra-peat variations are observed. The geophysical response variations are [...] Read more.
This study considers recent airborne radiometric (gamma ray) survey data, obtained at high-resolution, across various regions of the UK. The datasets all display a very evident attenuation of signal in association with peat, and intra-peat variations are observed. The geophysical response variations are examined in detail using example data sets across lowland areas (raised bogs, meres, fens and afforested peat) and upland areas of blanket bog, together with associated wetland zones. The radiometric data do not map soils per se. The bedrock (the radiogenic parent) provides a specific amplitude level. Attenuation of this signal level is then controlled by moisture content in conjunction with the density and porosity of the soil cover. Both soil and bedrock variations need to be jointly assessed. The attenuation theory, reviewed here, predicts that the behaviour of wet peat is distinct from most other soil types. Theory also predicts that the attenuation levels observed across wet peatlands cannot be generally used to map variations in peat thickness. Four survey areas at various scales, across England, Scotland, Wales and Ireland are used to demonstrate the ability of the airborne data to map peat zones. A 1:50 k national mapping of deep peat is used to provide control although variability in the definition of peat zones across existing databases is also demonstrated. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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<p>(<b>a</b>) HiRES airborne geophysical surveys in the UK (2008–2013), coloured areas. The recent survey in the Republic of Ireland is labeled (TB, Tellus Border). Arrows denote the four areas studied here. (<b>b</b>) The airborne survey areas (red polygons) overlaid on the DiGMAPGB50 UK mapping of peat (onshore black contours). BNG refers to British National Grid.</p>
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<p>Theoretical attenuation behaviour of soil/bedrock types. The parameters defining the soil types are discussed in the text. (<b>a</b>) Variation with thickness assuming a uniform half-space. A 90% attenuation level provides a reference level. (<b>b</b>) Variation with degree of saturation (soil moisture or moisture content). Other soil parameters are noted in the text.</p>
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<p>(<b>a</b>) Radiometric data from the Ayrshire survey shown as a ternary colour image (a 3-way colour stretch of the Potassium, Thorium and Uranium components) of the data. WB denotes a water body. Circles denote two lowland areas of peat. (<b>b</b>) Areas of peat defined by DiGMAPGB50 mapping of peat (zones in black). Infilled area (in the south and east) defines elevations greater than 200 m. Red rectangle is a 3 × 3 km area of peat that is used for a more detailed study.</p>
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<p>The 3 × 3 km detailed study area from the Ayrshire survey. (<b>a</b>) 1:50 k base topographic map with peat areas shown with transparent infill. “A” denotes a water body and “B” identifies a landfill. (<b>b</b>) Colour image of the radiometric DOSE data with peat outline in white. White dots denote airborne survey sampling along E–W flight lines.</p>
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<p>The 11 × 10 km study area from the northern Midlands survey. (<b>a</b>) 1:50 k base topographic map with peat areas shown with red line polygons. Areas with grey transparent infill denote eight Ramsar wetland sites. Sites identified are: FM = Fenn’s and other Mosses, HM = Hanmer Mere (non-peat), LB = Llyn Bedydd (non-peat), WM = White Mere (largely non-peat), CLM = Clarepool Moss, CM = Cole Mere, SCM = Sweat and Crose Mere, BM = Brownheath Mere. Note that Ellesmere Mere (EM, non-Ramsar) is largely water. (<b>b</b>) Radiometric DOSE response across the area, shown as coloured contours with an upper limit of 15 nGy·h<sup>−1</sup>. Peat areas shown with red line polygons.</p>
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<p>The 11 × 10 km study area from the northern Midlands survey. Peat areas are shown with red line polygons. Blue areas denote water bodies. Grey areas denote areas in which radiometric DOSE response is &lt; 12 nGy·h<sup>−1</sup>. Black dots show airborne survey sampling along E–W flight lines (400 m line separation) and N–S tie lines (1,200 m line separations).</p>
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<p>The 2 × 3 km study area (mainland area of Anglesey survey) across an area of afforested peat. (<b>a</b>) Perspective view, looking north, of aeriel image draped on DTM, with white dots showing airborne sampling locations. White polygons denote areas of peat. (<b>b</b>) 1:50 k base topographic map with colour contours of the radiometic DOSE response limited to values &lt;22 nGy·h<sup>−1</sup>. Red polygons denote areas of peat. WB denotes a water body.</p>
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<p>The 12 × 10 km study area across the Slieve Beagh blanket bog in both Northern Ireland and the Republic of Ireland. (<b>a</b>) Perspective view, looking north, with ariel image draped on DTM. Ramsar site shown by red contour (extends to border). Mapped peat locations (DiGMAPGB50) shown by white contours. (<b>b</b>) Orthographic view of ariel image with mapped peat locations (DiGMAPGB50, UK) shown by white contours. Red contours denote peat (soil) mapping in the Republic of Ireland (RoI). Yellow contours denote CORINE land-use mapping of peat bogs (UK and R0I). ING refers to Irish National Grid coordinates.</p>
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<p>The 12 × 10 km study area across the Slieve Beagh blanket bog in both Northern Ireland and the Republic of Ireland. (<b>a</b>) Radiometric DOSE data shown as continuous colour image, limited to 20 nGy·h<sup>−1</sup>. Black contours denote peat locations. Two water bodies are labelled WB. Areas “A” and “B” are discussed in the text. (<b>b</b>) Radiometric DOSE data shown as contours, limited to 6 nGy·h<sup>−1</sup>. Black contours denote peat locations. Infill background colours denote bedrock geology: LG (Leitrim Group), MSF (Marine Shelf Facies), TG (Tyrone Group).</p>
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707 KiB  
Review
Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review
by Thuan Chu and Xulin Guo
Remote Sens. 2014, 6(1), 470-520; https://doi.org/10.3390/rs6010470 - 31 Dec 2013
Cited by 169 | Viewed by 17263
Abstract
The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how [...] Read more.
The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre- and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions. Full article
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<p>Schematic representation of fire-related environments and assessment of post-fire effects on forest conditions concerning this comprehensive review. This review will particularly focus on studies of post-fire environments with respect to remote sensing approaches.</p>
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<p>A brief schematic diagram of the relationships between ecological factors, fire effects and their influence on the recovery of post-fire boreal forests in permafrost ecosystems. Post-fire forest recovery patterns can be defined by factors directly available to the plant, such as light (e.g., solar radiation), water (e.g., soil moisture) and mechanical factors (e.g., fire regime). The review will focus on the interrelationship between those drivers in order to discuss the challenges and research opportunities in monitoring post-fire boreal forests using remote sensing.</p>
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<p>Summary of field-based and remote sensing-based measurements of burn severity along with potential challenges and gaps in research in boreal forest ecosystems; LAI, Leaf Area Index; SAVI, Soil Adjusted Vegetation Index; dSAVI, Differenced Soil Adjusted Vegetation Index.</p>
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3560 KiB  
Article
Airborne Measurements of CO2 Column Concentration and Range Using a Pulsed Direct-Detection IPDA Lidar
by James B. Abshire, Anand Ramanathan, Haris Riris, Jianping Mao, Graham R. Allan, William E. Hasselbrack, Clark J. Weaver and Edward V. Browell
Remote Sens. 2014, 6(1), 443-469; https://doi.org/10.3390/rs6010443 - 30 Dec 2013
Cited by 81 | Viewed by 14092
Abstract
We have previously demonstrated a pulsed direct detection IPDA lidar to measure range and the column concentration of atmospheric CO2. The lidar measures the atmospheric backscatter profiles and samples the shape of the 1,572.33 nm CO2 absorption line. We participated [...] Read more.
We have previously demonstrated a pulsed direct detection IPDA lidar to measure range and the column concentration of atmospheric CO2. The lidar measures the atmospheric backscatter profiles and samples the shape of the 1,572.33 nm CO2 absorption line. We participated in the ASCENDS science flights on the NASA DC-8 aircraft during August 2011 and report here lidar measurements made on four flights over a variety of surface and cloud conditions near the US. These included over a stratus cloud deck over the Pacific Ocean, to a dry lake bed surrounded by mountains in Nevada, to a desert area with a coal-fired power plant, and from the Rocky Mountains to Iowa, with segments with both cumulus and cirrus clouds. Most flights were to altitudes >12 km and had 5–6 altitude steps. Analyses show the retrievals of lidar range, CO2 column absorption, and CO2 mixing ratio worked well when measuring over topography with rapidly changing height and reflectivity, through thin clouds, between cumulus clouds, and to stratus cloud tops. The retrievals shows the decrease in column CO2 due to growing vegetation when flying over Iowa cropland as well as a sudden increase in CO2 concentration near a coal-fired power plant. For regions where the CO2 concentration was relatively constant, the measured CO2 absorption lineshape (averaged for 50 s) matched the predicted shapes to better than 1% RMS error. For 10 s averaging, the scatter in the retrievals was typically 2–3 ppm and was limited by the received signal photon count. Retrievals were made using atmospheric parameters from both an atmospheric model and from in situ temperature and pressure from the aircraft. The retrievals had no free parameters and did not use empirical adjustments, and >70% of the measurements passed screening and were used in analysis. The differences between the lidar-measured retrievals and in situ measured average CO2 column concentrations were <1.4 ppm for flight measurement altitudes >6 km. Full article
(This article belongs to the Special Issue Optical Remote Sensing of the Atmosphere)
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<p>(<b>a</b>) NASA DC-8 aircraft. CO<sub>2</sub> Sounder instrument installed above Port 9 on the DC-8. (<b>b</b>) Rack with CO<sub>2</sub> lidar electro-optics. (<b>c</b>) Enclosure with transmit optics and receiver telescope that is coupled, via fiber optics, to racks. (<b>d</b>) Rack with O<sub>2</sub> lidar electro-optics in the telescope side of the rack, with flight computers on the opposite side for operators, and Picarro <span class="html-italic">in situ</span> gas analyzer.</p>
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<p>(<b>Left</b>) Illustration of wavelength sampling approach, that samples the 1,572.33 nm CO<sub>2</sub> absorption line at 30 wavelengths at a 300 Hz rate. The lidar parameters are summarized in <a href="#t1-remotesensing-06-00443" class="html-table">Table 1</a>. (<b>Right</b>) Block diagram of the CO<sub>2</sub> channel of the airborne lidar.</p>
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<p>Measurement processing approach, using examples from lidar receiver measurements with 1 s integration time from the flight over Iowa. (<b>a</b>) The 30 wavelength stepped laser pulses reflecting from the surface are evident in the time averaged backscatter profile, plotted with 8 ns/MCS bin. (<b>b</b>) A time-expanded view of a sample backscatter profile recorded for an offline pulse integrated over 1 s. This sample shows the reflected signal from a thin cloud at a distance of 5.7 km from the aircraft and the echo pulse from the ground at a range of 7.6 km. (<b>c</b>) Time expanded view of two pulse reflections (overlayed) from the surface, with red an offline pulse, and blue a pulse at a wavelength near the CO<sub>2</sub> line absorption peak. (<b>d</b>) The photon counts for each pulse are computed by summing the counts between the pulse edges, and with the wavelength scan calibration give the transmission shape for the CO<sub>2</sub> line. Although the atmospheric scattering above the ground causes a loss of energy, the measured shape of the CO<sub>2</sub> line in the column to the ground is otherwise unaffected.</p>
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<p>Data analysis and retrieval approach used to fit the CO<sub>2</sub> lineshape and determine column average CO<sub>2</sub> abundance and other parameters. The MCS record of the backscatter profile is analyzed to identify the pulse edges, solve for range, and determine the integrated counts as shown in <a href="#f3-remotesensing-06-00443" class="html-fig">Figure 3</a>. These are then normalized by the transmit energy monitor data to give the measured lineshape. In parallel, the algorithm computes the predicted lineshape based on the optical path traveled by the lidar pulses, the atmospheric conditions of the time. The vertically resolved atmospheric state is computed using either <span class="html-italic">in situ</span> measurements from the aircraft’s spiral down manuever or data from the MERRA atmospheric model. Initially the concentration of 390 ppm was used at all altitudes. The measured and predicted line shapes were independently averaged over 10 s, the measurements and calculations were compared, and the residual was computed. The algorithm then adjusts parameters to optimize the fit. It first varied the wavelength offset and instrument baseline slope (<b>left hand side</b>) to adjust measurements and then it varied the CO<sub>2</sub> column concentration (<b>right hand side</b>) in the prediction. The result is the retrieved CO<sub>2</sub> column concentration that minimizes the error. The RMS error in the fit, the confidence interval of the measurement, and OD values are also retrieved and are used for screening out poor fits and bad data, as described in <a href="#app2" class="html-app">Appendix B</a>.</p>
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<p>An example line shape comparison before and after the concentration adjustment. A sample 10 s averaged transmission CO<sub>2</sub> lineshape measured (blue dots) for the Iowa flight from 4.4 km altitude. The initial comparison is to a calculated lineshape with 390 ppm concentration that gives a noticeable fit error (blue circles in top plot). After optimizing the XCO<sub>2</sub> in the calculations to 378 ppm, there is smaller residual (green circles in top plot) and rms error. The difference between the initial calculations (red line in bottom plot) and the optimized fit for 378 ppm (green line) is small when compared to the overall transmission lineshape.</p>
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<p>Example of fitted CO<sub>2</sub> line shapes, plotted as optical depths, for previous airborne lidar measurements at the altitudes indicated. The optical depths at the fitted line peak and the average of the fitted values at the sides, at peak wavelength ±50 pm, are used to compute the value of DOD(pk,50) in <a href="#FD1" class="html-disp-formula">Equation (1)</a> shown in subsequent plots.</p>
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<p>Ground track patterns for the flights described in this paper. (<b>a</b>) Pacific Ocean west of Baja California, (<b>b</b>) Railroad Valley NV and surrounding mountains, (<b>c</b>) near Four Corners NM, and (<b>d</b>) near Iowa City Iowa. The flight patterns were based on surface topography, air traffic and other considerations.</p>
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<p>(<b>a</b>) <span class="html-italic">In situ</span> CO<sub>2</sub> concentration vertical profile measurements from AVOCET in the spiral-down segments of the four flights in August 2011 over the Pacific Ocean (in blue), in Railroad Valley, Nevada (in black), the Four Corners, NM (in red), and West Branch, Iowa (in green). (<b>b</b>) The column water vapor mixing ratios as a function of flight altitude above sea-level from the DC-8 <span class="html-italic">in situ</span> water vapor measurements for the four flights, measured during their the spiral down segment.</p>
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<p>Plot of DOD(pk,50) calculated for the four flights from AVOCET and DC-8 <span class="html-italic">in situ</span> measurements as a function of range to surface. In order to highlight the differences, the same calculation for a US Standard Atmosphere with a vertically uniform CO<sub>2</sub> concentration of 390 ppm was subtracted. The colder temperatures for flight 2 (over the Pacific Ocean) increased its air density and resulted in a lower difference with the US Standard atmosphere.</p>
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1862 KiB  
Article
Improved Accuracy of Chlorophyll-a Concentration Estimates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Northern Vietnam)
by Nguyen Thi Thu Ha, Katsuaki Koike and Mai Trong Nhuan
Remote Sens. 2014, 6(1), 421-442; https://doi.org/10.3390/rs6010421 - 30 Dec 2013
Cited by 53 | Viewed by 9783
Abstract
Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable [...] Read more.
Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable for estimating chlorophyll-a concentrations in tropical coastal waters with abundant phytoplankton using Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra imagery and to improve the spatial resolution of MODIS/Terra-based estimation from 1 km to 100 m by geostatistics. A model based on the ratio of green and blue band reflectance (rGBr) is proposed considering the bio-optical property of chlorophyll-a. Tien Yen Bay in northern Vietnam, a typical phytoplankton-rich coastal area, was selected as a case study site. The superiority of rGBr over two existing representative models, based on the blue-green band ratio and the red-near infrared band ratio, was demonstrated by a high correlation of the estimated chlorophyll-a concentrations at 40 sites with values measured in situ. Ordinary kriging was then shown to be highly capable of predicting the concentration for regions of the image covered by clouds and, thus, without sea surface data. Resultant space-time maps of concentrations over a year clarified that Tien Yen Bay is characterized by natural eutrophic waters, because the average of chlorophyll-a concentrations exceeded 10 mg/m3 in the summer. The temporal changes of chlorophyll-a concentrations were consistent with average monthly air temperatures and precipitation. Consequently, a combination of rGBr and ordinary kriging can effectively monitor water quality in tropical shallow waters. Full article
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Graphical abstract

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<p>Location of Tien Yen Bay in northern Vietnam and the positions of 40 sampling points for measuring Chl-a concentrations.</p>
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<p>(<b>a</b>) The relationship between chlorophyll-a (Chl-a) concentration of the water sample and the ratio of two reflectances at MODIS Band 12 (551 nm) <span class="html-italic">vs.</span> Band 9 (443 nm). A regression line is drawn to show the relationship. (<b>b</b>) Scattergram of Chl-a concentrations between the sample value and estimation by the ratio of green and blue band reflectance (rGBr) of <a href="#FD17" class="html-disp-formula">Equation (17)</a>. The 45-degree dotted line denotes perfect estimation.</p>
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<p>Scattergram of Chl-a concentrations between the sample value and estimation by three algorithms. The 45-degree dotted line denotes perfect estimation.</p>
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<p>Spatial distribution of Chl-a concentrations over Tien Yen Bay on 6 July 2010, with a 1-km interval using MODIS image data and rGBr.</p>
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<p>Histogram of Chl-a concentrations estimated from MODIS/Terra image data and rGBr.</p>
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<p>(<b>a</b>) Omnidirectional experimental semivariogram and exponential model (curve) of the Chl-a concentrations estimated from MODIS data and rGBr. (<b>b</b>) Scattergram for cross-validation of ordinary kriging prediction. The 45-degree dotted line is superimposed.</p>
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<p>(<b>a</b>) Spatial distribution of Chl-a concentrations produced by interpolating the MODIS image-based estimation in <a href="#f4-remotesensing-06-00421" class="html-fig">Figure 4</a> using OK and a 100-m grid size. (<b>b</b>) Kriging variance for representing the uncertainty of estimation of Chl-a concentrations by OK. in the image, no space before and after en dash representing ranges.</p>
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<p>(<b>a</b>) Spatial distribution of Chl-a concentrations produced by interpolating the MODIS image-based estimation in <a href="#f4-remotesensing-06-00421" class="html-fig">Figure 4</a> using OK and a 100-m grid size. (<b>b</b>) Kriging variance for representing the uncertainty of estimation of Chl-a concentrations by OK. in the image, no space before and after en dash representing ranges.</p>
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<p>Spatio-temporal changes of Chl-a concentrations for the year from May 2010, to May 2011, over Tien Yen Bay produced from 21 MODIS scene data, rGBr and OK.</p>
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<p>Variations of monthly averaged, maximum and minimum Chl-a concentrations over Tien Yen Bay calculated from the maps in <a href="#f8-remotesensing-06-00421" class="html-fig">Figure 8</a> and their correlations with monthly averaged air temperature (<b>a</b>) and monthly precipitation (<b>b</b>). The <span class="html-italic">r</span> values denote the correlations of the monthly averaged Chl-a concentration with those meteorological factors.</p>
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1732 KiB  
Article
Remote Sensing-Derived Bathymetry of Lake Poopó
by Adalbert Arsen, Jean-François Crétaux, Muriel Berge-Nguyen and Rodrigo Abarca Del Rio
Remote Sens. 2014, 6(1), 407-420; https://doi.org/10.3390/rs6010407 - 27 Dec 2013
Cited by 58 | Viewed by 12469
Abstract
Located within the Altiplano at 3,686 m above sea level, Lake Poopó is remarkably shallow and very sensitive to hydrologic recharge. Progressive drying has been observed in the entire Titicaca-Poopó-Desaguadero-Salar de Coipasa (TPDS) system during the last decade, causing dramatic changes to Lake [...] Read more.
Located within the Altiplano at 3,686 m above sea level, Lake Poopó is remarkably shallow and very sensitive to hydrologic recharge. Progressive drying has been observed in the entire Titicaca-Poopó-Desaguadero-Salar de Coipasa (TPDS) system during the last decade, causing dramatic changes to Lake Poopó’s surface and its regional water supplies. Our research aims to improve understanding of Lake Poopó water storage capacity. Thus, we propose a new method based on freely available remote sensing data to reproduce Lake Poopó bathymetry. Laser ranging altimeter ICESat (Ice, Cloud, and land Elevation Satellite) is used during the lake’s lowest stages to measure vertical heights with high precision over dry land. These heights are used to estimate elevations of water contours obtained with Landsat imagery. Contour points with assigned elevation are filtered and grouped in a points cloud. Mesh gridding and interpolation function are then applied to construct 3D bathymetry. Complementary analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) surfaces from 2000 to 2012 combined with bathymetry gives water levels and storage evolution every 8 days. Full article
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<p>Lake Poopó, (<b>a</b>) geographical overview, (<b>b</b>) Ice, Cloud, and land Elevation Satellite (ICESat) ascending and descending tracks (yellow lines) over Lake Poopó. White circles represent zones where ICESat elevation is intersected with water contours. Maximum (red) and minimal (blue) water extents of our bathymetry are shown.</p>
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<p>Typical ICESat profiles from northern Lake Poopó. Threshold black line 5 cm above water surface helps to exclude all points taken over water. (<b>a</b>) Example of profile taken during dry period (<b>b</b>) This profile shows many saturated points on the lake slope, which suggest crossing over surface with different optical properties (<span class="html-italic">i.e</span>., water).</p>
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<p>ICESat profile is superposed on a Landsat image (white, red and green crosses). In addition, 2D plots show ICESat elevation values at corresponding latitudes. Water level is approximated by orange horizontal threshold line. Points above water (red) and saturated points below water (green) are shown.</p>
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<p>Lake Poopó hypsometry.</p>
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<p>Moderate Resolution Imaging Spectroradiometer (MODIS) surfaces between 2000 and 2012.</p>
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<p>Landsat/MODIS correlation curve.</p>
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<p>Bathymetry of Lake Poopó (<b>a</b>) inclined view (<b>b</b>) viewed from top.</p>
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<p>Lake Poopó water and volumes reconstructed between 2000 and 2012.</p>
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6537 KiB  
Article
Envisat/ASAR Images for the Calibration of Wind Drag Action in the Doñana Wetlands 2D Hydrodynamic Model
by Anaïs Ramos-Fuertes, Belen Marti-Cardona, Ernest Bladé and Josep Dolz
Remote Sens. 2014, 6(1), 379-406; https://doi.org/10.3390/rs6010379 - 27 Dec 2013
Cited by 16 | Viewed by 9286
Abstract
Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of [...] Read more.
Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Doñana’s topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Doñana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Doñana’s basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment. Full article
(This article belongs to the Special Issue Hydrological Remote Sensing)
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<p>Location and digital terrain model of Doñana wetlands. The location of the modeled areas and gauging stations is indicated on the digital terrain model.</p>
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<p>Hydrometeorological records and ASAR acquisition dates from 20 February to 6 March 2006. Short-term wind induced variations of water depth are visible. Time series are recorded every 10 min and labeled according to measuring stations presented in <a href="#f1-remotesensing-06-00379" class="html-fig">Figure 1</a>.</p>
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<p>Membrillo pond on the calibrated ASAR images from (<b>a</b>) 1 March 2006, at swath IS4, HH polarization; (<b>b</b>) 4 March 2006, at swath IS2, HH polarization. Backscattering coefficient represented in dB. The study area is defined by the red line.</p>
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<p>Ánsares pond on the calibrated ASAR image from 19 October 2006, at swath IS1: (<b>a</b>) HV backscattering coefficient (dB); (<b>b</b>) HH backscattering coefficient (dB).</p>
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<p>View of a bare soil area recently emerged due to the wind-induced water displacement.</p>
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<p>Membrillo pond on the filtered ASAR images from (<b>a</b>) 1 March 2006, at swath IS4, HH polarization; (<b>b</b>) 4 March 2006, at swath IS2, HH polarization. Backscattering coefficient represented in dB. The study area is defined by the red line.</p>
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<p>Initial regions of interest (ROI’s) for the image from 4 March 2006: (<b>a</b>) selection of the ROIs in the backscattering coefficient-terrain elevation space; (<b>b</b>) view of the selected ROIs on the ASAR image.</p>
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<p>Wind drag coefficient <span class="html-italic">C<sub>D</sub></span> for a reference height of 2 m and for the range of observed values of wind speed <span class="html-italic">U</span><sub>2</sub> and similarity function ψ<span class="html-italic"><sub>m</sub></span>(<span class="html-italic">z</span>/<span class="html-italic">L</span>). Solution of the implicit <a href="#FD3" class="html-disp-formula">Equations (3)</a>, <a href="#FD7" class="html-disp-formula">(7)</a> and <a href="#FD8" class="html-disp-formula">(8)</a>.</p>
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<p>(<b>a</b>) Time series of simulated wind drag coefficient (notice that S1 and S2 are adapted to a 2 m reference height); (<b>b</b>) Time series of simulated wind stress at the water surface; (<b>c</b>) Time series of accumulated wind stress. Series are named according to <a href="#t2-remotesensing-06-00379" class="html-table">Table 2</a>.</p>
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2303 KiB  
Article
Homogeneity Analysis of the CM SAF Surface Solar Irradiance Dataset Derived from Geostationary Satellite Observations
by Sven Brinckmann, Jörg Trentmann and Bodo Ahrens
Remote Sens. 2014, 6(1), 352-378; https://doi.org/10.3390/rs6010352 - 27 Dec 2013
Cited by 14 | Viewed by 7928
Abstract
A satellite-based climate record of monthly mean surface solar irradiance (SIS) is investigated with regard to possible inhomogeneities in time. The data record is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM [...] Read more.
A satellite-based climate record of monthly mean surface solar irradiance (SIS) is investigated with regard to possible inhomogeneities in time. The data record is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) for the period of 1983 to 2005, covering a disk area between ±70° in latitude and longitude. The Standard Normal Homogeneity Test (SNHT) and two other homogeneity tests are applied with and without the use of reference SIS data (from the Baseline Surface Radiation Network (BSRN) and from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA -Interim reanalysis. The focus is on the detection of break-like inhomogeneities, which may occur due to satellite or SIS retrieval algorithm changes. In comparison with the few suitable BSRN SIS observation series with limited extension in time (no data before 1992), the CM SAF SIS time series do not show significant inhomogeneities, even though slight discrepancies in the surface measurements appear. The investigation of the full CM SAF SIS domain reveal inhomogeneities related to most of the documented satellite and retrieval changes, but only for relatively small domain fractions (especially in mountainous desert-like areas in Africa). In these regions the retrieval algorithm is not capable of adjusting for the changes of the satellite instruments. For other areas, e.g., Europe, no such breaks in the time series are found. We conclude that the CM SAF SIS data record has to be further assessed and regionally homogenized before climate trend investigations can be conducted. Full article
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<p>Synthetic time series with a break at 06/1994 (<b>A1, B1, C1</b>) and their respective Standard Normal Homogeneity Test (SNHT) test statistics, <span class="html-italic">T</span> (<b>A2, B2, C2</b>). For time series B and C, the breaks (same magnitude, but different signs) are superimposed by the same positive trend.</p>
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<p>Synthetic time series with two breaks (<b>A1</b>), after the first homogenization step (<b>B1</b>) and corresponding T statistics (<b>A2, B2</b>).</p>
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<p>Absolute SNHT analyses of Baseline Surface Radiation Network (BSRN) surface solar irradiance (SIS) observations at Bermuda (<b>A1, A2</b>) and Payerne (<b>B1, B2</b>).</p>
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<p>Absolute SNHT analysis of the ERA-Interim SIS record: (<b>A</b>) <span class="html-italic">T</span><sub>0</sub> values above a significance level of 95%; (<b>B</b>) number of detected inhomogeneities; and (<b>C</b>) distribution of the frequency of breaks (in percent of total area).</p>
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<p>Relative SNHT analyses of the Satellite Application Facility on Climate Monitoring (CM SAF) SIS series <span class="html-italic">versus</span> BSRN station series at the sites in Bermuda (<b>A1, A2</b>) and Payerne (<b>B1, B2</b>). Satellite changes are indicated by vertical grey lines; detected breaks are indicated with red lines.</p>
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<p>Full-domain mean series of the CM-SAF and ERA-Interim SIS data fields (<b>A</b>), difference time series (<b>B</b>) and SNHT test statistic for the difference series (<b>C</b>).</p>
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<p>Full-domain mean series of the CM-SAF and ERA-Interim SIS data fields (<b>A</b>), difference time series (<b>B</b>) and SNHT test statistic for the difference series (<b>C</b>).</p>
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<p><span class="html-italic">T</span><sub>0</sub> values above a significance level of 95% for CM SAF SIS data using absolute testing (<b>A</b>) and relative testing <span class="html-italic">versus</span> ERA-Interim SIS data (<b>B</b>).</p>
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<p>Relative SNHT analyses of the CM SAF SIS series <span class="html-italic">vs</span>. ERA-Interim SIS using the filter condition mentioned in the text. (<b>A</b>) <span class="html-italic">T</span><sub>0</sub> values above a significance level of 95% for CM SAF SIS data (grey shaded areas mark the filtered regions); (<b>B</b>) number of detected inhomogeneities; and (<b>C</b>) distribution of the frequency of breaks (in percent of total area). Satellite changes are indicated by vertical lines. The arrow marks the date of a retrieval change.</p>
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<p>Relative SNHT analysis of CM SAF SIS <span class="html-italic">versus</span> ERA-Interim SIS series at two grid points with largest <span class="html-italic">T</span><sub>0</sub> values over Africa: (<b>A1, B1</b>) time series; (<b>A2, B2</b>) corresponding SNHT test statistics. Satellite changes are indicated by grey lines and breaks by red (first analysis step) and orange (detected subsequently) lines.</p>
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1419 KiB  
Article
Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil
by Osmar Abílio De Carvalho, Júnior, Renato Fontes Guimarães, David R. Montgomery, Alan R. Gillespie, Roberto Arnaldo Trancoso Gomes, Éder De Souza Martins and Nilton Correia Silva
Remote Sens. 2014, 6(1), 330-351; https://doi.org/10.3390/rs6010330 - 27 Dec 2013
Cited by 80 | Viewed by 13913
Abstract
Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation [...] Read more.
Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the São Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst depressions using morphometric attributes. The advantages and limitations of the applied methodology using different DEMs are examined by comparison with a sinkhole map generated from traditional geomorphological investigations based on visual interpretation of the high-resolution remote sensing images and field surveys. The threshold values of the depth, area size and circularity index appropriate for distinguishing dolines were identified from the maximum overall accuracy obtained by comparison with a true doline map. Our results indicate that the best performance of the proposed methodology for meso-scale karst feature detection was using ALOS/PRISM data with a threshold depth > 2 m; areas > 13,125 m2 and circularity indexes > 0.3 (overall accuracy of 0.53). The overall correct identification of around half of the true dolines suggests the potential to substantially improve doline identification using higher-resolution LiDAR-generated DEMs. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
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<p>Study area location.</p>
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<p>Karst depression covered by herbaceous vegetation and stagnant surface water.</p>
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<p>Methodological procedures to determine the terrain attribute of sink depth. Digital elevation model (<b>A</b>) (DEM) Fillsink minus (<b>B</b>) original DEM results in the (<b>C</b>) sink-depth distribution.</p>
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<p>Hypothetical dolines enlargement from threshold depths equal to 1 m and “shallow” (<span class="html-italic">i.e.</span>, &gt; 0 m).</p>
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<p>Reference map from the visual interpretation of ALOS/PRISM and Google Earth images.</p>
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<p>(<b>A</b>) Depth image and (<b>B</b>) its derived binary mask made from ASTER-GDEM data using threshold value of 1 m.</p>
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<p>Depth image from (<b>A</b>) SRTM-DEM and binary mask images made with sink-depth thresholds of (<b>B</b>) 1 m, (<b>C</b>) 2 m and (<b>D</b>) 3 m.</p>
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<p>Depth image from (<b>A</b>) ALOS/PRISM-DEM and binary mask images made considering sink-depth thresholds of (<b>B</b>) 1 m, (<b>C</b>) 2 m and (<b>C</b>) 3 m.</p>
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<p>Spatial distribution of dolines considering the following attributes: area from (<b>A</b>) SRTM-DEM and (<b>B</b>) ALOS/PRISM-DEM, and circularity indexes from (<b>C</b>) SRTM-DEM and (<b>D</b>) ALOS/PRISM-DEM.</p>
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5242 KiB  
Article
Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors
by Peng Li, Luguang Jiang and Zhiming Feng
Remote Sens. 2014, 6(1), 310-329; https://doi.org/10.3390/rs6010310 - 27 Dec 2013
Cited by 232 | Viewed by 22657
Abstract
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology [...] Read more.
Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology (replacing of whisk-broom scanners with two separate push-broom OLI and TIRS scanners), an extended number of spectral bands (two additional bands provided) and narrower bandwidths. Therefore, cross-comparative analysis is very necessary for the combined use of multi-decadal Landsat imagery. In this study, 3,311 independent sample points of four major land cover types (primary forest, unplanted cropland, swidden cultivation and water body) were used to compare the spectral bands of ETM+ and OLI. Eight sample plots with different land cover types were manually selected for comparison with the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Land Surface Water Index (LSWI) and the Normalized Burn Ratio (NBR). These indices were calculated with six pairs of ETM+ and OLI cloud-free images, which were acquired over the border area of Myanmar, Laos and Thailand just two days apart, when Landsat-8 achieved operational obit. Comparative results showed that: (1) the average surface reflectance of each band differed slightly, but with a high degree of similarities between both sensors. In comparison with ETM+, the OLI had higher values for the near-infrared band for vegetative land cover types, but lower values for non-vegetative types. The new sensor had lower values for the shortwave infrared (2.11–2.29 µm) band for all land cover types. In addition, it also basically had higher values for the shortwave infrared (1.57–1.65 µm) band for non-water land cover types. (2) The subtle differences of vegetation indices derived from both sensors and their high linear correlation coefficient (R2 > 0.96) demonstrated that ETM+ and OLI imagery can be used as complementary data. (3) LSWI and NBR performed better than NDVI and MNDWI for cross-comparison analysis of satellite sensors, due to the spectral band difference effects. Full article
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<p>Relative spectral response (RSR) profiles showing the spectral band difference between Landsat-8 Operational Land Imager (OLI) (solid curve) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) (short dot curve). Note that these response metadata are available from the web page of the Spectral Characteristics Viewer ( <a href="http://landsat.usgs.gov/tools_spectralViewer.php" target="_blank">http://landsat.usgs.gov/tools_spectralViewer.php</a>) provided by the US Geological Survey (USGS). SWIR, short-wave infrared.</p>
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<p>Locations of the eight sample plots in Mainland Southeast Asia showing the corresponding land cover types imaged in the scenes of Landsat-7 ETM+ on 25 March 2013 and 3 April 2013. The two pairs of purple solid lines refer to the central part of ETM+ Scan Line Corrector (SLC)-off scenes with the eight sample plots included. WRS, Worldwide Reference System.</p>
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<p>Area proportions of five major land use and land cover types within the eight sample plots in this study. The land cover data was extracted from the GlobCover 2009 provided by the European Space Agency (ESA). The figures in the legend plot refer to the land cover type codes of the global land cover dataset.</p>
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<p>Locations of the 3,311 independent random sample points of four major land cover types in the border area of Myanmar and Thailand. The displayed images are Landsat-8 Pre-WRS-2 products acquired on 27 March 2013 (<b>left</b>) and 1 April 2013 (<b>right</b>).</p>
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<p>Band differences of Landsat-7 ETM+ and Landsat-8 OLI images (25 March 2013 and 27 March 2013) for four land-cover types (primary forest, unplanted cropland, swidden field and water body).</p>
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<p>Band differences of Landsat-7 ETM+ and Landsat-8 OLI images (3 April 2013 and 1 April 2013) for four land-cover types (primary forest, unplanted cropland, swidden field and water body).</p>
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<p>Mean values and standard deviation of differenced vegetation indices (dLSWI, dMNDWI, dNBR and dNDVI) derived from Landsat-7 ETM+ images (25 March 2013 and 1 April 2013) and Landsat-8 OLI images (27 March 2013 and 3 April 2013) within eight sample plots. LSWI, Land Surface Water Index; MNDWI, Modified Normalized Difference Water Index (MNDWI); NBR, Normalized Burn Ratio; NDVI, Normalized Difference Vegetation Index.</p>
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<p>Statistical relationship between Landsat-7 ETM+ and Landsat-8 OLI NDVI values for the eight sample plots. The linear regression equation and <span class="html-italic">R<sup>2</sup></span> value are given in each plot. The red solid line refers to the linear fitting curve.</p>
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<p>Statistical relationship between Landsat-7 ETM+ and Landsat-8 OLI LSWI values for the eight sample plots. The linear regression equation and <span class="html-italic">R<sup>2</sup></span> value are given in each plot. The red solid line refers to the linear fitting curve.</p>
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Article
Training Area Concept in a Two-Phase Biomass Inventory Using Airborne Laser Scanning and RapidEye Satellite Data
by Parvez Rana, Timo Tokola, Lauri Korhonen, Qing Xu, Timo Kumpula, Petteri Vihervaara and Laura Mononen
Remote Sens. 2014, 6(1), 285-309; https://doi.org/10.3390/rs6010285 - 27 Dec 2013
Cited by 13 | Viewed by 6959 | Correction
Abstract
This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated [...] Read more.
This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated by assessing the bias and the root mean square error (RMSE). In the phase I, ALS data from 50 field plots were used to predict AGB and volume for the 200 surrogate plots. In the phase II, the ALS-simulated surrogate plots were used as a ground-truth to estimate AGB and volume from the RapidEye data for the study area. The resulting RapidEye models were validated against a separate set of 28 plots. The RapidEye models showed a promising accuracy with a relative RMSE of 19%–20% for both volume and AGB. The evaluated concept of biomass inventory would be useful to support future forest monitoring and decision making for sustainable use of forest resources. Full article
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<p>Training, surrogate and validation plots location and administrative map of Finland (left side), and RapidEye image with study area marked (right side).</p>
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<p>Feature space images of each RapidEye band ((<b>a–e</b>) one to five band, consequently).</p>
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<p>Digital numbers in the overlapping area of the RapidEye blue band.</p>
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<p>Flowchart of the two-phase sampling design.</p>
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<p>The residuals plots of ALS predicted AGB at phase I.</p>
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<p>The residuals plots of ALS predicted AGB at phase I.</p>
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<p>The residuals plots of RapidEye for AGB at phase II.</p>
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<p>The residual plots of RapidEye for AGB at validation plots.</p>
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11713 KiB  
Article
Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series
by Clement Atzberger, Anja Klisch, Matteo Mattiuzzi and Francesco Vuolo
Remote Sens. 2014, 6(1), 257-284; https://doi.org/10.3390/rs6010257 - 27 Dec 2013
Cited by 101 | Viewed by 12055
Abstract
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with [...] Read more.
Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1/12° spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 × 40°), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well inter-calibrated before being used concurrently. Full article
(This article belongs to the Special Issue Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011))
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Graphical abstract

Graphical abstract
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<p>Study area and general environmental conditions. (<b>top left</b>) typical NDVI values in the study area in July, (<b>top right</b>) mean elevation [<a href="#b56-remotesensing-06-00257" class="html-bibr">56</a>], (<b>center left</b>) recoded land cover type 1 product MCD12Q1 v005 from MODIS [<a href="#b23-remotesensing-06-00257" class="html-bibr">23</a>,<a href="#b60-remotesensing-06-00257" class="html-bibr">60</a>], (<b>center right</b>) terrestrial ecoregions of World Wildlife Fund (WWF) [<a href="#b57-remotesensing-06-00257" class="html-bibr">57</a>], (<b>bottom left</b>) annual mean temperature [<a href="#b58-remotesensing-06-00257" class="html-bibr">58</a>], (<b>bottom right</b>) annual mean precipitation [<a href="#b58-remotesensing-06-00257" class="html-bibr">58</a>].</p>
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<p>Study area and general environmental conditions. (<b>top left</b>) typical NDVI values in the study area in July, (<b>top right</b>) mean elevation [<a href="#b56-remotesensing-06-00257" class="html-bibr">56</a>], (<b>center left</b>) recoded land cover type 1 product MCD12Q1 v005 from MODIS [<a href="#b23-remotesensing-06-00257" class="html-bibr">23</a>,<a href="#b60-remotesensing-06-00257" class="html-bibr">60</a>], (<b>center right</b>) terrestrial ecoregions of World Wildlife Fund (WWF) [<a href="#b57-remotesensing-06-00257" class="html-bibr">57</a>], (<b>bottom left</b>) annual mean temperature [<a href="#b58-remotesensing-06-00257" class="html-bibr">58</a>], (<b>bottom right</b>) annual mean precipitation [<a href="#b58-remotesensing-06-00257" class="html-bibr">58</a>].</p>
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<p>NDVI profiles (<b>top</b>) and first derivatives (<b>bottom</b>) for individual pixels from GIMMS (<b>left</b>) and MODIS (<b>right</b>). Example pixels were selected to represent different land cover types. Curves were obtained by averaging the respective NDVI of sample locations over the period of 2002–2011.</p>
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<p>Data distribution of GIMMS and MODIS time series (weekly data) extracted from the full image extent and pooled across all years (2002–2011).</p>
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<p>Spatial distribution of NDVI from GIMMS (<b>left</b>) and MODIS (<b>right</b>) time series. Displayed are the average NDVI (<b>top</b>) and standard deviation (<b>bottom</b>) calculated from weekly data over the full time period (2002–2011).</p>
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<p>Spatial distribution of weekly NDVI from GIMMS (<b>left</b>) and MODIS (<b>center</b>) time series averaged over 2002–2011 for each of the four climatological seasons. (<b>right</b>) Differences between the mean NDVI of GIMMS and MODIS.</p>
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<p>Spatial distribution of weekly NDVI from GIMMS (<b>left</b>) and MODIS (<b>center</b>) time series averaged over 2002–2011 for each of the four climatological seasons. (<b>right</b>) Differences between the mean NDVI of GIMMS and MODIS.</p>
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<p>Co-distribution of NDVI values from GIMMS and MODIS derived from the full image extent and weekly observations across all weeks between 2002 and 2011. (<b>left</b>) scatterplot with 1-to-1 line (red) and regression line (black); (<b>right</b>) frequency distribution of the differences between GIMMS and MODIS NDVI.</p>
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<p>Agreement/disagreement between weekly GIMMS and MODIS NDVI values. (<b>left</b>) Intra-annual analysis; (<b>right</b>) inter-annual analysis. (solid green) coefficient of determination (R<sup>2</sup>), (blue) root mean square difference (RMSD), (dashed green) time course of the average NDVI. Per week, or year, one value is shown per indicator. Lines are only shown for reader’s convenience.</p>
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<p>(<b>left</b>) Coefficient of determination (R<sup>2</sup>) and (<b>right</b>) root mean square difference (RMSD) between the temporal GIMMS and MODIS series calculated across all weekly observations between 2002 and 2011.</p>
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<p>Cumulated distribution function of the pixel-wise estimated start of season (SOS) for GIMMS (red) and MODIS (blue) for all years (2003–2011) using the relative threshold approach. Smoothed products at daily temporal resolution were used for the calculations. SOS of zero corresponds to a turn of the year.</p>
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