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Remote Sens., Volume 6, Issue 11 (November 2014) – 62 articles , Pages 10252-11672

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1989 KiB  
Article
Evaluation of Satellite Rainfall Estimates over the Chinese Mainland
by Yaxin Qin, Zhuoqi Chen, Yan Shen, Shupeng Zhang and Runhe Shi
Remote Sens. 2014, 6(11), 11649-11672; https://doi.org/10.3390/rs61111649 - 24 Nov 2014
Cited by 120 | Viewed by 9951
Abstract
Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is [...] Read more.
Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is crucial to quantify and document their error characteristics. In this study, four satellite-based precipitation products (TRMM-3B42, TRMM-3B42RT, CMORPH, GSMaP) were evaluated using gauge-based rainfall analysis based on a high-density gauge network throughout the Chinese Mainland during 2003–2006. To quantitatively evaluate satellite-based precipitation products, continuous (e.g., ME, RMSE, CC) and categorical (e.g., POD, FAR) verification statistics were used in this study. The results are as follows: (1) GSMaP and CMORPH underestimated precipitation (about −0.53 and −0.14 mm/day, respectively); TRMM-3B42RT overestimated precipitation (about 0.73 mm/day); TRMM-3B42, which is the only dataset corrected by gauges, had the best estimation of precipitation amongst all four products; (2) GSMaP, CMORPH and TRMM-3B42RT overestimated the frequency of low-intensity rainfall events; TRMM-3B42 underestimated the frequency of low-intensity rainfall events; GSMaP underestimated the frequency of high-intensity rainfall events; TRMM-3B42RT tended to overestimate the frequency of high-intensity rainfall events; TRMM-3B42 and CMORPH produced estimations of high-intensity rainfall frequency that best aligned with observations; (3) All four satellite-based precipitation products performed better in summer than in winter. They also had better performance over wet southern region than dry northern or high altitude regions. Overall, this study documented error characteristics of four satellite-based precipitation products over the Chinese Mainland. The results help to understand features of these datasets for users and improve algorithms for algorithm developers in the future. Full article
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Full article ">Figure 1
<p>Distribution of rain gauge stations.</p> Full article ">Figure 2
<p>Intensity distribution of precipitation events calculated from four satellite based precipitation datasets and measurements (CMA-GRA).</p> Full article ">Figure 3
<p>Seasonal variations of validation statistics. (<b>a</b>) Mean precipitation (mm/day); (<b>b</b>) ratio between satellite products and observations (%); (<b>c</b>) RMSE (mm/day); (<b>d</b>) Correlation coefficients; (<b>e</b>) POD and (<b>f</b>) FAR.</p> Full article ">Figure 4
<p>Snapshots of daily precipitation patterns on an arbitrary winter day (12 January 2005) for (<b>a</b>) CMA, (<b>b</b>) GSMaP, (<b>c</b>) CMORPH, (<b>d</b>) TRMM 3B42 and (<b>e</b>) TRMM 3B42RT. Units are mm/day.</p> Full article ">Figure 5
<p>Snapshots of daily precipitation patterns on an arbitrary summer day (1 July 2005) for (<b>a</b>) CMA, (<b>b</b>) GSMaP, (<b>c</b>) CMORPH, (<b>d</b>) TRMM 3B42 and (<b>e</b>) TRMM 3B42RT. Units are mm/day.</p> Full article ">Figure 6
<p>Distribution of daily mean precipitation over the Chinese Mainland from 2003 to 2006. Units are mm/day. (<b>a</b>) CMA, (<b>b</b>) GSMaP, (<b>c</b>) CMORPH, (<b>d</b>) TRMM 3B42 and (<b>e</b>) TRMM 3B42RT.</p> Full article ">Figure 7
<p>Distribution of RMSE over the Chinese Mainland from 2003 to 2006. (<b>a</b>) GSMaP, (<b>b</b>) CMORPH, (<b>c</b>) TRMM 3B42 and (<b>d</b>) TRMM 3B42RT.</p> Full article ">Figure 8
<p>Distribution of correlation coefficients over the Chinese Mainland from 2003 to 2006. (<b>a</b>) GSMaP, (<b>b</b>) CMORPH, (<b>c</b>) TRMM 3B42 and (<b>d</b>) TRMM 3B42RT.</p> Full article ">Figure 9
<p>Distribution of POD over the Chinese Mainland from 2003 to 2006. (<b>a</b>) GSMaP, (<b>b</b>) CMORPH, (<b>c</b>) TRMM 3B42 and (<b>d</b>) TRMM 3B42RT.</p> Full article ">Figure 10
<p>Distribution of FAR over the Chinese Mainland from 2003 to 2006. (<b>a</b>) GSMaP, (<b>b</b>) CMORPH, (<b>c</b>) TRMM 3B42 and (<b>d</b>) TRMM 3B42RT.</p> Full article ">
1290 KiB  
Article
A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data
by Rocío Hernández-Clemente, Rafael M. Navarro-Cerrillo, Francisco J. Romero Ramírez, Alberto Hornero and Pablo J. Zarco-Tejada
Remote Sens. 2014, 6(11), 11627-11648; https://doi.org/10.3390/rs61111627 - 21 Nov 2014
Cited by 36 | Viewed by 9329
Abstract
Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands [...] Read more.
Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories. Full article
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<p>(<b>a</b>) Digital aerial image acquisition planning: spatial distribution of images acquired over the area. (<b>b</b>) Flight log showing the grid from east to west.</p> Full article ">Figure 2
<p>(<b>a</b>) Example of the ALS-based canopy height model. (<b>b</b>) Digital aerial imagery (DAI)-based canopy height model. (<b>c</b>) ALS cloud points overlaid on the DAI-based canopy height model.</p> Full article ">Figure 3
<p>Example of a side view of a profile of an ALS-based and a DAI-based canopy height model overlaid with a 3D photogrammetric digital surface model reconstruction. Profile width = 1 m, length = 105 m.</p> Full article ">Figure 4
<p>(<b>a</b>) Soil buffering around overlapping and non-overlapping crowns as a reference for the spatial interpolation of ground points under the canopy. (<b>b</b>) Object-based delineation of crown trees based on DAI data. (<b>c</b>) Object-based delineation of crown trees based on ALS data. In both cases, manual delineation (dotted orange line) was used as the background.</p> Full article ">Figure 5
<p>Coefficient of determination (R<sup>2</sup>) derived from the relationship between tree height and maximum, mean, variance, average absolute deviation (AAD), L-moments (L1, L2), and percentile values (5th, 10th, 20th, 25th, ..., 95th percentiles) derived from the ALS-based and the DAI-based data.</p> Full article ">Figure 6
<p>(<b>a</b>) Relationship between observed height and ALS-based data using the 90th percentile. (<b>b</b>) Relationship between observed height and the DAI-based data using the 90th percentile. Regression function (solid lines) and 1:1 correspondence (dashed lines). CHM, canopy height model.</p> Full article ">Figure 7
<p>(<b>a</b>) Difference between predicted and observed height (H − Ho)<sup>2</sup> in relation to the percentage of crown overlapping using the DAI-based canopy height model and ALS data. (<b>b</b>) Difference between predicted and observed height (H − Ho)<sup>2</sup> in relation to the slope using the DAI-based canopy height model and ALS data.</p> Full article ">Figure 8
<p>Field-observed <span class="html-italic">versus</span> ALS-predicted tree height. (<b>a</b>) Field-observed tree height <span class="html-italic">versus</span> (<b>b</b>) DAI-predicted tree height. The graphs show the regression function (solid lines) and 1:1 correspondence (dashed lines).</p> Full article ">Figure 9
<p>Relationship between observed effective (LAIe) and the laser penetration index (LPI) (1) obtained from ALS data.</p> Full article ">Figure 10
<p>Coefficient of determination (R<sup>2</sup>) derived from the relationship between LAIe and maximum, mean and percentile values (5th, 10th, 20th, 25th, ..., 95th percentiles) derived from the DAI-derived Normalized Difference Vegetation Index (NDVI).</p> Full article ">Figure 11
<p>(<b>a</b>) Field-observed effective leaf area index (LAIe) <span class="html-italic">versus</span> ALS predicted LAI (<b>b</b>) Field-observed effective leaf area index (LAIe) <span class="html-italic">versus</span> DAI predicted LAI. Graphs show the regression function (solid lines) and 1:1 correspondence (dashed lines).</p> Full article ">
3255 KiB  
Article
Landsat-8 Thermal Infrared Sensor (TIRS) Vicarious Radiometric Calibration
by Julia A. Barsi, John R. Schott, Simon J. Hook, Nina G. Raqueno, Brian L. Markham and Robert G. Radocinski
Remote Sens. 2014, 6(11), 11607-11626; https://doi.org/10.3390/rs61111607 - 21 Nov 2014
Cited by 337 | Viewed by 23605
Abstract
Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 [...] Read more.
Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m2·sr·μm or −2.1 K and −4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m2·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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Full article ">Figure 1
<p>TIRS optical diagram. The SSM mirror rotates between the nadir viewport, blackbody and deep space viewport to provide calibration at least once an orbit.</p> Full article ">Figure 2
<p>TIRS focal plane diagram. The areas of the three SCAs covered by the spectral filters are indicated, along with the region on each SCA used to measure the dark signal.</p> Full article ">Figure 3
<p>The relative spectral responses (RSR) of the TIRS bands (B10 and 11). Also shown for comparison are the RSRs of ETM+ band (B6) and the equivalent MODIS bands (B31 and 32). The TIRS and ETM+ RSRs are band-average but the MODIS RSRs are each for a specific detector (detector 5 in both cases).</p> Full article ">Figure 4
<p>The SCA-average per-calibration sequence responsivity metric for both TIRS bands along with the per-SCA lifetime average. Based on this and other metrics, the TIRS instrument is internally stable.</p> Full article ">Figure 5
<p>Distribution of vicarious calibration data across the TIRS focal plane. Dashed red lines indicate the boundaries between SCAs; SCA1 consists of detectors 1–640, SCA2 contains detectors 641–1280, and SCA3 is detectors 1281–1920. Note that all the JPL day data since April 2013 falls in SCA2 and for most of the night acquisitions, the satellite has been pointed such that Tahoe falls in SCA2. The RIT acquisitions are based on eight different buoys and are distributed across the focal plane.</p> Full article ">Figure 6
<p>The estimated root-mean-squared error (RMSE) error in comparing the ETM+ (<b>left</b>) or MODIS (<b>right</b>) to TIRS brightness temperatures over a range of surface temperatures and through a variety of atmosphere types. The largest errors are due to less likely conditions, <span class="html-italic">i.e.</span>, a very warm surface in a very cold atmosphere.</p> Full article ">Figure 7
<p>TIRS Band 11 image of Lake Superior (47.5N, 88W) illustrating the discontinuities between the Sensor Chip Assemblies (SCAs) and time-varying nature of the difference. The edges of the SCAs are clearly defined (red arrows) and the differences between the SCAs change from north to south in the image. In a stable system, even with a calibration error, the differences between the SCAs should remain constant for the length of the lake. However, in this example, SCA3 is warmer than SCA2 by 0.2 K at the region marked 1 but is cooler by 0.8 K at region 2. SCA1 is warmer than SCA2 by 0.2 K at region 3 but cooler than SCA2 by 0.7 K at region 4.</p> Full article ">Figure 8
<p>The 185 km wide scene boundaries of the standard Lake Tahoe, located at 39N, 120W (<b>left</b>), and Salton Sea, located at 33.3N, 115.8W (<b>right</b>), image frames are indicated by the green box. The blue circle indicates the 15° ring source of stray light (though the stray light does not necessarily come from the whole circle). In both cases, the source of the stray light is primarily from land (given that no snow or clouds are covering the surface) outside the area observed by Landsat-8.</p> Full article ">Figure 9
<p>The initial vicarious calibration results for both TIRS bands, based on the day JPL and RIT buoy data. If the instrument were perfectly calibrated, the data would fall scattered about the 1:1 line. All results for both bands are above the 1:1 line indicating that the instrument is predicting too high.</p> Full article ">Figure 10
<p>The initial vicarious calibration results for Band 10, based on the JPL data only, displayed as difference between the predicted vicarious radiance minus the image radiance. The data are split into the day and night series. There is a statistically significant difference between the day and night results so only the results for the day data were used to calculate the bias error.</p> Full article ">Figure 11
<p>Seasonal effect of the residual bias error for the Band 11 JPL data (includes both Lake Tahoe and Salton Sea). Data are plotted versus day of year so the change in the residual error over the year is apparent.</p> Full article ">Figure 12
<p>Seasonal effect of the residual bias error for the RIT data, including the average error for the ETM+ comparison. Data are plotted versus day of year but the seasonal effect is not as apparent in the RIT data as in the JPL data (<a href="#remotesensing-06-11607-f011" class="html-fig">Figure 11</a>). The ETM+ comparison data point sits within the residual errors of the buoy data.</p> Full article ">Figure 13
<p>JPL buoy and MODIS comparison results plotted versus target brightness temperature. The trends do not overlap but they both indicate that the residual error is not dependent on target temperature. The trend in the Band 11 data is a function of the seasonal effect of the stray light.</p> Full article ">Figure 14
<p>Current vicarious calibration results for the two TIRS bands, including both RIT and JPL data for all SCAs, but only displaying day data. The data are scattered about the 1:1 line, indicating that the residual error has been removed. Neither the slope nor the offset is statistically significant.</p> Full article ">Figure 15
<p>Current vicarious calibration results for the two TIRS bands over time. The residual difference between the vicarious radiance and the image radiance is scattered about the zero axis.</p> Full article ">
13361 KiB  
Article
Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch
by Gang Liu, Scott F. Heron, C. Mark Eakin, Frank E. Muller-Karger, Maria Vega-Rodriguez, Liane S. Guild, Jacqueline L. De La Cour, Erick F. Geiger, William J. Skirving, Timothy F. R. Burgess, Alan E. Strong, Andy Harris, Eileen Maturi, Alexander Ignatov, John Sapper, Jianke Li and Susan Lynds
Remote Sens. 2014, 6(11), 11579-11606; https://doi.org/10.3390/rs61111579 - 20 Nov 2014
Cited by 238 | Viewed by 25146
Abstract
The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for [...] Read more.
The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for higher resolution products by taking advantage of new satellites, sensors and algorithms. Improvements of the 5-km products over CRW’s heritage global 50-km products are derived from: (1) the higher resolution and greater data density of NOAA’s next-generation operational daily global 5-km geo-polar blended sea surface temperature (SST) analysis; and (2) implementation of a new SST climatology derived from the Pathfinder SST climate data record. The new products increase near-shore coverage and now allow direct monitoring of 95% of coral reefs and significantly reduce data gaps caused by cloud cover. The 5-km product suite includes SST Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks and Bleaching Alert Area, matching existing CRW products. When compared with the 50-km products and in situ bleaching observations for 2013–2014, the 5-km products identified known thermal stress events and matched bleaching observations. These near reef-scale products significantly advance the ability of coral reef researchers and managers to monitor coral thermal stress in near-real-time. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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<p>Results of user needs survey conducted by the University of Colorado Cooperative Institute for Research in Environmental Sciences in 2010 indicating the overwhelming request for higher-resolution Coral Reef Watch (CRW) products.</p> Full article ">Figure 2
<p>NOAA CRW’s daily global 5-km products for 3 October 2013: (<b>a</b>) sea surface temperature (SST); (<b>b</b>) SST Anomaly; (<b>c</b>) Coral Bleaching HotSpots; (<b>d</b>) Degree Heating Weeks; and (<b>e</b>) 7-day maximum composite Bleaching Alert Area, in which Alert Levels 1 and 2 values around Guam and the Commonwealth of the Northern Mariana Islands (CNMI) identified areas where bleaching was underway at that time.</p> Full article ">Figure 3
<p>CRW’s twice-weekly, 50-km, night-only SST time series of 2001–2014 from a pixel near Tao Island, Thailand. The flat portions of the SST time series indicate the periods with no SST update. Note that the longest period with no SST update spanned from May to October 2010. The MMM (maximum of the monthly mean) + 1 °C (31.1 °C) is shown as a red dashed-line across the figure.</p> Full article ">Figure 4
<p>CRW’s twice-weekly, 50-km, night-only SST (purple line) from a 50-km pixel near Apo Reef, Philippines (see arrows in <a href="#remotesensing-06-11579-f005" class="html-fig">Figure 5</a>c,d), overlaid with the daily, 5-km, night-only SST (red line) from a 5-km pixel located at the center of the 50-km pixel, for January, 2013–August, 2014. The lower portion of the graph shows the twice-weekly, 50-km DHW and bleaching stress levels (colored polygons). The flat portions of the purple 50-km SST time series indicate periods with no SST update. The persistent data gap during May–July 2014, caused the 50-km DHW product to overestimate the accumulated thermal stress.</p> Full article ">Figure 5
<p>Comparison of CRW products in the South China Sea on 10 July 2014: (<b>a</b>) twice-weekly 50-km DHW product and (<b>b</b>) daily 5-km DHW product; also shown are (<b>c</b>) NESDIS’ twice-weekly 50-km SST analysis (<span class="html-italic">i.e.</span>, SST composite of 7–10 July 2014) used by the 50-km DHW; and (<b>d</b>) the age (in days) of the newest SST retrievals used in the 50-km SST analysis for that region. The overestimation of the 50-km DHW shown in (a) is a result of persistent cloud cover partially demonstrated in the SST analysis (c) and explained by the retrieval ages plotted in (d); red arrows in (c) and (d) point to the pixel whose time series data are displayed in <a href="#remotesensing-06-11579-f004" class="html-fig">Figure 4</a>. (Dark grey pixels in the 50-km images are land pixels).</p> Full article ">Figure 6
<p>CRW’s twice-weekly 50-km Coral Bleaching HotSpots product (<b>a</b>) and daily global 5-km Coral Bleaching HotSpots product (<b>b</b>) of 9 May 2013, for the Gulf of Panama; CRW’s twice-weekly 50-km DHW product (<b>c</b>) and daily global 5-km DHW product (<b>d</b>) of 1 July 2013, for the Gulf of Panama. (Dark grey pixels in the 50-km images are land pixels).</p> Full article ">Figure 7
<p>Comparison of NOAA CRW’s (<b>a</b>) twice-weekly 50-km DHW product and (<b>b</b>) daily 5-km DHW product for 21 July 2014, showing accumulated bleaching thermal stress from 29 April to 21 July (Dark grey pixels in the 50-km images are land pixels).</p> Full article ">Figure 8
<p>CRW’s daily 5-km DHW maps of the tropical northwestern Pacific Ocean (<b>a</b>) and CNMI/Guam (<b>b</b>) of 20 September 2013; the box in (a) shows the spatial coverage of (b). Location names: 1, Uracas; 2, Maug Islands; 3, Asuncion; 4, Agrihan; 5, Pagan; 6, Alamagan; 7, Guguan; 8, Sarigan; 9, Anathan; 10, Saipan; 11, Tinian; 12, Aguijan; 13, Rota; 14, Guam.</p> Full article ">Figure 9
<p>CRW’s daily 5-km DHW maps of the tropical northwestern Pacific Ocean (<b>a</b>) and CNMI/Guam (<b>b</b>) of 24 August 2014; the box in (a) shows the spatial coverage of (b). Location names: 1, Uracas; 2, Maug Islands; 3, Asuncion; 4, Agrihan; 5, Pagan; 6, Alamagan; 7, Guguan; 8, Sarigan; 9, Anathan; 10, Saipan; 11, Tinian; 12, Aguijan; 13, Rota; 14, Guam.</p> Full article ">Figure 10
<p>CRW’s daily 5-km regional DHW image for Bermuda on 28 August 2013.</p> Full article ">
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Article
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification
by Ian Olthof and Robert H. Fraser
Remote Sens. 2014, 6(11), 11558-11578; https://doi.org/10.3390/rs61111558 - 20 Nov 2014
Cited by 27 | Viewed by 8808
Abstract
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories [...] Read more.
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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<p>Study area covered by Landsat image stack from 1985 to 2011. Region 2 (R2) provided training data while Region 1 (R1) and Region 3 (R3) were used for validation.</p> Full article ">Figure 2
<p>Inuvik summer (June–August) and winter (December–February) temperature trends from 1985 to 2011.</p> Full article ">Figure 3
<p>Reference and classified brightness profile examples of lake erosion with Frechet distance that is calculated as the minimum, maximum length that connects points on two curves. Frechet distance is commonly described as the minimum length of a leash required to connect a dog and its owner travelling along the two separate curves without either one backtracking.</p> Full article ">Figure 4
<p>Examples of changes with relatively consistent Tasseled Cap temporal profiles (left column) and inconsistent profiles due to temporal misalignment or the nature of change itself (right column).</p> Full article ">Figure 5
<p>Change map of the Mackenzie Delta region classified to eight change processes. Timoney’s (1992) treeline isolines are in red, the NWT/Yukon border is in blue.</p> Full article ">Figure 6
<p>TCB, TCG, TCW displayed as R, G, B and interpretation legend (Fraser <span class="html-italic">et al.</span>, this issue [<a href="#B5-remotesensing-06-11558" class="html-bibr">5</a>]) and classified change process map for a region around Inuvik.</p> Full article ">
31263 KiB  
Article
Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization
by Robert H. Fraser, Ian Olthof, Steven V. Kokelj, Trevor C. Lantz, Denis Lacelle, Alexander Brooker, Stephen Wolfe and Steve Schwarz
Remote Sens. 2014, 6(11), 11533-11557; https://doi.org/10.3390/rs61111533 - 20 Nov 2014
Cited by 52 | Viewed by 14085
Abstract
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here [...] Read more.
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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<p>Two study regions analyzed using Landsat TM/ETM+ satellite imagery from 1985 to 2011. The treeline was digitized from Timoney <span class="html-italic">et al.</span> [<a href="#B22-remotesensing-06-11533" class="html-bibr">22</a>] and represents the 1:1 tree:upland tundra cover isoline.</p> Full article ">Figure 2
<p>Tasseled Cap (TC) trend compositing method used by LARCH. Individual trend (slope) images from linear regression analysis of the Landsat image stack are composited as an RGB image to yield unique colors representing the three-dimensional TC trajectory.</p> Full article ">Figure 3
<p>Color wheel legend showing the major types of landscape changes that can be interpreted from RGB compositing of the linear TC trends. The changes in each TC index that characterize each color family are shown on the outside of the color wheel.</p> Full article ">Figure 4
<p>NWT fire history perimeters (1965–2011) overlaid on the TC trend image composite. Regenerating stands initiated by fires before 1965 appear dark blue.</p> Full article ">Figure 5
<p>General trajectory of regenerating wildfires in RGB trend space from recently burned forest (1), expanding broadleaf cover (2–3) and succession towards needleleaf species (4–5). Also shown are the linear regression TC slopes averaged by age of burn with third-order polynomial curves overlaid to show the shape of the trajectories. The three TC slope values on the graphs are combined to generate the RGB colour trajectory (left) labelled as 1–5.</p> Full article ">Figure 6
<p>TC trend composite image (<b>top left</b>) showing the boundary between a mature forest and stand initiated following a pre-1965 fire, which is also observable in a 2006 SPOT Image (<b>top right</b>). The broadleaf-needleleaf composition of forest in the area is visible in a recent oblique air photo captured by the NWT Government (<b>bottom</b>). The small arrow shows the direction from which the photo was taken.</p> Full article ">Figure 7
<p>Area along the treeline in study region 1 containing large regenerating fire complexes, which burned in 1954 (dark blue color). Also observable are smaller, more recently regenerating burns (light and yellow colors) with year of burning indicated and greening tundra vegetation (teal color). The 1976 tundra burn is included in the NWT fire history survey. The treeline was digitized from Timoney <span class="html-italic">et al.</span> [<a href="#B22-remotesensing-06-11533" class="html-bibr">22</a>] and represents the 1:1 tree:upland tundra cover isoline.</p> Full article ">Figure 8
<p>Area east of Tuktoyaktuk showing widespread increases in wetness in the trend image (<b>top</b>) over ice-wedge polygon terrain (dark blue), some expanding lakes (dark blue), and eroding coastline (dark blue and red). Areas of surface ponding can be observed in recent oblique air photos captured by the NWT Government (<b>bottom</b>). The red arrows show the direction from which the photos were taken.</p> Full article ">Figure 9
<p>TC trend image (<b>top</b>) showing draining or drying of shallow lakes (yellow) within a 1986 burn. Historical lake perimeter from the National Hydro Network are overlaid showing ~1950 lake extents. Exposed and vegetating lake beds are visible in recent oblique air photos from the NWT Government (<b>bottom</b>). The red arrow shows the direction from which the photo was taken.</p> Full article ">Figure 10
<p>Area along the north shore of Great Slave Lake west of Yellowknife showing receding shorelines that are being colonized by vegetation in the TC trend image (yellow). The new Yellowknife Highway (red) and old highway where vegetation is regenerating (teal) are also visible. A photo captured from helicopter shows one of the bays with growth of new shoreline vegetation. Mean annual water levels from Environment Canada’s Hydrometric Data are shown for Great Slave Lake at Yellowknife Bay for the 1985–2011 Landsat analysis period.</p> Full article ">Figure 11
<p>Example TC trend images for three retrogressive thaw slumps within the Peel Plateau region (<b>top</b>) with recent SPOT imagery (<b>bottom</b>) and an air photo (top right) shown for reference.</p> Full article ">Figure 12
<p>TC trend image for a wide, braided river channel in the Mackenzie Mountain foothills west of Norman Wells (<b>upper left</b>). Comparison to single-date Landsat imagery (<b>upper middle</b>, <b>upper right</b>) and NWT air photos (<b>bottom</b>) show that the TC trends capture dynamic changes resulting from the shifting of stream channels and loss or gain of vegetation.</p> Full article ">Figure 13
<p>Air photos acquired by the NWT Government in 2007 (<b>left</b>) showing land use changes visible in the TC trend image for the city of Yellowknife (<b>right</b>). These changes relate to post-1985 development (red) and regeneration of previously disturbed areas (teal) and include the construction of a golf course (1), a gravy quarry (2), new housing subdivisions (3), a solid waste facility (4), and open mining pits (5).</p> Full article ">Figure 14
<p>Examples of TC trend imagery north of Yellowknife showing abandoned mining sites with regeneration (teal) and the footprint of recently developed diamond mines (red and dark blue).</p> Full article ">Figure 15
<p>TC trend image for area surrounding Norman Wells, which has been heavily developed for oil and gas during the past 60 years.</p> Full article ">
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Article
Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data
by Kun Jia, Shunlin Liang, Xiangqin Wei, Yunjun Yao, Yingru Su, Bo Jiang and Xiaoxia Wang
Remote Sens. 2014, 6(11), 11518-11532; https://doi.org/10.3390/rs61111518 - 19 Nov 2014
Cited by 145 | Viewed by 13378
Abstract
Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. [...] Read more.
Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to obtain NDVI data at the Landsat spatial resolution. Next, phenological features, including the beginning and ending dates of the growing season, the length of the growing season, seasonal amplitude, and the maximum fitted NDVI value, were extracted from the fused time series NDVI data using the TIMESAT tool. The extracted data were integrated with the spectral data of the Landsat data to improve classification accuracy using a maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. The results indicated that phenological features had a statistically significant effect on improving the land cover classification accuracy of single Landsat data (an approximately 3% increase in overall classification accuracy), especially for vegetation type discrimination. However, the phenological features did not improve on statistical measures including the maximum, the minimum, the mean, and the standard deviation values of the time series NDVI dataset, especially for human-managed vegetation types. Regarding different classifiers, SVM could achieve better classification accuracy than the traditional MLC classifier, but the improvement in accuracy obtained using advanced classifiers was inferior to that achieved by involving the temporally derived features for land cover classification. Full article
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<p>The location of the study area and the Landsat OLI data acquired on 12 May 2013 (presented in false color image: R = NIR, G = red, B = green).</p> Full article ">Figure 2
<p>The flowchart of land cover classification of Landsat data with phenological features extracted from time series MODIS NDVI data.</p> Full article ">Figure 3
<p>Land cover classification results of MLC and SVM classifiers with different configurations.</p> Full article ">
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Article
Evaluation of MODIS LST Products Using Longwave Radiation Ground Measurements in the Northern Arid Region of China
by Wenping Yu, Mingguo Ma, Xufeng Wang, Liying Geng, Junlei Tan and Jinan Shi
Remote Sens. 2014, 6(11), 11494-11517; https://doi.org/10.3390/rs61111494 - 19 Nov 2014
Cited by 31 | Viewed by 6767
Abstract
This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. [...] Read more.
This study presents preliminary results of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST products (MOD/MYD11A1, Version 5) using longwave radiation ground measurements obtained at 12 stations in the North Arid and Semi-Arid Area Cooperative Experimental Observation Integrated Research program. In this evaluation process, the broadband emissivity at each station was obtained from the ASTER Spectral Library or estimated from the MODIS narrowband emissivity Collection 5. A comparison of the validation results based on those two methods shows that no significant differences occur in the short-term validation, and a sensitivity analysis of the broadband emissivity demonstrates that it has a limited effect on the evaluation results. In general, the results at the 12 stations indicate that the LST products have a lower accuracy in China’s arid and semi-arid areas than in other areas, with a mean absolute error of 2–3 K. Compared with the temporal mismatch, the spatial mismatch has a stronger effect on the validation results in this study, and the stations with homogeneous land cover have more comparable MODIS LST accuracies. Comparisons between the stations indicate that the spatial mismatch can increase the influence of the temporal mismatch. Full article
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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<p>Study area and locations of the validation stations.</p> Full article ">Figure 2
<p>The flowchart of the study. “MODIS NB_Emissivity” is for the MODIS narrowband emissivity products retrieved by the day/night algorithm. “GB_LW Data” is the ground-based longwave radiation data. “ASTER SL Data” is the ASTER spectral library data. “NB_AW Data” is the ground-based LST obtained from “GB_LW Data” and the broadband emissivity retrieved based on “MODIS NB_Emissivity”. “ASTER_AW Data” is the ground-based LST retrieved from “GB_LW Data” and broadband emissivity from the “ASTER SL Data”. “BE_MODIS Results” is a comparison of results between the “NB_AW Data” and “MODIS LST MOD/MYD11A1”. “ASTER_MODIS Results” is a comparison of results between the “ASTER_AW Data” and “MODIS LST MOD/MYD11A1”.</p> Full article ">Figure 3
<p>Plots of the ground-measured LSTs <span class="html-italic">vs.</span> MODIS LSTs at the 12 sites. The black diamonds (ASTER_MODIS(C)) indicate the evaluation results for the ground-station LSTs based on the broadband emissivity data from the ASTER Spectral Library compared with the MODIS LSTs. The pink circles (BE_MODIS(C)) indicate the evaluation results for the ground station LSTs based on the broadband emissivity data from the MODIS narrowband emissivity products compared with the MODIS LSTs. MS_LST (<span class="html-italic">x</span>-axis) is the LST obtained from a station’s measured longwave radiation. The linear fit of ASTER_MODIS corresponds to the ASTER_MODIS(C) plots, and the linear fit of BE_MODIS(C) corresponds to BE_MODIS(C). (<b>a</b>) The plot of AR station; (<b>b</b>) the plot of DS station; (<b>c</b>) the plot of HZZ station; (<b>d</b>) the plot of JZ station; (<b>e</b>) the plot of MQ station; (<b>f</b>) the plot of MY station; (<b>g</b>) the plot of NM station; (<b>h</b>) the plot of SPT station; (<b>i</b>) the plot of TYF station; (<b>j</b>) the plot of YK station; (<b>k</b>) the plot of TYG station; (<b>l</b>) the plot of YZ station.</p> Full article ">Figure 3 Cont.
<p>Plots of the ground-measured LSTs <span class="html-italic">vs.</span> MODIS LSTs at the 12 sites. The black diamonds (ASTER_MODIS(C)) indicate the evaluation results for the ground-station LSTs based on the broadband emissivity data from the ASTER Spectral Library compared with the MODIS LSTs. The pink circles (BE_MODIS(C)) indicate the evaluation results for the ground station LSTs based on the broadband emissivity data from the MODIS narrowband emissivity products compared with the MODIS LSTs. MS_LST (<span class="html-italic">x</span>-axis) is the LST obtained from a station’s measured longwave radiation. The linear fit of ASTER_MODIS corresponds to the ASTER_MODIS(C) plots, and the linear fit of BE_MODIS(C) corresponds to BE_MODIS(C). (<b>a</b>) The plot of AR station; (<b>b</b>) the plot of DS station; (<b>c</b>) the plot of HZZ station; (<b>d</b>) the plot of JZ station; (<b>e</b>) the plot of MQ station; (<b>f</b>) the plot of MY station; (<b>g</b>) the plot of NM station; (<b>h</b>) the plot of SPT station; (<b>i</b>) the plot of TYF station; (<b>j</b>) the plot of YK station; (<b>k</b>) the plot of TYG station; (<b>l</b>) the plot of YZ station.</p> Full article ">Figure 3 Cont.
<p>Plots of the ground-measured LSTs <span class="html-italic">vs.</span> MODIS LSTs at the 12 sites. The black diamonds (ASTER_MODIS(C)) indicate the evaluation results for the ground-station LSTs based on the broadband emissivity data from the ASTER Spectral Library compared with the MODIS LSTs. The pink circles (BE_MODIS(C)) indicate the evaluation results for the ground station LSTs based on the broadband emissivity data from the MODIS narrowband emissivity products compared with the MODIS LSTs. MS_LST (<span class="html-italic">x</span>-axis) is the LST obtained from a station’s measured longwave radiation. The linear fit of ASTER_MODIS corresponds to the ASTER_MODIS(C) plots, and the linear fit of BE_MODIS(C) corresponds to BE_MODIS(C). (<b>a</b>) The plot of AR station; (<b>b</b>) the plot of DS station; (<b>c</b>) the plot of HZZ station; (<b>d</b>) the plot of JZ station; (<b>e</b>) the plot of MQ station; (<b>f</b>) the plot of MY station; (<b>g</b>) the plot of NM station; (<b>h</b>) the plot of SPT station; (<b>i</b>) the plot of TYF station; (<b>j</b>) the plot of YK station; (<b>k</b>) the plot of TYG station; (<b>l</b>) the plot of YZ station.</p> Full article ">Figure 4
<p>TM land surface temperature image of a 2 × 2-km region centered on each site. (<b>a</b>) AR station; (<b>b</b>) DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>)YZ station.</p> Full article ">Figure 4 Cont.
<p>TM land surface temperature image of a 2 × 2-km region centered on each site. (<b>a</b>) AR station; (<b>b</b>) DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>)YZ station.</p> Full article ">Figure 4 Cont.
<p>TM land surface temperature image of a 2 × 2-km region centered on each site. (<b>a</b>) AR station; (<b>b</b>) DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>)YZ station.</p> Full article ">Figure 5
<p>Variograms of all stations. (<b>a</b>)AR station; (<b>b</b>)DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>)YZ station.</p> Full article ">Figure 5 Cont.
<p>Variograms of all stations. (<b>a</b>)AR station; (<b>b</b>)DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>)YZ station.</p> Full article ">Figure 6
<p>Results of the sensitivity analyses of the broadband emissivities for each station. The <span class="html-italic">y</span>-axis indicates the <span class="html-italic">ΔT<sub>S</sub></span> in Equation (8). The x-axis indicates the sequence numbers of longwave radiation data used to evaluate the MODIS LSTs during the daytime or nighttime. (<b>a</b>) AR station; (<b>b</b>) DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>) YZ station.</p> Full article ">Figure 6 Cont.
<p>Results of the sensitivity analyses of the broadband emissivities for each station. The <span class="html-italic">y</span>-axis indicates the <span class="html-italic">ΔT<sub>S</sub></span> in Equation (8). The x-axis indicates the sequence numbers of longwave radiation data used to evaluate the MODIS LSTs during the daytime or nighttime. (<b>a</b>) AR station; (<b>b</b>) DS station; (<b>c</b>) HZZ station; (<b>d</b>) JZ station; (<b>e</b>) MQ station; (<b>f</b>) MY station; (<b>g</b>) NM station; (<b>h</b>) SPT station; (<b>i</b>) TYF station; (<b>j</b>) YK station; (<b>k</b>) TYG station; (<b>l</b>) YZ station.</p> Full article ">
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Article
TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 2: Line Segments Linking and Merging
by Mehdi Rahnama and Richard Gloaguen
Remote Sens. 2014, 6(11), 11468-11493; https://doi.org/10.3390/rs61111468 - 18 Nov 2014
Cited by 37 | Viewed by 11645
Abstract
Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and [...] Read more.
Extraction and interpretation of tectonic lineaments is one of the routines for mapping large areas using remote sensing data. However, this is a subjective and time-consuming process. It is difficult to choose an optimal lineament extraction method in order to reduce subjectivity and obtain vectors similar to what an analyst would manually extract. The objective of this study is the implementation, evaluation and comparison of Hough transform, segment merging and polynomial fitting methods towards automated tectonic lineament mapping. For this purpose we developed a new MATLAB-based toolbox (TecLines). The proposed toolbox capabilities were validated using a synthetic Digital Elevation Model (DEM) and tested along in the Andarab fault zone (Afghanistan) where specific fault structures are known. In this study, we used filters in both frequency and spatial domains and the tensor voting framework to produce binary edge maps. We used the Hough transform to extract linear image discontinuities. We used B-spline as a polynomial curve fitting method to eliminate artificial line segments that are out of interest and to link discontinuous segments with similar trends. We performed statistical analyses in order to compare the final image discontinuities maps with existing references map. Full article
(This article belongs to the Special Issue Remote Sensing in Geomorphology)
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<p>The synthetic Digital Elevation Model (DEM) that is the result of landscape evolution algorithm created using set river incision and different uplift rates across tectonic faults. The drainage system adapts to the evolving surface conditions.</p> Full article ">Figure 2
<p>(<b>A</b>) location of the study area in northeast Afghanistan; (<b>B</b>) panchromatic band of the Quickbird-2 (1 m spatial resolution) for 2 March 2006 of the study area.</p> Full article ">Figure 3
<p>Overview of the essential components of linear image discontinuities extraction and grouping using TecLines.</p> Full article ">Figure 4
<p>Lines through a point in the image space and (<span class="html-italic">m</span>, <span class="html-italic">c</span>) space.</p> Full article ">Figure 5
<p>Simplified flow chart for Hough transform procedure.</p> Full article ">Figure 6
<p>(<b>A</b>) Merging of two non-overlapping, <b>(B)</b> partially overlapping and <b>(C</b>) totally overlapping segments by Tavares-Padilha method. The red segments are merged to the green lines.</p> Full article ">Figure 7
<p>Approximation of line segments into a smooth curve using B-spline.</p> Full article ">Figure 8
<p>(<b>A</b>) The reference discontinuity map for real dataset that is based on manual extraction from panchromatic band of QuickBird-2; (<b>B</b>) The reference map of the synthetic DEM consists in the digitized traces of the modeled discontinuities (black line).</p> Full article ">Figure 9
<p>(<b>A</b>–<b>C</b>) the line segments extracted by Hough transform, Tavares-Padilha algorithm, and final resulting lineament map was obtained by B-spline method, respectively.</p> Full article ">Figure 10
<p>(<b>A</b>–<b>C</b>)<b>:</b> Rose diagrams for discontinuities extracted by Hough transform, Tavares-Padilha algorithm and B-spline method, respectively. (<b>D</b>): Rose diagram for reference lineament map.</p> Full article ">Figure 11
<p>(<b>A</b>–<b>C</b>) The binary edge datasets that are produced by Sobel, LOG and Canny edge detection methods and tensor voting, respectively. (<b>D</b>–<b>F</b>): Hough domains from Sobel, LOG and Canny binary edge maps, respectively. Points on (D–F) images show peak values in matrix H.</p> Full article ">Figure 12
<p>(<b>A</b>–<b>C</b>): Extracted line segments using HT for binary edge data sources from Sobel, LOG method and Canny methods, respectively; (<b>D</b>–<b>F</b>): Intermediate discontinuity map after applying Tavares-Padilha algorithm; (<b>G</b>–<b>I</b>): Final lineament maps from polynomial interpolation using B-spline method for Sobel, LOG and Canny edge data sources, respectively. (<b>J</b>): Extracted discontinuities using PCI.</p> Full article ">Figure 12 Cont.
<p>(<b>A</b>–<b>C</b>): Extracted line segments using HT for binary edge data sources from Sobel, LOG method and Canny methods, respectively; (<b>D</b>–<b>F</b>): Intermediate discontinuity map after applying Tavares-Padilha algorithm; (<b>G</b>–<b>I</b>): Final lineament maps from polynomial interpolation using B-spline method for Sobel, LOG and Canny edge data sources, respectively. (<b>J</b>): Extracted discontinuities using PCI.</p> Full article ">Figure 13
<p>(<b>A</b>–<b>C</b>): Rose diagram for extracted line segments by Hough transform from binary edge maps produced by Sobel, LOG and Canny methods, respectively; (<b>D</b>–<b>F</b>): Rose diagrams for intermediate discontinuities map extracted using Tavares-Padilha algorithm from three data sources (Sobel, LOG and Canny); (<b>G</b>–<b>I</b>): Rose diagrams for final discontinuities map extracted using B-spline method from three data sources (Sobel, LOG and Canny). (<b>J</b>) and (<b>K</b>): Rose diagrams for manually and automatically (PCI) extracted discontinuities, respectively.</p> Full article ">Figure 13 Cont.
<p>(<b>A</b>–<b>C</b>): Rose diagram for extracted line segments by Hough transform from binary edge maps produced by Sobel, LOG and Canny methods, respectively; (<b>D</b>–<b>F</b>): Rose diagrams for intermediate discontinuities map extracted using Tavares-Padilha algorithm from three data sources (Sobel, LOG and Canny); (<b>G</b>–<b>I</b>): Rose diagrams for final discontinuities map extracted using B-spline method from three data sources (Sobel, LOG and Canny). (<b>J</b>) and (<b>K</b>): Rose diagrams for manually and automatically (PCI) extracted discontinuities, respectively.</p> Full article ">Figure 14
<p>(<b>A</b>–<b>C</b>): Frequency of extracted discontinuities length by polynomial interpolation method from Sobel, LOG and Canny data sources, respectively; (<b>D-F1)</b>: Frequency of length for automatically (PCI) extracted discontinuities; (<b>D-F2</b>): Enlarged image of (<b>D</b>-<b>F1</b>). (<b>E-G1</b>): Frequency of length for manually extracted discontinuities; (<b>E-G2</b>): Enlarge the image of (E-G1).</p> Full article ">Figure 15
<p>Superimposition of discontinuities extrapolated from Canny data sources (black lines) and the reference discontinuities, which are manually extracted (green lines), and automatically lineaments extracted by PCI Geomatica software (violet lines).</p> Full article ">Figure 16
<p>Comparison of run time between TecLines and PCI steps on the same datasets.</p> Full article ">
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Article
An Effective Method for Detecting Potential Woodland Vernal Pools Using High-Resolution LiDAR Data and Aerial Imagery
by Qiusheng Wu, Charles Lane and Hongxing Liu
Remote Sens. 2014, 6(11), 11444-11467; https://doi.org/10.3390/rs61111444 - 17 Nov 2014
Cited by 47 | Viewed by 13254
Abstract
Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and [...] Read more.
Effective conservation of woodland vernal pools—important components of regional amphibian diversity and ecosystem services—depends on locating and mapping these pools accurately. Current methods for identifying potential vernal pools are primarily based on visual interpretation and digitization of aerial photographs, with variable accuracy and low repeatability. In this paper, we present an effective and efficient method for detecting and mapping potential vernal pools using stochastic depression analysis with additional geospatial analysis. Our method was designed to take advantage of high-resolution light detection and ranging (LiDAR) data, which are becoming increasingly available, though not yet frequently employed in vernal pool studies. We successfully detected more than 2000 potential vernal pools in a ~150 km2 study area in eastern Massachusetts. The accuracy assessment in our study indicated that the commission rates ranged from 2.5% to 6.0%, while the proxy omission rate was 8.2%, rates that are much lower than reported errors of previous vernal pool studies conducted in the northeastern United States. One significant advantage of our semi-automated approach for vernal pool identification is that it may reduce inconsistencies and alleviate repeatability concerns associated with manual photointerpretation methods. Another strength of our strategy is that, in addition to detecting the point-based vernal pool locations for the inventory, the boundaries of vernal pools can be extracted as polygon features to characterize their geometric properties, which are not available in the current statewide vernal pool databases in Massachusetts. Full article
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
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<p>The number of vernal pools certified by the Natural Heritage and Endangered Species Program (NHESP) in Massachusetts.</p> Full article ">Figure 2
<p>The combined vernal pool (CVP + PVP) density map in towns of Massachusetts. CVPs—certified vernal pools; PVPs—potential vernal pools; NHESP—Natural Heritage and Endangered Species Program.</p> Full article ">Figure 3
<p>Location of the study area—towns of Attleboro and Norton, in Bristol County, Massachusetts. The imagery is a false-color composite image mosaic from U.S. Geological Survey color orthoimagery acquired in April 2013.</p> Full article ">Figure 4
<p>The distribution of Natural Heritage and Endangered Species Program (NHESP) certified/potential vernal pool locations across the study area.</p> Full article ">Figure 5
<p>The bare-earth LiDAR DEM shaded relief of Attleboro and Norton, Massachusetts.</p> Full article ">Figure 6
<p>Probability distribution of LiDAR data error.</p> Full article ">Figure 7
<p>The number of detected depression pixels with corresponding number of iterations.</p> Full article ">Figure 8
<p>The histograms of averaged NDVI (<b>a</b>) and NDWI (<b>b</b>) for 8733 depressions.</p> Full article ">Figure 9
<p>Examples of different certified vernal pools (CVPs) and potential vernal pools (PVPs) overlaid on color-infrared aerial photographs (2013). The green circular dots represent CVPs and the green triangles represent PVPs. The polygons with blue outlines represent our detected depressions.</p> Full article ">Figure 10
<p>Examples of different depression classes, as described in <a href="#remotesensing-06-11444-t005" class="html-table">Table 5</a>, overlaid on color-infrared aerial photographs (2013). The green circular dots represent certified vernal pools (CVPs), and the green triangles represent potential vernal pools (PVPs). The polygons with blue outlines represent detected depressions.</p> Full article ">
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Article
Use of Satellite SAR for Understanding Long-Term Human Occupation Dynamics in the Monsoonal Semi-Arid Plains of North Gujarat, India
by Francesc C. Conesa, Núria Devanthéry, Andrea L. Balbo, Marco Madella and Oriol Monserrat
Remote Sens. 2014, 6(11), 11420-11443; https://doi.org/10.3390/rs61111420 - 14 Nov 2014
Cited by 21 | Viewed by 10782
Abstract
This work explores the spatial distribution of monsoonal flooded areas using ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR) in the semi-arid region of N. Gujarat, India. The amplitude component of SAR Single Look Complex (SLC) images has been used to estimate the extent [...] Read more.
This work explores the spatial distribution of monsoonal flooded areas using ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR) in the semi-arid region of N. Gujarat, India. The amplitude component of SAR Single Look Complex (SLC) images has been used to estimate the extent of surface and near-surface water dynamics using the mean amplitude (MA) of monsoonal (July to September) and post-monsoonal (October to January) seasons. The integration of SAR-derived maps (seasonal flooding maps and seasonal MA change) with archaeological data has provided new insights to understand present-day landscape dynamics affecting archaeological preservation and visibility. Furthermore, preliminary results suggest a good correlation between Mid-Holocene settlement patterns and the distribution and extension of seasonal floodable areas within river basin areas, opening interesting inroads to study settlement distribution and resource availability in past socio-ecological systems in semi-arid areas. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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<p>Location map of the NoGAP study area (thick dotted red line). Alluvial flood-prone areas are indicated as medium grey shading (after Attri and Tyagi [<a href="#B28-remotesensing-06-11420" class="html-bibr">28</a>], p. 104). Isohyet lines and climatic zones are from Juyal <span class="html-italic">et al.</span> [<a href="#B25-remotesensing-06-11420" class="html-bibr">25</a>] (p. 2633), and the maximum fossil extent of Thar Desert is after Singhvi <span class="html-italic">et al.</span> [<a href="#B29-remotesensing-06-11420" class="html-bibr">29</a>] (p. 3097).</p> Full article ">Figure 2
<p>(<b>a</b>) Regional slope (SRTM 90 v4.1, <a href="http://srtm.csi.cgiar.org" target="_blank">http://srtm.csi.cgiar.org</a>); (<b>b</b>) Archaeological scatters within NoGAP research area (red dotted line) and extent of ENVISAT ASAR scenes in ascending mode, Track 127, Swath I6 (base map: ESRI World Imagery); (<b>c</b>) Extent of Track 127 displayed as LANDSAT 8 OLI false colour composite (5-4-3, 3 April 2014); (<b>d</b>) Fossilized dune surface (Kalrio Timbo archaeological site) during post-monsoon (November 2011; (<b>e</b>) Interdune black cotton soils waterlogged during post-monsoon (November 2010); (<b>f</b>) West Banas riverbed (November 2011).</p> Full article ">Figure 3
<p>Workflow employed for the ENVISAT ASAR image processing. After co-registration and equalization, a (<b>1</b>) multi-look (MLI) processing was applied to each amplitude scene. (<b>2</b>) Images were rotated, transposed, classified upon acquisition time and integrated in two seasonal stacks (MA seasonal images). (<b>3</b>) A water threshold was applied to obtain (<a href="#sec3dot1-remotesensing-06-11420" class="html-sec">Section 3.1</a>) seasonal flooding maps. Besides. (<b>4</b>) MA images were divided to obtain the seasonal MA change map. (<b>5</b>) Final products were georeferenced and integrated into GIS platforms.</p> Full article ">Figure 4
<p>(<b>a</b>) Monsoonal mean amplitude (MA) image generated from two monsoonal scenes; (<b>b</b>) Post-monsoonal MA image generated from three monsoonal scenes; (<b>c</b>) Detail of radar speckle in ASAR scene (13 September 2006, after MLI processing); (<b>d</b>) MA radar speckle reduction (monsoonal MA, after temporal filter).</p> Full article ">Figure 5
<p>Extraction of water threshold values in equalized MA seasonal images from clearly identified water tanks: (<b>a</b>) Zinzuwada village; (<b>b</b>) Kuvarad village; (<b>c</b>) Mujpur village. Image composition shows, in descending order: MA monsoonal image (horizontal blue line); MA post-monsoonal image (horizontal red line); and MA spatial profile (spatially indicated by the horizontal blue and red lines) for monsoonal and post-monsoonal seasons. The water threshold at 0.25 MA value is indicated as a grey dashed line.</p> Full article ">Figure 6
<p>(<b>a</b>) Monsoonal flooding map (enhanced in blue) superimposed to monsoonal MA and (<b>b</b>) post-monsoonal flooding map (enhanced in red) superimposed to post-monsoonal MA; (<b>c</b>) Integrated seasonal flooding map. Pixels flooded on both seasons are represented in green. Dotted areas indicate the subsets of interest displayed in <a href="#remotesensing-06-11420-f007" class="html-fig">Figure 7</a> (KR), <a href="#remotesensing-06-11420-f008" class="html-fig">Figure 8</a> (DI) and <a href="#remotesensing-06-11420-f009" class="html-fig">Figure 9</a>c (DS).</p> Full article ">Figure 7
<p>Khari River (KR) subset of interest showing the flooding maps for (<b>a</b>) MA monsoonal (blue pixels) and (<b>b</b>) MA post-monsoonal images (red pixels). Note the post-monsoonal low amplitude (dark pixels) in the field banks located within the Khari River basin area (yellow dotted line).</p> Full article ">Figure 8
<p>(<b>a</b>) Dune-interdune (DI) subset of interest in orthorectified ASTER false colour composite (3N-2-1) from 1 October 2004; (<b>b</b>) Integrated seasonal flooding map (blue for monsoon, red for post-monsoon and green for no change); (<b>c</b>) Seasonal MA change values (stretched histogram).</p> Full article ">Figure 9
<p>(<b>a</b>) Georeferenced seasonal flooding map (blue for monsoon, red for post-monsoon and green for no change) and regional distribution of archaeological sites; (<b>b)</b> Integrated seasonal flooding map superimposed to regional elevation (SRTM 90 v4.1, <a href="http://srtm.csi.cgiar.org" target="_blank">http://srtm.csi.cgiar.org</a>); (<b>c</b>) Integrated seasonal flooding map superimposed to the MA image for the Dune fields/Silt-b<span class="html-italic">e</span>lt (DS) subset of interest (dotted areas indicates the extent of <a href="#remotesensing-06-11420-f010" class="html-fig">Figure 10</a>a,b).</p> Full article ">Figure 10
<p>Subset detail of two archaeological areas within the Khari River basin area: (<b>a</b>) Loteshwar/Kalrio Timbo and (<b>b</b>) Vaharvo Timbo. Image composition shows, in descending order: orthorectified ASTER false colour composite (3N-2-1) from 1 October 2004; integrated seasonal flooding maps (blue for monsoon, red for post-monsoon and green for no change) superimposed to MA images; and seasonal MA change map (stretched histogram).</p> Full article ">
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Article
Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case Study for Spain
by Ana Pérez-Hoyos, Beatriz Martínez, Francisco Javier García-Haro, Álvaro Moreno and María Amparo Gilabert
Remote Sens. 2014, 6(11), 11391-11419; https://doi.org/10.3390/rs61111391 - 14 Nov 2014
Cited by 26 | Viewed by 11916
Abstract
Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a [...] Read more.
Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a major identification of Ecosystem Functional Types (EFTs) in Spain to characterize the patterns of ecosystem functional diversity and status, from several functional attributes as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Albedo. For this purpose, several metrics, related to the spatial variability in seasonal and annual patterns (e.g., relative range), have been derived from remote sensing time series of 1 km MODIS over the period 2000–2009. Moreover, precipitation maps from data provided by the AEMet (Agencia Estatal de Meteorología) and the corresponding aridity and humidity indices were also included in the analysis. To create the EFTs, the potential of the joint use of Kohonen’s Self-Organizing Map (SOM) and the k-means clustering algorithm was tested. The EFTs were analyzed using different remote sensing (i.e., Gross Primary Production) and climatic variables. The relationship of the EFTs with existing land cover datasets and climatic data were analyzed through a correspondence analysis (CA). The trained SOM have shown feasible in providing a comprehensive view on the functional attributes patterns and a remarkable potential for the quantification of ecosystem function. The results highlight the potential of this technique to delineate ecosystem functional types as well as to monitor the spatial pattern of the ecosystem status as a reference for changes due to human or climate impacts. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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<p>Study area with the localization of the regions, the Basin Rivers and the mountains.</p> Full article ">Figure 2
<p>Flowchart of the methodology used to derive the ecosystem functional types (EFTs).</p> Full article ">Figure 3
<p>Schematic layout of a self-organizing map adapted from [<a href="#B74-remotesensing-06-11391" class="html-bibr">74</a>].</p> Full article ">Figure 4
<p>Example of several functional variables (Date of maximum Normalized Difference Vegetation Index (DNDVI), Normalized Difference Vegetation Index (NDVI), Mean Land Surface Temperature (MeanLST), IAlbedo, Aridity Index).</p> Full article ">Figure 5
<p>Visualization of the two-plane components of the Self-Organizing Map (SOM) trained from time series of NDVI, Albedo, LST and climatic data of Spain.</p> Full article ">Figure 6
<p>Visualization of the two-plane components of the SOM trained from time series of NDVI, Albedo, LST and climatic data in Spain used as input to derive the EFTS.</p> Full article ">Figure 7
<p>(<b>Left-Upper</b>) Clusters obtained with the modified <span class="html-italic">k-means</span>, (<b>Right-Upper</b>) validity index values for Davies-Bouldin as a function of the number of clusters ranging from 2 to 36 and (<b>Down</b>) similarity among the dendrogram resulting from hierarchical agglomerative cluster analysis.</p> Full article ">Figure 8
<p>Distribution of ecosystem functional types (EFTs) in Spain.</p> Full article ">Figure 9
<p>Mean seasonal profile of the Gross Primary Production (GPP) and the rate of Evapotranspiration (ETP) for the ecosystem functional types (EFTs).</p> Full article ">Figure 10
<p>Correspondence analysis between Ecosystem Functional Types (EFTs) and (<b>Left</b>) ecoregions (SISW: Southeastern Iberian Shrub and Woodlands, SISM: Southwest Iberian Mediterranean Sclerophyllous and Mixed Forest, ISSF: Iberian Sclerophyllous and Semi-deciduous Forest, CMF: Cantabrian Mixed Forest, PCM: Pyrenees Conifer and Mixed Forests, NIMF: Northwest Iberian Montane Forest, ICF: Iberian Conifer Forest, NSMF: Norhteastern Spain and Southern France Mediterranean Forest) and (<b>Right</b>) hybrid land cover (CUL: Cultivated and Managed areas, IRR: Irrigated Areas, MOS: Mosaic of cropland/natural, NEED: Needleleaved Forest, BROAD: Broadleaved Forest, MIX: Mixed Forest, SHR: Shrubland, HER: Herbaceous, SPA: Sparse Vegetation and BAR: Bare areas).</p> Full article ">Figure 11
<p>Box-plots of the three ecosystem function variables (LST, Albedo and NDVI) for the twelve EFTs.</p> Full article ">Figure 11 Cont.
<p>Box-plots of the three ecosystem function variables (LST, Albedo and NDVI) for the twelve EFTs.</p> Full article ">Figure 12
<p>Distribution of the 36 EFTs in Spain.</p> Full article ">
2858 KiB  
Article
Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA
by Xiaoxiao Li and Guofan Shao
Remote Sens. 2014, 6(11), 11372-11390; https://doi.org/10.3390/rs61111372 - 14 Nov 2014
Cited by 84 | Viewed by 12356
Abstract
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods [...] Read more.
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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<p>A visual comparison of the subset of a digital aerial photograph which was taken in 2006, and NLCD 2006 in an area of 6.0 × 7.5 km<sup>2</sup>.</p> Full article ">Figure 2
<p>The study area is the Tippecanoe County that locates in the northwest quadrant of the U.S. state of Indiana.</p> Full article ">Figure 3
<p>The image spectral response profiles showed that mixtures of spectral characteristics exited in the class of buildings and roads, and between different vegetation types in the original aerial photography bands and principal component analysis (PCA) bands. Lines in the figures with different colors represent different land-cover types. (<b>a</b>) The mixture in the spectral response of building and road objects, and the mixture in the spectral response of trees, crop and grass objects of 4-band aerial photos. (<b>b</b>) The mixture in the spectral response of trees, crop and grass objects of PCA bands.</p> Full article ">Figure 4
<p>The quadtree-based (QT) segmentation partitions an image into objects of trees (small squares) and crops (large squares). (<b>a</b>) Image of PCA2 band within a 800 × 1000 pixels area. (<b>b</b>) After the QT segmentation, most of the large squres represent the crops, and most of the small squares represent the trees. The blue squares are the non-vegetation image objects with assigned classes, whose borders were inherated from the upper level of Multi-threshold (MT) segmentation results.</p> Full article ">Figure 5
<p>A binary diagram of the object-based rule used in the classification.</p> Full article ">Figure 6
<p>The building and road image objects were effectively refined and merged after using multi-resolution image region grow algorithm (MRSRG). (<b>a</b>) The building and road image objects after quadtree-based segmentation. (<b>b</b>) Image objects after the first time of MRSRG with parameters: scale = 10,000, shape = 0.1, compactness = 0.5. (<b>c</b>) Image objects after the second time of MRSRG with parameters: scale = 250, shape = 0.3, compactness = 0.5. (<b>d</b>) Image objects after the third time of MRSRG with parameters: scale = 50, shape = 0.3, compactness = 0.8.</p> Full article ">Figure 7
<p>A comparison between pixel-based and object-based classification results. The <b>first</b> <b>row</b> shows the image of original DOQQ with RGB band combination display. The <b>second</b> <b>row</b> of images is the pixel-based classification results. The <b>third</b> <b>row</b> of image is the object-based classification results. Each column shows the same location on the map.</p> Full article ">
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Article
Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard
by Marcos Carrasco-Benavides, Samuel Ortega-Farías, Luis Octavio Lagos, Jan Kleissl, Luis Morales-Salinas and Ayse Kilic
Remote Sens. 2014, 6(11), 11342-11371; https://doi.org/10.3390/rs61111342 - 14 Nov 2014
Cited by 45 | Viewed by 9176
Abstract
A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi [...] Read more.
A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (αi), net radiation (Rni) and soil heat flux (Gi), sensible (Hi) and latent heat (LEi) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure αi, Rni and Gi. In the same tower an Eddy Covariance (EC) system was mounted to measure Hi and LEi. Specific sub-models to estimate Gi, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (zom) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, zom and Gi were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated αi in 21% and Rni in 11%. Also, METRIC using the calibrated functions overestimated Hi and LEi with errors of 16% and 17%, respectively while it using the original functions overestimated Hi and LEi with errors of 13% and 15%, respectively. Finally, LEi was estimated with root mean square error (RMSE) between 43 and 60 W∙m−2 and mean absolute error (MAE) between 35 and 48 W∙m−2 for both calibrated and original functions, respectively. These results suggested that biases observed for instantaneous pixel-by-pixel values of Rni, Gi and other intermediate components of the algorithm were presumably absorbed into the computation of sensible heat flux as a result of the internal self-calibration of METRIC. Full article
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<p>True color cropped image from Landsat 5 (TM) of the area of study (AOS) (image taken in 25 January 2008) (Universal Transverse Mercator-UTM 19, WGS 84, South). Red square indicates the vineyard allocation.</p> Full article ">Figure 2
<p>Site detail showing: (<b>a</b>) vine varieties distribution scheme; (<b>b</b>) image that shows the experimental plot (vineyard block N°22A indicated as a segmented square). White dot inside the experimental plot indicates the Eddy Covariance system position; (<b>c</b>,<b>d</b>) show the analytical footprint model for all evaluated days under unstable conditions in terms of relative and cumulative contribution, respectively; (<b>e</b>) and (<b>f</b>) depict the wind-rose for unstable conditions at the peak (Xpeak) of the footprint function and during the time of satellite overpasses, respectively.</p> Full article ">Figure 3
<p>Hourly variation of albedo (α), Bowen ratio (β), net radiation (<span class="html-italic">R<sub>n</sub></span>), soil heat flux (<span class="html-italic">G</span>), sensible heat flux (<span class="html-italic">H</span><sub>β</sub>) and latent heat flux (<span class="html-italic">LE</span><sub>β</sub>) measured above a drip-irrigated vineyard for the days when satellite scenes were available. Dotted rectangles denote the satellite overpass hour (about 1124 h). Subscript “β” denotes that the fluxes were corrected using the Bowen-ratio approach.</p> Full article ">Figure 4
<p>Comparisons at the time of satellite overpass between observed (axis X) and estimated (axis Y) values of net radiation (<span class="html-italic">R<sub>n</sub></span>), soil heat flux (<span class="html-italic">G</span>), sensible heat flux (<span class="html-italic">H</span>), and latent heat flux (<span class="html-italic">LE</span>) for a drip irrigated Merlot vineyard. Estimated values were obtained using METRIC with the calibrated functions. Subscript “<span class="html-italic">i</span>” denotes instantaneous values.</p> Full article ">Figure 5
<p>Comparisons between the measured (black bars) and modeled values using METRIC with the original (grey bars) and calibrated (white bars) functions to estimate (<b>a</b>) net radiation (<span class="html-italic">R<sub>ni</sub></span>); (<b>b</b>) soil heat flux (<span class="html-italic">G<sub>i</sub></span>); (<b>c</b>) sensible heat flux (<span class="html-italic">H<sub>i</sub></span>); and (<b>d</b>) latent heat flux (<span class="html-italic">LE<sub>i</sub></span>) at the time of satellite overpass. Modeled values correspond to average from pixels inside the experimental plot. DOY is the day of the year. Subscript “<span class="html-italic">i</span>” denotes instantaneous values.</p> Full article ">Figure 6
<p>Averaged spatial distribution of net radiation (<span class="html-italic">R<sub>n_M</sub></span>) (<b>a</b>–<b>e</b>); soil heat flux (<span class="html-italic">G<sub>_M</sub></span>) (<b>f</b>–<b>j</b>); sensible heat flux (<span class="html-italic">H<sub>_M</sub></span>) (Figure <b>k</b>–<b>o</b>); and latent heat flux (<span class="html-italic">LE<sub>_M</sub></span>) (<b>p</b>–<b>t</b>) over a drip-irrigated vineyard computed by the calibrated METRIC model. Column headers indicate the phenological stages. Averages were done combining data from the 2006–2007, 2007–2008 and 2008–2009 growing seasons; A panchromatic scene (<b>u</b>) from Landsat 7 (ETM+; DOY 17, 2008) is included as reference. The red square indicates the experimental plot location.</p> Full article ">
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Article
An Operational System for Estimating Road Traffic Information from Aerial Images
by Jens Leitloff, Dominik Rosenbaum, Franz Kurz, Oliver Meynberg and Peter Reinartz
Remote Sens. 2014, 6(11), 11315-11341; https://doi.org/10.3390/rs61111315 - 13 Nov 2014
Cited by 104 | Viewed by 10815
Abstract
Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic [...] Read more.
Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks. Full article
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<p>Overview of the airborne component of the on-board sensor system.</p> Full article ">Figure 2
<p>Ground coverage of the 3K/3K+ sensor system in continuous across track mode (<b>lower left</b>), across track burst mode (<b>upper left</b>) and continuous along track mode (<b>right</b>).</p> Full article ">Figure 3
<p>Results of the boosted classifier. (<b>a</b>) Straightened image; (<b>b</b>) boosting results; (<b>c</b>) boosting results after threshold.</p> Full article ">Figure 4
<p>Clustering and final classification. (<b>a</b>) Zero crossings of the confidence image; (<b>b</b>) Lepetit points (red circles) and final detections by SVM (green crosses); (<b>c</b>) final detections on the original image.</p> Full article ">Figure 5
<p>Typical matching result of the vehicle tracking algorithm between the first (<b>left</b>) and second image (<b>right</b>) of a camera burst (example from the nadir camera, Cologne campaign on 17 September 2011).</p> Full article ">Figure 6
<p>Typical result of the traffic data extraction obtained online during the Cologne campaign 2011 (3K+ system). It shows traffic congestion at the “Heumar” three-leg interchange. Vehicle velocities are color coded.</p> Full article ">Figure 7
<p>(<b>Top</b>) Results of original vehicle detection on a complex bridge scene that resulted in a low quality of 48% (Cologne campaign 2011 with 3K+ system). (<b>Bottom</b>) Results of vehicle detection on the same scene after retraining the AdaBoost classifier.</p> Full article ">Figure 8
<p>Matching result of the vehicle tracking algorithm between the first (<b>left</b>) and second image (<b>right</b>) of a camera burst in the case of thin clouds (Cologne campaign 2011 with the 3K+ system).</p> Full article ">
24126 KiB  
Article
Daily Area of Snow Melt Onset on Arctic Sea Ice from Passive Microwave Satellite Observations 1979–2012
by Angela C. Bliss and Mark R. Anderson
Remote Sens. 2014, 6(11), 11283-11314; https://doi.org/10.3390/rs61111283 - 13 Nov 2014
Cited by 10 | Viewed by 7690
Abstract
Variability in snow melt onset (MO) on Arctic sea ice since 1979 is examined by determining the area of sea ice experiencing the onset of melting during the melt season on a daily basis. The daily MO area of the snow and ice [...] Read more.
Variability in snow melt onset (MO) on Arctic sea ice since 1979 is examined by determining the area of sea ice experiencing the onset of melting during the melt season on a daily basis. The daily MO area of the snow and ice surface is determined from passive microwave satellite-derived MO dates for the Arctic Ocean and sub-regions. Annual accumulations of MO area are determined by summing the time series of daily MO area through the melt season. Daily areas and annual accumulations of MO area highlight inter-annual and regional variability in the timing of MO area, which is sensitive to day-to-day variations in spring weather conditions. Two distinct spatial patterns in MO area accumulations including an intense, fast accumulating melt area pattern and a slow accumulating melt pattern are examined for two melting events in the Kara Sea. In comparing the 34 years of MO dates for the Arctic Ocean and sub-regions, melt accumulations have changed during the period. In the earlier years, 1979–1987, the MO generally was later in the year than the mean, while in more recent years, the MO accumulations have been occurring earlier in the melt season. The sub-regions of the Arctic Ocean also exhibit greater annual variability than the Arctic Ocean. Full article
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Full article ">Figure 1
<p>Map illustrating sea ice locations used in analysis. Colored pixels indicate locations where a melt onset date exists for all 34 years in the study period. White pixels represent fewer than 34 years of data within a region and were not included in the analysis. Different colors distinguish the boundaries between sub-regions within the Arctic Ocean.</p> Full article ">Figure 2
<p>Kara Sea 3 day running mean of daily melt onset (MO) area for (<b>a</b>) Scanning Multichannel Microwave Radiometer (SMMR) years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 3
<p>Progression of Kara Sea MO area for DOY 121–127 1992. MO locations for the current DOY and where MO has already occurred on a previous day are highlighted. Pixels outside of the Kara Sea region (see <a href="#remotesensing-06-11283-f001" class="html-fig">Figure 1</a>) are de-emphasized to indicate MO conditions adjacent to the Kara Sea.</p> Full article ">Figure 4
<p>Daily average (<b>a</b>–<b>c</b>) mean sea level pressures; (<b>d</b>–<b>f</b>) vector wind directions and magnitude; and (<b>g</b>–<b>i</b>) surface temperatures for DOY 124–126 1992. Boxes define the region surrounding the Kara Sea as shown in <a href="#remotesensing-06-11283-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Progression of Kara Sea MO area for DOY 130–164 1985. MO locations for the current DOY and where MO has already occurred on a previous day are highlighted. Pixels outside of the Kara Sea region (see <a href="#remotesensing-06-11283-f001" class="html-fig">Figure 1</a>) are de-emphasized to indicate MO conditions adjacent to the Kara Sea.</p> Full article ">Figure 6
<p>Composite mean atmospheric (<b>a</b>) mean sea level pressures; (<b>b</b>) sea level pressure anomalies; (<b>c</b>) surface temperatures; (<b>d</b>) surface temperature anomalies; and (<b>e</b>) vector wind directions and magnitude for DOY 130–164 1985. Daily (<b>f</b>) mean sea level pressures are shown for DOY 136 1985. Boxes define the region surrounding the Kara Sea as shown in <a href="#remotesensing-06-11283-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 7
<p>Arctic Ocean annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 8
<p>Barents Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 9
<p>Kara Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 10
<p>Laptev Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 11
<p>East Siberian Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 12
<p>Chukchi Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 13
<p>Beaufort Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 14
<p>Canadian Arctic Archipelago annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 15
<p>Central Arctic annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 16
<p>Baffin Bay annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">Figure 17
<p>Greenland Sea annual accumulated MO areas for (<b>a</b>) SMMR years; (<b>b</b>) early SSM/I years; and (<b>c</b>) late SSM/I and SSMIS years.</p> Full article ">
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Article
Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas
by Lingli Zhu and Juha Hyyppä
Remote Sens. 2014, 6(11), 11267-11282; https://doi.org/10.3390/rs61111267 - 13 Nov 2014
Cited by 94 | Viewed by 11336
Abstract
High-voltage power lines can be quite easily mapped using laser scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On [...] Read more.
High-voltage power lines can be quite easily mapped using laser scanning data, because vegetation close to high-voltage lines is typically removed and also because the power lines are located higher off the ground in contrast to regional networks and lower voltage networks. On the contrary, lower voltage power lines are located in the middle of dense forests, and it is difficult to classify power lines in such an environment. This paper proposes an automated power line detection method for forest environments. Our method was developed based on statistical analysis and 2D image-based processing technology. During the process of statistical analysis, a set of criteria (e.g., height criteria, density criteria and histogram thresholds) is applied for selecting the candidates for power lines. After transforming the candidates to a binary image, image-based processing technology is employed. Object geometric properties are considered as criteria for power line detection. This method was conducted in six sets of airborne laser scanning (ALS) data from different forest environments. By comparison with reference data, 93.26% of power line points were correctly classified. The advantages and disadvantages of the methods were analyzed and discussed. Full article
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
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<p>Examples of the test areas (with red marks) in Kirkkonummi.</p> Full article ">Figure 2
<p>Illustration of the study areas on an aerial image (from the Finnish National Land Survey). The numbers on the map indicate the IDs of the test fields.</p> Full article ">Figure 3
<p>Method for power line extraction. Red frame: steps in the statistical analysis; green frame: steps in the image-based processing.</p> Full article ">Figure 4
<p>The complete power line process in Area 4. (<b>a</b>) Airborne laser scanning (ALS) point cloud (3D); (<b>b</b>) power line candidate selection (3D); (<b>c</b>) binary image of (b) (2D); (<b>d</b>) after binary image filtering: power line image (2D); (<b>e</b>) extracted power lines (3D).</p> Full article ">Figure 4 Cont.
<p>The complete power line process in Area 4. (<b>a</b>) Airborne laser scanning (ALS) point cloud (3D); (<b>b</b>) power line candidate selection (3D); (<b>c</b>) binary image of (b) (2D); (<b>d</b>) after binary image filtering: power line image (2D); (<b>e</b>) extracted power lines (3D).</p> Full article ">Figure 5
<p>Power line extraction in Area 5. (<b>a</b>) ALS point cloud (3D); (<b>b</b>) power line candidates (3D); (<b>c</b>) binary image of (b) (2D); (<b>d</b>) after binary image filtering: power line image (2D); (<b>e</b>) extracted power lines (3D).</p> Full article ">Figure 6
<p>Power line extraction in Area 1. (<b>Left</b>) ALS point cloud; (<b>Right</b>) the result of power line extraction.</p> Full article ">Figure 7
<p>Power line extraction in Area 2. (<b>Left</b>) ALS point cloud; (<b>Right</b>) the result of power line extraction.</p> Full article ">Figure 8
<p>Power line extraction in Area 3. (<b>Left</b>) ALS point cloud; (<b>Right</b>) the result of power line extraction.</p> Full article ">Figure 9
<p>Power line extraction in Area 6. (<b>Left</b>) ALS point cloud; (<b>Right</b>) the result of power line extraction.</p> Full article ">Figure 10
<p>The relationship between the number of points in the ALS data and the run time.</p> Full article ">
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Article
Development of an Operational Calibration Methodology for the Landsat Thermal Data Archive and Initial Testing of the Atmospheric Compensation Component of a Land Surface Temperature (LST) Product from the Archive
by Monica Cook, John R. Schott, John Mandel and Nina Raqueno
Remote Sens. 2014, 6(11), 11244-11266; https://doi.org/10.3390/rs61111244 - 13 Nov 2014
Cited by 164 | Viewed by 16735
Abstract
The Landsat program has been producing an archive of thermal imagery that spans the globe and covers 30 years of the thermal history of the planet at human scales (60–120 m). Most of that archive’s absolute radiometric calibration has been fixed through vicarious [...] Read more.
The Landsat program has been producing an archive of thermal imagery that spans the globe and covers 30 years of the thermal history of the planet at human scales (60–120 m). Most of that archive’s absolute radiometric calibration has been fixed through vicarious calibration techniques. These calibration ties to trusted values have often taken a year or more to gather sufficient data and, in some cases, it has been over a decade before calibration certainty has been established. With temperature being such a critical factor for all living systems and the ongoing concern over the impacts of climate change, NASA and the United States Geological Survey (USGS) are leading efforts to provide timely and accurate temperature data from the Landsat thermal data archive. This paper discusses two closely related advances that are critical steps toward providing timely and reliable temperature image maps from Landsat. The first advance involves the development and testing of an autonomous procedure for gathering and performing initial screening of large amounts of vicarious calibration data. The second advance discussed in this paper is the per-pixel atmospheric compensation of the data to permit calculation of the emitted surface radiance (using ancillary sources of emissivity data) and the corresponding land surface temperature (LST). Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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<p>Illustration of parameters used in Equations (1) and (2).</p> Full article ">Figure 2
<p>Graphic representation of the unknown diurnal surface temperature at time t (<b>left</b>), and the Zeng empirical correction method (<b>right</b>).</p> Full article ">Figure 3
<p>Example of possible Landsat 8 paths, buoys, and specific scene rows for 19 July 2013; triangles indicate buoy location.</p> Full article ">Figure 4
<p>Selection of specific Landsat scenes and corresponding buoys for a given date using the buoy meteorological database.</p> Full article ">Figure 5
<p>Workflow illustrating: assembling the necessary inputs, Landsat scene filtering, buoy to surface temperature adjustment, and atmospheric compensation.</p> Full article ">Figure 6
<p>Plot of top of atmosphere (ToA) buoy predicted radiance (apparent temperature) <span class="html-italic">vs.</span> satellite observed radiance for Landsat 5 after removal of six cloud contaminated points (mean error −0.52 K ± 0.72 K).</p> Full article ">Figure 7
<p>Plot of ToA buoy predicted radiance<span class="html-italic"> vs.</span> satellite observed radiance for Landsat 7 for all data processed automatically (mean error −0.24 K ± 0.81 K).</p> Full article ">Figure 8
<p>Plot of ToA buoy predicted radiance<span class="html-italic"> vs.</span> satellite observed radiance for Landsat 8 band 10 for all data processed automatically (mean error −0.01 K ± 0.90 K).</p> Full article ">Figure 9
<p>Plot of ToA buoy predicted radiance<span class="html-italic"> vs.</span> satellite observed radiance for Landsat 8 band 11 for all data processed automatically (mean error −0.91 K ± 1.28 K).</p> Full article ">Figure 10
<p>Illustration of regression to calculate transmission and upwelled radiance.</p> Full article ">Figure 11
<p>Histogram of errors for all Landsat 5 scenes in validation data set.</p> Full article ">Figure 12
<p>Histogram of errors for Landsat 5 scenes with possible clouds in the vicinity but not over the buoy.</p> Full article ">Figure 13
<p>Histogram of errors for Landsat 5 scenes without clouds near the buoy.</p> Full article ">Figure 14
<p>Histogram of errors for Landsat 8 band 10 including only cloud free scenes.</p> Full article ">Figure 15
<p>Histogram of errors for Landsat 8 band 11 including only cloud free scenes.</p> Full article ">
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Article
Hybrid Ensemble Classification of Tree Genera Using Airborne LiDAR Data
by Connie Ko, Gunho Sohn, Tarmo K. Remmel and John Miller
Remote Sens. 2014, 6(11), 11225-11243; https://doi.org/10.3390/rs61111225 - 13 Nov 2014
Cited by 14 | Viewed by 5331
Abstract
This paper presents a hybrid ensemble method that is comprised of a sequential and a parallel architecture for the classification of tree genus using LiDAR (Light Detection and Ranging) data. The two classifiers use different sets of features: (1) features derived from geometric [...] Read more.
This paper presents a hybrid ensemble method that is comprised of a sequential and a parallel architecture for the classification of tree genus using LiDAR (Light Detection and Ranging) data. The two classifiers use different sets of features: (1) features derived from geometric information, and (2) features derived from vertical profiles using Random Forests as the base classifier. This classification result is also compared with that obtained by replacing the base classifier by LDA (Linear Discriminant Analysis), kNN (k Nearest Neighbor) and SVM (Support Vector Machine). The uniqueness of this research is in the development, implementation and application of three main ideas: (1) the hybrid ensemble method, which aims to improve classification accuracy, (2) a pseudo-margin criterion for assessing the quality of predictions and (3) an automatic feature reduction method using results drawn from Random Forests. An additional point-density analysis is performed to study the influence of decreased point density on classification accuracy results. By using Random Forests as the base classifier, the average classification accuracies for the geometric classifier and vertical profile classifier are 88.0% and 88.8%, respectively, with improvement to 91.2% using the ensemble method. The training genera include pine, poplar, and maple within a study area located north of Thessalon, Ontario, Canada. Full article
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<p>Map of the study area and the locations of the eight field survey sites.</p> Full article ">Figure 2
<p>Return distribution examples of maple (<b>a</b>) at 40 pulses∙m<sup>−2</sup>, maple (<b>b</b>) at 1.25 pulses∙m<sup>−2</sup>, pine (<b>c</b>) at 40 pulses∙m<sup>−2</sup>, pine (<b>d</b>) at 1.25 pulses∙m<sup>−2</sup>, poplar (<b>e</b>) trees at 40 pulses∙m<sup>−2</sup> and poplar (<b>f</b>) trees at 1.25 pulses∙m<sup>−2</sup>.</p> Full article ">Figure 3
<p>Cumulative MDA (mean decrease accuracy) values for geometric classifiers (<b>a</b>) and cumulative MDA values for vertical profile classifiers (<b>b</b>); the residual sum of squares residual for fitting two linear lines through the cumulative MDA curve; dotted line shows where the residual sum of square minimizes. Solid lines represent <span class="html-italic">l<sub>i</sub></span> and <span class="html-italic">l<sub>j</sub></span> at optimized <span class="html-italic">J</span>.</p> Full article ">Figure 4
<p>Summary of the ensemble method using the geometric classifier as first classifier: MG<sub>g</sub> and MG<sub>v </sub>indicate the margin obtained from the geometric and the vertical profile classifiers, respectively; V<sub>g</sub> and V<sub>v</sub> represent the vote proportions obtained from the geometric and vertical profile classifiers, respectively; Y<sub>g*</sub> and Y<sub>v</sub><sup>*</sup> correspond to the final predictions from geometric and vertical profile classifiers respectively.</p> Full article ">Figure 5
<p>Frequency distribution of the LiDAR (Light Detection and Ranging) trees that are correctly classified for at least 80% (and less than 80%) with 20 randomly selected sample subsets (<b>a</b>). Normalized frequency distribution of (<b>a</b>) is shown in (<b>b</b>). Margin for incorrect classification (for more and less than 80% chance) and total margin for incorrect classification at different σ (<b>c</b>).</p> Full article ">Figure 6
<p>Classification accuracy of the geometric, vertical profile, and ensemble classifiers, using the geometric classifier as the first classifier at different LiDAR pulse density levels.</p> Full article ">
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Article
Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment
by Veronika Kopačková and Lenka Hladíková
Remote Sens. 2014, 6(11), 11204-11224; https://doi.org/10.3390/rs61111204 - 13 Nov 2014
Cited by 10 | Viewed by 8117
Abstract
Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral [...] Read more.
Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral (HS) sensors means that image spectroscopy has the potential to become a modern method for monitoring polluted surface waters. This paper describes an approach employing linear Spectral Unmixing (LSU) for analysis of hyperspectral image data to map the relative abundances of mine water components (dissolved Fe—Fediss, dissolved organic carbon—DOC, undissolved particles). The ground truth data (8 monitored ponds) were used to validate the results of spectral mapping. The same approach applied to HS data was tested using the image data resampled to WorldView2 (WV2) spectral resolution. A key aspect of the image data processing was to define the proper pure image end members for the fundamental water types. The highest correlations detected between the studied water parameters and the fractional images using the HyMap and the resampled WV2 data, respectively, were: dissolved Fe (R2 = 0.74 and R2vw2 = 0.6), undissolved particles (R2 = 0.57 and R2vw2 = 0.49) and DOC (R2 = 0.42 and R2vw2 < 0.40). These fractional images were further classified to create semi-quantitative maps. In conclusion, the classification still benefited from the higher spectral resolution of the HyMap data; however the WV2 reflectance data can be suitable for mapping specific inherent optical properties (SIOPs), which significantly differ from one another from an optical point of view (e.g., mineral suspension, dissolved Fe and phytoplankton), but it seems difficult to differentiate among diverse suspension particles, especially when the waters have more complex properties (e.g., mineral particles, DOC together with tripton or other particles, etc.). Full article
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<p>Test site: sampling/measuring points displayed on the HyMap 2009 data (corrected reflectance, true color coding).</p> Full article ">Figure 2
<p>Sampled waters: boxplots of the major parameters further discussed in the text. Boxes indicate the variations as defined by the standard deviation; the median is indicated as a horizontal line in the box; Quartile 1 and Quartile 3 indicate the minimum and maximum values, respectively.</p> Full article ">Figure 3
<p>Processing scheme used for the HyMap image reflectance data as well as for the image reflectance resampled to WV2 spectral resolution.</p> Full article ">Figure 4
<p>Image spectra (scaled reflectance) of the selected sampling points that best illustrated the discussed gradients: (<b>a</b>) Samples differing in their undissolved particle content. (<b>b</b>) Samples differing in their Fe<sub>diss</sub> content. (<b>c</b>) Samples differing in their DOC content, but whose spectral properties show that they are rather complex and contain DOC together with tripton and a mineral suspension. Diagnostic scattering and absorbance features for particular matter (PM), Fe<sub>diss</sub> and DOC are also indicated.</p> Full article ">Figure 5
<p>Image-derived end members used for the LSU: (<b>a</b>) The end members (EM) of the corresponding fractional images exhibiting strongest correlations with the studied hydrochemical parameters are shown in bold (EM 7: Fe<sub>diss</sub>, EM10: DOC and EM11: undissolved particles). (<b>b</b>) The same end members resampled to WV2 spectral resolution. (<b>c</b>) Each end member corresponding geographic position: water with high contents of diverse suspended matter (EM1, 4, 10), water with Fe precipitated on mineral suspended matter (EM2), water with high chlorophyll <span class="html-italic">a</span> concentrations (EM9), clear water (EM8), water with high contents of dissolved Fe (EM7), water with high levels of DOC and solid tripton or other particulate matter (EM3 and 11).</p> Full article ">Figure 6
<p>Sampled water characteristics: high contents of dissolved Fe were present when there were low DOC concentrations (&lt;5 mg/L).</p> Full article ">Figure 7
<p><b>Lomnice-Georg:</b> Semi-quantitative maps derived for dissolved organic carbon (DOC), undissolved particles and dissolved Fe. The legend is uniform for both the classified map and for the sampling point.</p> Full article ">Figure 8
<p><b>Medard:</b> Semi-quantitative maps derived for dissolved organic carbon (DOC), undissolved particles and dissolved Fe. The legend is uniform for both the classified map and for the sampling point.</p> Full article ">Figure 9
<p>Validation of semi-quantitative mapping: the corresponding pixel values of the classified maps (classified LSU fractional images) are compared to the classified values of the ground truth data. Classes 1, 2 and 3 (Y axis) correspond to low, middle and high content classes for DOC, undissolved particles and Fe<sub>diss</sub> displayed in <a href="#remotesensing-06-11204-f007" class="html-fig">Figure 7</a> and <a href="#remotesensing-06-11204-f008" class="html-fig">Figure 8</a>.</p> Full article ">
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Article
A Life-Size and Near Real-Time Test of Irrigation Scheduling with a Sentinel-2 Like Time Series (SPOT4-Take5) in Morocco
by Michel Le Page, Jihad Toumi, Saïd Khabba, Olivier Hagolle, Adrien Tavernier, M. Hakim Kharrou, Salah Er-Raki, Mireille Huc, Mohamed Kasbani, Abdelilah El Moutamanni, Mohamed Yousfi and Lionel Jarlan
Remote Sens. 2014, 6(11), 11182-11203; https://doi.org/10.3390/rs61111182 - 11 Nov 2014
Cited by 28 | Viewed by 9538
Abstract
This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. [...] Read more.
This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. For the purpose of this experiment, the irrigation of a reference plot was driven by the farmer according to, mainly empirical, irrigation scheduling while test plot irrigations were being managed following the FAO-56 method, driven by remote sensing. Images were issued from the SPOT4 (Take5) data set, which aimed at delivering image time series at a decametric resolution with less than five-day satellite overpass similar to the time series ESA Sentinel-2 satellites will produce in the coming years. With a Root Mean Square Error (RMSE) of 0.91mm per day, the comparison between daily actual evapotranspiration measured by eddy-covariance and the simulated one is satisfactory, but even better at a five-day integration (0.59mm per day). Finally, despite a chaotic beginning of the experiment—the experimental plot had not been irrigated to get rid of a slaking crust, which prevented good emergence—our plot caught up and yielded almost the same grain crop with 14% less irrigation water. This experiment opens up interesting opportunities for operational scheduling of flooding irrigation sectors that dominate in the semi-arid Mediterranean area. Full article
(This article belongs to the Special Issue Earth Observation for Water Resource Management in Africa)
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<p>The picture shows the area covered with the SPOT4-Take5 images (background is the first image of the Time Series taken on 31 January 2013), and the location of R3 scheme (green), 40 km east of Marrakech, Morocco. The scheme is fed by the Rocade Canal (dark blue).</p> Full article ">Figure 2
<p>Setup at the south of the scheme: The experimental plot (yellow) is located north of the reference plot (pink) at a distance of about 1km from the main canal. A flux tower (green circle) was installed at the center of each plot (E stands for “experimental”, and R, for “Reference”). The meteorological station (green square) is located 2 km west of the plots.</p> Full article ">Figure 3
<p>Time series of satellite images Spot4-Take5, Spot5 and Landsat8 during the 2012/13 season. Landsat8 scenes were not used during the experiment and are only indicated as reference. The gaps in the SPOT4 series are only due to cloud cover.</p> Full article ">Figure 4
<p>Time evolution of Rainfall and Reference Evapotranspiration (ET<sub>0</sub>) during the experimental setup.</p> Full article ">Figure 5
<p>Evolution of NDVI for the two plots: Reference (Ref) and Experimental (Exp). Note that the first three points come from SPOT5 and others from SPOT4. The DaS of each input image has been labeled.</p> Full article ">Figure 6
<p>Kc<sub>b</sub> derived from NDVI (dashed line) and evolution of extrapolations as new images are arriving (dark to light gray) for the experimental plot. Each time a new image is received (dot and DaS small label), the Kc<sub>b</sub> trajectory is corrected. The three inflexion points extracted from the FAO-56 tables are indicated by a star and a bold label.</p> Full article ">Figure 7
<p>Comparison between the daily values of estimated <math display="inline"> <semantics> <mrow> <mi>E</mi> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>d</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics> </math> by the FAO-56 model and the measured one <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <mi>E</mi> <msub> <mi>T</mi> <mrow> <mi>E</mi> <mi>C</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> by the Eddy-covariance system for the experimental plot. Error indicators are MSE (Mean Square Error), RMSE (Root Mean Square Error), MBE (Mean Bias Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error).</p> Full article ">Figure 8
<p>Forcing (ET<sub>0</sub>, Rainfall and Irrigation) of the Water Balance and comparison of measured Evapotranspiration (ET_EC) and Calculated Evapotranspiration (ETadj) for the experimental plot.</p> Full article ">Figure 9
<p>Comparison of irrigation events between the two plots. Time intervals between events are indicated in the same figure. NDVI is for the experimental plot.</p> Full article ">
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Article
Landsat 8 Thermal Infrared Sensor Geometric Characterization and Calibration
by James Storey, Michael Choate and Donald Moe
Remote Sens. 2014, 6(11), 11153-11181; https://doi.org/10.3390/rs61111153 - 11 Nov 2014
Cited by 20 | Viewed by 9327
Abstract
The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization [...] Read more.
The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization challenging. TIRS geometric calibration thus relied heavily on on-orbit measurements. Since the two Landsat 8 payloads are complementary and generate combined Level 1 data products, the TIRS geometric performance requirements emphasize the co-alignment of the OLI and TIRS instrument fields of view and the registration of the OLI reflective bands to the TIRS long-wave infrared emissive bands. The TIRS on-orbit calibration procedures include measuring the TIRS-to-OLI alignment, refining the alignment of the three TIRS sensor chips, and ensuring the alignment of the two TIRS spectral bands. The two key TIRS performance metrics are the OLI reflective to TIRS emissive band registration accuracy, and the registration accuracy between the TIRS thermal bands. The on-orbit calibration campaign conducted during the commissioning period provided an accurate TIRS geometric model that enabled TIRS Level 1 data to meet all geometric accuracy requirements. Seasonal variations in TIRS-to-OLI alignment have led to several small calibration parameter adjustments since commissioning. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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<p>Cutaway view of the Thermal Infrared Sensor (TIRS) instrument design showing major subsystems including: scene select mechanism; telescope and focal plane assembly (FPA); cryocooler.</p> Full article ">Figure 2
<p>TIRS focal plane layout showing the arrangement of the three sensor chip assemblies (SCAs, designated A, B, and C) as they are projected into object space. SCA-C is down-track of SCAs A and B; SCA-A covers the starboard side of the field of view while SCA-B covers the port side.</p> Full article ">Figure 3
<p>A simplified ray trace diagram shows the TIRS SCA along-track (<b>left</b>) and cross-track (<b>right</b>) pointing directions.</p> Full article ">Figure 4
<p>While in the Earth-view orientation, the scene select mechanism (SSM) redirects the detector lines-of-sight emerging from the telescope out the nadir view port.</p> Full article ">Figure 5
<p>Two-pixel diameter line-of-sight target image (inside red circle). The dark areas are detectors that are masked whereas the light bars are the active detector rows covered by the spectral filters for the two TIRS bands. In addition to the small round target, several anomalous detectors are also visible.</p> Full article ">Figure 6
<p>TIRS line-of-sight target field angles. SSM rotation allows the outboard SCA-A and SCA-B to view targets at field angles closer to the center of the field of view.</p> Full article ">Figure 7
<p>TIRS line-of-sight data analysis flow. The parameters in the shaded boxes are adjusted to minimize the root-sum-squared (RSS) of the differences between the adjusted ground support equipment (GSE) angles and the TIRS model angles.</p> Full article ">Figure 8
<p>Initial TIRS alignment calibration images, acquired prior to the first Operational Land Imager (OLI) imaging. (<b>a</b>) The interval on the left from WRS 042/030 acquired 9 March 2013 and (<b>b</b>) the interval on the right from WRS 038/037 acquired on 11 March 2013, provided the first complete TIRS on-orbit alignment calibration update, using Landsat 5 Thematic Mapper data as a reference.</p> Full article ">Figure 9
<p>TIRS-to-OLI (<b>a</b>) roll (<b>top</b>) and (<b>b</b>) pitch (<b>bottom</b>) alignment measurements over time show a step discontinuity at the spacecraft safe-hold anomaly that occurred in late-September 2013. An update to the alignment calibration was issued shortly after imaging operations resumed (green lines). The entire calibration time history was subsequently refined (blue lines) in preparation for the February 2014 data reprocessing campaign.</p> Full article ">Figure 10
<p>Standard deviations of band 11 (12.0-μm) to band 10 (10.9-μm) band alignment calibration Legendre coefficient updates in the X/along-track (AT) and Y/cross-track (XT) directions. All SCAs showed good consistency (better than 4 microradians) relative to the 142-microradian TIRS pixel size.</p> Full article ">Figure 11
<p>OLI-TIRS composite image with the TIRS 10.9-micrometer band (band 10) in the red channel, the OLI SWIR2 band (band 7) in the green channel, and the OLI coastal-aerosol band (band 1) in the blue channel. (<b>a</b>) The left image window was extracted from the western edge of the scene and (<b>b</b>) the right window was extracted from the eastern edge of the scene. Note that the TIRS coverage is contained within the OLI coverage.</p> Full article ">Figure 12
<p>TIRS emissive to OLI reflective band LE90 registration (in meters) in the line and sample directions. Summary results for every TIRS-to-OLI band combination. The 30-meter LE90 registration accuracy requirement is shown by the red line.</p> Full article ">Figure 13
<p>TIRS emissive to OLI reflective band LE90 registration (in meters) in the line and sample directions. Worst-case band combination results by calendar quarter. The 30-meter LE90 registration accuracy requirement is shown by the red line.</p> Full article ">Figure 14
<p>TIRS 10.9-micrometer band to 12.0-micrometer band LE90 registration (in meters) in the line and sample directions by calendar quarter. The 18-meter LE90 registration accuracy requirement is shown by the red line.</p> Full article ">
3130 KiB  
Article
Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance
by James Storey, Michael Choate and Kenton Lee
Remote Sens. 2014, 6(11), 11127-11152; https://doi.org/10.3390/rs61111127 - 11 Nov 2014
Cited by 208 | Viewed by 14594
Abstract
The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric [...] Read more.
The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric calibration activities were performed to refine the prelaunch calibration parameters. The results of these calibration activities were subsequently used to measure geometric performance characteristics in order to verify the OLI geometric requirements. Three types of geometric calibrations were performed including: (1) updating the OLI-to-spacecraft alignment knowledge; (2) refining the alignment of the sub-images from the multiple OLI sensor chips; and (3) refining the alignment of the OLI spectral bands. The aspects of geometric performance that were measured and verified included: (1) geolocation accuracy with terrain correction, but without ground control (L1Gt); (2) Level 1 product accuracy with terrain correction and ground control (L1T); (3) band-to-band registration accuracy; and (4) multi-temporal image-to-image registration accuracy. Using the results of the on-orbit calibration update, all aspects of geometric performance were shown to meet or exceed system requirements. Full article
(This article belongs to the Special Issue Landsat-8 Sensor Characterization and Calibration)
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<p>The Operational Land Imager (OLI) focal plane layout showing: top—the arrangement of the visible and near infrared (VNIR) and short-wave infrared (SWIR) spectral bands in the along-track direction; and bottom—the 14 focal plane modules across the OLI field of view.</p> Full article ">Figure 2
<p>The OLI line-of-sight pattern with the along-track scale, exaggerated by a factor of ~8 to highlight the off-nadir focal plane module (FPM) rotation intended to compensate for optical distortion.</p> Full article ">Figure 3
<p>Left—schematic of the OLI detector pattern showing even/odd detector offset and redundant detector rows. Right—example of final active detector set after primary/redundant detector selection.</p> Full article ">Figure 4
<p>Using the example active detector pattern from <a href="#remotesensing-06-11127-f003" class="html-fig">Figure 3</a> above, red X’s indicate the ideal detector locations represented by the operational Legendre polynomial line-of-sight model. The Legendre polynomials model the trailing row of primary detectors with the departures of the actual detector locations from these ideal locations being captured in a detector offset table.</p> Full article ">Figure 5
<p>Three-dimensional grid of Level 1R line/sample/height coordinate to Level 1T line/sample mappings. The grid provides a convenient means to rapidly convert coordinates between the unresampled Level 1R image and the output Level 1T product. The nominal grid resolution for the multispectral bands is: Δl = 30 lines, Δs = 40 samples, and Δh = 500 meters.</p> Full article ">Figure 6
<p>On-orbit FPM-to-FPM alignment corrections before (blue and red bars) and after (green and purple bars) the OLI-to-spacecraft yaw alignment was adjusted.</p> Full article ">Figure 7
<p>On-orbit band-to-band alignment corrections; top—in the along-track direction; and bottom—in the across-track direction. The bands are plotted in the focal plane order.</p> Full article ">Figure 8
<p>OLI absolute geodetic/geolocation accuracy by calendar quarter. Anchor sites are the subset of Global Land Survey (GLS) scenes that contained control points provided by the National Geospatial Intelligence Agency (NGA).</p> Full article ">Figure 9
<p>Level 1T product accuracy by calendar quarter. The accuracy estimates are highly sensitive to the quality of the ground control used to measure the accuracy.</p> Full article ">Figure 10
<p>OLI band-to-band registration accuracy by band pair. Pairs including the cirrus band are grouped at the right end of the plot.</p> Full article ">Figure 11
<p>OLI image-to-image registration accuracy; blue bars—average over all test scenes; red bars—worst-case test scene.</p> Full article ">Figure 12
<p>Landsat 8 ground control improvement sites. The 15 highest priority areas are shown in red, second priority areas are shown in green, and high latitude areas are shown in tan.</p> Full article ">
13414 KiB  
Case Report
The Integration of Geotechnologies in the Evaluation of a Wine Cellar Structure through the Finite Element Method
by Alberto Villarino, Belén Riveiro, Diego Gonzalez-Aguilera and Luis Javier Sánchez-Aparicio
Remote Sens. 2014, 6(11), 11107-11126; https://doi.org/10.3390/rs61111107 - 11 Nov 2014
Cited by 9 | Viewed by 6314
Abstract
This paper presents a multidisciplinary methodology to evaluate an underground wine cellar structure using non-invasive techniques. In particular, a historical subterranean wine cellar that presents a complex structure and whose physical properties are unknown is recorded and analyzed using geomatics and geophysics synergies. [...] Read more.
This paper presents a multidisciplinary methodology to evaluate an underground wine cellar structure using non-invasive techniques. In particular, a historical subterranean wine cellar that presents a complex structure and whose physical properties are unknown is recorded and analyzed using geomatics and geophysics synergies. To this end, an approach that integrates terrestrial laser scanning and ground penetrating radar is used to properly define a finite element-based structural model, which is then used as a decision tool to plan architectural restoration actions. The combination of both techniques implies the registration of external and internal information that eases the construction of structural models. Structural simulation for both stresses and deformations through FEM allowed identifying critical structural elements under great stress or excessive deformations. In this investigation, the ultimate limit state of cracking was considered to determine allowable loads due to the brittle nature of the material. This allowed us to set limit values of loading on the cellar structure in order to minimize possible damage. Full article
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<p>(<b>a</b>) Location of the wine cellar: Toro, Zamora, Spain; (<b>b</b>) top view and location of the “Toro’s Council” wine cellar.</p> Full article ">Figure 2
<p>(<b>a</b>) Terrestrial laser scanner used for the recording of the external geometry, Trimble GX; (<b>b</b>) ground penetrating radar equipment, RIS MF HI-MOD 200–600 MHz.</p> Full article ">Figure 3
<p>Boundary conditions at the floor of the wine cellar.</p> Full article ">Figure 4
<p>Values of soil thrust on two walls of the wine cellar.</p> Full article ">Figure 5
<p>(<b>a</b>) Ground-vault model; (<b>b</b>) ground-vault mesh detail; (<b>c</b>) vertical and horizontal thrust (due to weight soil and the permanent load of the building above the wine cellar) over two vault elements.</p> Full article ">Figure 6
<p>Service load of 4 kN/m<sup>2</sup> on the floor of the wine cellar.</p> Full article ">Figure 7
<p>3D laser model with the GPR profiles georeferenced.</p> Full article ">Figure 8
<p>(<b>a</b>) 3D surface model of the wine cellar and the area of study selected for performing the structural evaluation (top view); (<b>b</b>) radargrams that show the presence of air voids and porous materials.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) 3D surface model of the wine cellar and the area of study selected for performing the structural evaluation (top view); (<b>b</b>) radargrams that show the presence of air voids and porous materials.</p> Full article ">Figure 9
<p>Generation of the FEM model of the cellar from laser data.</p> Full article ">Figure 10
<p>(<b>a</b>) Maximum principal stresses (kN/m<sup>2</sup>) in <span class="html-italic">ULS1</span> (with averaged thickness values); (<b>b</b>) maximum principal stresses (kN/m<sup>2</sup>) in <span class="html-italic">ULS2</span> (with averaged thickness values).</p> Full article ">Figure 11
<p>(<b>a</b>) Area of the wall with tensile stresses (kN/m<sup>2</sup>) exceeding the tensile breaking point of limestone and the output table of calculation results; (<b>b</b>) photograph of the cracked masonry inside the cellar.</p> Full article ">Figure 12
<p>(<b>a</b>) Maximum principal tensile (kN/m<sup>2</sup>) for the elevation wall with a thickness of 0.38 m; (<b>b</b>) maximum principal tensile (kN/m<sup>2</sup>) for the elevation wall with a thickness of 0.42 m.</p> Full article ">Figure 13
<p>The deformation of the cellar (×200) along the Y-axis of the global model and in the <span class="html-italic">SLS1</span> model.</p> Full article ">
15695 KiB  
Article
Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions
by Christian Rogass, Christian Mielke, Daniel Scheffler, Nina K. Boesche, Angela Lausch, Christin Lubitz, Maximilian Brell, Daniel Spengler, Andreas Eisele, Karl Segl and Luis Guanter
Remote Sens. 2014, 6(11), 11082-11106; https://doi.org/10.3390/rs61111082 - 11 Nov 2014
Cited by 36 | Viewed by 8720
Abstract
Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics [...] Read more.
Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics based units such as radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing the photoelectric effect. Temporal variations of the detector characteristics that differ with foregoing radiometric calibration cause visually perceptible along-track stripes in the at-sensor radiance data that aggravate succeeding image-based analyses. Especially, variations of the thermally induced dark current dominate and have to be reduced. In this work, a new approach is presented that efficiently reduces dark current related stripe noise. It integrates an across-effect gradient minimization principle. The performance has been evaluated using artificially degraded whiskbroom (reference) and real pushbroom acquisitions from EO-1 Hyperion and AISA DUAL that are significantly covered by stripe noise. A set of quality indicators has been used for the accuracy assessment. They clearly show that the new approach outperforms a limited set of tested state-of-the-art approaches and achieves a very high accuracy related to ground-truth for selected tests. It may substitute recent algorithms in the Reduction of Miscalibration Effects (ROME) framework that is broadly used to reduce radiometric miscalibrations of pushbroom data takes. Full article
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<p>Overview on study regions as Landsat 8 false color composite subsets overlaid with vectorized acquisition borders for airborne data takes (<b>a</b>)-two scenes over the Fichtwald region (AISA DUAL; red), spaceborne data takes (<b>b</b>)-two scenes over the Haib River Complex of Namibia and the Republic of South Africa (Hyperion; green) and airborne data takes (<b>c</b>,<b>d</b>) three scenes over the cities of Dresden (c), Berlin, Potsdam (HyMap; blue).</p> Full article ">Figure 1 Cont.
<p>Overview on study regions as Landsat 8 false color composite subsets overlaid with vectorized acquisition borders for airborne data takes (<b>a</b>)-two scenes over the Fichtwald region (AISA DUAL; red), spaceborne data takes (<b>b</b>)-two scenes over the Haib River Complex of Namibia and the Republic of South Africa (Hyperion; green) and airborne data takes (<b>c</b>,<b>d</b>) three scenes over the cities of Dresden (c), Berlin, Potsdam (HyMap; blue).</p> Full article ">Figure 2
<p>False colored representations of subsets of HyMAP acquisitions over (<b>a</b>) Potsdam (CIR: Red 864.5 nm, Green 652.6 nm, Blue 546.3 nm), (<b>b</b>) Berlin (CIR as in (a), (<b>c</b>) Dresden and of subsets of Hyperion acquisitions over (<b>d</b>,<b>e</b>) the Haib River Complex (Red 2304.71 nm, Green 915.23 nm, Blue 447.17 nm), and subsets of AISA acquisitions over the Fichtwald region (<b>f</b>) and (<b>g</b>) (Red 1574.37 nm, Green 964.39 nm, Blue 730.05 nm).</p> Full article ">Figure 3
<p>Examples of false colored (CIR: Red 864.5 nm, Green 652.6 nm, Blue 546.3 nm) artificially degraded subsets of HyMAP acquisitions over Potsdam (<b>a</b>–<b>d</b>), over Berlin (<b>e</b>–<b>h</b>) and Dresden (<b>i</b>–<b>l</b>); noise degradation levels (a,e,i) 0.1%, (b,f,j) 0.5%, (c,g,b) 1% and (d,h,l) 5% noise degradation.</p> Full article ">Figure 3 Cont.
<p>Examples of false colored (CIR: Red 864.5 nm, Green 652.6 nm, Blue 546.3 nm) artificially degraded subsets of HyMAP acquisitions over Potsdam (<b>a</b>–<b>d</b>), over Berlin (<b>e</b>–<b>h</b>) and Dresden (<b>i</b>–<b>l</b>); noise degradation levels (a,e,i) 0.1%, (b,f,j) 0.5%, (c,g,b) 1% and (d,h,l) 5% noise degradation.</p> Full article ">Figure 4
<p>False colored near infrared image subsets that show the process of the estimation of the across track gradients considering Equation (5) for an HyMAP data set acquired over Potsdam in 2004 (<b>a</b>), that was artificially degraded with uncorrelated across track noise (<b>b</b>), the across track gradient from (a) in (<b>c</b>), the across track gradient from (b) in (<b>d</b>) and the estimation of <math display="inline"> <semantics> <mrow> <mfrac> <mrow> <mo>∂</mo> <mo> </mo> <mi>i</mi> <mi>m</mi> </mrow> <mrow> <mo>∂</mo> <mi>x</mi> </mrow> </mfrac> </mrow> </semantics> </math> from <span class="html-italic">im<sub>s</sub></span> in (<b>e</b>).</p> Full article ">Figure 5
<p>Average MSSIM of the two best performing approaches for artificially degraded images.</p> Full article ">Figure 6
<p>Exemplary results as false colored contrast enhanced subsets of one HyMAP scene (<b>a</b>); Potsdam original; figured again for visual comparison) that has been artificially degraded with 5% noise (<b>b</b>) for (<b>c</b>) the proposed approach, (<b>d</b>) Rogass et al. [<a href="#B9-remotesensing-06-11082" class="html-bibr">9</a>], (<b>e</b>) Goodenough et al. and Datt et al. [<a href="#B10-remotesensing-06-11082" class="html-bibr">10</a>,<a href="#B11-remotesensing-06-11082" class="html-bibr">11</a>], (<b>f</b>) Staenz et al. [<a href="#B12-remotesensing-06-11082" class="html-bibr">12</a>], (<b>g</b>) Pande-Chhetri and Abd-Elrahman ([<a href="#B13-remotesensing-06-11082" class="html-bibr">13</a>] and (<b>h</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B14-remotesensing-06-11082" class="html-bibr">14</a>].</p> Full article ">Figure 6 Cont.
<p>Exemplary results as false colored contrast enhanced subsets of one HyMAP scene (<b>a</b>); Potsdam original; figured again for visual comparison) that has been artificially degraded with 5% noise (<b>b</b>) for (<b>c</b>) the proposed approach, (<b>d</b>) Rogass et al. [<a href="#B9-remotesensing-06-11082" class="html-bibr">9</a>], (<b>e</b>) Goodenough et al. and Datt et al. [<a href="#B10-remotesensing-06-11082" class="html-bibr">10</a>,<a href="#B11-remotesensing-06-11082" class="html-bibr">11</a>], (<b>f</b>) Staenz et al. [<a href="#B12-remotesensing-06-11082" class="html-bibr">12</a>], (<b>g</b>) Pande-Chhetri and Abd-Elrahman ([<a href="#B13-remotesensing-06-11082" class="html-bibr">13</a>] and (<b>h</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B14-remotesensing-06-11082" class="html-bibr">14</a>].</p> Full article ">Figure 7
<p>Average AAHPD performance of the two best performing approaches for real images.</p> Full article ">Figure 8
<p>Subsets of one Hyperion scene (<b>a</b>) as across track spatially stretched false color composites (Red 2304.71 nm, Green 915.23 nm, Blue 447.17 nm) of the destriping results of (<b>b</b>) the proposed approach, (<b>c</b>) Rogass <span class="html-italic">et al.</span> [<a href="#B9-remotesensing-06-11082" class="html-bibr">9</a>], (<b>d</b>) Goodenough <span class="html-italic">et al.</span> and Datt <span class="html-italic">et al.</span> [<a href="#B10-remotesensing-06-11082" class="html-bibr">10</a>,<a href="#B11-remotesensing-06-11082" class="html-bibr">11</a>], (<b>e</b>) Staenz <span class="html-italic">et al.</span> [<a href="#B12-remotesensing-06-11082" class="html-bibr">12</a>], (<b>f</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B13-remotesensing-06-11082" class="html-bibr">13</a>] and (<b>g</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B14-remotesensing-06-11082" class="html-bibr">14</a>].</p> Full article ">Figure 9
<p>Subsets of one AISA Dual scene (<b>a</b>) as across track spatially stretched false color composites (Red 1574.37 nm, Green 964.39 nm, Blue 730.05 nm) of the destriping results of (<b>b</b>) the proposed approach, (<b>c</b>) Rogass <span class="html-italic">et al</span>. [<a href="#B9-remotesensing-06-11082" class="html-bibr">9</a>], (<b>d</b>) Goodenough <span class="html-italic">et al</span>. and Datt <span class="html-italic">et al</span>. [<a href="#B10-remotesensing-06-11082" class="html-bibr">10</a>,<a href="#B11-remotesensing-06-11082" class="html-bibr">11</a>], (<b>e</b>) Staenz <span class="html-italic">et al</span>. [<a href="#B12-remotesensing-06-11082" class="html-bibr">12</a>], (<b>f</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B13-remotesensing-06-11082" class="html-bibr">13</a>] and (<b>g</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B14-remotesensing-06-11082" class="html-bibr">14</a>].</p> Full article ">Figure 9 Cont.
<p>Subsets of one AISA Dual scene (<b>a</b>) as across track spatially stretched false color composites (Red 1574.37 nm, Green 964.39 nm, Blue 730.05 nm) of the destriping results of (<b>b</b>) the proposed approach, (<b>c</b>) Rogass <span class="html-italic">et al</span>. [<a href="#B9-remotesensing-06-11082" class="html-bibr">9</a>], (<b>d</b>) Goodenough <span class="html-italic">et al</span>. and Datt <span class="html-italic">et al</span>. [<a href="#B10-remotesensing-06-11082" class="html-bibr">10</a>,<a href="#B11-remotesensing-06-11082" class="html-bibr">11</a>], (<b>e</b>) Staenz <span class="html-italic">et al</span>. [<a href="#B12-remotesensing-06-11082" class="html-bibr">12</a>], (<b>f</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B13-remotesensing-06-11082" class="html-bibr">13</a>] and (<b>g</b>) Pande-Chhetri and Abd-Elrahman [<a href="#B14-remotesensing-06-11082" class="html-bibr">14</a>].</p> Full article ">
9784 KiB  
Review
UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas
by Esther Salamí, Cristina Barrado and Enric Pastor
Remote Sens. 2014, 6(11), 11051-11081; https://doi.org/10.3390/rs61111051 - 11 Nov 2014
Cited by 247 | Viewed by 30661
Abstract
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting [...] Read more.
The miniaturization of electronics, computers and sensors has created new opportunities for remote sensing applications. Despite the current restrictions on regulation, the use of unmanned aerial vehicles equipped with small thermal, laser or spectral sensors has emerged as a promising alternative for assisting modeling, mapping and monitoring applications in rangelands, forests and agricultural environments. This review provides an overview of recent research that has reported UAV flight experiments on the remote sensing of vegetated areas. To provide a differential trend to other reviews, this paper is not limited to crops and precision agriculture applications, but also includes forest and rangeland applications. This work follows a top-down categorization strategy and attempts to fill the gap between application requirements and the characteristics of selected tools, payloads and platforms. Furthermore, correlations between common requirements and the most frequently used solutions are highlighted. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops)
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<p>UAV experiments classification by vegetation type (colors), airframe (symbols), altitude, size, and endurance.</p> Full article ">Figure 2
<p>Visible mosaic of Robison Ridge site in Antarctica (left), moss health derived from MTVI2 vegetation index (upper right), and moss surface temperature at ultra-high spatial resolution (lower right); the red circle highlights a thermal shadow (reprinted from [<a href="#B85-remotesensing-06-11051" class="html-bibr">85</a>]).</p> Full article ">Figure 3
<p>Very high-resolution (<span class="html-italic">≈</span>7 cm/pixel) RGB images showing delineated gaps in two different regions in Germany (<b>a</b>, <b>b</b>), and the gap map obtained for the same plot as (b) with a manned LIDAR flight (<b>c</b>) (reprinted from [<a href="#B87-remotesensing-06-11051" class="html-bibr">87</a>]).</p> Full article ">Figure 4
<p>Interpolated DEMs of Constitution Hill in Wales, UK, using (<b>a</b>) TLS and (<b>b</b>) SfM, (<b>c</b>) aerial photograph of the site, and (<b>d</b>) point density map.‘A’ and ‘B’ labels correspond to the headwall at the highest point and near-vertical faces respectively. ‘VF’ and ‘DF’ labels refer to vegetation-free and desenly vegetated sub-regions respectively (reprinted from [<a href="#B91-remotesensing-06-11051" class="html-bibr">91</a>]).</p> Full article ">Figure 5
<p>Imaging sensors used in UAV-based systems for vegetation remote sensing.</p> Full article ">Figure 6
<p>Different UAV platforms used in vegetation remote sensing.</p> Full article ">
1483 KiB  
Article
Surface Daytime Net Radiation Estimation Using Artificial Neural Networks
by Bo Jiang, Yi Zhang, Shunlin Liang, Xiaotong Zhang and Zhiqiang Xiao
Remote Sens. 2014, 6(11), 11031-11050; https://doi.org/10.3390/rs61111031 - 11 Nov 2014
Cited by 38 | Viewed by 7935
Abstract
Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical R [...] Read more.
Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W∙m−2, and a bias of −0.61 W∙m−2 in global mode based on the validation dataset. This study concluded that ANN methods are a potentially powerful tool for global Rn estimation. Full article
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Figure 1

Figure 1
<p>Distribution of 251 observing sites in 12 measurement networks.</p> Full article ">Figure 2
<p>General regression neural networks (GRNN) with multi-input-one-output architecture. The inputs <span class="html-italic">x<sub>i</sub></span> <sub>(<span class="html-italic">i</span> = 1, …, <span class="html-italic">n</span>)</sub> were shown in <a href="#remotesensing-06-11031-t004" class="html-table">Table 4</a>, and the output <span class="html-italic">y</span> represents <span class="html-italic">R<sub>n</sub></span>.</p> Full article ">Figure 3
<p>Scatter plot of predicted and measured <span class="html-italic">R<sub>n</sub></span> by (<b>a</b>) GRNN and (<b>b</b>) Neuroet model in global mode.</p> Full article ">Figure 4
<p>Scatter plots for (<b>a</b>, <b>b</b>, <b>c</b>, <b>d</b>) GRNN global and (<b>e</b>, <b>f</b>, <b>g</b>, <b>h</b>) conditional models for the four categories, scatter plots for (<b>i</b>, <b>j</b>, <b>k</b>, <b>l</b>) Neuroet global and (<b>m</b>, <b>n</b>, <b>o</b>, <b>p</b>) conditional models.</p> Full article ">Figure 5
<p>Sensitivity analysis of the variables used to predict <span class="html-italic">R<sub>n</sub></span>.</p> Full article ">
846 KiB  
Article
A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles
by Juha Suomalainen, Niels Anders, Shahzad Iqbal, Gerbert Roerink, Jappe Franke, Philip Wenting, Dirk Hünniger, Harm Bartholomeus, Rolf Becker and Lammert Kooistra
Remote Sens. 2014, 6(11), 11013-11030; https://doi.org/10.3390/rs61111013 - 10 Nov 2014
Cited by 136 | Viewed by 16890
Abstract
During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. [...] Read more.
During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops)
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Full article ">Figure 1
<p>Schematic figure of the HYMSY frame and main components (<b>Left</b>) Photo of HYMSY mounted on an Aerialtronics Altura AT8 v1 octocopter UAV (<b>Right</b>).</p> Full article ">Figure 2
<p>Schematic figure of the HYMSY synchronization pulses and the data flow. The DSP outputs from I/O-1 a 1-ms pulse on every spectrometer exposure (25 Hz) to trigger the GPS-INS. Every 2 s the DSP triggers the photogrammetric camera (Panasonic GX1) with a 83-ms wide pulse from I/O-2. The time lag of 83 ms is the delay between the trigger rising edge and the camera exposure, allowing use of the falling edge of the trigger pulse to synchronize GPS-INS to the camera exposure.</p> Full article ">Figure 3
<p>Flowchart of the HYMSY geometric processing chain. The radiometric (reflectance factor) processing (<a href="#sec3dot1-remotesensing-06-11013" class="html-sec">Section 3.1</a>) is done prior to geometric processing. The yellow cylinders are input data, white cylinders are temporary data, and green cylinders are final output data products. Orange boxes are processing steps.</p> Full article ">Figure 4
<p>A collage image of the HYMSY dataset from a single flight at 120 m altitude. A RGB orthomosaic at 34 mm GSD generated from the aerial images (<b>Left background</b>). The Digital Surface Model at 77 mm resolution visualized with hillshading effect (<b>Right background</b>). A false color composite (RGB = 800, 650, 550 nm respectively) of the hyperspectral dataset of the first flight line at 320 mm GSD (<b>Front</b>). The circles mark the locations of spectra displayed in <a href="#remotesensing-06-11013-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 5
<p>Comparison of HYMSY spectra and ground reference (<span class="html-italic">ASD FieldSpec HandHeld 2</span>) spectra. The HYMSY spectra were picked from the datacube shown in <a href="#remotesensing-06-11013-f004" class="html-fig">Figure 4</a> by averaging pixels over a small area (10–100 pixels) close to the estimated location of the ground reference spectra. The sampled areas do not match perfectly and thus some deviation is expected especially with the soil and onion samples. Especially in the wheat spectra in this dataset HYMSY overestimates reflectance factors in the blue region due to poorly performed Spectralon calibration phase where part of the diffuse light got blocked by the person holding the UAV.</p> Full article ">
15997 KiB  
Article
Integrated Geophysical and Aerial Sensing Methods for Archaeology: A Case History in the Punic Site of Villamar (Sardinia, Italy)
by Carlo Piga, Luca Piroddi, Elisa Pompianu, Gaetano Ranieri, Stefano Stocco and Antonio Trogu
Remote Sens. 2014, 6(11), 10986-11012; https://doi.org/10.3390/rs61110986 - 10 Nov 2014
Cited by 14 | Viewed by 11298
Abstract
In this paper, the authors present a recent integrated survey carried out on an archaeological urban site, generally free of buildings, except some temporary structures related to excavated areas where multi-chamber tombs were found. The two methods used to investigate this site were [...] Read more.
In this paper, the authors present a recent integrated survey carried out on an archaeological urban site, generally free of buildings, except some temporary structures related to excavated areas where multi-chamber tombs were found. The two methods used to investigate this site were thermal infrared and ground penetrating radar (GPR). The thermography was carried out with the sensor mounted under a helium balloon simultaneously with a photographic camera. In order to have a synthetic view of the surface thermal behavior, a simplified version of the existing night thermal gradient algorithm was applied. By this approach, we have a wide extension of thermal maps due to the balloon oscillation, because we are able to compute the maps despite collecting few acquisition samples. By the integration of GPR and the thermal imaging, we can evaluate the depth of the thermal influence of possible archaeological targets, such as buried Punic tombs or walls belonging to the succeeding medieval buildings, which have been subsequently destroyed. The thermal anomalies present correspondences to the radar time slices obtained from 30 to 50 cm. Furthermore, by superimposing historical aerial pictures on the GPR and thermal imaging data, we can identify these anomalies as the foundations of the destroyed buildings. Full article
(This article belongs to the Special Issue New Perspectives of Remote Sensing for Archaeology)
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Full article ">Figure 1
<p>(<b>a</b>) Localization of Villamar town in Sardinia; (<b>b</b>) localization of the test site.</p> Full article ">Figure 2
<p>(<b>a</b>) Tomb with a hypogeum chamber, with the plan and cross-section; (<b>b</b>) tomb in a stone cist, with the plan and cross-section; (<b>c</b>) tomb in a pit, with the plan and cross-section; (<b>d</b>) tomb in an <span class="html-italic">enchytrismós</span>, with the cross-section; (<b>e</b>) tomb at the “capuchin”, with the plan and cross-section.</p> Full article ">Figure 3
<p>GPR maps and interpretation of the anomalies (on the left of every GPR map, an interpretative scheme is drawn reporting linear and concentrated anomalies) at various depths: (<b>a</b>) 0.30 m; (<b>b</b>) 0.50 m; (<b>c</b>) 0.90 m; (<b>d</b>) 1.30 m.</p> Full article ">Figure 3 Cont.
<p>GPR maps and interpretation of the anomalies (on the left of every GPR map, an interpretative scheme is drawn reporting linear and concentrated anomalies) at various depths: (<b>a</b>) 0.30 m; (<b>b</b>) 0.50 m; (<b>c</b>) 0.90 m; (<b>d</b>) 1.30 m.</p> Full article ">Figure 4
<p>(<b>a</b>) The thermocamera and the camera mounted on the rocker element in the acquisition layout; (<b>b</b>) the monitor for the real-time control of acquisitions.</p> Full article ">Figure 5
<p>The monitoring conditions.</p> Full article ">Figure 6
<p>The thermal map of apparent intercept temperature (in Kelvin degrees), resulting from the merging of many thermograms and processing temperature over time.</p> Full article ">Figure 7
<p>The thermal map of the night thermal gradient (NTG) index (in Kelvin/minute, negative values), resulting from the merging of many thermograms and processing temperature over time.</p> Full article ">Figure 8
<p>The historical aerial pictures of the test site. (<b>a</b>) 1977; (<b>b</b>) 1981; (<b>c</b>) 1987; (<b>d</b>) 1996.</p> Full article ">Figure 8 Cont.
<p>The historical aerial pictures of the test site. (<b>a</b>) 1977; (<b>b</b>) 1981; (<b>c</b>) 1987; (<b>d</b>) 1996.</p> Full article ">Figure 9
<p>The aerial pictures of the test site acquired by the camera hanging from the balloon.</p> Full article ">Figure 10
<p>The filtered zoom of the picture in <a href="#remotesensing-06-10986-f009" class="html-fig">Figure 9</a> (central part, near the gangway).</p> Full article ">Figure 11
<p>(<b>a</b>) The aerial photograph of 1977 with the tracking of some alignments of some of the main walls in yellow; (<b>b</b>) the GPR map at a 0.50-m depth with tracking of alignments in gray.</p> Full article ">Figure 12
<p>Radargrams crossing the two big anomalies of the GPR map at a 90-cm depth: (<b>a</b>) The GPR map; (<b>b</b>) Profile a-a; (<b>c</b>) Profile b-b.</p> Full article ">Figure 13
<p>The apparent intercept temperature map, in Kelvin, in a stretched grayscale image shows correspondences with previous building alignments (see <a href="#remotesensing-06-10986-f011" class="html-fig">Figure 11</a>a) in the northern sector.</p> Full article ">Figure 14
<p>The NTG map, after histogram enhancing of middle range values, shows weak, but existing correspondences with previous building alignments (see <a href="#remotesensing-06-10986-f011" class="html-fig">Figure 11</a>a) in the north-central sector. Other darker regions are shown.</p> Full article ">Figure 15
<p>Thermal and visible maps of the area in <a href="#remotesensing-06-10986-f010" class="html-fig">Figure 10</a>. (<b>a</b>) The visible bands view; (<b>b</b>) the NTG map; (<b>c</b>) the intercept temperature map.</p> Full article ">Figure 15 Cont.
<p>Thermal and visible maps of the area in <a href="#remotesensing-06-10986-f010" class="html-fig">Figure 10</a>. (<b>a</b>) The visible bands view; (<b>b</b>) the NTG map; (<b>c</b>) the intercept temperature map.</p> Full article ">Figure 16
<p>Map obtained by multiplying normalized intercept temperature by normalized NTG maps (both centered on the mean value). Positive values are darker, while negative ones are brighter. In the south-western part of the map, the border of the most recent digging area is indicated in yellow, while dug (after the survey) tombs are in violet.</p> Full article ">Figure 17
<p>Details of the archaeological analysis of the map from <a href="#remotesensing-06-10986-f016" class="html-fig">Figure 16</a> (obtained by multiplying normalized intercept temperature by normalized NTG maps). (<b>a</b>) Detail of the thermal map with Thermal Pattern A in correspondence with the GPR anomaly indicated as A in <a href="#remotesensing-06-10986-f012" class="html-fig">Figure 12</a>; (<b>b</b>) detail of thermal patterns superimposed by dug (after the survey) tombs (B and D), and in yellow, the border of the most recent digging area.</p> Full article ">
7018 KiB  
Article
A Synergistic Methodology for Soil Moisture Estimation in an Alpine Prairie Using Radar and Optical Satellite Data
by Binbin He, Minfeng Xing and Xiaojing Bai
Remote Sens. 2014, 6(11), 10966-10985; https://doi.org/10.3390/rs61110966 - 10 Nov 2014
Cited by 82 | Viewed by 7757
Abstract
This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The methodology adequately represents the scattering behavior of the vegetation-covered area by defining the scattering of the vegetation and the soil below. The Integral Equation Method (IEM) was [...] Read more.
This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The methodology adequately represents the scattering behavior of the vegetation-covered area by defining the scattering of the vegetation and the soil below. The Integral Equation Method (IEM) was employed to determine the backscattering of the underlying soil. The modified Water Cloud Model (WCM) was used to reduce the effect of vegetation. Vegetation coverage, which can be easily derived from optical data, was incorporated in this method to account for the vegetation gap information. Then, an inversion scheme of soil moisture was developed that made use of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data. The method developed in this study was assessed by comparing the reproduction of the backscattering, which was calculated from an area with full vegetation cover to that with relatively sparse cover. The accuracy and sources of error in this soil moisture retrieval method were evaluated. The results showed a good correlation between the measured and estimated soil moisture (R2 = 0.71, RMSE = 3.32 vol.%, p < 0.01). Therefore, this method has operational potential for estimating soil moisture under the vegetated area of an alpine prairie. Full article
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Graphical abstract

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Full article ">Figure 1
<p>Location of study region and the distribution of sampling plots in the study area. The background image is a charge-coupled device (CCD) composite image of bands 4 (near-infrared), 3 (red), and 2 (green) (corresponding to R, G, B color space) showing the study area.</p> Full article ">Figure 2
<p>Scatterplot illustrating the relationship between the measured backscattering from images and predicted backscattering values from the vegetation backscattering model described in <a href="#sec3dot2dot1-remotesensing-06-10966" class="html-sec">Section 3.2.1</a> for (<b>a</b>) HH polarization and (<b>b</b>) VV polarization.</p> Full article ">Figure 3
<p>Scatterplot illustrating the relationship between the measured backscattering from the images and predicted backscattering values from the modified vegetation backscattering model described in <a href="#sec3dot2dot2-remotesensing-06-10966" class="html-sec">Section 3.2.2</a> for (<b>a</b>) HH polarization and (<b>b</b>) VV polarization.</p> Full article ">Figure 4
<p>The absolute error map of the model simulation.</p> Full article ">Figure 5
<p>Scatterplot illustrating the relationship between the measured and predicted soil moisture when using the WCM to reduce the effect of vegetation.</p> Full article ">Figure 6
<p>Scatterplot illustrating the relationship between the measured and predicted soil moisture when using the modified WCM to reduce the effect of vegetation.</p> Full article ">
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