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Remote Sens., Volume 8, Issue 1 (January 2016) – 80 articles

Cover Story (view full-size image): We used a very low-cost unmanned aerial system to collect images over an oak-juniper woodland ecosystem in the Texas Hill Country, USA. Images were processed in AgiSoft Photoscan to produce a dense, fully georeferenced 3D point cloud for the 15-ha site. Our study had two objectives: 1) compare digital terrain product accuracies between the SfM product and spatially coincident lidar data, and 2) use the SfM non-ground points to estimate tree canopy height. Our results demonstrate that image-based point cloud products obtained over our vegetated site can be used to provide a reasonably accurate terrain product and that non-ground SfM points serve as suitable predictors of tree canopy height when coupled with an accurate terrain model. In summary, although lidar data are increasingly available, SfM can serve as a suitable proxy under specific canopy conditions. View this paper
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749 KiB  
Editorial
Acknowledgement to Reviewers of Remote Sensing in 2015
by Remote Sensing Editorial Office
Remote Sens. 2016, 8(1), 81; https://doi.org/10.3390/rs8010081 - 21 Jan 2016
Viewed by 9564
Abstract
The editors of Remote Sensing would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...] Full article
7259 KiB  
Article
Multispectral Radiometric Analysis of Façades to Detect Pathologies from Active and Passive Remote Sensing
by Susana Del Pozo, Jesús Herrero-Pascual, Beatriz Felipe-García, David Hernández-López, Pablo Rodríguez-Gonzálvez and Diego González-Aguilera
Remote Sens. 2016, 8(1), 80; https://doi.org/10.3390/rs8010080 - 21 Jan 2016
Cited by 45 | Viewed by 8265
Abstract
This paper presents a radiometric study to recognize pathologies in façades of historical buildings by using two different remote sensing technologies covering part of the visible and very near infrared spectrum (530–905 nm). Building materials deteriorate over the years due to different extrinsic [...] Read more.
This paper presents a radiometric study to recognize pathologies in façades of historical buildings by using two different remote sensing technologies covering part of the visible and very near infrared spectrum (530–905 nm). Building materials deteriorate over the years due to different extrinsic and intrinsic agents, so assessing these affections in a non-invasive way is crucial to help preserve them since in many cases they are valuable and some have been declared monuments of cultural interest. For the investigation, passive and active remote acquisition systems were applied operating at different wavelengths. A 6-band Mini-MCA multispectral camera (530–801 nm) and a FARO Focus3D terrestrial laser scanner (905 nm) were used with the dual purpose of detecting different materials and damages on building façades as well as determining which acquisition system and spectral range is more suitable for this kind of studies. The laser scan points were used as base to create orthoimages, the input of the two different classification processes performed. The set of all orthoimages from both sensors was classified under supervision. Furthermore, orthoimages from each individual sensor were automatically classified to compare results from each sensor with the reference supervised classification. Higher overall accuracy with the FARO Focus3D, 74.39%, was obtained with respect to the Mini MCA6, 66.04%. Finally, after applying the radiometric calibration, a minimum improvement of 24% in the image classification results was obtained in terms of overall accuracy. Full article
(This article belongs to the Special Issue Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The workflow of the methodology presented. Acronyms: RC = Radiometric calibration and Mp = Millions of points.</p> Full article ">Figure 2
<p>ASD FieldSpec3 spectroradiometer collecting spectral radiation reflected from (<b>a</b>) the Spectralon target and (<b>b</b>) mortar between contiguous stones of the examined façade.</p> Full article ">Figure 3
<p>Sketch of the test performed to analyze the internal radiometric behavior of the FARO Focus3D.</p> Full article ">Figure 4
<p>FARO Focus3D backscattered intensity behavior for the measurements of the four Spectralon panels at 1 m distance increments related to the signal attenuation (Equation (3)).</p> Full article ">Figure 5
<p>Relationship between TLS raw intensity data and reflectance for each spectralon panel at 10 m distance.</p> Full article ">Figure 6
<p>South façade of the Church of San Segundo in Ávila (Spain) (<b>left</b>) and a sketch of the acquisition setup with the different sensor’s stations (MCA6-multispectral camera, FARO Focus3D) (<b>right</b>).</p> Full article ">Figure 7
<p>Set of 7 orthoimages of the façade in reflectance values from the two analyzed sensors (MCA6 multispectral camera and FARO Focus3D) and a false colorcolor composite orthoimage.</p> Full article ">Figure 8
<p>Mini MCA6 map for the 5-clusters unsupervised classification.</p> Full article ">Figure 9
<p>FARO Focus3D map for the 5-clusters unsupervised classification.</p> Full article ">Figure 10
<p>Multisensory map for the 5 informational classes supervised classification.</p> Full article ">Figure 11
<p>Spectral signatures of the two different types of granites, (<b>a</b>) unaltered and (<b>b</b>) altered, measured with the ASD spectroradiometer for the wavelength interval covered by the sensors used (Mini MCA6 and FARO Focus3D) where points are obtained from the orthoimages in reflectance values.</p> Full article ">
5082 KiB  
Article
Improved VIIRS and MODIS SST Imagery
by Irina Gladkova, Alexander Ignatov, Fazlul Shahriar, Yury Kihai, Don Hillger and Boris Petrenko
Remote Sens. 2016, 8(1), 79; https://doi.org/10.3390/rs8010079 - 21 Jan 2016
Cited by 27 | Viewed by 10731
Abstract
Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) radiometers, flown onboard Terra/Aqua and Suomi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) satellites, are capable of providing superior sea surface temperature (SST) imagery. However, the swath data of these [...] Read more.
Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) radiometers, flown onboard Terra/Aqua and Suomi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) satellites, are capable of providing superior sea surface temperature (SST) imagery. However, the swath data of these multi-detector sensors are subject to several artifacts including bow-tie distortions and striping, and require special pre-processing steps. VIIRS additionally does two irreversible data reduction steps onboard: pixel aggregation (to reduce resolution changes across the swath) and pixel deletion, which complicate both bow-tie correction and destriping. While destriping was addressed elsewhere, this paper describes an algorithm, adopted in the National Oceanic and Atmospheric Administration (NOAA) Advanced Clear-Sky Processor for Oceans (ACSPO) SST system, to minimize the bow-tie artifacts in the SST imagery and facilitate application of the pattern recognition algorithms for improved separation of ocean from cloud and mapping fine SST structure, especially in the dynamic, coastal and high-latitude regions of the ocean. The algorithm is based on a computationally fast re-sampling procedure that ensures a continuity of corresponding latitude and longitude arrays. Potentially, Level 1.5 products may be generated to benefit a wide range of MODIS and VIIRS users in land, ocean, cryosphere, and atmosphere remote sensing. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p><b>Top-left</b>: Example of bow-tie deletions when the Visible Infrared Imaging Radiometer Suite (VIIRS) sea surface temperature (SST) image is displayed in the original swath projection. Deleted pixels are rendered in black and the land is shown in brown. <b>Top-right</b>: Location of the 10 min Advanced Clear-Sky Processor for Oceans (ACSPO) granule (18 October 2015 UTC) is shown by blue rectangle and its portion, displayed in the top-left, is shown in magenta. <b>Bottom</b>: Schematic representation of the left half of a single scan, showing bow-tie distortions, on-board deletions and aggregation for a single-gain M-band.</p> Full article ">Figure 2
<p>Schematic representation of VIIRS sampling for three consecutive whiskbroom (rendered in blue, red and green respectively), for several selected VZAs from 0° (nadir) to 70° (approximately representing the left edge of the scan) in 10° increments. The two axis of each ellipse represent the horizontal and vertical sampling intervals, respectively. The vertical dashed lines show positions where the aggregation factor changes.</p> Full article ">Figure 3
<p>The sampling interval as a function of VZA, in along-track and along-scan directions.</p> Full article ">Figure 4
<p>(<b>a</b>) Original VIIRS SST with bow-tie distortions and deletions (note the jumps and repeats along the SST thermal fronts, and that onboard deleted pixels are shown in white); (<b>b</b>) original latitudes; (<b>c</b>) unfolded latitudes; (<b>d</b>) corresponding resampled SSTs.</p> Full article ">Figure 5
<p>Three consecutive VIIRS whiskbrooms for the left half of the scan (from the edge of the swath to the nadir): (<b>a</b>) in swath projection (along with sorting patterns); (<b>b</b>) in the mapped projection. The detectors are shown with distinct colors ranging from yellow (detector 1) to blue (detector 16); and (<b>c</b>) reordering scheme corresponding to the left half of the VIIRS swath. The top and bottom halves of the reordering table are displaced intentionally, to emphasize that the corresponding sorting patterns may be different (see text for details).</p> Full article ">Figure 6
<p>(<b>a</b>) Intersection of grid lines of <span class="html-italic">S<sub>k</sub></span> (green) and <span class="html-italic">S<sub>k+1</sub></span> (blue). Break points are shown with red asterisks; (<b>b</b>) Iterative adjustment procedure: black dash-dotted line shows the column with the initial break point approximation; gray dashed lines correspond to intermediate steps of the iterative adjustment procedure with arrows indicating the direction in which adjustments occur, pointing from the current position to the next approximation. The red dashed line is the final position of the break point, which is the approximation before last in the iterative sequence, which continues until the difference between the latitude values changes the sign.</p> Full article ">Figure 7
<p>(<b>a</b>) The geo-locations of three consecutive VIIRS scans near the end of the swath are shown in green, red and blue. Portions of the scans corresponding to onboard deletions are marked by dashed line; (<b>b</b>) Relative scan displacement as a function of latitude and scan position.</p> Full article ">Figure 8
<p>(<b>a</b>) Geo-locations around the transition from the 3:1 to the 2:1 sample aggregation scheme; magenta path connects interleaved footprints from different scans; (<b>b</b>) two alternate geo reordering schemes, involving column shifts, marked by yellow and orange paths.</p> Full article ">Figure 9
<p>Geo-locations around the transition from the 2:1 to the 1:1 sample aggregation scheme. Unfolding order for this region corresponds to the N<sub>5</sub> +1:N<sub>6</sub> column of <a href="#remotesensing-08-00079-f005" class="html-fig">Figure 5</a>c with only six middle detectors, 6 through 11, outside the bow-tie region. The insert on the left shows detailed overlap of the S<sub>k</sub> and S<sub>k+1</sub> grids. Black zigzagging line represents reordered path and black dotted line corresponds to proposed grid with adjusted longitude values.</p> Full article ">Figure 10
<p><b>Top</b>: Latitude values for the middle column of the grid portion shown in <a href="#remotesensing-08-00079-f009" class="html-fig">Figure 9</a>. Blue line represents original latitude values and black line corresponds to latitudes reordered according to <a href="#remotesensing-08-00079-f005" class="html-fig">Figure 5</a>c. <b>Bottom</b>: Longitude values for the same column. Black line corresponds to longitudes reordered according to <a href="#remotesensing-08-00079-f005" class="html-fig">Figure 5</a>c. Zigzagging effect caused by Earth rotation is present at the bow-tie regions after reordering. Magenta line, representing adjusted longitudes, is monotonic. The adjustments are performed only at the overlapping portions of the consecutive scans.</p> Full article ">Figure 11
<p>BT at 12 μm: (<b>a</b>) Original; (<b>b</b>) Reordered; (<b>c</b>) Resampled with missing values filled.</p> Full article ">Figure 12
<p>(<b>a</b>) Schematic representation of Moderate Resolution Imaging Spectroradiometer (MODIS) sampling for three consecutive whiskbrooms (rendered in blue, red and green respectively), for several selected VZAs from 0° (nadir) to 65° (approximately representing the left edge of the scan) in 10° increments. The two axis of each ellipse represent the horizontal and vertical sampling intervals, respectively; (<b>b</b>) The reordering scheme for the left half of the MODIS swath.</p> Full article ">Figure 13
<p>Number of clear sky observations for one day (18 October 2015) of global ACSPO SST data as a function of view zenith angle (VZA) for (<b>a</b>) S-NPP VIIRS; (<b>b</b>) Aqua MODIS; (<b>c</b>) Terra MODIS. Original data are shown in light gray and resampled in dark gray. Day and night data are combined together. Corresponding percent increase for (<b>d</b>) S-NPP VIIRS; (<b>e</b>) Aqua MODIS and (<b>f</b>) Terra MODIS (separated by night and day).</p> Full article ">Figure 14
<p>(<b>a</b>) Small crop from the end of the swath showing both grids: original swath (blue) and GTM (black). Pixels deleted onboard (magenta) would decrease the number of duplications in the GTM projection; (<b>b</b>) Distribution of GTM duplicates: (<b>top</b>) horizontal and (<b>bottom</b>) vertical. The bar graph uses 121 columns in each bin, except for the last bin.</p> Full article ">Figure 15
<p>(<b>a</b>) Difference between original M4 (0.55 μm) and M10 (1.6 μm) bands; (<b>b</b>) difference between resampled M4 and M10.</p> Full article ">Figure 16
<p>(<b>a</b>) Difference between terrain-corrected and ellipsoid longitudes in the original scan order (from VIIRS Moderate Bands SDR Terrain Corrected Geolocation (GMTCO) and Original (elliptical) Geolocation (GMODO) files); (<b>b</b>) De-bowtized difference after re-sampling.</p> Full article ">
3326 KiB  
Article
Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs
by Eric Ariel L. Salas, Kenneth G. Boykin and Raul Valdez
Remote Sens. 2016, 8(1), 78; https://doi.org/10.3390/rs8010078 - 21 Jan 2016
Cited by 42 | Viewed by 8629
Abstract
We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense [...] Read more.
We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar’s test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the study area in the southeastern region of Tajikistan, based on a single Landsat 8 OLI scene from 15 July 2014.</p> Full article ">Figure 2
<p>Summer fieldwork involved geotagging of vegetation communities based on three classes: (<b>a</b>) dense; (<b>b</b>) medium dense; and (<b>c</b>) sparse. The sample photo in (<b>d</b>) shows the transition between different classes. Geotagged locations are essential for classification and accuracy assessment.</p> Full article ">Figure 3
<p>Schematic diagram of MDI applied on a sample spectral reflectance curve of a green vegetation. Note that the number of points between LP and RP pivots can vary depending on the number of bands analyzed or the width of the pivot wavelength region.</p> Full article ">Figure 4
<p>Sample illustrations of MDI applied to the reflectance responses of: (<b>a</b>) dense vegetation; (<b>b</b>) medium dense vegetation; (<b>c</b>) sparse vegetation; and (<b>d</b>) barren land, derived from the Landsat OLI image. The figures demonstrate the changes of the MDI values with varying PWR, moving from reference point 1 to 2, and <span class="html-italic">vice versa</span>. Maximum values are observed at maximum shape differences, usually occurring at the inclusion of a spectral curve peak or a spectral curve dip.</p> Full article ">Figure 5
<p>Averaged correlogram and semivariogram plots from the six bands of the 2014 Landsat image, showing the optimal lag distance (~5 pixels) with the highest variance.</p> Full article ">Figure 6
<p>Rankings of the 50 most important object features in the RF model. Variables with high Normalized Variable Importance values, such as NDVI and MDI, are deemed highly important in the classification. The scales are also listed after the “@” symbol. HOM = homogeneity, CON = contrast, M2 = second moment, ENT = entropy, VAR = variance, MEA = mean, DIS = dissimilarity, COR = correlation, DEM = Digital Elevation Model. B2 to B7 are Landsat OLI bands 2 to 7.</p> Full article ">Figure 7
<p>Map classifications showing the five landcover classes. Set 1 uses all object features with MDI, while Set 2 uses all object features without MDI.</p> Full article ">Figure 8
<p>Map of the spatial distribution of the vegetation classes relative to the rivers/streams and the elevation layers. Note that dense vegetation is mostly found near rivers and water bodies.</p> Full article ">Figure 9
<p>In (<b>a</b>), sample spectral curves of the barren land and sparse vegetation classes are shown with the original curve plus the curves with mixed reflectances. In (<b>b</b>) are values of the MDI at varying mixture of spectral reflectance. A mix of 90b10s indicates 90% barren land and 10% sparse vegetation, while a mix of 10b90s indicates 10% barren land and 90% sparse vegetation.</p> Full article ">Figure 10
<p>Classification results zoomed near a body of water. In Set 1 (<b>a</b>) with MDI, more patches of sparse vegetation exist compared to Set 2 (<b>b</b>) without MDI that classified the patches as barren land.</p> Full article ">
2440 KiB  
Letter
An Investigation of a Novel Cross-Calibration Method of FY-3C/VIRR against NPP/VIIRS in the Dunhuang Test Site
by Caixia Gao, Yongguang Zhao, Chuanrong Li, Lingling Ma, Ning Wang, Yonggang Qian and Lu Ren
Remote Sens. 2016, 8(1), 77; https://doi.org/10.3390/rs8010077 - 21 Jan 2016
Cited by 12 | Viewed by 5806
Abstract
Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral [...] Read more.
Radiometric cross-calibration of Earth observation sensors is an effective approach to evaluate instrument calibration performance, identify and diagnose calibration anomalies, and quantify the consistency of measurements from different sensors. In this study a novel cross-calibration method is proposed, taking into account the spectral and viewing angle differences adequately; the method is applied to the FY-3C/Visible Infrared Radiometer (VIRR), taking the Suomi National Polar-Orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) as a reference. The results show that the relative difference between the two sets increases from January to May 2014, and becomes lower for the data on 24 July, 11 September, and 16 September, within approximately 10%. This phenomenon is caused by the updating of the calibration coefficients in the VIRR datasets with results from a vicarious method on June 2014. After performing an approximate estimation of the uncertainty, it is demonstrated that this calibration has a total uncertainty of 5.5%–6.0%, which is mainly from the uncertainty of the Bidirectional Reflectance Distribution Function model. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Spectral response functions of VIIRS and VIRR.</p> Full article ">Figure 2
<p>The images of VIIRS band I2 (<b>a</b>) and VIRR band 2 (<b>b</b>) over the Dunhuang test site on 16 September 2014.</p> Full article ">Figure 3
<p>Measured Bidirectional Reflectance Factor of the Dunhuang site. (<b>a</b>) VIIRS M3; (<b>b</b>) VIIRS M4; (<b>c</b>) VIIRS I1; and (<b>d</b>) VIIRS I2.</p> Full article ">Figure 3 Cont.
<p>Measured Bidirectional Reflectance Factor of the Dunhuang site. (<b>a</b>) VIIRS M3; (<b>b</b>) VIIRS M4; (<b>c</b>) VIIRS I1; and (<b>d</b>) VIIRS I2.</p> Full article ">Figure 4
<p>The flowchart of the cross-calibration procedure.</p> Full article ">Figure 5
<p>Scatterplot of the VIIRS TOA reflectance and its surface reflectance after atmospheric correction.</p> Full article ">Figure 6
<p>Scatterplot of the VIIRS surface reflectance and the BRDF corrected reflectance values under the geometry condition of VIRR.</p> Full article ">Figure 7
<p>Spectral band adjustment factor between VIIRS and VIRR on 24 July 2014. (<b>a</b>) VIIRS M3 <span class="html-italic">vs.</span> VIRR B8; (<b>b</b>) VIIRS M4 <span class="html-italic">vs.</span> VIRR B9; (<b>c</b>) VIIRS I1 <span class="html-italic">vs.</span> VIRR B1; (<b>d</b>) VIIRS I2 <span class="html-italic">vs.</span> VIRR B2.</p> Full article ">Figure 8
<p>Scatterplot of the simulated VIRR TOA reflectance before spectral adjustment and the corresponding values after spectral adjustment.</p> Full article ">Figure 9
<p>Scatterplot of the simulated VIRR TOA reflectance values after the spectral adjustment and the observed values. (<b>a</b>) VIRR B8; (<b>b</b>) VIRR B9; (<b>c</b>) VIRR B1; and (<b>d</b>) VIRR B2.</p> Full article ">
3414 KiB  
Article
Fast and Accurate Collocation of the Visible Infrared Imaging Radiometer Suite Measurements with Cross-Track Infrared Sounder
by Likun Wang, Denis Tremblay, Bin Zhang and Yong Han
Remote Sens. 2016, 8(1), 76; https://doi.org/10.3390/rs8010076 - 21 Jan 2016
Cited by 45 | Viewed by 9469
Abstract
Given the fact that Cross-track Infrared Sounder (CrIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) are currently onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and will continue to be carried on the same platform as future Joint Polar Satellite System [...] Read more.
Given the fact that Cross-track Infrared Sounder (CrIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) are currently onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and will continue to be carried on the same platform as future Joint Polar Satellite System (JPSS) satellites for the next decade, it is desirable to develop a fast and accurate collocation scheme to collocate VIIRS products and measurements with CrIS for applications that rely on combining measurements from two sensors such as inter-calibration, geolocation assessment, and cloud detection. In this study, an accurate and fast collocation method to collocate VIIRS measurements within CrIS instantaneous field of view (IFOV) directly based on line-of-sight (LOS) pointing vectors is developed and discussed in detail. We demonstrate that this method is not only accurate and precise from a mathematical perspective, but also easy to implement computationally. More importantly, with optimization, this method is very fast and efficient and thus can meet operational requirements. Finally, this collocation method can be extended to a wide variety of sensors on different satellite platforms. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>CrIS spectra from the LWIR and SWIR bands (bands 1 and 3) and the VIIRS spectral response functions of I5, M13, M15, and M16 bands.</p> Full article ">Figure 2
<p>Examples of VIIRS I5 (<b>a</b>) and CrIS (<b>b</b>) images from 1024UTC to 1032UTC on 5 September 2015. The CrIS spectra have been convolved with VIIRS spectral response function to match VIIRS I5 band radiances.</p> Full article ">Figure 3
<p>Enlarged plots four CrIS scans in <a href="#remotesensing-08-00076-f002" class="html-fig">Figure 2</a>, including (<b>a</b>) projected CrIS FOV footprints overlapped with VIIRS image and (<b>b</b>) CrIS FOV images.</p> Full article ">Figure 4
<p>Illustration of the coordinate systems listed in <a href="#remotesensing-08-00076-t001" class="html-table">Table 1</a>, including (<b>a</b>) ENU (red color), LLA (green color), ECEF (black color) coordinate systems; and (<b>b</b>) local spherical coordinate (blue color).</p> Full article ">Figure 5
<p>Schematic diagrams showing how to collocate VIIRS pixels CrIS FOV through VIIRS and CrIS LOS pointing vector, including (<b>a</b>) computation of the VIIRS and CrIS LOS vectors in ECEF and (<b>b</b>) examination of the angle between CrIS and VIIRS LOS vectors. Note that φ is the CrIS detector FOV angle of 0.963°.</p> Full article ">Figure 6
<p>(<b>Top</b>) Magnitudes from satellite position vectors in ECEF contained in CrIS geolocation datasets (red color) and derived from Equation (2) (black) as well as their differences (<b>bottom</b>).</p> Full article ">Figure 7
<p>Collocated VIIRS pixels within the CrIS FOVs, including (<b>a</b>) the four scans shown in <a href="#remotesensing-08-00076-f003" class="html-fig">Figure 3</a>; (<b>b</b>) the enlarged images for FOR 1 and 15 in the first scan line (from the bottom up) with scan angles of −48.3° and −1.65°; and (<b>c</b>) the enlarged plot for the center FOV (FOV 5) in the FOR 15 in the first scan, where the colorful points indicate VIIRS pixels falling within the CrIS FOV and the black ones represent those outside CrIS FOVs. Note that CrIS FOV shapes shown as black lines are independently computed using CrIS geolocation dataset and the FOV angle of 0.963°.</p> Full article ">Figure 8
<p>CrIS-VIIRS BT difference image map (<b>a</b>) and scatter plot of VIIRS BT <span class="html-italic">versus</span> CrIS BT for VIIRS I5 band (<b>b</b>). CrIS spectra are convolved with VIIRS SFRs to simulate VIIRS I5 band radiances, while collocated VIIRS radiances are spatially averaged within CrIS FOVs. The original CrIS and VIIRS images can be found in <a href="#remotesensing-08-00076-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 9
<p>CrIS image (<b>a</b>) at the North Polar Region on 1 October 2015 and CrIS-VIIRS BT difference map (<b>b</b>).</p> Full article ">Figure 10
<p>CrIS clear sky FOVs’ BTs detected using VIIRS cloud mask product. The original CrIS and VIIRS images as well as the color bar can be found in <a href="#remotesensing-08-00076-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 11
<p>Examples of CrIS (<b>a</b>) and AIRS (<b>b</b>) images at 900 cm<sup>−1</sup> as well as a collocated CrIS image that matches AIRS (<b>c</b>).</p> Full article ">
11521 KiB  
Article
Evaluation of ASTER-Like Daily Land Surface Temperature by Fusing ASTER and MODIS Data during the HiWATER-MUSOEXE
by Guijun Yang, Qihao Weng, Ruiliang Pu, Feng Gao, Chenhong Sun, Hua Li and Chunjiang Zhao
Remote Sens. 2016, 8(1), 75; https://doi.org/10.3390/rs8010075 - 21 Jan 2016
Cited by 41 | Viewed by 8207
Abstract
Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. [...] Read more.
Land surface temperature (LST) is an important parameter that is highly responsive to surface energy fluxes and has become valuable to many disciplines. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between them. Thus, various algorithms/models have been developed to enhance the spatial or the temporal resolution of thermal infrared (TIR) data or LST, but rarely both. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like surface reflectance. In order to extend the STARFM application over heterogeneous areas, an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. The aim of this study is to conduct a comprehensive evaluation of the ESTARFM algorithm for generating ASTER-like daily LST by three approaches: simulated data, ground measurements and remote sensing products, respectively. The datasets of LST ground measurements, MODIS, and ASTER images were collected in an arid region of Northwest China during the first thematic HiWATER-Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012 from May to September. Firstly, the results of the simulation test indicated that ESTARFM could accurately predict background with temperature variations, even coordinating with small ground objects and linear ground objects. Secondly, four temporal ASTER and MODIS data fusion LSTs (i.e., predicted ASTER-like LST products) were highly consistent with ASTER LST products. Here, the four correlation coefficients were greater than 0.92, root mean square error (RMSE) reached about 2 K and mean absolute error (MAE) ranged from 1.32 K to 1.73 K. Finally, the results of the ground measurement validation indicated that the overall accuracy was high (R2 = 0.92, RMSE = 0.77 K), and the ESTARFM algorithm is a highly recommended method to assemble time series images at ASTER spatial resolution and MODIS temporal resolution due to LST estimation error less than 1 K. However, the ESTARFM method is also limited in predicting LST changes that have not been recorded in MODIS and/or ASTER pixels. Full article
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<p>Location of the study area and the WATERNET and AWS stations’ spatial distribution. (<b>a</b>) ASTER L1B visible near infrared (VNIR) image of the study area on 27 August 2012. RGB components are channels 3 (0.81 μm), 2 (0.66 μm), and 1 (0.56 μm), respectively, with 15-m resolution; (<b>b</b>) true color image acquired with an airborne digital camera, which covers the core experimental area (5.5 km × 5.5 km); (<b>c</b>) one node of WATERNET; (<b>d</b>) AWS-10. station; and (<b>e</b>) thermal infrared sensor.</p> Full article ">Figure 2
<p>Two schemes for estimation of LST at ASTER resolution by ESTARFM. Scheme 1 was a segment-based prediction made by taking turns of each two adjacent temporal data. Scheme 2 was only using first and last temporal data for the direct estimation.</p> Full article ">Figure 3
<p>Simulation of temporal changes in temperature for water (inside circle) and vegetation (outside circle). (<b>a</b>–<b>c</b>) were fine resolution images for three temporal; (<b>d</b>–<b>f</b>) were coarse-resolution images aggregated from fine resolution images (<b>a</b>–<b>c</b>); Image in (<b>g</b>) was estimated with ESTARFM, for comparison with image (<b>b</b>).</p> Full article ">Figure 3 Cont.
<p>Simulation of temporal changes in temperature for water (inside circle) and vegetation (outside circle). (<b>a</b>–<b>c</b>) were fine resolution images for three temporal; (<b>d</b>–<b>f</b>) were coarse-resolution images aggregated from fine resolution images (<b>a</b>–<b>c</b>); Image in (<b>g</b>) was estimated with ESTARFM, for comparison with image (<b>b</b>).</p> Full article ">Figure 4
<p>Simulation of an object with changing shape. (<b>a</b>–<b>c</b>) were fine resolution images for three temporal; (<b>d</b>–<b>f</b>) were coarse-resolution images aggregated from fine resolution images (<b>a</b>–<b>c</b>); Image in (<b>g</b>) was estimated with ESTARFM, for comparison with image (<b>b</b>); Image in (<b>h</b>) was difference between (<b>b</b>,<b>g</b>).</p> Full article ">Figure 5
<p>Simulation test on a small object. (<b>a</b>–<b>c</b>) were fine resolution images for three temporal; (<b>d</b>–<b>f</b>) were coarse-resolution images aggregated from fine resolution images (<b>a</b>–<b>c</b>); Image in (<b>g</b>) was estimated with ESTARFM, for comparison with image (<b>b</b>).</p> Full article ">Figure 6
<p>Simulation test on a linear object. (<b>a</b>–<b>c</b>) were fine resolution images for three temporal; (<b>d</b>–<b>f</b>) were coarse-resolution images aggregated from fine resolution images (<b>a</b>–<b>c</b>); Image in (<b>g</b>) was estimated with ESTARFM, for comparison with image (<b>b</b>).</p> Full article ">Figure 6 Cont.
<p>Simulation test on a linear object. (<b>a</b>–<b>c</b>) were fine resolution images for three temporal; (<b>d</b>–<b>f</b>) were coarse-resolution images aggregated from fine resolution images (<b>a</b>–<b>c</b>); Image in (<b>g</b>) was estimated with ESTARFM, for comparison with image (<b>b</b>).</p> Full article ">Figure 7
<p>Estimated LST with ASTER resolution using ESTARFM according to Scheme 1. (<b>a</b>) 2 August 2012; (<b>b</b>) 18 August 2012; (<b>c</b>) 20 August 2012; (<b>d</b>) 21 August 2012; (<b>e</b>) 22 August 2012; (<b>f</b>) 23 August 2012; (<b>g</b>) 25 August 2012; (<b>h</b>) 27 August 2012; (<b>i</b>) 29 August 2012; (<b>j</b>) 2 September 2012; (<b>k</b>) 3 September 2012.</p> Full article ">Figure 7 Cont.
<p>Estimated LST with ASTER resolution using ESTARFM according to Scheme 1. (<b>a</b>) 2 August 2012; (<b>b</b>) 18 August 2012; (<b>c</b>) 20 August 2012; (<b>d</b>) 21 August 2012; (<b>e</b>) 22 August 2012; (<b>f</b>) 23 August 2012; (<b>g</b>) 25 August 2012; (<b>h</b>) 27 August 2012; (<b>i</b>) 29 August 2012; (<b>j</b>) 2 September 2012; (<b>k</b>) 3 September 2012.</p> Full article ">Figure 8
<p>Estimated LST with ASTER resolution using ESTARFM according to Scheme 2. (<b>a</b>) 2 August 2012; (<b>b</b>) 18 August 2012; (<b>c</b>) 20 August 2012; (<b>d</b>) 21 August 2012; (<b>e</b>) 22 August 2012; (<b>f</b>) 23 August 2012; (<b>g</b>) 25 August 2012; (<b>h</b>) 27 August 2012; (<b>i</b>) 29 August 2012; (<b>j</b>) 2 September 2012; (<b>k</b>) 3 September 2012.</p> Full article ">Figure 8 Cont.
<p>Estimated LST with ASTER resolution using ESTARFM according to Scheme 2. (<b>a</b>) 2 August 2012; (<b>b</b>) 18 August 2012; (<b>c</b>) 20 August 2012; (<b>d</b>) 21 August 2012; (<b>e</b>) 22 August 2012; (<b>f</b>) 23 August 2012; (<b>g</b>) 25 August 2012; (<b>h</b>) 27 August 2012; (<b>i</b>) 29 August 2012; (<b>j</b>) 2 September 2012; (<b>k</b>) 3 September 2012.</p> Full article ">Figure 9
<p>Scatter density plots between the observed product and estimated LSTs with Scheme 1 on 8 August 2012 (<b>a</b>); 18 August 2012 (<b>b</b>); 27 August 2012 (<b>c</b>); and 3 September 2012 (<b>d</b>).</p> Full article ">Figure 10
<p>Scatter density plots between the observed product and estimated LSTs with Scheme 2 on 2 August 2012 (<b>a</b>); 18 August 2012 (<b>b</b>); 27 August 2012 (<b>c</b>); and 3 September 2012 (<b>d</b>).</p> Full article ">Figure 11
<p>The average STD of LSTs at all AWS and WATERNET stations.</p> Full article ">Figure 12
<p>Error image and correlation analysis plot for LST ground validation. (<b>a</b>) Image of errors between estimated and measured LSTs for each date (horizontal axis) and stations (vertical axis); (<b>b</b>) Scatter plot of measured and estimated LSTs on 11 dates (2 August–3 September] and overall accuracy; (<b>c</b>) Correlation coefficient and RMSE between estimated and measured LSTs for each ground observation including all dates.</p> Full article ">Figure 12 Cont.
<p>Error image and correlation analysis plot for LST ground validation. (<b>a</b>) Image of errors between estimated and measured LSTs for each date (horizontal axis) and stations (vertical axis); (<b>b</b>) Scatter plot of measured and estimated LSTs on 11 dates (2 August–3 September] and overall accuracy; (<b>c</b>) Correlation coefficient and RMSE between estimated and measured LSTs for each ground observation including all dates.</p> Full article ">Figure 13
<p>Variation range of 23 selected stations’ FVC from 30 May to 19 September 2012.</p> Full article ">Figure 14
<p>Impact analyses of the number of land cover types and the size of search window for ESTARFM, (<b>a</b>) NEI with different land cover type; (<b>b</b>) NEI with different size of window.</p> Full article ">
5407 KiB  
Article
On the Use of Cross-Correlation between Volume Scattering and Helix Scattering from Polarimetric SAR Data for the Improvement of Ship Detection
by Jujie Wei, Jixian Zhang, Guoman Huang and Zheng Zhao
Remote Sens. 2016, 8(1), 74; https://doi.org/10.3390/rs8010074 - 20 Jan 2016
Cited by 20 | Viewed by 5951
Abstract
Synthetic Aperture Radar (SAR) ship detection is an important maritime application. However, azimuth ambiguities caused by the finite sampling of the Doppler spectrum are often visible in SAR images and are always mistaken as ships by classic detection techniques, like the Constant False [...] Read more.
Synthetic Aperture Radar (SAR) ship detection is an important maritime application. However, azimuth ambiguities caused by the finite sampling of the Doppler spectrum are often visible in SAR images and are always mistaken as ships by classic detection techniques, like the Constant False Alarm Rate (CFAR). It is known that radar targets and azimuth ambiguities have different characteristics in polarimetric SAR (PolSAR) data, i.e., first ambiguities usually have strong odd- or double-bounce scattering and the maximum amplitude of the first ambiguity in SHV is always considerably smaller than that of the corresponding target for zero or high velocity. On the basis of this characteristics, this paper finds that first ambiguities usually have low volume scattering power relative to ships and almost have no helix scattering by Yamaguchi decomposition. But some residual ambiguities still exit in the volume scattering power and have similar scattering intensity to small ships, and some parts of a ship also have zero helix scattering owing to some physical factors (e.g., ship structure, radar incidence angle, etc.). Thus, for high-precision ship detection, a new ship detection method based on cross-correlation between the volume and helix scattering mechanisms derived from Yamaguchi decomposition is proposed to avoid false alarms caused by azimuth ambiguities and enhance Target-to-Clutter Ratio (TCR) for improving the miss detection rate of small ships. By experiments, it is proved that our method can work effectively and has high detection accuracy. Full article
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<p>The Pauli RGB images of the AIRSAR C-band and L-band PolSAR data, color coded by: red = <span class="html-italic">T</span><sub>22</sub> = 1/2|<span class="html-italic">S</span><sub>HH</sub> − <span class="html-italic">S</span><sub>VV</sub>|<sup>2</sup>, green = <span class="html-italic">T</span><sub>33</sub> = 2|<span class="html-italic">S</span><sub>HV</sub>|<sup>2</sup>, and bue = <span class="html-italic">T</span><sub>11</sub> = 1/2|<span class="html-italic">S</span><sub>HH</sub> + <span class="html-italic">S</span><sub>VV</sub>|<sup>2</sup>. The test area is selected by the yellow dash line.</p> Full article ">Figure 2
<p>The pseudo-color RGB image (<b>Left</b>) color coded by: red = C-band Span image (<b>Right Top</b>), green = L-band Span image (<b>Right Bottom</b>), and blue = C-band Span image. All ships were interpreted visually and labeled as S1 to S22 (white spots). Correspondingly, the azimuth ambiguities were labeled as A1<sup>1</sup>, A1<sup>2</sup>, A2, …, and A20 (magenta spots).</p> Full article ">Figure 3
<p>Yamaguchi decomposition results. (<b>a</b>) odd-bounce scattering; (<b>b</b>) double-bounce scattering; (<b>c</b>) volume scattering; and (<b>d</b>) helix scattering. All the powers are recalculated in decibels by 10 × 1og<sub>10</sub>(power + 1e−5).</p> Full article ">Figure 4
<p>The variation of mean target-to-clutter ratio (TCR) of ships and ambiguities in each decomposition component. (<b>a</b>) odd-bounce scattering; (<b>b</b>) double-bounce scattering; (<b>c</b>) volume scattering; and (<b>d</b>) helix scattering.</p> Full article ">Figure 5
<p>Zoom in S3, S12, and S15. (<b>a</b>) S3; (<b>b</b>) S12; and (<b>c</b>) S15. The powers are also calculated in decibels by 10 × 1og<sub>10</sub>(power + 1e−5).</p> Full article ">Figure 6
<p>The quantitative comparison among the helix and volume scattering powers from the original Yamaguchi decomposition [<a href="#B17-remotesensing-08-00074" class="html-bibr">17</a>] and T33 for azimuth ambiguities.</p> Full article ">Figure 7
<p>Flowchart of the proposed ship detection method.</p> Full article ">Figure 8
<p>The coherence image derived from cross-correlation between the volume and helix scattering. And the coherence result is also recalculated in decibels by 10 × 1og<sub>10</sub>(power + 1e−5).</p> Full article ">Figure 9
<p>Zoom in S3, S12, and S15 after the coherence. (<b>a</b>) S3; (<b>b</b>) S12; and (<b>c</b>) S15. The powers are also calculated in decibels by 10 × 1og<sub>10</sub>(power + 1e−5).</p> Full article ">Figure 10
<p>Various ship detection results. (<b>a</b>) The detection result by the new method; (<b>b</b>) the detection result by Wang’s method; (<b>c</b>) the detection result by the helix scattering power; and (<b>d</b>) the detection result by T33.</p> Full article ">Figure 11
<p>The quantitative comparison among the methods. (<b>a</b>) ROC plots for the different detectors; and (<b>b</b>) the quantitative comparison among the methods by FoM <span class="html-italic">vs</span>. <span class="html-italic">P</span><sub>d</sub>.</p> Full article ">Figure 12
<p>The quantitative comparison between our method and Wang’s method. From the top down, each row corresponds to the test area <b>A</b>, <b>B</b>, <b>C</b>, and <b>D</b>, respectively. From left to right, each column corresponds to the volume scattering, the helix scattering, the coherence result derived from cross-correlation between the volume and helix scattering, and the third eigenvalue derived from Cloude decomposition, respectively. The scattering powers and the coherence intensity are all calculated in decibels (dB) by 10 × 1og<sub>10</sub>(power + 1e−5).</p> Full article ">
6789 KiB  
Article
Post-Eruption Deformation Processes Measured Using ALOS-1 and UAVSAR InSAR at Pacaya Volcano, Guatemala
by Lauren N. Schaefer, Zhong Lu and Thomas Oommen
Remote Sens. 2016, 8(1), 73; https://doi.org/10.3390/rs8010073 - 19 Jan 2016
Cited by 36 | Viewed by 8107
Abstract
Pacaya volcano is a persistently active basaltic cone complex located in the Central American Volcanic Arc in Guatemala. In May of 2010, violent Volcanic Explosivity Index-3 (VEI-3) eruptions caused significant topographic changes to the edifice, including a linear collapse feature 600 m long [...] Read more.
Pacaya volcano is a persistently active basaltic cone complex located in the Central American Volcanic Arc in Guatemala. In May of 2010, violent Volcanic Explosivity Index-3 (VEI-3) eruptions caused significant topographic changes to the edifice, including a linear collapse feature 600 m long originating from the summit, the dispersion of ~20 cm of tephra and ash on the cone, the emplacement of a 5.4 km long lava flow, and ~3 m of co-eruptive movement of the southwest flank. For this study, Interferometric Synthetic Aperture Radar (InSAR) images (interferograms) processed from both spaceborne Advanced Land Observing Satellite-1 (ALOS-1) and aerial Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired between 31 May 2010 and 10 April 2014 were used to measure post-eruptive deformation events. Interferograms suggest three distinct deformation processes after the May 2010 eruptions, including: (1) subsidence of the area involved in the co-eruptive slope movement; (2) localized deformation near the summit; and (3) emplacement and subsequent subsidence of about a 5.4 km lava flow. The detection of several different geophysical signals emphasizes the utility of measuring volcanic deformation using remote sensing techniques with broad spatial coverage. Additionally, the high spatial resolution of UAVSAR has proven to be an excellent compliment to satellite data, particularly for constraining motion components. Measuring the rapid initiation and cessation of flank instability, followed by stabilization and subsequent influence on eruptive features, provides a rare glimpse into volcanic slope stability processes. Observing these and other deformation events contributes both to hazard assessment at Pacaya and to the study of the stability of stratovolcanoes. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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<p>Location and major topographical features of Pacaya Volcano. (<b>a</b>) Pacaya volcano (red triangle) is located in the Central American Volcanic Arc (CAVA), with other CAVA volcanoes marked with black triangles; (<b>b</b>) Map showing the location of topographic features, including the summit vent, ancestral collapse scarp, and the linear collapse and lava flow as a result of the May 2010 eruptions. The colored boxes show the extent of <a href="#remotesensing-08-00073-f002" class="html-fig">Figure 2</a>, <a href="#remotesensing-08-00073-f003" class="html-fig">Figure 3</a>, <a href="#remotesensing-08-00073-f004" class="html-fig">Figure 4</a> and <a href="#remotesensing-08-00073-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 2
<p>UAVSAR amplitude images of Pacaya volcano (<b>a</b>) before and (<b>b</b>) after the May 2010 eruptions clearly show changes to the edifice as a result of the eruption, including the emplacement of the 5.4 km long lava flow, an increase in the summit crater size, and the linear collapse oriented NNW from the summit; Panel (<b>c</b>) is an averaged image produced from eight ALOS-1 acquisitions between 2010 and 2011.</p> Full article ">Figure 3
<p>Slope deformation surrounding the May 2010 eruptions, with the black dashed line marking the outline of the slope instability in panel 3b. (<b>a</b>) No deformation of the southwest slope is seen prior to the eruption; (<b>b</b>) During the eruptions on the 27 and 28 of May, ~3 m of LOS slope displacement can be seen on the southwest sector of the edifice (modified from [<a href="#B22-remotesensing-08-00073" class="html-bibr">22</a>]); (<b>c</b>) Immediately after the eruption, subsidence encompasses a similar extent of the southwest flank that moved during the slide; (<b>d</b>,<b>e</b>) This deformation decreases in magnitude and spatial extent until late December; (<b>f</b>) Deformation measured using the first available UAVSAR data after the May 2010 eruption spanning 26 April 2011–8 March 2013 is magnified to show the similarities in the fringe pattern over the southwest flank of the volcano. Similar fringe patterns between this UAVSAR interferogram and the ALOS-1 interferogram in <a href="#remotesensing-08-00073-f003" class="html-fig">Figure 3</a>c suggests that the deformation observed after the slide is likely dominated by vertical subsidence, as the sensors have nearly opposite looking geometries.</p> Full article ">Figure 4
<p>Localized deformation to the north of the summit. (<b>a</b>) Several fringes to the north of the summit are difficult to discern from subsidence of the southwest flank in the ALOS-1 interferogram immediately after the eruptions on 27 and 28 May; (<b>b</b>,<b>c</b>) This signal becomes the only deformation event in subsequent months, reducing in magnitude and spatial extent; (<b>d</b>) Fringes are more distinct in a later UAVSAR interferogram, which shows that the area was still subsiding at very slow rates (~10 cm over 682 days) between 2011 and 2013; (<b>e</b>) Several recent lava flows (outlined from Landsat satellite images and [<a href="#B20-remotesensing-08-00073" class="html-bibr">20</a>]) have flowed to the north of the summit, the contraction of which could account for some of the measured deformation.</p> Full article ">Figure 5
<p>InSAR images showing post-emplacement surface movement of the lava flow that began on 28 May 2010, with the approximate extent of the lava flow outlined. (<b>a</b>) Emplacement of the flow continues until 30 June, and then subsides at a decreasing rate over time, shown in the following time frames: (<b>b</b>) 29 June 2010–14 August 2010; (<b>c</b>) 14 August 2010–29 September 2010; (<b>d</b>) 29 September 2010–30 December 2010; (<b>e</b>) 30 December 2010–14 February 2011; (<b>f</b>) 14 February 2011–1 April 2011; (<b>g</b>) 26 April 2011–8 March 2013; and (<b>h</b>) 2 April 2013–10 April 2014. Line A-A’ in 5d crosses the approximate area of maximum subsidence, with values in <a href="#remotesensing-08-00073-t001" class="html-table">Table 1</a> and a time series of profiles of range change rate deformation rates along this profile in <a href="#remotesensing-08-00073-f006" class="html-fig">Figure 6</a>.</p> Full article ">Figure 6
<p>Deformation rate of cross section A-A’ in <a href="#remotesensing-08-00073-f005" class="html-fig">Figure 5</a>d shows that lava flow subsidence is greatest in the center, which can be inferred to be the thickest part of the flow.</p> Full article ">Figure 7
<p>Eruptive trends at Pacaya. (<b>a</b>) A continued NNW-oriented pattern of vents and cracks is seen post-2010 eruption. Intrusions along this weakness zone could have provided the push to induce slope displacement during the May 2010 eruption. Cracks were mapped using Google Earth aerial images (inset image taken in December 2010, courtesy of Google 2015 DigitalGlobe); (<b>b</b>) High volume lava flows tend to erupt from lower flank vents such as in 1961, 1975, 2010, and 2014, suggesting a cyclical draining of a shallow magma system in the cone. Outlines for the 1961 and 1975 lava flows from [<a href="#B21-remotesensing-08-00073" class="html-bibr">21</a>].</p> Full article ">
2010 KiB  
Article
Inter-Comparison of S-NPP VIIRS and Aqua MODIS Thermal Emissive Bands Using Hyperspectral Infrared Sounder Measurements as a Transfer Reference
by Yonghong Li, Aisheng Wu and Xiaoxiong Xiong
Remote Sens. 2016, 8(1), 72; https://doi.org/10.3390/rs8010072 - 19 Jan 2016
Cited by 16 | Viewed by 6393
Abstract
This paper compares the calibration consistency of the spectrally-matched thermal emissive bands (TEB) between the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), using observations from their simultaneous nadir overpasses (SNO). Nearly-simultaneous [...] Read more.
This paper compares the calibration consistency of the spectrally-matched thermal emissive bands (TEB) between the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), using observations from their simultaneous nadir overpasses (SNO). Nearly-simultaneous hyperspectral measurements from the Aqua Atmospheric Infrared Sounder(AIRS) and the S-NPP Cross-track Infrared Sounder (CrIS) are used to account for existing spectral response differences between MODIS and VIIRS TEB. The comparison uses VIIRS Sensor Data Records (SDR) in MODIS five-minute granule format provided by the NASA Land Product and Evaluation and Test Element (PEATE) and Aqua MODIS Collection 6 Level 1 B (L1B) products. Each AIRS footprint of 13.5 km (or CrIS field of view of 14 km) is co-located with multiple MODIS (or VIIRS) pixels. The corresponding AIRS- and CrIS-simulated MODIS and VIIRS radiances are derived by convolutions based on sensor-dependent relative spectral response (RSR) functions. The VIIRS and MODIS TEB calibration consistency is evaluated and the two sensors agreed within 0.2 K in brightness temperature. Additional factors affecting the comparison such as geolocation and atmospheric water vapor content are also discussed in this paper. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Spectral distribution of VIIRS and MODIS TEBs, as well as AIRS and CrIS spectra coverage. (radiance unit: W/m<sup>2</sup>/µm/sr).</p> Full article ">Figure 2
<p>VIIRS-MODIS-AIRS SNO FOV number distribution.</p> Full article ">Figure 3
<p>Distribution of VIIRS-MODIS inter-comparison without/with RSR correction from AIRS measurements. (<b>a</b>) M13_B22 without RSR correction; (<b>b</b>) M13_B22 with RSR correction; (<b>c</b>) M13_B23 without RSR correction; (<b>d</b>) M13_B23 with RSR correction; (<b>e</b>) M15_B31 without RSR correction; (<b>f</b>) M15_B31 with RSR correction; (<b>g</b>) M16_B32 without RSR correction; (<b>h</b>) M16_B32 with RSR correction.</p> Full article ">Figure 4
<p>VIIRS-MODIS inter-comparison without/with RSR correction from AIRS (black stars) and CrIS (blue diamonds) measurements, (<b>a</b>) without RSR correction; and (<b>b</b>) with RSR correction.</p> Full article ">Figure 5
<p>VIIRS-MODIS-CrIS SNO FOV number distribution.</p> Full article ">Figure 6
<p>Distribution of VIIRS-MODIS inter-comparison without/with RSR correction from CrIS measurements. (<b>a</b>) M13_B22 without RSR correction; (<b>b</b>) M13_B22 with RSR correction; (<b>c</b>) M13_B23 without RSR correction; (<b>d</b>) M13_B23 with RSR correction; (<b>e</b>) M15_B31 without RSR correction; (<b>f</b>) M15_B31 with RSR correction; (<b>g</b>) M16_B32 without RSR correction; (<b>h</b>) M16_B32 with RSR correction.</p> Full article ">Figure 7
<p>RSR correction factors from AIRS (black stars) and CrIS (blue diamonds) measurements.</p> Full article ">
16780 KiB  
Article
A Point Cloud Filtering Approach to Generating DTMs for Steep Mountainous Areas and Adjacent Residential Areas
by Qi Chen, Huan Wang, Hanchao Zhang, Mingwei Sun and Xiuguo Liu
Remote Sens. 2016, 8(1), 71; https://doi.org/10.3390/rs8010071 - 19 Jan 2016
Cited by 48 | Viewed by 8070
Abstract
Digital terrain models (DTMs) are considered important basic geographic data. They are widely used in the fields of cartography, land utilization, urban planning, communications, and remote sensing. Digital photogrammetry mainly based on stereo image matching is a frequently applied technique to generate DTMs. [...] Read more.
Digital terrain models (DTMs) are considered important basic geographic data. They are widely used in the fields of cartography, land utilization, urban planning, communications, and remote sensing. Digital photogrammetry mainly based on stereo image matching is a frequently applied technique to generate DTMs. Generally, the process of ground filtering should be applied to the point cloud derived from image matching to separate terrain and off-terrain points before DTM generation. However, many of the existing filtering methods perform unsatisfactorily for steep mountainous areas, particularly when residential neighborhoods exist in the proximity of the test areas. In this study, an improved automated filtering method based on progressive TIN (triangulated irregular networks) densification (PTD) is proposed to generate DTMs for steep mountainous areas and adjacent residential areas. Our main improvement on the classic method is the acquisition of seed points with better distribution and reliability to enhance its adaptability to different types of terrain. A rule-based method for detecting ridge points is first applied. The detected points are used as additional seed points. Subsequently, a locally optimized seed point selection method based on confidence interval estimation theory is applied to remove the erroneous points. The experiments on two sets of stereo-matched point clouds indicate that the proposed method performs well for both residential and mountainous areas. The total accuracy values in the form of root-mean-square errors of the generated DTMs by the proposed method are 0.963 and 1.007 m; respectively; which are better than the 1.286 and 1.309 m achieved by the classic PTD method. Full article
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<p>Seed point selection for a steep mountainous area with buildings nearby. The real terrain and the corresponding point cloud are separately drawn in two parts. In part (<b>a</b>), the buildings, trees, and topography are indicated with different colors; In part (<b>b</b>), the cyan dashed line defines the grid cell for seed point selection; the small red diamond shape represents the lowest point selected as seed point in each grid, and the brown dashed line represents the initial terrain constructed with the seed points.</p> Full article ">Figure 2
<p>Adjacent triangles for determining the ridge triangle. The green triangle represents the judging triangle, the dark blue triangles represent the triangles directly adjacent to the judging triangle, and the light blue triangles represent the triangles connected to the directly adjacent triangles.</p> Full article ">Figure 3
<p>Results of ridge point detection. (<b>a</b>) Initial seed points selected from the stereo-matched point cloud that are colored according to their elevations; (<b>b</b>) Enlarged view of the rectangular area in (<b>a</b>); (<b>c</b>) Ridge points detected based on the initial seed points; (<b>d</b>) Enlarged view of the rectangular area in (<b>c</b>); (<b>e</b>,<b>f</b>) Vertical views of the TIN surfaces constructed using the points in (<b>b</b>,<b>d</b>), respectively.</p> Full article ">Figure 3 Cont.
<p>Results of ridge point detection. (<b>a</b>) Initial seed points selected from the stereo-matched point cloud that are colored according to their elevations; (<b>b</b>) Enlarged view of the rectangular area in (<b>a</b>); (<b>c</b>) Ridge points detected based on the initial seed points; (<b>d</b>) Enlarged view of the rectangular area in (<b>c</b>); (<b>e</b>,<b>f</b>) Vertical views of the TIN surfaces constructed using the points in (<b>b</b>,<b>d</b>), respectively.</p> Full article ">Figure 4
<p>Triangulated irregular network (TIN) surfaces constructed with seed points before and after optimal selection. (<b>a</b>) Results of the initial seed points; (<b>b</b>) Results of the seed points after adding the detected ridge points; (<b>c</b>) Results of the seed points after optimal selection. The yellow ellipses indicate the areas where gross errors among the initial seed points appear, and the black ellipses indicate the areas where gross errors among additional ridge points appear.</p> Full article ">Figure 4 Cont.
<p>Triangulated irregular network (TIN) surfaces constructed with seed points before and after optimal selection. (<b>a</b>) Results of the initial seed points; (<b>b</b>) Results of the seed points after adding the detected ridge points; (<b>c</b>) Results of the seed points after optimal selection. The yellow ellipses indicate the areas where gross errors among the initial seed points appear, and the black ellipses indicate the areas where gross errors among additional ridge points appear.</p> Full article ">Figure 5
<p>Example of TIN densification in a local area. The brown line represents the actual terrain, and the blue dashed line represents the TIN surface constructed with the detected terrain points. The red diamond shapes represent the points labeled as terrain points, and the white diamonds represent the points labeled as off-terrain points. (<b>a</b>) Sectional view of the original point cloud, where <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mi>A</mi> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mi>B</mi> </msub> </mrow> </semantics> </math> are the distances from <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>B</mi> </msub> </mrow> </semantics> </math> to the corresponding triangle, respectively, and <math display="inline"> <semantics> <mi>m</mi> </semantics> </math> is the grid cell size for determining the lowest point; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> </mrow> </semantics> </math> cannot be labeled as a terrain point if the points are subsampled in advance; (<b>c</b>,<b>d</b>) Densification results using the proposed strategy in two iterations, where <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> </mrow> </semantics> </math> is successfully labeled.</p> Full article ">Figure 6
<p>Experimental and evaluation areas. (<b>a</b>,<b>b</b>) TIN surfaces constructed with the point clouds of the two datasets, which are colored based on the texture of the aerial images. The red rectangles indicate the areas for DTM accuracy evaluation of the residential areas, and the yellow rectangles indicate the areas for evaluating mountainous areas.</p> Full article ">Figure 7
<p>TIN surfaces constructed with the points of Site 1 and its filtering results. (<b>a</b>) Original stereo-matched point cloud; (<b>b</b>) Reference DTM obtained from manual editing; (<b>c</b>–<b>g</b>) Filtering results of PM, MC, LP, PTD and the proposed method, respectively.</p> Full article ">Figure 8
<p>Orthographs of TIN surfaces of Site 4 and its filtering results. (<b>a</b>) Original stereo-matched point cloud; (<b>b</b>) Reference DTM obtained from manual editing; (<b>c</b>–<b>g</b>) Filtering results of PM, MC, LP, PTD and the proposed method, respectively. The white ellipses indicate the areas where the terrain points near the ridge are obviously rejected during the filtering process.</p> Full article ">Figure 8 Cont.
<p>Orthographs of TIN surfaces of Site 4 and its filtering results. (<b>a</b>) Original stereo-matched point cloud; (<b>b</b>) Reference DTM obtained from manual editing; (<b>c</b>–<b>g</b>) Filtering results of PM, MC, LP, PTD and the proposed method, respectively. The white ellipses indicate the areas where the terrain points near the ridge are obviously rejected during the filtering process.</p> Full article ">Figure 9
<p>TIN surfaces constructed with the points of Site 6 and its filtering results. (<b>a</b>) Original stereo-matched point cloud; (<b>b</b>) Reference DTM obtained from manual editing; (<b>c</b>–<b>g</b>) Filtering results of PM, MC, LP, PTD and the proposed method, respectively.</p> Full article ">Figure 10
<p>Orthographs of TIN surfaces of Site 7 and its filtering results. (<b>a</b>) Original stereo-matched point cloud; (<b>b</b>) Reference DTM obtained from manual editing; (<b>c</b>–<b>g</b>) Filtering results of PM, MC, LP, PTD and the proposed method, respectively. The white ellipses indicate the areas where the terrain points near the ridge are obviously rejected during the filtering process.</p> Full article ">Figure 10 Cont.
<p>Orthographs of TIN surfaces of Site 7 and its filtering results. (<b>a</b>) Original stereo-matched point cloud; (<b>b</b>) Reference DTM obtained from manual editing; (<b>c</b>–<b>g</b>) Filtering results of PM, MC, LP, PTD and the proposed method, respectively. The white ellipses indicate the areas where the terrain points near the ridge are obviously rejected during the filtering process.</p> Full article ">Figure 11
<p>DTM accuracy values obtained by PTD and the proposed method using different <math display="inline"> <semantics> <mi>w</mi> </semantics> </math> values. (<b>a</b>–<b>c</b>) Comparative results of <math display="inline"> <semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <msub> <mi>E</mi> <mi>r</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>R</mi> <mi>M</mi> <mi>S</mi> <msub> <mi>E</mi> <mi>m</mi> </msub> </mrow> </semantics> </math> and<math display="inline"> <semantics> <mrow> <mo> </mo> <mi>R</mi> <mi>M</mi> <mi>S</mi> <msub> <mi>E</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics> </math> respectively.</p> Full article ">Figure 12
<p>Three types of errors of the comparative filtering methods for the eight testing sites. (<b>a</b>–<b>c</b>) Type I errors, type II errors and total errors of the comparative methods for the testing sites, respectively.</p> Full article ">
29178 KiB  
Article
A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery
by Tengfei Long, Weili Jiao, Guojin He and Zhaoming Zhang
Remote Sens. 2016, 8(1), 56; https://doi.org/10.3390/rs8010056 - 19 Jan 2016
Cited by 43 | Viewed by 8239
Abstract
Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents [...] Read more.
Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images. Full article
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<p>Flowchart of the proposed matching method.</p> Full article ">Figure 2
<p>Blocks of an image and tiles of a block.</p> Full article ">Figure 3
<p>The numbers of matched pairs and correct match rates for different values of <math display="inline"> <msub> <mi>T</mi> <mi>σ</mi> </msub> </math> and <math display="inline"> <msub> <mi>T</mi> <mi>θ</mi> </msub> </math>. (<b>a</b>) The results with respect to different values of <math display="inline"> <msub> <mi>T</mi> <mi>σ</mi> </msub> </math>; (<b>b</b>) the results with respect to different values of <math display="inline"> <msub> <mi>T</mi> <mi>θ</mi> </msub> </math>.</p> Full article ">Figure 4
<p>The matched keypoints in a sensed image tile and a reference image tile. (<b>a</b>) The sensed image tile; and (<b>b</b>) the reference image tile.</p> Full article ">Figure 5
<p>(<b>a</b>) The template image around the SIFT keypoint (marked with a cross) in the sensed image tile; (<b>b</b>) the search image around the SIFT keypoint (marked with a cross) in the reference image tile; (<b>c</b>) an image fragment warped from the template image using the geometric model and the radiometric model in Equation (<a href="#FD8-remotesensing-08-00056" class="html-disp-formula">8</a>), and the cross denotes the SIFT keypoint in the sensed image tile after geometric transformation; (<b>d</b>) the search image, and the cross denotes the refined keypoint in the reference image tile.</p> Full article ">Figure 6
<p>Example of online matching, and the matched points are marked by cross. (<b>a</b>) to (<b>d</b>) are matching results using Google satellite images, Bing aerial maps, MapQuest satellite maps and Mapbox satellite images, respectively. In each figure, the left is the RapidEye image tile, while the right is the online aerial map.</p> Full article ">Figure 7
<p>Matching results of the AVIRIS visible image tile (left) and the AVIRIS infrared image tile (right), using three different methods. (<b>a</b>) The result of original SIFT (four matches are found, including a wrong match); (<b>b</b>) the result of SR-SIFT (six correct matches are found); and (<b>c</b>) the result of the proposed method (20 correct matches are found).</p> Full article ">Figure 8
<p>Matching results of GF-2 PAN images, using SiftGPU and the proposed method. In each sub-figure, the left is the image captured on 3 September 2015 and the right is the image captured on 12 September 2015. The matched points are labeled by the same numbers, and red crosses stand for correct matches, while yellow crosses stand for wrong matches. (<b>a</b>) The result of SiftGPU (71 correct matches and 11 wrong matches); and (<b>b</b>) the result of proposed method (30 correct matches are found).</p> Full article ">Figure 9
<p>Root mean square biases of matched points before and after least squares match (LSM) refinement.</p> Full article ">Figure 10
<p>Distribution of matched points (marked by a cross) and sensed images in online image matching tests. (<b>a</b>–<b>h</b>) are the results of Landsat-5, Cbers-2, Cbers-4, ZY-3, GF-2, Spot-5, Theos and GF-1 images, respectively.</p> Full article ">Figure 11
<p>Layer swiping between sensed images and online aerial maps in Tests 4, 6, 7 and 8. (<b>a</b>) and (<b>b</b>) are from Test 4, and the top ones are Bing aerial maps, while the lower one in (<b>a</b>) is the warped ZY-3 image using vendor-provided RPC and the lower one in (<b>b</b>) is the rectified ZY-3 image using RPC refinement; (<b>c</b>) and (<b>d</b>) are from Test 6, and the left are Bing aerial maps, while the right one in (<b>c</b>) is the Spot-5 image of Level 2 and the right one in (<b>d</b>) is the rectified Spot-5 image using terrain-dependent RPC; (<b>e</b>) and (<b>f</b>) are from Test 7, and the right are Bing aerial maps, while the left one in (<b>e</b>) is the Theos image of Level 2 and the left one in (<b>f</b>) is the rectified Theos image using terrain-dependent RPC; (<b>g</b>) and (<b>h</b>) are from Test 8, and the upper are Bing aerial maps, while the lower one in (<b>e</b>) is the warped GF-1 image using vendor-provided RPC and the lower one in (<b>f</b>) is the rectified GF-1 image using RPC refinement.</p> Full article ">
2647 KiB  
Review
A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring
by Neha Joshi, Matthias Baumann, Andrea Ehammer, Rasmus Fensholt, Kenneth Grogan, Patrick Hostert, Martin Rudbeck Jepsen, Tobias Kuemmerle, Patrick Meyfroidt, Edward T. A. Mitchard, Johannes Reiche, Casey M. Ryan and Björn Waske
Remote Sens. 2016, 8(1), 70; https://doi.org/10.3390/rs8010070 - 16 Jan 2016
Cited by 516 | Viewed by 42870
Abstract
The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique [...] Read more.
The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales. Full article
(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)
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<p>Conceptual sketch and examples of the relations between land use and land cover conversions and modifications.</p> Full article ">Figure 2
<p>Summary of land use/cover classes included in the studies.</p> Full article ">Figure 3
<p>Locations (countries) of study sites in land use-related studies selected for analysis. Studies that covered sites located in more than one country are mapped more than once.</p> Full article ">Figure 4
<p>Spatial extent of study sites in land use-related studies selected for analysis. Total area is reported for studies that covered more than one site. Plot level refers to studies that conducted assessments on individual field plots.</p> Full article ">Figure 5
<p>Optical- and radar-based sensors used in land use-related studies selected for analysis. Satellites with the same configuration and sensors (e.g., Landsat 4 and Landsat 5 or ERS-1 and ERS-2) are not distinguished.</p> Full article ">
3454 KiB  
Article
Global Gap-Free MERIS LAI Time Series (2002–2012)
by Markus Tum, Kurt P. Günther, Martin Böttcher, Frédéric Baret, Michael Bittner, Carsten Brockmann and Marie Weiss
Remote Sens. 2016, 8(1), 69; https://doi.org/10.3390/rs8010069 - 15 Jan 2016
Cited by 35 | Viewed by 8437
Abstract
This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial [...] Read more.
This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale. Full article
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<p>Map of the total number of available MERIS observations for the period July 2002–March 2012, at a spatial resolution of 0.25° × 0.25° [<a href="#B59-remotesensing-08-00069" class="html-bibr">59</a>].</p> Full article ">Figure 2
<p>Number of available 10-day composites of MERIS-LAI for selected years (2003, 2007 and 2011) and mean value for the period 2003–2011.</p> Full article ">Figure 3
<p>Observation range (maximum and minimum latitude) for each 10-day composite at which the solar zenith angle is constantly lower than 80° at data acquisition time (10 a.m. local time).</p> Full article ">Figure 4
<p>Example data of four 10° × 10° areas of the period 1–10 June 2007. Latitude/longitude values represent the top left corner of each 10° × 10° tile. Top row: MERIS 10-day composite Leaf Area Index (LAI) (0–6); second row: harmonic analysis (HA) LAI result (0–6); third row: missing data values (0–36); lower row: normalized root mean squared error (NRMSE) (0–0.3). White pixels in the top row show missing data. Blue pixels represent water.</p> Full article ">Figure 5
<p>Validation of time series-analyzed MERIS data with VALERI and BigFoot measurements. Red triangles represent the comparison of Leaf Area Index (LAI) MERIS with LAI reference maps. Black diamonds represent the comparison of LAI harmonic analysis (HA) with LAI reference maps.</p> Full article ">Figure 6
<p>Temporal development of the original MERIS (black crosses), processed (green solid line) and BigFoot/VALERI (blue Triangles) data for the period 2003–2011, for: (<b>a</b>) Fundulea (crops); (<b>b</b>) SEVI (grass); (<b>c</b>) Gnangara (broad-leaved forest); (<b>d</b>) Järvselja (mixed forest). Negative values (−1) represent missing observations.</p> Full article ">Figure 7
<p>Leaf Area Index inter-comparison of nine MERIS-LAI-HA and the corresponding Geoland2-SPOT-VGT-LAI at the Fundulea, Romania (crops), site for 2004. Top right: RGB image (Google Earth) of the Fundulea area with: grey rectangles, SPOT 1 km × 1 km pixel; colored rectangles, MERIS pixels. Graph: each colored line corresponds to the rectangle with the same color. Bold black line, average LAI of all nine colored LAI lines; dashed line, SPOT-VEGETATION LAI.</p> Full article ">Figure 8
<p>Data inter-comparison of Geoland2 (dotted line) and MERIS-HA (black line) LAI time series for the four sites, (<b>a</b>) Fundulea; (<b>b</b>) SEVI; (<b>c</b>) Gnangara and (<b>d</b>) Järvselja, for three exemplary years (2004–2006).</p> Full article ">
3706 KiB  
Article
Quantitative Estimation of Carbonate Rock Fraction in Karst Regions Using Field Spectra in 2.0–2.5 μm
by Xiangjian Xie, Shufang Tian, Peijun Du, Wenfeng Zhan, Alim Samat and Jike Chen
Remote Sens. 2016, 8(1), 68; https://doi.org/10.3390/rs8010068 - 15 Jan 2016
Cited by 4 | Viewed by 6792
Abstract
Considering the important roles of carbonate rock fraction in karst rocky desertification areas and their potential for indicating damage to vegetation, improved knowledge is desired to assess the application of spectroscopy and remote sensing to characterizing and quantifying the biophysical constituents of karst [...] Read more.
Considering the important roles of carbonate rock fraction in karst rocky desertification areas and their potential for indicating damage to vegetation, improved knowledge is desired to assess the application of spectroscopy and remote sensing to characterizing and quantifying the biophysical constituents of karst landscapes. In this study, we examined the spectra of major surface constituents in karst areas for direct evidence of absorption features attributable to carbonate rock fraction. Using spectral feature analysis with continuum removal, we observed that there are overlapping spectral absorption in 2.149–2.398 μm by soils and non-photosynthetic vegetation. These overlapping features complicated the carbonate absorption feature near 2.340 μm in synthetic mixed spectra. To remove the overprint signal, two hyperspectral carbonate rock indices (HCRIs) were developed. Compared to the absorption features including depths, areas, and KRDSIs (karst rocky desertification synthesis indices), linear regression of HCRIs with carbonate rock fraction in linear synthetic mixtures resulted in higher correlations and lower errors. This study demonstrates that spectral variation of the surface constituents spectra in 2.270–2.398 μm region can indicate carbonate rock fraction and be used to quantify them. Still, additional research is needed to advance our understanding of the spectral influences from carbonate petrography relative to carbonate mineralogy, components and physical state of rock surface. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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<p>The pictures depict examples of field reflectance spectra measured from different surface constituents in Eastern Yunnan, China.</p> Full article ">Figure 2
<p>Comparison of relative reflectance with uncorrected offsets and averaged, absolute reflectance spectrum with offsets corrected.</p> Full article ">Figure 3
<p>Definition of the continuum and continuum removal of absorption features [<a href="#B32-remotesensing-08-00068" class="html-bibr">32</a>,<a href="#B33-remotesensing-08-00068" class="html-bibr">33</a>,<a href="#B34-remotesensing-08-00068" class="html-bibr">34</a>,<a href="#B35-remotesensing-08-00068" class="html-bibr">35</a>,<a href="#B36-remotesensing-08-00068" class="html-bibr">36</a>,<a href="#B37-remotesensing-08-00068" class="html-bibr">37</a>]. The carbonate rock spectrum is the average of measured spectra.</p> Full article ">Figure 4
<p>Reflectance spectra of surface constituents in karst rocky desertification areas and the parameters of HCRIs: <math display="inline"> <msub> <mi>λ</mi> <mn>0</mn> </msub> </math>, <math display="inline"> <msub> <mi>λ</mi> <mn>1</mn> </msub> </math> and <math display="inline"> <msub> <mi>λ</mi> <mn>2</mn> </msub> </math>.</p> Full article ">Figure 5
<p>Reflectance spectra of the surface constituents in 2.0–2.5 μm: (<b>a</b>) exposed carbonate rocks; (<b>b</b>) soils; (<b>c</b>) green vegetation; and (<b>d</b>) NPV. The mean reflectance spectra are colored red.</p> Full article ">Figure 6
<p>Continuum-removed spectra for absorption features in the spectra of: (<b>a</b>) selected surface constituents; (<b>b</b>) mixtures of the soil and carbonate rock; (<b>c</b>) mixtures of the green vegetation and carbonate rock; (<b>d</b>) mixtures of the NPV and carbonate rock.</p> Full article ">Figure 7
<p>Linear regression results for 2.340 μm absorption features of the second synthetic karst surface mixtures and carbonate rock fraction: (<b>a</b>) feature KRDSI<math display="inline"> <msub> <mrow/> <mn>3</mn> </msub> </math>; (<b>b</b>) feature absorption area; (<b>c</b>) feature HCRI<math display="inline"> <msub> <mrow/> <mn>1</mn> </msub> </math>; (<b>d</b>) and feature HCRI<math display="inline"> <msub> <mrow/> <mn>2</mn> </msub> </math>.</p> Full article ">
21861 KiB  
Article
Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis
by Benjamin Aubrey Robson, Daniel Hölbling, Christopher Nuth, Tazio Strozzi and Svein Olaf Dahl
Remote Sens. 2016, 8(1), 67; https://doi.org/10.3390/rs8010067 - 15 Jan 2016
Cited by 25 | Viewed by 10404
Abstract
In this paper, we semi-automatically classify clean and debris-covered ice for 145 glaciers within Hohe Tauern National Park in the Austrian Alps for the years 1985, 2003, and 2013. We also map the end-summer transient snowline (TSL), which approximates the annual Equilibrium Line [...] Read more.
In this paper, we semi-automatically classify clean and debris-covered ice for 145 glaciers within Hohe Tauern National Park in the Austrian Alps for the years 1985, 2003, and 2013. We also map the end-summer transient snowline (TSL), which approximates the annual Equilibrium Line Altitude (ELA). By comparing our results with the Austrian Glacier Inventories from 1969 and 1998, we calculate a mean reduction in glacier area of 33% between 1969 and 2013. The total ice area reduced at a mean rate of 1.4 km2 per year. This TSL rose by 92 m between 1985 and 2013 to an altitude of 3005 m. Despite some limitations, such as some seasonal snow being present at higher elevations, as well as uncertainties related to the range of years that the LiDAR DEM was collected, our results show that the glaciers within Hohe Tauern National Park conform to the heavy shrinkage experienced in other areas of the European Alps. Moreover, we believe that Object-Based Image Analysis (OBIA) is a promising methodology for future glacier mapping. Full article
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<p>Location map of Hohe Tauern National Park (HTNP) within Austria.</p> Full article ">Figure 2
<p>Pasterze Glacier, the largest glacier in Austria. Pasterze Glacier contains significant debris cover on the southern part of the glacier tongue. Photo: Hanna Siiki.</p> Full article ">Figure 3
<p>An illustration of the four segmentation levels within eCognition that were used within the classification procedure. An overview of each level and what it was used for is given in <a href="#remotesensing-08-00067-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 4
<p>Edge detection on the LiDAR-derived DEM was effective for identifying debris-covered termini based on a break in surface morphology. (<b>A</b>) Shows the Landsat 2013 image with glacier outlines overlaid; (<b>B</b>) shows the edge detection dataset. It can be seen that the edge detection can be used in helping delineate the extent of debris-covered ice. The glacier outlines shown are the manually corrected 2013 outlines (2013_Man).</p> Full article ">Figure 5
<p>The glacier outlines (<b>A</b>) were manually corrected with reference to thermal data (<b>B</b>); SAR coherence data (<b>C</b>); the profile curvature (<b>D</b>); difference in elevation between 2000 and 2006 (<b>E</b>); and a hillshade model (<b>F</b>).</p> Full article ">Figure 6
<p>A cumulative distribution graph of the total glacier area against the size of individual glaciers in 1969 and 2013. Note that the x-axis is logarithmic.</p> Full article ">Figure 7
<p>Total glacier area change between 1969 and 2013 over Schlaten Kees, Untersulzbach Kees, Viltragen Kees and Obersulzbach Kees. The background image is a false color (SWIR1, NIR, Red) Landsat image in order to help highlight snow and ice.</p> Full article ">Figure 8
<p>Decadal changes around Großglockner, including the largest glacier in Austria, the Pasterze Glacier, which includes a heavily debris-covered glacier tongue. The background image is a false color (SWIR1, NIR, Red) Landsat image in order to help highlight snow and ice.</p> Full article ">Figure 9
<p>Percentage change in glacier area between 1969 and 2013 against mean glacier elevation.</p> Full article ">Figure 10
<p>Percentage area change between 1969 and 2013 for individual glaciers within Western (<b>A</b>); Central (<b>B</b>); and Eastern (<b>C</b>) portions of the Hohe Tauern National Park.</p> Full article ">Figure 11
<p>Percentage change in glacier area between 1969 and 2013 against glacier area as of 1969. Note that the two largest glaciers are excluded.</p> Full article ">Figure 12
<p>Percentage change in glacier area between 1969 and 2013 against mean glacier aspect. Note that no glaciers had a mean aspect towards the north (345°–15°).</p> Full article ">Figure 13
<p>Snowline elevation as mapped for individual glaciers in 2013 within within Western (<b>A</b>); Central (<b>B</b>); and Eastern (<b>C</b>) portions of the Hohe Tauern National Park.</p> Full article ">Figure 14
<p>Mapped transient snowlines (TSLs) from 1985 and 2013 against aspect. Note that no snowlines were mapped with NWN aspects in 1985 or NWN and WNW in 1985, and these glaciers were more effected by shadows which masked the TSL.</p> Full article ">
3671 KiB  
Article
Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal)
by Soukèye Cissé, Laurence Eymard, Catherine Ottlé, Jacques André Ndione, Amadou Thierno Gaye and Françoise Pinsard
Remote Sens. 2016, 8(1), 66; https://doi.org/10.3390/rs8010066 - 15 Jan 2016
Cited by 14 | Viewed by 6658
Abstract
During the monsoon season, the spatiotemporal variability of rainfall impacts the growth of vegetation in the Sahel. This study evaluates this effect for the Ferlo basin in central northern Senegal. Relationships between rainfall, soil moisture (SM), and vegetation are assessed using remote sensing [...] Read more.
During the monsoon season, the spatiotemporal variability of rainfall impacts the growth of vegetation in the Sahel. This study evaluates this effect for the Ferlo basin in central northern Senegal. Relationships between rainfall, soil moisture (SM), and vegetation are assessed using remote sensing data (TRMM3B42 and RFE 2.0 for rainfall, ESA-CCI.SM for soil moisture and MODIS Leaf Area Index (LAI)). The principal objective was to analyze the response of vegetation growth to water availability during the rainy season using statistical criteria at the scale of homogeneous vegetation-soil zones. The study covers the period from June to September for the years 2000 to 2010. The surface SM is well correlated with both rainfall products. On ferruginous soils, better correlation of intra-seasonal variations and stronger sensitivity of the vegetation to rainfall are found compared to lithosols soils. LAI responds, on average, two to three weeks after a rainfall anomaly. Moreover, dry spells (negative anomalies) of seven days’ length (three days for SM anomaly) significantly affect vegetation growth (maximum LAI within the season). A strong and significant link is also found between total precipitation and the number of dry spells. These datasets proved to be sufficiently reliable to assess the impacts of rainfall variability on vegetation dynamics. Full article
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<p>(<b>a</b>) Location of the Ferlo watershed study area; (<b>b</b>) land-cover map obtained from the FAO for 2005 [<a href="#B17-remotesensing-08-00066" class="html-bibr">17</a>] (<span class="html-italic">Centre de Suivi Écologique (CSE), Dakar</span>); (<b>c</b>) Soil type map extracted from the Senegalese <span class="html-italic">Plan National d’Aménagement du Territoire</span> <span class="html-italic">(PNAT)</span> published in 1986.</p> Full article ">Figure 2
<p>Vegetation-Soil Zones (VSZ) map obtained from the superposition of the land-cover and soil-type maps, the heterogeneous transition zones were masked. The legend items are spelled out in <a href="#remotesensing-08-00066-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 3
<p>Spatial variations of rainy season rainfall (<span class="html-italic">i.e.</span>, for months June through September) from 2000 to 2010, (<b>a</b>–<b>c</b>) show 11-year average seasonal rainfall from TRMM3B42 and 10-year average for RFE 2.0, and 11-year average soil moisture; (<b>d</b>–<b>f</b>) quantify the year-to-year variability with the standard deviations.</p> Full article ">Figure 4
<p>Average rainfall over eight-day intervals from (<b>a</b>) RFE and (<b>b</b>) TRMM; and (<b>c</b>) soil moisture for each Vegetation-Soil Zone (VSZ) averaged over the 2000–2010 decade (see <a href="#remotesensing-08-00066-t003" class="html-table">Table 3</a> for legend items).</p> Full article ">Figure 5
<p>The total amount of rain from June through September averaged for the period 2000–2010 over the area of each vegetation-soil zone (VSZ, see <a href="#remotesensing-08-00066-t003" class="html-table">Table 3</a> for legend items): (<b>a</b>) comparison between RFE rainfall and the soil moisture, and (<b>b</b>) comparison between TRMM and RFE.</p> Full article ">Figure 6
<p>Comparison between some parameters characterizing the rainy season from TRMM3B42, RFE 2.0 and soil moisture (SM) in average on each vegetation soil zone (VSZ, see <a href="#remotesensing-08-00066-t003" class="html-table">Table 3</a> for legend items) over the 2000–2010 decade: (<b>a</b>) cumulative rainfall and SM; (<b>b</b>) dates of onset and maximum (respectively in dark and clear colors); (<b>c</b>) number of dry spells and (<b>d</b>) the longest dry spells).</p> Full article ">Figure 7
<p>(<b>a</b>) Variations of the mean LAI smoothed with the cubic spline, each color representing a homogeneous vegetation-soil zone (VSZ, see <a href="#remotesensing-08-00066-t003" class="html-table">Table 3</a> for legend items); (<b>b</b>) into box plot, the standard deviation of LAI (a red segment inside the rectangle shows the median, the “whiskers (black horizontal dash)” above and below the box show the minimum and maximum standard deviation and the blue box around the median is the lower quartile (median value of the lower half of the data) and the upper quartile (median value of the upper half of the data)) for each zone over the 2000–2010 decade.</p> Full article ">Figure 8
<p>Spatial variations of some characteristic parameters in the vegetation phenological cycle over the period 2000 to 2010: (<b>a</b>) start of the growing season; (<b>b</b>) end of the growing season; (<b>c</b>) date of maximum LAI; and (<b>d</b>) the maximum value of LAI.</p> Full article ">Figure 9
<p>Comparison of correlation coefficients and lags in number of weeks between anomalies of rainfall and SM, and anomalies of LAI (TRMM3B42/LAI (blue) and RFE/LAI (green) and SM/LAI (red)) over the period 2000-2010 and on all the VSZs (see <a href="#remotesensing-08-00066-t003" class="html-table">Table 3</a> for legend items) in the Ferlo watershed; with (<b>a</b>) correlation coefficients between positive anomalies; (<b>b</b>) correlation coefficients between negative anomalies; (<b>c</b>) lags between positive anomalies and (<b>d</b>) lags between negative anomalies.</p> Full article ">Figure 10
<p>Comparison of correlation coefficients between water availability and the maximum of vegetation growth on the Ferlo VSZs with (<b>a</b>) for non-lithosol VSZs and (<b>b</b>) lithosol VSZs over the period 2000–2010. TA: Total Amount; N-DS: Number of Dry Spells; I-DS: Intensity of Dry Spells; LDS-D: Longest Dry Spell Duration and LDS-I: Longest Dry Spell Intensity. The digits 3, 5 and 7 are the numbers of days corresponding to the thresholds of dry spell duration. The colors are dark blue for TRMM3B42; green for RFE 2.0 and dark red for SM. Horizontal solid and dashed lines correspond to the levels of significance for <span class="html-italic">p</span> &lt; 5%.</p> Full article ">
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Article
Using Remotely-Sensed Land Cover and Distribution Modeling to Estimate Tree Species Migration in the Pacific Northwest Region of North America
by Nicholas C. Coops, Richard H. Waring, Andrew Plowright, Joanna Lee and Thomas E. Dilts
Remote Sens. 2016, 8(1), 65; https://doi.org/10.3390/rs8010065 - 15 Jan 2016
Cited by 19 | Viewed by 7700
Abstract
Understanding future tree species migration is challenging due to the unprecedented rate of climate change combined with the presence of human barriers that may limit or impede species movement. Projected changes in climatic conditions outpace migration rates, and more realistic rates of range [...] Read more.
Understanding future tree species migration is challenging due to the unprecedented rate of climate change combined with the presence of human barriers that may limit or impede species movement. Projected changes in climatic conditions outpace migration rates, and more realistic rates of range expansion are needed to make sound environmental policies. In this paper, we develop a modeling approach that takes into account both the geographic changes in the area suitable for the growth and reproduction of tree species, as well as limits imposed geographically on their potential migration using remotely-sensed land cover information. To do so, we combined a physiologically-based decision tree model with a remotely-sensed-derived diffusion-dispersal model to identify the most likely direction of future migration for 15 native tree species in the Pacific Northwest Region of North America, as well as the degree that landscape fragmentation might limit movement. Although projected changes in climate through to 2080 are likely to create favorable environments for range expansion of the 15 tree species by 65% on average, by limiting the potential movement by previously published migration rates and landscape fragmentation, range expansion will likely be 50%–90% of the potential. The hybrid modeling approach using distribution modeling and remotely-sensed data fills a gap between naïve and more complex approaches to take into account major impediments on the potential migration of native tree species. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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<p>The maritime influence coniferous forests ecoregion (green) is among the most productive in North America. The Northwest Forested Mountain Ecoregion contains a much broader mix of species on less productive sites (olive), whereas the North American Desert Ecoregion includes drought-adapted species, such as ponderosa pine and western juniper.</p> Full article ">Figure 2
<p>Pacific Northwest Study Area with barriers to migration recognized at three levels of increasing resistance to migration. Land cover classes are weighed to reflect the relative difficulty that they might represent to migration. The lowest impedance was assigned to natural vegetation (dark grey), then developed and mosaic types (black) to impenetrable landscape barrier, such as water (light grey).</p> Full article ">Figure 3
<p>Depiction of the cost distance and impedance functions. (<b>a</b>) Coordinates and details of the map subset; (<b>b</b>) current distribution of Douglas-fir; (<b>c</b>) with the addition of other landscape barriers, principally representing inhospitable habitat for tree growth; (<b>d</b>) expansion by 2020 limited by unsuitable climate; (<b>e</b>) expansion from 2020–2050 limited by suitable climate; (<b>f</b>) predicted maximum distribution by 2080 constrained by predicted climate and landscape barriers.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>f</b>) Predicted direction and area (length of the ray) of potential migration by six tree species in response to expected shifts in climatic conditions by 2080. (<b>Left</b>) Migration constrained by realistic maximum rates (200 m/y) and landscape barriers; (<b>right</b>) migration constrained by realistic maximum rates (200 m/y), but unconstrained by landscape barriers.</p> Full article ">Figure 5
<p>Predicted distributions of Douglas-fir (DF), Sitka spruce (SS), ponderosa pine (PP) and western redcedar (WWR). For each of the species, the maps depict: (<b>a</b>) current distributions; (<b>b</b>) 2080 distributions with climate and landscape barriers imposed; (<b>c</b>) distributions with a maximum 200-m/y limit constraint (LC) and favorable climatic conditions; and (<b>d</b>) limits imposed by climate alone.</p> Full article ">Figure 5 Cont.
<p>Predicted distributions of Douglas-fir (DF), Sitka spruce (SS), ponderosa pine (PP) and western redcedar (WWR). For each of the species, the maps depict: (<b>a</b>) current distributions; (<b>b</b>) 2080 distributions with climate and landscape barriers imposed; (<b>c</b>) distributions with a maximum 200-m/y limit constraint (LC) and favorable climatic conditions; and (<b>d</b>) limits imposed by climate alone.</p> Full article ">Figure 6
<p>Species ranges (km<sup>2</sup> and % difference from current distribution) predicted by 2080 using realistic migration rates (maximum of 200 km or m/year) and with barriers imposed by land cover and topography. Species abbreviations: Pacific silver fir (PSF), grand fir (GF), Douglas-fir (DF), western hemlock (WH), subalpine fir (SAF), ponderosa pine (PP), noble fir (NF), mountain hemlock (MH), lodgepole pine (LLP), Alaska yellow-cedar (YC), western redcedar (WRC), western white pine (WP), western larch (WL) and Sitka spruce (SS).</p> Full article ">
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Article
Snow Depth Variations Estimated from GPS-Reflectometry: A Case Study in Alaska from L2P SNR Data
by Shuanggen Jin, Xiaodong Qian and Hakan Kutoglu
Remote Sens. 2016, 8(1), 63; https://doi.org/10.3390/rs8010063 - 15 Jan 2016
Cited by 77 | Viewed by 7636
Abstract
Snow is a water resource and plays a significant role in the water cycle. However, traditional ground techniques for snow monitoring have many limitations, e.g., high-cost and low resolution. Recently, the new Global Positioning System-Reflectometry (GPS-R) technique has been developed and applied for [...] Read more.
Snow is a water resource and plays a significant role in the water cycle. However, traditional ground techniques for snow monitoring have many limitations, e.g., high-cost and low resolution. Recently, the new Global Positioning System-Reflectometry (GPS-R) technique has been developed and applied for snow sensing. However, most previous studies mainly used GPS L1C/A and L2C Signal-to-Noise Ratio (SNR) data to retrieve snow depth. In this paper, snow depth variations are retrieved from new weak GPS L2P SNR data at three stations in Alaska and evaluated by comparing with in situ measurements. The correlation coefficients for the three stations are 0.79, 0.88 and 0.98, respectively. The GPS-estimated snow depths from the L2P SNR data are further compared with L1C/A results at three stations, showing a high correlation of 0.94, 0.93 and 0.95, respectively. These results indicate that geodetic GPS observations with SNR L2P data can well estimate snow depths. The samplings of 15 s or 30 s have no obvious effect on snow depth estimation using GPS SNR L2P measurements, while the range of 5°–35°elevation angles has effects on results with a decreasing correlation of 0.96 and RMSE of 0.04 m when compared to the range of 5°–30° with correlation of 0.98 and RMSE of 0.03 m. GPS SNR data below 30° elevation angle are better to estimate snow depth. Full article
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<p>Location of GPS stations in Alaska.</p> Full article ">Figure 2
<p>Antenna of three GPS stations SG27 (<b>a</b>); AB39 (<b>b</b>) and AB33 (<b>c</b>).</p> Full article ">Figure 3
<p>L2P SNR data, multipath modulations and Lomb–Scargle periodogram of multipath pattern at AB33. (<b>a</b>) L2P SNR data from satellite PRN10; (<b>b</b>) Multipath modulations; (<b>c</b>) Lomb-Scargle periodogram of multipath pattern.</p> Full article ">Figure 4
<p>Comparison of simulated L1C/A and L2P SNR observables. (<b>a</b>) Simulated L1C/A and L2P SNR; (<b>b</b>) Multipath modulations; (<b>c</b>) Lomb–Scargle periodogram of multipath pattern.</p> Full article ">Figure 5
<p>Comparison of L1C/A and L2P SNR data, multipath modulations and Lomb–Scargle periodogram of multipath pattern at AB33. (<b>a</b>) L1C/A and L2P SNR from satellite PRN10; (<b>b</b>) Multipath modulations; (<b>c</b>) Lomb–Scargle periodogram of multipath pattern.</p> Full article ">Figure 6
<p>Coherent scattering coefficients of different snow densities (g/cm<sup>2</sup>) at linear (VV and HH) and circular polarizations (RR and LR).</p> Full article ">Figure 7
<p>Simulated Signal-to-Noise Ratio (SNR) modulations of different snow densities (g/cm<sup>2</sup>). (<b>a</b>) SNR modulations after removing the direct trend; (<b>b</b>) Lomb–Scargle periodogram of multipath pattern.</p> Full article ">Figure 8
<p>Comparison of simulated L2P SNR modulations at different surface roughness. (<b>a</b>) Multipath modulations with different surface roughness; (<b>b</b>) Lomb–Scargle periodogram of multipath pattern.</p> Full article ">Figure 9
<p>Comparison of GPS and <span class="html-italic">in situ</span> snow depth at AB33, AB39 and SG27. (<b>a</b>) AB33; (<b>b</b>) AB39; (<b>c</b>) SG27.</p> Full article ">Figure 10
<p>Comparison of L1C/A and L2P results at AB33. (<b>a</b>) Snow depth estimation from L1C/A; (<b>b</b>) Snow depth estimation from L1C/A and L2P.</p> Full article ">Figure 11
<p>Comparison of L1C/A and L2P results at AB39. (<b>a</b>) Snow depth estimation from L1C/A; (<b>b</b>) Snow depth estimation from L1C/A and L2P.</p> Full article ">Figure 12
<p>Comparison of L1C/A and L2P results at SG27. (<b>a</b>) Snow depth estimation from L1C/A and L2P; (<b>b</b>) Correlation between L1C/A and L2P.</p> Full article ">Figure 13
<p>The First Fresnel zone for a typical 2 m antenna height with different elevation angles.</p> Full article ">Figure 14
<p>Comparison of snow depth estimations from different ranges of 5°–25°, 5°–30°and 5°–35° elevation angles at AB39.</p> Full article ">Figure 15
<p>Comparison of 15 s and 60 s sampling multipath patterns and Lomb Scargle Periodograms at AB39.</p> Full article ">Figure 16
<p>Comparison of snow depth estimations from different sampling rates of 15 s, 30 s and 60 s at AB39. (<b>a</b>) Correlation between 15 s and 30 s; (<b>b</b>) Correlation between 15 s and 60 s.</p> Full article ">
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Correction
Correction: Pimont, F. et al. Estimating Leaf Bulk Density Distribution in a Tree Canopy Using Terrestrial LiDAR and a Straightforward Calibration Procedure. Remote Sens. 2015, 7(6), 7995-8018
by François Pimont, Jean-Luc Dupuy, Eric Rigolot, Vincent Prat and Alexandre Piboule
Remote Sens. 2016, 8(1), 64; https://doi.org/10.3390/rs8010064 - 14 Jan 2016
Viewed by 4245
Abstract
After publication of the research paper [1] an error during the data analysis process was recognized. [...] Full article
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<p>Correction of Figure 6 in [<a href="#B1-remotesensing-08-00064" class="html-bibr">1</a>]. Calibration of indices <span class="html-italic">I</span><sub>1</sub> (<b>a</b>,<b>b</b>), <span class="html-italic">I</span><sub>2</sub> (<b>c</b>,<b>d</b>), <span class="html-italic">I</span><sub>3</sub> (<b>e</b>,<b>f</b>) against leaf bulk densities in 35 CVs. Left figures (<b>a</b>, <b>c</b> and <b>e</b>) used the largest number of intercepted beams in the CV for scan selection (N<sub>i</sub>)<sub>max</sub>; Right figures (<b>b</b>, <b>d</b> and <b>f</b>) used the largest number of incident beams on the CV for scan selection (N<sub>t</sub> − N<sub>t</sub>)<sub>max</sub>. Dashed lines are 95% confidence interval.</p> Full article ">Figure 2
<p>Correction of Figure 7 [<a href="#B1-remotesensing-08-00064" class="html-bibr">1</a>]. Analysis of residuals of the model based on I<sub>3</sub> and the (N<sub>i</sub>)<sub>max</sub>. (<b>a</b>) function of I<sub>3</sub> on the (N<sub>i</sub>)<sub>max</sub> data set (<b>b</b>) function of I<sub>3</sub> on the N<sub>i</sub> &gt; 1000 data set (<b>c</b>) function of calibration volume heights on the (N<sub>i</sub>)<sub>max</sub> data set (<b>d</b>) function of calibration volume heights on the N<sub>i</sub> &gt; 1000 data set.</p> Full article ">
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Article
Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery
by Xiaoyi Wang, Huabing Huang, Peng Gong, Gregory S. Biging, Qinchuan Xin, Yanlei Chen, Jun Yang and Caixia Liu
Remote Sens. 2016, 8(1), 62; https://doi.org/10.3390/rs8010062 - 14 Jan 2016
Cited by 20 | Viewed by 6949
Abstract
Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover [...] Read more.
Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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<p>Youyu county in Shanxi Province, China. The imagery represent a false-color composite of one Landsat image acquired on 10 July 2009.</p> Full article ">Figure 2
<p>Flowchart for the sub-pixel forest cover process.</p> Full article ">Figure 3
<p>Sub-pixel forest cover estimated from airborne LiDAR data in 2009.</p> Full article ">Figure 4
<p>Scatter plots of predicted forest cover against LiDAR data results. (<b>a</b>) Validation of forest cover with temporal trajectory fitting algorithm; (<b>b</b>) validation of forest cover with original spectral indices. The blue area represents the 95% confidence intervals for the regression line.</p> Full article ">Figure 5
<p>Scatterplot of predicted forest cover against field data in 2003.</p> Full article ">Figure 6
<p>Forest cover change from 1987 to 2012. (<b>a</b>) Plantation area with forest cover increases; (<b>b</b>) native forest; (<b>c</b>) plantation area with unchanged forest cover; and (<b>d</b>) plantation area.</p> Full article ">Figure 7
<p>Annual forest cover condition and dynamic for plantation area.</p> Full article ">Figure 8
<p>Forest cover dynamic for native forest area from 1987 to 2012. (<b>a</b>) Decrease; and (<b>b</b>) fluctuation.</p> Full article ">
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Article
Photogrammetric, Geometrical, and Numerical Strategies to Evaluate Initial and Current Conditions in Historical Constructions: A Test Case in the Church of San Lorenzo (Zamora, Spain)
by Luis Javier Sánchez-Aparicio, Alberto Villarino, Jesús García-Gago and Diego González-Aguilera
Remote Sens. 2016, 8(1), 60; https://doi.org/10.3390/rs8010060 - 13 Jan 2016
Cited by 22 | Viewed by 6899
Abstract
Identifying and quantifying the potential causes of damages to a construction and evaluating its current stability have become an imperative task in today’s world. However, the existence of variables, unknown conditions and a complex geometry hinder such work, by hampering the numerical results [...] Read more.
Identifying and quantifying the potential causes of damages to a construction and evaluating its current stability have become an imperative task in today’s world. However, the existence of variables, unknown conditions and a complex geometry hinder such work, by hampering the numerical results that simulate its behavior. Of the mentioned variables, the following can be highlighted: (i) the lack of historical information; (ii) the mechanical properties of the material; (iii) the initial geometry and (iv) the interaction with other structures. Within the field of remote sensors, the laser scanner and photogrammetric systems have become especially valuable for construction analysis. Such sensors are capable of providing highly accurate and dense geometrical data with which to assess a building’s condition. It is also remarkable, that the latter provide valuable radiometric data with which to identify the properties of the materials, and also evaluate and monitor crack patterns. Motivated by this, the present article investigates the potential offered by the combined use of photogrammetric techniques (DIC and SfM), as well as geometrical (NURBs and Hausdorff distance) and numerical strategies (FEM) to assess the origin of the damage (through an estimation of the initial conditions) and give an evaluation of the current stability (considering the deformation and the damage). Full article
(This article belongs to the Special Issue Remote Sensed Data and Processing Methodologies for 3D Virtual Reconstruction and Visualization of Complex Architectures)
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<p>(<b>a</b>) Detail view of the brick and speckle pattern applied during the Digital Image Correlation (DIC) test; (<b>b</b>) Histogram of the speckle pattern.</p> Full article ">Figure 2
<p>Digital Image Correlation general outline. In red the reference subset, in blue the initial seed, and in yellow, the final location of the subset.</p> Full article ">Figure 3
<p>Results after the experimental campaign (2D DIC). (<b>a</b>) Deformation measurement, expressed in pixels, between two captures and positioning of the virtual extensometers; (<b>b</b>) Stress-strain curve obtained with the virtual extensometer A-A’.</p> Full article ">Figure 4
<p>(<b>a</b>) 3D model obtained by the proposed methodology; (<b>b</b>) Detail view of the most damaged section through the texture model.</p> Full article ">Figure 5
<p>San Lorenzo church: (<b>a</b>) Orthophoto of the main façade through the methodology proposed; (<b>b</b>) Orthophoto of the west façade (chancel) of the construction; (<b>c</b>) Floor plan-view of the church, red color indicates the damaged area of the dome.</p> Full article ">Figure 6
<p>(<b>a</b>) Constructive section of the church’s transept; (<b>b</b>) Transversal section of the dome geometry (initial state estimated by the Structure from Motion (SfM) point cloud) with dimensions in meters.</p> Full article ">Figure 7
<p>Results of the visual inspection over the different photogrammetric products: (<b>a</b>) Surface comparison between the initial proposed model and the most deformed one estimated by the SfM point cloud; (<b>b</b>) Damage inspection in the orthophoto, in green the main observed cracks, in blue the secondary cracks, in yellow the material removal.</p> Full article ">Figure 8
<p>(<b>a</b>) Isometric view of the mesh and the control points (nodes) used for the numerical simulations; (<b>b</b>) First principal stress distribution, expressed in N/mm<sup>2</sup> for the self-weight case; (<b>c</b>) First principal stress distribution, expressed in N/mm<sup>2</sup>, for the numerical model which considers the asymmetric load.</p> Full article ">Figure 9
<p>Graphical distribution of the different considered symmetrical Hausdorff distance (d<span class="html-italic"><sub>SH</sub></span>) (expressed in m) for the base model.</p> Full article ">Figure 10
<p>(<b>a</b>) First principal stress distribution, expressed in N/mm2 of the updated model; (<b>b</b>) Geometrical accuracy, in terms of Local Hausdorff metric (<span class="html-italic">LHm<sub>s</sub></span>) of the updated model; in green, values where the geometrical model improves the results, in orange values where no improvements are carried out and in red, areas where the updated numerical model displays worse behavior.</p> Full article ">Figure 11
<p>Proposed workflow for the study of the current stability of the construction.</p> Full article ">Figure 12
<p>(<b>a</b>) Isometric view of the considered mesh model; (<b>b</b>) Discrepancies, expressed in mm, between the Non-Uniform Rational B-Splines (NURBs) and the photogrammetric models.</p> Full article ">Figure 13
<p>(<b>a</b>) Parametric analysis of different tensile strengths and shear retention factors; (<b>b</b>) Parametric analysis of different masonry and infill’s Young modulus; (<b>c</b>) Maximum principal stress (σ<sub>1</sub>), expressed in N/mm<sup>2</sup>, at collapse of the initial considered model.</p> Full article ">
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Article
Validation of Reef-Scale Thermal Stress Satellite Products for Coral Bleaching Monitoring
by Scott F. Heron, Lyza Johnston, Gang Liu, Erick F. Geiger, Jeffrey A. Maynard, Jacqueline L. De La Cour, Steven Johnson, Ryan Okano, David Benavente, Timothy F. R. Burgess, John Iguel, Denise I. Perez, William J. Skirving, Alan E. Strong, Kyle Tirak and C. Mark Eakin
Remote Sens. 2016, 8(1), 59; https://doi.org/10.3390/rs8010059 - 12 Jan 2016
Cited by 70 | Viewed by 11943
Abstract
Satellite monitoring of thermal stress on coral reefs has become an essential component of reef management practice around the world. A recent development by the U.S. National Oceanic and Atmospheric Administration’s Coral Reef Watch (NOAA CRW) program provides daily global monitoring at 5 [...] Read more.
Satellite monitoring of thermal stress on coral reefs has become an essential component of reef management practice around the world. A recent development by the U.S. National Oceanic and Atmospheric Administration’s Coral Reef Watch (NOAA CRW) program provides daily global monitoring at 5 km resolution—at or near the scale of most coral reefs. In this paper, we introduce two new monitoring products in the CRW Decision Support System for coral reef management: Regional Virtual Stations, a regional synthesis of thermal stress conditions, and Seven-day Sea Surface Temperature (SST) Trend, describing recent changes in temperature at each location. We describe how these products provided information in support of management activities prior to, during and after the 2014 thermal stress event in the Commonwealth of the Northern Mariana Islands (CNMI). Using in situ survey data from this event, we undertake the first quantitative comparison between 5 km satellite monitoring products and coral bleaching observations. Analysis of coral community characteristics, historical temperature conditions and thermal stress revealed a strong influence of coral biodiversity in the patterns of observed bleaching. This resulted in a model based on thermal stress and generic richness that explained 97% of the variance in observed bleaching. These findings illustrate the importance of using local benthic characteristics to interpret the level of impact from thermal stress exposure. In an era of continuing climate change, accurate monitoring of thermal stress and prediction of coral bleaching are essential for stakeholders to direct resources to the most effective management actions to conserve coral reefs. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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<p>(<b>a</b>) Map of the study region identifying islands at which surveys were undertaken; (<b>b</b>) Spatial coverage of the Regional Virtual Stations for CNMI (north of dashed line) and Guam include satellite pixels within 20 km of reef-containing pixels (outlined by polygons). The color of customized Google Maps pins (inverted teardrops) indicates the 90th percentile Bleaching Alert Area level for pixels within each Regional Virtual Station. The background image shows the Bleaching Alert Area level on 13 August 2014 displayed in Google Maps [<a href="#B16-remotesensing-08-00059" class="html-bibr">16</a>]); (<b>c</b>) Regional Virtual Station gauges for CNMI showing the Bleaching Alert level on 13 August 2014 (top gauge) and the forecast stress levels in the subsequent three months. Grey arrows indicate change from the previous gauge reading.</p> Full article ">Figure 2
<p>Regional Virtual Station time series for CNMI in 2014. The purple SST trace is the temperature at the location of the 90th percentile HotSpot value from among 5 km pixels for CNMI (see <a href="#remotesensing-08-00059-f001" class="html-fig">Figure 1</a>b) for each date. Similarly, the red DHW trace is the 90th percentile DHW value and the color under this trace reflects the 90th percentile Bleaching Alert Area value. For each pixel, DHW accumulates when the SST value exceeds the maximum (blue dashed, MMM) of the monthly mean climatology values (blue plus) by at least 1 °C (blue solid, Bleaching Threshold)—the time series shows the spatial average of each of these values. DHW thresholds of 4 and 8 °C-weeks (red dashed) have been associated with significant coral bleaching, and widespread bleaching and significant mortality, respectively.</p> Full article ">Figure 3
<p>Observed (<b>a</b>) coral cover; (<b>b</b>) percent of coral bleached (white shading); and (<b>c</b>) number of coral genera present; with calculated (<b>d</b>) bleaching susceptibility, compiled by island and survey month. Bars show the island-and-month average, while whiskers show 1 SD. Numbers of sites for each island/month grouping are shown. Grey shading in all panels indicates the survey month (light-dark: June–August); white shading in (<b>b</b>) indicates percent bleached. Note that Maug was surveyed twice (in June and August 2014).</p> Full article ">Figure 4
<p>Comparison of percent coral bleached against Degree Heating Weeks (DHW). Site data (dots) were averaged by island and date (filled square), with the whiskers showing 1 SD. Symbol shading indicates the month of observation. The dashed line shows the linear regression of the island-scale data (<span class="html-italic">y</span> = 7.0177<span class="html-italic">x</span> − 7.5183, <span class="html-italic">r</span><sup>2</sup> = 0.411).</p> Full article ">Figure 5
<p>Maps of 5 km Seven-day SST Trend for the Mariana Archipelago during June 2014. Regional SST peaked on (<b>a</b>) 10 June 2014 and (<b>b</b>) 21 June 2014, with a rapid cooling event culminating on (<b>c</b>) 30 June 2014 (see also <a href="#remotesensing-08-00059-f002" class="html-fig">Figure 2</a>).</p> Full article ">Figure 6
<p>Maps of 5 km Degree Heating Week (DHW) for the Mariana Archipelago when bleaching observation surveys were conducted. (<b>a</b>) 30 June 2014; (<b>b</b>) 19 July 2014; (<b>c</b>) 13 August 2014. Arrows indicate locations of surveys taken within 1–11 days prior to the date shown. Shade of arrows corresponds to the different survey months (also in <a href="#remotesensing-08-00059-f003" class="html-fig">Figure 3</a> and <a href="#remotesensing-08-00059-f004" class="html-fig">Figure 4</a>).</p> Full article ">Figure 7
<p>Modeled predictions of percent bleaching, as functions of thermal stress accumulation (DHW) and generic richness. Models based on (<b>a</b>) the product of DHW and generic richness (Equation (1)); and (<b>b</b>) the product of exponential factors of DHW and generic richness (Equation (2)). Symbol color indicates the month of observation. The line of unity is also shown (dashed).</p> Full article ">
9433 KiB  
Article
A Multi-Sensor Approach for Volcanic Ash Cloud Retrieval and Eruption Characterization: The 23 November 2013 Etna Lava Fountain
by Stefano Corradini, Mario Montopoli, Lorenzo Guerrieri, Matteo Ricci, Simona Scollo, Luca Merucci, Frank S. Marzano, Sergio Pugnaghi, Michele Prestifilippo, Lucy J. Ventress, Roy G. Grainger, Elisa Carboni, Gianfranco Vulpiani and Mauro Coltelli
Remote Sens. 2016, 8(1), 58; https://doi.org/10.3390/rs8010058 - 12 Jan 2016
Cited by 68 | Viewed by 8831
Abstract
Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and [...] Read more.
Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne “fine” ash retrieved from the satellite is estimated to be about 1%–2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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<p>Different phases of the 23 November 2013 lava fountain event seen by the visible camera (upper panels) and the thermal camera (lower panels) of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) video-surveillance system located in Nicolosi and Monte Cagliato, respectively.</p> Full article ">Figure 2
<p>Top panels: ash cloud centre of mass at different times. Bottom left plot: distance (red squares) and direction (blue triangles, defined as the direction from which the wind was blowing) of the volcanic ash cloud centre of mass considering the different SEVIRI images. Bottom right plot: comparison between the wind speed retrieved (vertical dashed line) with the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) mean wind speed profile extracted in the area occupied by the volcanic cloud (red solid line). The dashed blue lines represent the standard deviation of the mean NCEP/NCAR wind speed profile.</p> Full article ">Figure 3
<p>Volcanic ash cloud altitudes computed from SEVIRI images using the dark pixel (black diamonds) and centre of mass (orange diamonds) procedures. The curve of the red diamonds represents the volcanic cloud altitude used for all of the SEVIRI image processing.</p> Full article ">Figure 4
<p>Volcanic ash M<sub>a</sub> (<b>left panel</b>), mean R<sub>e</sub> (<b>middle panel</b>) and mean AOD (<b>right panel</b>) for the series of SEVIRI images.</p> Full article ">Figure 5
<p>RGB image (R:28, G:29, B:31, <b>upper left panel</b>), volcanic ash M<sub>a</sub> (<b>upper right panel</b>), R<sub>e</sub> (<b>lower left panel</b>) and AOD (<b>lower right panel</b>) for the MODIS image collected at 12:45 UTC.</p> Full article ">Figure 5 Cont.
<p>RGB image (R:28, G:29, B:31, <b>upper left panel</b>), volcanic ash M<sub>a</sub> (<b>upper right panel</b>), R<sub>e</sub> (<b>lower left panel</b>) and AOD (<b>lower right panel</b>) for the MODIS image collected at 12:45 UTC.</p> Full article ">Figure 6
<p>Upper panels: vertical cross-sections, along the prevailing wind direction A–B, of the radar variables used in this work sampled at 10:10 UTC on 23 November 2013 from the DPX4 radar in Catania (Sicily, Italy); lower panels: horizontal radar maps at a 3° radar antenna view angle from the horizon. The dashed lines in the top left and middle panels highlight the areas probably affected by particle updraft, whereas the area contoured in the top right panel marks the region most affected by cross winds.</p> Full article ">Figure 7
<p>DPX4 retrievals. <b>Left panel</b>: gamma-modelled ash number density distribution; <b>middle panel</b>: ash plume maximum altitudes as a function of the distance from Mt. Etna for the different DPX4 acquisition times (UTC); <b>right panel</b>: total ash mass of the ash plume.</p> Full article ">Figure 8
<p>Parallax configurations. <b>Left panel (panel a)</b>: two satellite sensors view the same target along two different lines of sight; <b>right panel (panel b)</b>: a satellite and a ground station form the parallax view.</p> Full article ">Figure 9
<p>Example of parallax displacement when retrievals from SEVIRI and DPX4 radar (<b>left</b>), and MODIS and SEVIRI (<b>right</b>) are compared to each other at nearly simultaneous acquisition times.</p> Full article ">Figure 10
<p>Volcanic cloud altitude retrieved from SEVIRI images, SEVIRI-DPX4 and SEVIRI-MODIS parallaxes. The light purple curve represents the volcanic cloud altitude obtained from the multi-sensor integration.</p> Full article ">Figure 11
<p><b>Left panel</b>: SEVIRI ash masses retrieved before (red curve) and after (yellow curve) the multi-sensor approach; <b>Right panel</b>: SEVRI and DPX4 ash masses comparison.</p> Full article ">Figure 12
<p>Classification maps for the ash concentration following the European Aviation Safety Agency (EASA) guidelines. Low contamination (cyan); medium contamination (grey); high contamination (red).</p> Full article ">Figure 13
<p><b>Left panel</b>: effective number density computed from the SEVIRI images. The solid black line represent the mean value. <b>Right panel</b>: same as the left panel (grey shaded area), but including the DPX4 retrievals (the dashed curve is the radar average).</p> Full article ">Figure 14
<p>Calibrated images of the ECV camera on 23 November 2013. The images show that the column height reached 5 km, &gt;10 km and 5 km at 9:30, 10:00 and 10:30 UTC.</p> Full article ">Figure 15
<p>Plots of the volcanic cloud top altitudes retrieved from the VIS camera and SEVIRI-DPX4 parallax approach. The dotted line at 10 km indicates the sensitivity limit of the VIS camera.</p> Full article ">Figure 16
<p><b>Left panels</b>: SEVIRI brightness temperature difference (BTD) images collected at different times; <b>Right panels</b>: same SEVIRI BTD images with the ash volcanic cloud (red contours) and ice volcanic cloud (cyan contours) indicated.</p> Full article ">Figure 16 Cont.
<p><b>Left panels</b>: SEVIRI brightness temperature difference (BTD) images collected at different times; <b>Right panels</b>: same SEVIRI BTD images with the ash volcanic cloud (red contours) and ice volcanic cloud (cyan contours) indicated.</p> Full article ">Figure 17
<p>Left panel: twelve hours of HYSPLIT forward trajectories for volcanic clouds altitudes at 6 (red dotted line) and 11 km (cyan dotted line), drawn on the SEVIRI BTD image collected at 12:30 UTC; right panel: zoom on the area occupied by the volcanic ash and ice volcanic clouds identified by red and cyan contours, respectively.</p> Full article ">Figure 18
<p>Volcanic ash cloud retrievals from the IASI image collected at 21:00 UTC. In the right upper panel, also the HYSPLIT trajectories at 6 km (red dotted line) and 11 km (cyan dotted line) have been drawn.</p> Full article ">Figure 19
<p>VIS camera image collected at 09:45 UTC. The yellow and red lines confine the advection and fallout areas.</p> Full article ">
8119 KiB  
Article
Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data
by Hyangsun Han, Jungho Im, Miae Kim, Seongmun Sim, Jinwoo Kim, Duk-jin Kim and Sung-Ho Kang
Remote Sens. 2016, 8(1), 57; https://doi.org/10.3390/rs8010057 - 12 Jan 2016
Cited by 31 | Viewed by 8434
Abstract
Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in [...] Read more.
Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches—decision trees (DT) and random forest (RF)—in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 × 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data. Full article
(This article belongs to the Special Issue Sea Ice Remote Sensing and Analysis)
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<p>TerraSAR-X (TSX) amplitude images (HH-polarization) obtained in the Chukchi Sea on (<b>a</b>) 12 August 2011 and (<b>b</b>) 21 July 2010, respectively. The airborne SAR images are overlaid on the 2011 TerraSAR-X amplitude image (yellow box). The TerraSAR-X imaging area corresponds to the white solid box in each map.</p> Full article ">Figure 2
<p>Processing flow of the melt pond retrieval from the classification of the TerraSAR-X dual-polarization SAR data based on machine learning approaches. SLC, single-look complex; DT, decision tree; RF, random forest.</p> Full article ">Figure 3
<p>Boxplots of the polarimetric parameters used for the melt pond retrieval: (<b>a</b>) backscattering coefficient at HH polarization; (<b>b</b>) backscattering coefficient at VV polarization; (<b>c</b>) co-polarization ratio; (<b>d</b>) co-polarization phase difference; (<b>e</b>) co-polarization correlation coefficient; (<b>f</b>) alpha angle, (<b>g</b>) entropy; and (<b>h</b>) anisotropy. Colored boxes represent the interquartile range of the samples, while a line inside the box means the median value of the samples. The vertical lines above and below the box represent 1.5-times the interquartile range beyond the lower and upper quartiles, and the dots represent the outliers. The red asterisks on the right side of boxes represent the mean value of the samples, while the red vertical lines above and below the asterisk mean the standard deviation of the samples. Co-pol, OW, SI, and MP indicate co-polarization, open water, sea ice, and melt pond, respectively.</p> Full article ">Figure 4
<p>The variations of the overall accuracy and the kappa coefficient for the DT and RF model that were developed using the polarimetric parameters and their texture features based on a pixel window, ranging from 5–35: (<b>a</b>) the variations of the overall accuracies; and (<b>b</b>) the variations of the kappa coefficients.</p> Full article ">Figure 5
<p>Boxplots of the texture features of the polarimetric parameters used as the supplementary variables for melt pond retrieval: (<b>a</b>–<b>h</b>) the average of the polarimetric parameters based on a 15 × 15 pixel window; and (<b>i</b>–<b>p</b>) the standard deviation of the polarimetric parameters based on a 15 × 15 pixel window. Co-pol, Avg, and Std indicate co-polarization, average, and standard deviation, respectively.</p> Full article ">Figure 6
<p>Attribute usage of the DT model that was developed using the polarimetric parameters and their texture features based on a 15 × 15 pixel window.</p> Full article ">Figure 7
<p>Mean decrease accuracy of the RF model that was developed using the polarimetric parameters and their texture features based on a 15 × 15 pixel window.</p> Full article ">Figure 8
<p>The comparison between (<b>a</b>) the airborne SAR- and machine learning results-based melt pond maps: (<b>b</b>) the DT model and (<b>c</b>) the RF model.</p> Full article ">Figure 9
<p>Visual comparisons between the 2010 TerraSAR-X HH-polarized amplitude images (<b>a</b>,<b>b</b>) and the RF model-based melt pond maps (<b>c</b>,<b>d</b>).</p> Full article ">Figure 10
<p>The comparison between the airborne SAR- and the RF model-derived statistics for melt pond and sea ice: (<b>a</b>) sea ice concentration; (<b>b</b>) melt pond fraction; (<b>c</b>) number density of ponds and (<b>d</b>) mean pond area. NRMSD, normalized RMSD.</p> Full article ">Figure 11
<p>Maps of the statistics for melt pond and sea ice generated from the RF model-derived melt pond map: (<b>a</b>) sea ice concentration; (<b>b</b>) melt pond fraction; (<b>c</b>) number density of ponds; and (<b>d</b>) mean pond area.</p> Full article ">
5544 KiB  
Article
Comparison of Two Independent Mapping Exercises in the Primeiras and Segundas Archipelago, Mozambique
by Luisa Teixeira, John Hedley, Aurélie Shapiro and Kathryn Barker
Remote Sens. 2016, 8(1), 52; https://doi.org/10.3390/rs8010052 - 12 Jan 2016
Cited by 4 | Viewed by 6797
Abstract
Production of coral reef habitat maps from high spatial resolution multispectral imagery is common practice and benefits from standardized accuracy assessment methods and many informative studies on the merits of different processing algorithms. However, few studies consider the full production workflow, including factors [...] Read more.
Production of coral reef habitat maps from high spatial resolution multispectral imagery is common practice and benefits from standardized accuracy assessment methods and many informative studies on the merits of different processing algorithms. However, few studies consider the full production workflow, including factors such as operator influence, visual interpretation and a-priori knowledge. An end-user might justifiably ask: Given the same imagery and field data, how consistent would two independent production efforts be? This paper is a post-study analysis of a project in which two teams of researchers independently produced maps of six coral reef systems of the archipelago of the Primeiras and Segundas Environmental Protected Area (PSEPA), Mozambique. Both teams used the same imagery and field data, but applied different approaches—pixel based vs. object based image analysis—and used independently developed classification schemes. The results offer a unique perspective on the map production process. Both efforts resulted in similar merged classes accuracies, averaging at 63% and 64%, but the maps were distinct in terms of scale of spatial patterns, classification disparities, and in other aspects where the mapping process is reliant on visual interpretation. Despite the difficulty in aligning the classification schemes clear patterns of correspondence and discrepancy were identified. The maps were consistent with respect to geomorphological level mapping (17 out of 30 paired comparisons at more than 75% agreement), and also agreed in the extent of coral containing areas within a difference of 16% across the archipelago. However, more detailed benthic habitat level classes were inconsistent. Mapping of deep benthic cover was the most subjective result and dependent on operator visual interpretation, yet this was one of the results of highest interest for the PSEPA management since it revealed a continuity of benthos between the islands and the impression of a proto-barrier reef. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
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<p>Locational map of the study area.</p> Full article ">Figure 2
<p>Example of (<b>a</b>,<b>b</b>) raw data (min and max percentage clip = 0.2); (<b>c</b>,<b>d</b>) GEM and (<b>e</b>,<b>f</b>) Lund mapping results for Baixo Santo Antonio and Mafamede; BC=benthic cover, BM = brown macroalgae, C = coral, FR = fore reef, L = land, NI = No information, R=rock(s), RC = reef crest, RF = reef front, RS = reef slope, Ru = rubble, S = sand, SG = seagrass, UP=unprocessed,V = vegetation, W = water.</p> Full article ">Figure 3
<p>Example of (<b>a</b>,<b>b</b>) raw data (min and max percentage clip = 0.2); (<b>c</b>,<b>d</b>) GEM and (<b>e</b>,<b>f</b>) Lund mapping results for PugaPuga and Baixo Miguel; BC = benthic cover, BM = brown macroalgae, C = coral, FR = fore reef, L = land, NI = No information, R = rock(s), RC = reef crest, RF = reef front, RS = reef slope, Ru = rubble, S = sand, SG = seagrass, UP=unprocessed,V = vegetation, W = water.</p> Full article ">Figure 4
<p>Example of (<b>a</b>,<b>b</b>) raw data (min and max percentage clip = 0.2); (<b>c</b>,<b>d</b>) GEM and (<b>e</b>,<b>f</b>) Lund mapping results for Njovo and Caldeira; BC = benthic cover, BM = brown macroalgae, C = coral, FR = fore reef, L = land, NI = No information, R = rock(s), RC = reef crest, RF = reef front, RS = reef slope, Ru = rubble, S = sand, SG = seagrass, UP=unprocessed,V = vegetation, W = water.</p> Full article ">Figure 5
<p>Paired class comparison across the different coral reef systems at the geomorphological level; GEM refers to the pixel based mapping products, and Lund to the OBIA mapping.</p> Full article ">Figure 6
<p>Paired class comparison across the different coral reef systems at the bottom cover and benthic habitat levels.</p> Full article ">Figure 7
<p>Extent of coral, vegetation and deep benthic cover according to the GEM and Lund maps.</p> Full article ">
14015 KiB  
Article
Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions
by Silvia Valero, David Morin, Jordi Inglada, Guadalupe Sepulcre, Marcela Arias, Olivier Hagolle, Gérard Dedieu, Sophie Bontemps, Pierre Defourny and Benjamin Koetz
Remote Sens. 2016, 8(1), 55; https://doi.org/10.3390/rs8010055 - 11 Jan 2016
Cited by 109 | Viewed by 14046
Abstract
The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring [...] Read more.
The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring for many agricultural systems across the globe. In the framework of this project, this article studies the construction of a dynamic cropland mask. This mask consists of a binary “annual-cropland/no-annual-cropland” map produced several times during the season to serve as a mask for monitoring crop growing conditions over the growing season. The construction of the mask relies on two classical pattern recognition techniques: feature extraction and classification. One pixel- and two object-based strategies are proposed and compared. A set of 12 test sites are used to benchmark the methods and algorithms with regard to the diversity of the agro-ecological context, landscape patterns, agricultural practices and actual satellite observation conditions. The classification results yield promising accuracies of around 90% at the end of the agricultural season. Efforts will be made to transition this research into operational products once Sentinel-2 data become available. Full article
(This article belongs to the Special Issue Lessons Learned from the SPOT4 (Take5): Experiment in Preparation for Sentinel-2)
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<p>Number of images with less than 90% cloud cover for the different Test Sites. Mo: Morocco, Mad: Madagascar, Ar: Argentina, Be: Belgium, Ch: China, So: South Africa, Uk: Ukraine, US: United States (Maricopa), Fr: France, Ru: Russia, Pa: Pakistan, Bu: Burkina Faso.</p> Full article ">Figure 2
<p>Percentage of images covered by clouds. Mo: Morocco, Mad: Madagascar, Ar: Argentina, Be: Belgium, Ch: China, So: South Africa, Uk: Ukraine, US: United States (Maricopa), Fr: France, Ru: Russia, Pa: Pakistan, Bu: Burkina Faso.</p> Full article ">Figure 3
<p>Number of pixels from Sentinel proxy data, having a spatial resolution of 20 m, used in the validation of cropland masks. Mo: Morocco, Mad: Madagascar, Ar: Argentina, Be: Belgium, Ch: China, So: South Africa, Uk: Ukraine, US: United States (Maricopa), Fr: France, Ru: Russia, Pa: Pakistan, Bu: Burkina Faso.</p> Full article ">Figure 4
<p>The proposed dynamic classification system.</p> Full article ">Figure 5
<p>Supervised classification strategy for pixel-based approach.</p> Full article ">Figure 6
<p>The proposed object-based classification system with a pre-filtering task.</p> Full article ">Figure 7
<p>MS segmentation results obtained for different agricultural landscapes. The MS segmentation algorithm was applied to the six first principal components for all the sites. The same set of MS parameters was used for the four examples : <math display="inline"> <mrow> <msub> <mi>h</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </math>, <math display="inline"> <mrow> <msub> <mi>h</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>0</mn> <mo>.</mo> <mn>65</mn> </mrow> </math> and <math display="inline"> <mrow> <msub> <mi>N</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> </mrow> </math>. From left to right, the first row contains the Argentina, France, Pakistan, Maricopa (US) true color RGB compositions. The second row contains the MS segmentation results obtained.</p> Full article ">Figure 8
<p>The proposed object-based classification system with a post-processing task.</p> Full article ">Figure 9
<p>On the left, the 2x2 confusion matrix displaying the number of correct and incorrect predictions made by the classifier. On the right, equations of the classical quality measures used in the following.</p> Full article ">Figure 10
<p>Precision and Recall evaluation for the cropland areas for the different data sets in 12 sites (<b>a–l</b>). Three methodologies are compared: Pixel-Based (PB), Object-Based with a Pre-filtering task (P-OB) and Object-Based with a Post-filtering task (OB-P)</p> Full article ">Figure 11
<p>Precision and Recall evaluation for the no-crop areas for the different data sets in 12 sites (<b>a–l</b>). Three methodologies are compared: Pixel-Based (PB), Object-Based with a Pre-filtering task (P-OB) and Object-Based with a Post-filtering task (OB-P)</p> Full article ">Figure 12
<p>Kappa and Overall Accuracy (OA) evaluation for the cropland masks for the different data sets in 12 sites (<b>a–l</b>). Three methodologies are compared: Pixel-Based (PB), Object-Based with a Pre-filtering task (P-OB) and Object-Based with a Post-filtering task (OB-P)</p> Full article ">Figure 13
<p>From top to bottom: Cropland mask results obtained after 12 months for test sites in Argentina, Belgium, BurkinaFaso, Maricopa, Pakistan, and France. From left to right: true color RGB composition, PB cropland results, P-OB cropland results and OB-P cropland results. Crops appear in white whereas no-crop areas appear in black.</p> Full article ">
6170 KiB  
Article
Examination of Surface Temperature Modification by Open-Top Chambers along Moisture and Latitudinal Gradients in Arctic Alaska Using Thermal Infrared Photography
by Nathan C. Healey, Steven F. Oberbauer and Robert D. Hollister
Remote Sens. 2016, 8(1), 54; https://doi.org/10.3390/rs8010054 - 11 Jan 2016
Cited by 6 | Viewed by 7368
Abstract
Passive warming manipulation methodologies, such as open-top chambers (OTCs), are a meaningful approach for interpretation of impacts of climate change on the Arctic tundra biome. The magnitude of OTC warming has been studied extensively, revealing an average plot-level warming of air temperature that [...] Read more.
Passive warming manipulation methodologies, such as open-top chambers (OTCs), are a meaningful approach for interpretation of impacts of climate change on the Arctic tundra biome. The magnitude of OTC warming has been studied extensively, revealing an average plot-level warming of air temperature that ranges between 1 and 3 °C as measured by shielded resistive sensors or thermocouples. Studies have also shown that the amount of OTC warming depends in part on location climate, vegetation, and soil properties. While digital infrared thermometers have been employed in a few comparisons, most of the focus of the effectiveness of OTC warming has been on air or soil temperature rather than tissue or surface temperatures, which directly translate to metabolism. Here we used thermal infrared (TIR) photography to quantify tissue and surface temperatures and their spatial variability at a previously unavailable resolution (3–6 mm2). We analyzed plots at three locations that are part of the International Tundra Experiment (ITEX)-Arctic Observing Network (AON-ITEX) network along both moisture and latitudinal gradients spanning from the High Arctic (Barrow, AK, USA) to the Low Arctic (Toolik Lake, AK, USA). Our results show a range of OTC surface warming from 2.65 to 1.27 °C (31%–10%) at our three sites. The magnitude of surface warming detected by TIR imagery in this study was comparable to increases in air temperatures previously reported for these sites. However, the thermal images revealed wide ranges of surface temperatures within the OTCs, with some surfaces well above ambient unevenly distributed within the plots under sunny conditions. We note that analyzing radiometric temperature may be an alternative for future studies that examine data acquired at the same time of day from sites that are in close geographic proximity to avoid the requirement of emissivity or atmospheric correction for validation of results. We foresee future studies using TIR photography to describe species-level thermodynamics that could prove highly valuable toward a better understanding of species-specific responses to climate change in the Arctic. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Graphical abstract

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Full article ">Figure 1
<p>Map of the three locations used in this study along with weather data during image acquisition. Soil moisture measurements recorded within three days of image acquisition are from plots along the ITEX transects immediately adjacent to the study sites as described in Healey <span class="html-italic">et al.</span> [<a href="#B26-remotesensing-08-00054" class="html-bibr">26</a>] that represent the wet/moist and dry ITEX OTC/Control plots.</p> Full article ">Figure 2
<p>Aggregate percent cover of major plant growth forms and surface features at each site (BRW = Barrow; ATQ = Atqasuk, TLK = Toolik Lake) for dry (<b>a</b>) and wet/moist (<b>b</b>) moisture regimes (ctl = control, otc = open top chamber). Note: cover data were not available for all plots at Toolik Lake [<a href="#B25-remotesensing-08-00054" class="html-bibr">25</a>].</p> Full article ">Figure 3
<p>Images depicting four Barrow dry site plots (<b>a</b>–<b>d</b>) and the coinciding thermal images below (<b>e</b>–<b>h</b>); and images of four additional plots (<b>i</b>–<b>l</b>) with coinciding thermal images below (<b>m</b>–<b>p</b>). Note: the ring appearing in panels (<b>d</b>,<b>h</b>) is gas exchange collar not related to this study. Areas of image analysis are outlined with dotted white lines. Data within the transparent circular area has been removed. All temperatures are in degrees Celsius. Plant growth forms and surface features are depicted with the following abbreviations—BRYO: bryophyte/moss; DSHR: deciduous shrub; ESHR: evergreen shrub; FORB: forb; GRAM: graminoid; LICH: lichen; LITT: leaf litter; SOIL: bare soil/rock.</p> Full article ">Figure 4
<p>Images depicting four Barrow wet site plots (<b>a</b>–<b>d</b>) and the coinciding thermal images below (<b>e</b>–<b>h</b>); and images of four additional plots (<b>i</b>–<b>l</b>) with coinciding thermal images below (<b>m</b>–<b>p</b>). Note: the rings appearing in panels (<b>b</b>,<b>f</b>,<b>i</b>–<b>p</b>) are gas exchange collars not related to this study. Areas of image analysis are outlined with dotted white lines. Data in the transparent circular areas have been removed. All temperatures are in degrees Celsius. Plant growth forms and surface features are depicted with the following abbreviations—BRYO: bryophyte/moss; DSHR: deciduous shrub; ESHR: evergreen shrub; FORB: forb; GRAM: graminoid; LICH: lichen; LITT: leaf litter; SOIL: bare soil/rock. White grids symbolize chamber base points or plant identification tags.</p> Full article ">Figure 5
<p>Images depicting four Atqasuk dry site plots (<b>a</b>–<b>d</b>) and the coinciding thermal images below (<b>e</b>–<b>h</b>); and images of four additional plots (<b>i</b>–<b>l</b>) with coinciding thermal images below (<b>m</b>–<b>p</b>). Areas of image analysis are outlined with dotted black lines. All temperatures are in degrees Celsius. Plant growth forms and surface features are depicted with the following abbreviations—BRYO: bryophyte/moss; DSHR: deciduous shrub; ESHR: evergreen shrub; FORB: forb; GRAM: graminoid; LICH: lichen; LITT: leaf litter; SOIL: bare soil/rock.</p> Full article ">Figure 6
<p>Images depicting four Atqasuk wet site plots (<b>a</b>–<b>d</b>) and the coinciding thermal images below (<b>e</b>–<b>h</b>); and images of four additional plots (<b>i</b>–<b>l</b>) with coinciding thermal images below (<b>m</b>–<b>p</b>). Note: the rings appearing in panels (<b>i</b>–<b>p</b>) are gas exchange collars not related to this study. Areas of image analysis are outlined with dotted white lines. Data in the transparent circular areas have been removed. All temperatures are in degrees Celsius. Plant growth forms and surface features are depicted with the following abbreviations—BRYO: bryophyte/moss; DSHR: deciduous shrub; ESHR: evergreen shrub; FORB: forb; GRAM: graminoid; LICH: lichen; LITT: leaf litter; SOIL: bare soil/rock. White grids symbolize chamber base points or plant identification tags.</p> Full article ">Figure 7
<p>Images depicting four Toolik Lake dry site plots (<b>a</b>–<b>d</b>) and the coinciding thermal images below (<b>e</b>–<b>h</b>); and images of four additional plots (<b>i</b>–<b>l</b>) with coinciding thermal images below (<b>m</b>–<b>p</b>). Areas of image analysis are outlined with dotted white lines. All temperatures are in degrees Celsius. Plant growth forms and surface features are depicted with the following abbreviations—BRYO: bryophyte/moss; DSHR: deciduous shrub; ESHR: evergreen shrub; FORB: forb; GRAM: graminoid; LICH: lichen; LITT: leaf litter; SOIL: bare soil/rock. Note: cover data were not available for all plots.</p> Full article ">Figure 8
<p>Images depicting four Toolik Lake moist site plots (<b>a</b>–<b>d</b>) and the coinciding thermal images below (<b>e</b>–<b>h</b>); and images of four additional plots (<b>i</b>–<b>l</b>) with coinciding thermal images below (<b>m</b>–<b>p</b>). Areas of image analysis are outlined with dotted white lines. All temperatures are in degrees Celsius. Plant growth forms and surface features are depicted with the following abbreviations—BRYO: bryophyte/moss; DSHR: deciduous shrub; ESHR: evergreen shrub; FORB: forb; GRAM: graminoid; LICH: lichen; LITT: leaf litter; SOIL: bare soil/rock. Note: cover data were not available for all plots.</p> Full article ">Figure 9
<p>Mean difference between Open Top Chamber (OTC) and control plot surface temperatures (OTC—control).</p> Full article ">Figure 10
<p>Boxplots of the plot-level average surface temperature data for all controls (CTL) and all Open Top Chambers (OTC) (<b>a</b>); all dry plots (<b>b</b>); and all wet/moist plots (<b>c</b>).</p> Full article ">Figure 11
<p>Histograms of pixel temperatures within the OTCs (<b>gray</b>) and the coinciding control plots (<b>black</b>) at each of the three locations. Pixel values for all four plots and each treatment (OTC or control) at Barrow dry (<b>a</b>); Barrow wet (<b>b</b>); Atqasuk dry (<b>c</b>); Atqasuk wet (<b>d</b>); Toolik Lake dry (<b>e</b>); and Toolik Lake moist (<b>f</b>) are presented.</p> Full article ">Figure 11 Cont.
<p>Histograms of pixel temperatures within the OTCs (<b>gray</b>) and the coinciding control plots (<b>black</b>) at each of the three locations. Pixel values for all four plots and each treatment (OTC or control) at Barrow dry (<b>a</b>); Barrow wet (<b>b</b>); Atqasuk dry (<b>c</b>); Atqasuk wet (<b>d</b>); Toolik Lake dry (<b>e</b>); and Toolik Lake moist (<b>f</b>) are presented.</p> Full article ">
6095 KiB  
Technical Note
Derivation of Sea Surface Wind Directions from TerraSAR-X Data Using the Local Gradient Method
by Yi-Ran Wang and Xiao-Ming Li
Remote Sens. 2016, 8(1), 53; https://doi.org/10.3390/rs8010053 - 8 Jan 2016
Cited by 7 | Viewed by 6338
Abstract
Derivation of sea surface wind direction is a key step of sea surface wind retrieval from spaceborne Synthetic Aperture Radar (SAR) data. This technical note describes an implementation of the Local Gradient (LG) method to derive sea surface wind directions at a scale [...] Read more.
Derivation of sea surface wind direction is a key step of sea surface wind retrieval from spaceborne Synthetic Aperture Radar (SAR) data. This technical note describes an implementation of the Local Gradient (LG) method to derive sea surface wind directions at a scale of a few kilometers from X-band spaceborne SAR TerraSAR-X (TS-X) data in wide swath mode. The 180° ambiguity in the derived sea surface wind direction is automatically eliminated using a single reference wind direction from external data sources. Several typical cases acquired in the North Sea were presented to demonstrate the derivation of sea surface wind direction under different wind situations using this method. The derived sea surface wind direction were further compared to atmospheric model prediction results. In addition, a practical method is introduced to address ambiguity in the derived sea surface wind directions using the LG method in a typhoon case with rotating surface wind structure. By interpolating the derived wind directions at a scale of kilometers, sea surface wind speeds with a spatial resolution of 500 m are subsequently retrieved using the X-band SAR sea surface wind Geophysical Model Function (GMF). The approach accomplished by combining the LG method with the X-band GMF for deriving sea surface wind in high spatial resolution demonstrates its potential for operational service. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>A TerraSAR-X (TS-X) ScanSAR (SC) image acquired on 18 July 2012 at 17:28 UTC over the North Sea. The sub-image on the upper right shows wind streaks that are oriented southwest-northeast, and the sub-image in the lower-right panel shows ocean swell patterns that are oriented northwest-southeast.</p> Full article ">Figure 2
<p>Distribution of the complex numbers in <math display="inline"> <semantics> <mrow> <msub> <mi>G</mi> <mi>f</mi> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 3
<p>The histogram and the smoothed histogram of <math display="inline"> <semantics> <mrow> <msub> <mi>G</mi> <mi>f</mi> </msub> </mrow> </semantics> </math> derived from the sub-image shown in <a href="#remotesensing-08-00053-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 4
<p>(<b>a</b>) The derived sea surface wind direction with a 180° ambiguity using the local gradient (LG) method applied to the TS-X case acquired on 18 July 2012; (<b>b</b>–<b>d</b>) the resolved sea surface wind directions at spatial resolutions of 20 km, 10 km and 5 km, respectively, using a single reference wind direction from the European Wave Model (EWAM) model to eliminate the 180° ambiguity.</p> Full article ">Figure 5
<p>Comparison of the retrieved sea surface wind field from the TS-X data acquired on 18 July 2012 at 17:28 UTC with the EWAM wind model result at 18:00 UTC. The white arrows indicate the retrieved sea surface wind directions from the TS-X data using the LG method, and the EWAM sea surface wind vectors are represented by the color-coded arrows.</p> Full article ">Figure 6
<p>(<b>a</b>) A TS-X SC image acquired on 12 July 2012 at 17:37 UTC over the North Sea and (<b>b</b>) the comparison of the TS-X retrieved sea surface wind field with the EWAM model result at 18:00 UTC.</p> Full article ">Figure 7
<p>(<b>a</b>) A TS-X SC image acquired on 18 May 2012 at 17:36 UTC over the North Sea and (<b>b</b>) the comparison of the TS-X retrieved sea surface wind field with the EWAM model results at 18:00 UTC.</p> Full article ">Figure 8
<p>Scatterplot of the wind directions from the EWAM model and the retrieved wind directions from TS-X/ TanDEM-X (TD-X) using the LG method.</p> Full article ">Figure 9
<p>Sketch of the relationship between the typhoon eye and sea surface wind directions.</p> Full article ">Figure 10
<p>(<b>a</b>) The TS-X SC image acquired on 17 July 2011 at 08:59 UTC over typhoon Ma-On in the west Pacific and the superimposed derived sea surface wind directions using the LG method; (<b>b</b>) the retrieved sea surface wind field using the derived wind direction and XMOD2.</p> Full article ">
4134 KiB  
Article
Differentiating among Four Arctic Tundra Plant Communities at Ivotuk, Alaska Using Field Spectroscopy
by Sara N. Bratsch, Howard E. Epstein, Marcel Buchhorn and Donald A. Walker
Remote Sens. 2016, 8(1), 51; https://doi.org/10.3390/rs8010051 - 8 Jan 2016
Cited by 37 | Viewed by 8969
Abstract
Warming in the Arctic has resulted in changes in the distribution and composition of vegetation communities. Many of these changes are occurring at fine spatial scales and at the level of individual species. Broad-band, coarse-scale remote sensing methods are commonly used to assess [...] Read more.
Warming in the Arctic has resulted in changes in the distribution and composition of vegetation communities. Many of these changes are occurring at fine spatial scales and at the level of individual species. Broad-band, coarse-scale remote sensing methods are commonly used to assess vegetation changes in the Arctic, and may not be appropriate for detecting these fine-scale changes; however, the use of hyperspectral, high resolution data for assessing vegetation dynamics remains scarce. The aim of this paper is to assess the ability of field spectroscopy to differentiate among four vegetation communities in the Low Arctic of Alaska. Primary data were collected from the North Slope site of Ivotuk, Alaska (68.49°N, 155.74°W) and analyzed using spectrally resampled hyperspectral narrowbands (HNBs). A two-step sparse partial least squares (SPLS) and linear discriminant analysis (LDA) was used for community separation. Results from Ivotuk were then used to predict community membership at five other sites along the Dalton Highway in Arctic Alaska. Overall classification accuracy at Ivotuk ranged from 84%–94% and from 55%–91% for the Dalton Highway test sites. The results of this study suggest that hyperspectral data acquired at the field level, along with the SPLS and LDA methodology, can be used to successfully discriminate among Arctic tundra vegetation communities in Alaska, and present an improvement over broad-band, coarse-scale methods for community classification. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>NDVI throughout the 1999 growing season for four different vegetation communities at Ivotuk, Alaska. Adapted from [<a href="#B17-remotesensing-08-00051" class="html-bibr">17</a>]. Measurements were taken using an Analytical Spectral Devices FieldSpec spectro-radiometer.</p> Full article ">Figure 2
<p>Location of Ivotuk, Deadhorse, Franklin Bluffs, Sagwon-MNT, Sagwon-MAT, and Happy Valley sites on the North Slope of Alaska within the Alaskan bioclimatic subzones of the Circumpolar Arctic Vegetation Map [<a href="#B44-remotesensing-08-00051" class="html-bibr">44</a>].</p> Full article ">Figure 3
<p>The four vegetation communities at Ivotuk, Alaska along with the grid and spectral sampling locations. Each point represents one sampling gridpoint. Study site images were taken between 16–31 July 1999. All grid images were taken on 19 July 1999 [<a href="#B60-remotesensing-08-00051" class="html-bibr">60</a>].</p> Full article ">Figure 4
<p>Mean reflectance spectra for the four vegetation communities during early (<b>a</b>) and peak (<b>b</b>) growing season at Ivotuk, Alaska in 1999. Reflectance spectra are shown along with the top ten percent of optimal hyperspectral narrowbands (HNBs) and their normalized coefficients for the first discriminant function.</p> Full article ">Figure 5
<p>First two functions derived from linear discriminant analysis for the four vegetation communities at Ivotuk, Alaska using the top ten percent of optimal hyperspectral narrowbands (HNBs) during early (<b>a</b>) and peak (<b>b</b>) growing season.</p> Full article ">Figure 6
<p>Average continuum-removed and scaled continuum-removed reflectance spectra at Ivotuk, Alaska for the blue (<b>a</b>,<b>b</b>), red (<b>c</b>,<b>d</b>), and water (<b>e</b>,<b>f</b>) absorption features during early growing season.</p> Full article ">Figure 7
<p>Average continuum-removed and scaled continuum-removed reflectance spectra at Ivotuk, Alaska for the blue (<b>a</b>,<b>b</b>), red (<b>c</b>,<b>d</b>), and water (<b>e</b>,<b>f</b>) absorption features during peak growing season.</p> Full article ">Figure 8
<p>Mean reflectance spectra for the five Dalton Highway test sites. Reflectance spectra are shown along with the top ten percent of optimal hyperspectral narrowbands (HNBs) and their normalized coefficients for the first discriminant function.</p> Full article ">
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