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Volume 11
Issue 13
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Remote Sens., Volume 11, Issue 13 (July-1 2019) – 123 articles

Cover Story (view full-size image): Despite recent research on the potential of dual polarimetry (DP) and full polarimetry (FP) synthetic aperture radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. The inclusion of these beam modes on the Canadian RADARSAT Constellation Mission (RCM), launched in June 2019, makes such a study important and timely from both a research and operational perspective. While previous studies have illustrated the potential for accurate crop mapping using DP and FP SAR features, the contributions of each feature to model accuracy are not well understood. This study examines the potential of early- to mid-season RADARSAT-2 images for crop mapping in an agricultural region in Manitoba, Canada. View this paper.
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22 pages, 5880 KiB  
Article
Influence of Topographic Resolution and Accuracy on Hydraulic Channel Flow Simulations: Case Study of the Versilia River (Italy)
by Marco Luppichini, Massimiliano Favalli, Ilaria Isola, Luca Nannipieri, Roberto Giannecchini and Monica Bini
Remote Sens. 2019, 11(13), 1630; https://doi.org/10.3390/rs11131630 - 9 Jul 2019
Cited by 10 | Viewed by 4407
Abstract
The Versilia plain, a well-known and populated tourist area in northwestern Tuscany, is historically subject to floods. The last hydrogeological disaster of 1996 resulted in 13 deaths and in loss worth hundreds of millions of euros. A valid management of the hydraulic and [...] Read more.
The Versilia plain, a well-known and populated tourist area in northwestern Tuscany, is historically subject to floods. The last hydrogeological disaster of 1996 resulted in 13 deaths and in loss worth hundreds of millions of euros. A valid management of the hydraulic and flooding risks of this territory is therefore mandatory. A 7.5 km-long stretch of the Versilia River was simulated in one-dimension using river cross-sections with the FLO-2D Basic model. Simulations of the channel flow and of its maximum flow rate under different input conditions highlight the key role of topography: uncertainties in the topography introduce much larger errors than the uncertainties in roughness. The best digital elevation model (DEM) available for the area, a 1-m light detection and ranging (LiDAR) DEM dating back to 2008–2010, does not reveal all the hydraulic structures (e.g., the 40 cm thick embankment walls), lowering the maximum flow rate to only 150 m3/s, much lower than the expected value of 400 m3/s. In order to improve the already existing input topography, three different possibilities were considered: (1) to add the embankment walls to the LiDAR data with a targeted Differential GPS (DGPS) survey, (2) to acquire the cross section profiles necessary for simulation with a targeted DGPS survey, and (3) to achieve a very high resolution topography using structure from motion techniques (SfM) from images acquired using an unmanned aerial vehicle (UAV). The simulations based on all these options deliver maximum flow rates in agreement with estimated values. Resampling of the 10 cm cell size SfM-DSM allowed us to investigate the influence of topographic resolution on hydraulic channel flow, demonstrating that a change in the resolution from 30 to 50 cm alone introduced a 10% loss in the maximum flow rate. UAV-SfM-derived DEMs are low cost, relatively fast, very accurate, and they allow for the monitoring of the channel morphology variations in real time and to keep the hydraulic models updated, thus providing an excellent tool for managing hydraulic and flooding risks. Full article
(This article belongs to the Special Issue Unmanned Aerial Systems and Digital Terrain Modeling)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Study area showing the extent of the four DEMs derived from UAV surveys.</p> Full article ">Figure 2
<p>FLO-2D model set up. (<b>a</b>) Computational domain with FLO-2D model input sections (in green): sections extrapolated from LiDAR (in black) and sections extrapolated from LiDAR or UAV models as described in the text (in red). The blue flag shows the location of input hydrographs, while red flags show locations of output hydrographs. The yellow and blue polygons represent the Manning value of 0.036 and 0.029 used in the models. Numbers indicate the location of the cross-section profiles in Figure 7. The hill-shaded background is from the LiDAR DTM. (<b>b</b>) Ortomosaic zoom-in of a river segment, and location of the FLO-2D input sections (in red). (<b>c</b>) Hill-shaded zoom of the UAV DSM with the location of the FLO-2D input sections (in red).</p> Full article ">Figure 3
<p>Spatial distribution of the GCPs used for the model construction (see <a href="#remotesensing-11-01630-t002" class="html-table">Table 2</a> and <a href="#remotesensing-11-01630-t003" class="html-table">Table 3</a>). Color coding refers to all the models and shows the vertical differences between DEM and GCP elevation. (<b>a</b>) Model 1; (<b>b</b>) model 4, inset shows an example of the targets captured in the images; (<b>c</b>) model 2; and (<b>d</b>) model 3.</p> Full article ">Figure 4
<p>Model 1 error evaluation. (<b>a</b>) Area distribution of the GPS points used for georeferencing and/or error evaluation. GCPs refer to markers used in the Photoscan model construction (see <a href="#remotesensing-11-01630-t002" class="html-table">Table 2</a> and <a href="#remotesensing-11-01630-t003" class="html-table">Table 3</a>). Check points refer to the independent set of points to check DEM accuracy. (<b>b</b>) Control points RMSE (blue dots) and check points RMSE (red dots) of Photoscan models were built as a function of the number of control points used. The green strip shows the value range of the number of GCPs suggested by the Photoscan Tutorial for accurate model georeferencing. See the text for details.</p> Full article ">Figure 5
<p>Difference between SfM DSM and LiDAR DSM inside the channel. a) Studied area; b and c) magnified areas corresponding to the black boxes in frame a.</p> Full article ">Figure 6
<p>DEM analysis of relevant hydraulic features. (<b>a</b>,<b>b</b>) Hillshaded maps of the 1-m LiDAR DTM and SfM DSM, respectively. (<b>c</b>) Difference between SfM DSM and LiDAR DSM. (<b>d</b>) Longitudinal profiles along the embankment walls (yellow lines in frames a and b). (<b>e</b>) Profiles of the river cross section (black lines in frame a and b). Flow rate values show the maximum flow rate for the different DEMs according to FLO-2D simulations.</p> Full article ">Figure 7
<p>Comparison of the cross-section profiles derived from different topographic data. See <a href="#remotesensing-11-01630-f002" class="html-fig">Figure 2</a> for the locations of the cross-sections along the channel. (<b>a</b>) Profiles derived from SfM DSM, LiDAR DSM and DTM. (<b>b</b>) Profiles derived from SfM DSM and Simplified Sections.</p> Full article ">Figure 8
<p>Flow rates computed using FLO-2D at four different sections (see <a href="#remotesensing-11-01630-f002" class="html-fig">Figure 2</a>) for an input flow step function with 50 m<sup>3</sup>/s increments (maximum input rate 600 m<sup>3</sup>/s) in the simulations based on LiDAR DTM, LiDAR DSM, and SfM DSM. The shaded area highlights the flow rate range without flooding.</p> Full article ">Figure 9
<p>Flow rates computed using FLO-2D at four different points (see <a href="#remotesensing-11-01630-f002" class="html-fig">Figure 2</a>) for an input flow step function with 50 m<sup>3</sup>/s increments (maximum input rate 600 m<sup>3</sup>/s) in the simulations based on simplified sections extracted from the SfM DSM, SfM DSM+, and LiDAR DTM with bank walls updated using the SfM DSM. The shaded area highlights the flow rate range without flooding.</p> Full article ">Figure 10
<p>The relation between flow rates and flow height for different topographic data: (<b>a</b>) position of the section, (<b>b</b>) section profiles, and (<b>c</b>) flow height vs. flow rates plot where dots are calculated using FLO-2D and dashed lines are the rating curves calculated using the Manning Formula (Equation (1)).</p> Full article ">Figure 11
<p>Effect of topography resolution on the maximum flow rate for three selected sections. (<b>a</b>) Hill-shaded map of a reach of the Versilia River with the location of the sections. (<b>b</b>) Plots of topographic profiles, maximum flow rate vs. maximum flow height, as function of the topography resolution and maximum flow rate vs. cell size.</p> Full article ">
24 pages, 6024 KiB  
Article
Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach
by Daniel Jensen, Marc Simard, Kyle Cavanaugh, Yongwei Sheng, Cédric G. Fichot, Tamlin Pavelsky and Robert Twilley
Remote Sens. 2019, 11(13), 1629; https://doi.org/10.3390/rs11131629 - 9 Jul 2019
Cited by 31 | Viewed by 5594
Abstract
The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability [...] Read more.
The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R2 = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids. Full article
(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)
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Graphical abstract
Full article ">Figure 1
<p>The study areas corresponding to water sample and spectroscopic measurement locations: Canada’s Peace–Athabasca Delta (top-left), Grizzly Bay in California’s San Francisco Bay–Delta Estuary (middle), and the Louisiana’s Wax Lake Delta and Atchafalaya River—this study’s primary study area (bottom-right).</p> Full article ">Figure 2
<p>(<b>a</b>) Sample spectra plots for the 2016 Louisiana field spectrometer reflectance measurements (left), representing the paired spectra for the sample set’s minimum (8.98 mg/L), median (30.94 mg/L), and maximum (74.39 mg/L) TSS measurements, and the corresponding first derivatives (right). (<b>b</b>) Partial least squares regression (PLSR) model summaries for simulated Airborne Visible–Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) reflectance and first derivative. The red lines indicate the Variable Importance in Projection value at each band (bottom) for the initial PLSR model, and the black lines (top) indicate the models’ coefficient value for each utilized band (also reported in <a href="#remotesensing-11-01629-t0A1" class="html-table">Table A1</a>). Shaded regions indicate the utilized wavelengths in the final PLSR models, where the associated Variable Importance in Projection (VIP) &gt; 1.</p> Full article ">Figure 3
<p>Validation scatterplots for the simulated MODIS OLSR and generalized (712.5 nm) [<a href="#B18-remotesensing-11-01629" class="html-bibr">18</a>] models based on the 2015 and 2016 Atchafalaya and Wax Lake Delta datasets. The (<b>a</b>) Band 1 and (<b>b</b>) Band 2 models mirror those developed by Chen et al. [<a href="#B21-remotesensing-11-01629" class="html-bibr">21</a>] but are parameterized with the 2016 Louisiana <span class="html-italic">R<sub>rs</sub></span> data to investigate the performance of established multispectral methods relative to imaging spectroscopy models. The generic model (<b>c</b>) utilizes the hyperspectrally tabulated model coefficients published by Nechad et al. [<a href="#B18-remotesensing-11-01629" class="html-bibr">18</a>] and <span class="html-italic">R<sub>rs</sub></span> for 712.5 nm.</p> Full article ">Figure 4
<p>Validation scatterplots for the PLSR models applied to the 2015 and 2016 coastal Louisiana datasets: (<b>a</b>) simulated MODIS ocean color band reflectance, (<b>b</b>) AVIRIS-NG reflectance, and (<b>c</b>) AVIRIS-NG derivative.</p> Full article ">Figure 5
<p>Validation results for total suspended solids (TSS, mg/L) retrieval from the San Francisco Bay–Delta Estuary PRISM dataset with the (<b>a</b>) simulated MODIS band 1 regression model, (<b>b</b>) generalized model at 712.5 nm [<a href="#B18-remotesensing-11-01629" class="html-bibr">18</a>], and (<b>c</b>) first derivative-based PLSR model. The PLSR model shows significantly better performance, with a lower RMSE and a line of best fit that has better agreement with the 1:1 line. Additional error metrics are listed in <a href="#remotesensing-11-01629-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 6
<p>Validation results for total suspended solids (TSS, mg/L) from the Peace–Athabasca Delta field spectrometer dataset with the (<b>a</b>) simulated MODIS band 1 regression model, (<b>b</b>) generalized model at 712.5 nm, and (<b>c</b>) first derivative-based PLSR model. Similar to <a href="#remotesensing-11-01629-f005" class="html-fig">Figure 5</a>, the PLSR results here again show superior performance, with a lower RMSE and a line of best fit closer to the 1:1 line. <a href="#remotesensing-11-01629-t003" class="html-table">Table 3</a> contains additional error metrics for the latter model.</p> Full article ">Figure 7
<p>Total suspended solids (TSS, mg/L) maps produced with the modified derivative-based PLSR algorithm applied to the 2015 and 2016 AVIRIS-NG mosaics.</p> Full article ">Figure 8
<p>Total suspended solids (TSS, mg/L) retrieval result maps from the (<b>a</b>) San Francisco Bay–Delta Estuary PRISM dataset and (<b>b</b>) Peace–Athabasca Delta field spectrometer sample points.</p> Full article ">Figure A1
<p>Remote sensing reflectance spectra derived from the ASD FieldSpec<sup>®</sup> 3 spectrometer and the corresponding AVIRIS-NG pixel spectra, with all corrections applied. Spectral angle, represented by <math display="inline"><semantics> <mi>α</mi> </semantics></math>, is a measure of the difference in overall spectral shape between the two samples, with 0 denoting a perfect similarity.</p> Full article ">Figure A2
<p>Statistical relationships between predicted TSS residuals and silicate (micromolar) for the 2015 and 2016 Louisiana in situ validation datasets. Silicate here serves as a proxy for particulate inorganic matter. The regression line is plotted for the 2015 data’s relationships that is significant at a <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure A3
<p>Statistical relationships between the predicted TSS residuals and other water constituents from corresponding water samples in the San Francisco Bay–Delta Estuary. These include chlorophyll <span class="html-italic">a</span> concentration (left) and the CDOM absorption coefficient at 350 nm (right) for both the simulated MODIS B1 model (top) and the AVIRIS-NG derivative-based PLSR model (bottom). Regression lines are plotted for relationships that are significant at a <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure A4
<p>Statistical relationships between the predicted TSS residuals and other water constituents from corresponding water samples in the Peace–Athabasca Delta. These include chlorophyll <span class="html-italic">a</span> (left) and CDOM (right) concentrations for both the simulated MODIS B1 model (top) and the AVIRIS-NG derivative-based PLSR model (bottom). CDOM is listed in parts per billion of a standard solution used to calibrate the fluorescence probe. Regression lines are plotted for relationships that are significant at a <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">
18 pages, 2776 KiB  
Article
Copula-Based Abrupt Variations Detection in the Relationship of Seasonal Vegetation-Climate in the Jing River Basin, China
by Jing Zhao, Shengzhi Huang, Qiang Huang, Hao Wang, Guoyong Leng, Jian Peng and Haixia Dong
Remote Sens. 2019, 11(13), 1628; https://doi.org/10.3390/rs11131628 - 9 Jul 2019
Cited by 46 | Viewed by 4311
Abstract
Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, [...] Read more.
Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982–2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Niño 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities (“Grain For Green” program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems. Full article
(This article belongs to the Special Issue Observations, Modeling, and Impacts of Climate Extremes)
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Graphical abstract
Full article ">Figure 1
<p>Location of the Jing River Basin. (<b>a</b>) The basin map of the Jing River Basin; (<b>b</b>) location of the Jing River Basin in the Yellow River Basin, China.</p> Full article ">Figure 2
<p>Flowchart of the framework for the change point identification.</p> Full article ">Figure 3
<p>The seasonal Normalized Difference Vegetation Index (NDVI) and precipitation/temperature (P/T) series covering 1982–2010 in the Jing River Basin (JRB).</p> Full article ">Figure 4
<p>The statistics of copula-based method in detecting the change points of the relationship between seasonal NDVI and P/T during 1982–2010 in the JRB.</p> Full article ">Figure 5
<p>The double mass curves of seasonal NDVI and P/T covering 1982–2010 in the JRB. (<b>a</b>) and (<b>b</b>) represent the double-mass curve of spring NDVI-P/T; (<b>c</b>,<b>d</b>) represent the double-mass curve of summer NDVI-P/T; (<b>e</b>,<b>f</b>) represent the double-mass curve of autumn NDVI-P/T.</p> Full article ">Figure 6
<p>Land use map in different periods of the Jing River Basin.</p> Full article ">
15 pages, 8794 KiB  
Article
Assessment of the Ice Wedge Polygon Current State by Means of UAV Imagery Analysis (Samoylov Island, the Lena Delta)
by Andrei Kartoziia
Remote Sens. 2019, 11(13), 1627; https://doi.org/10.3390/rs11131627 - 9 Jul 2019
Cited by 20 | Viewed by 5656
Abstract
Modern degradation of Arctic permafrost promotes changes in tundra landscapes and leads to degradation of ice wedge polygons, which are the most widespread landforms of Arctic wetlands. Status assessment of polygon degradation is important for various environmental studies. We have applied the geographic [...] Read more.
Modern degradation of Arctic permafrost promotes changes in tundra landscapes and leads to degradation of ice wedge polygons, which are the most widespread landforms of Arctic wetlands. Status assessment of polygon degradation is important for various environmental studies. We have applied the geographic information systems’ (GIS) analysis of data from unmanned aerial vehicles (UAV) to accurately assess the status of ice wedge polygon degradation on Samoylov Island. We used several modern models of polygon degradation for revealing polygon types, which obviously correspond to different stages of degradation. Manual methods of mapping and a high spatial resolution of used UAV data allowed for a high degree of accuracy in the identification of all land units. The study revealed the following: 41.79% of the first terrace surface was composed of non-degraded polygonal tundra; 18.37% was composed of polygons, which had signs of thermokarst activity and corresponded to various stages of degradation in the models; and 39.84% was composed of collapsed polygons, slopes, valleys, and water bodies, excluding ponds of individual polygons. This study characterizes the current status of polygonal tundra degradation of the first terrace surface on Samoylov Island. Our assessment reflects the landscape condition of the first terrace surface of Samoylov Island, which is the typical island of the southern part of the Lena Delta. Moreover, the study illustrates the potential of UAV data GIS analysis for highly accurate investigations of Arctic landscape changes. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of the study area. (<b>a</b>) An obtained orthophoto map mosaic of Samoylov Island. The red line is the border of the analyzed first terrace surface. (<b>b</b>) Samoylov Island (red square) in a mosaic of satellite Sentinel-2 images of the Lena Delta.</p> Full article ">Figure 2
<p>The obtained Samoylov Island digital terrain model (<b>a</b>) and maps of land morphometry parameters: (<b>b</b>) a shaded relief map, (<b>c</b>) a slope map, and (<b>d</b>) an aspect map.</p> Full article ">Figure 3
<p>Incipient polygons (IP) in (<b>a</b>) an orthophoto map that is mixed with a grey shaded relief (GSR) map and (<b>b</b>) an aspect map, which shows flat centers of polygons without elevated rims.</p> Full article ">Figure 4
<p>An example of low-centered polygons (LCP) in (<b>a</b>) an orthophoto map that is mixed with a GSR map and (<b>b,c</b>) a map of local differentiation of the relief that shows a difference between mean DTM, which was derived from DTM averaging with a moving window of 50 m, and actual DTM. Red color marks elevated terrains and green color marks local hollows. These maps show that the elevation of troughs was greater than polygon centers in LCP (2.1) (b), LCP with water-filled centers (2.2) (b), and even in LCP with water-filled troughs (2.3) (c).</p> Full article ">Figure 5
<p>Intermediate-centered polygons (ICP) with water-filled troughs in (<b>a</b>) an orthophoto map that is mixed with a GSR map. We obtained a topography profile (<b>b</b>) by using ArcGIS. This profile shows troughs, which subsided below the polygon centers and elevated rims.</p> Full article ">Figure 6
<p>High-centered polygons (HCP) in (<b>a</b>) an orthophoto map that is mixed with a GSR map, and in (<b>b</b>) a slope map that overlays an orthophoto map. Yellow color is used for slopes with inclination from 3° to 10°, and red color marks slopes with an inclination of more than 10°. These maps show flat centers of HCP, which are higher than slightly elevated rims and troughs.</p> Full article ">Figure 7
<p>An example of collapsed polygons (CP; number 1 in a white circle) and a mapped pond (2), which is related to lakes and other water bodies. This is an orthophoto map that is mixed with a GSR map.</p> Full article ">Figure 8
<p>Lakes and other water bodies (number 1 in a white circle), valleys (2), and slopes (3) in (<b>a</b>) an orthophoto map that is mixed with a GSR map, and in (<b>b</b>) an aspect map.</p> Full article ">Figure 9
<p>A geomorphology map that is the result of the described mapping procedure and shows the spatial distribution of all mapped land units. Legend: 1 – incipient polygons (IP); 2.1 – low-centered polygons (LCP); 2.2 – LCP with water-filled centers; 2.3 – LCP with water-filled troughs; 3.1 – intermediate-centered polygons (ICP); 3.2 – ICP with water-filled centers; 3.3 – ICP with water-filled troughs; 4.1 – high-centered polygons (HCP); 4.2 – HCP with water-filled troughs; 5 – collapsed polygons (CP); 6 – slopes; 7 – valleys; 8 – lakes and other water bodies; 9 – the Samoylov Island research station.</p> Full article ">Figure A1
<p>Schematic profiles of mapped polygon types: (<b>a</b>) incipient polygons (IP); (<b>b</b>) low-centered polygons (LCP); (<b>c</b>) intermediate-centered polygons (ICP); (<b>d</b>) high-centered polygons (HCP).</p> Full article ">
15 pages, 3310 KiB  
Article
Improving the Positioning Accuracy of Satellite-Borne GNSS-R Specular Reflection Point on Sea Surface Based on the Ocean Tidal Correction Positioning Method
by Fan Wu, Wei Zheng, Zhaowei Li and Zongqiang Liu
Remote Sens. 2019, 11(13), 1626; https://doi.org/10.3390/rs11131626 - 9 Jul 2019
Cited by 10 | Viewed by 3992
Abstract
The positioning error of the specular reflection point is the main error source of Global Navigation Satellite System Reflectometry (GNSS-R) satellite sea surface altimetry. The existing specular reflection point geometric positioning methods do not consider the static-state elevation difference of tens of meters [...] Read more.
The positioning error of the specular reflection point is the main error source of Global Navigation Satellite System Reflectometry (GNSS-R) satellite sea surface altimetry. The existing specular reflection point geometric positioning methods do not consider the static-state elevation difference of tens of meters and the decimeter-level time-varying elevation difference between the reflection reference surface and the instantaneous sea surface. The resulting positioning error restricts the GNSS-R satellite sea surface altimetry from reaching cm-level high accuracy on the reference datum. Under the premise of the basic static-state elevation positioning error correction, reducing the time-varying elevation positioning error is the key to improving positioning accuracy. In this study, based on the principle of elevation correction of GNSS-R reflection reference surface, the main parameter that determines the real-time variation of sea surface height, ocean tide, is used to correct the specular reflection point from geoid to ocean tidal surface. The positioning error caused by the time-varying elevation error of the reflection reference surface is reduced, the positioning accuracy is improved, and the improvement is quantified. According to the research results, the ocean tidal correction positioning (OTCP) method improves the positioning accuracy by 0.31 m. The positioning accuracy improvement has a good correlation with the corresponding tidal height modulo, and the improvement is 1.07 times of the tidal height modulo. In the offshore, the tidal height gradient modulo is greater than the deep sea, the gradient of the tidal positioning correction has a good response to the tidal height gradient modulo, while the sensitivity of this response decreases in the deep sea. Full article
(This article belongs to the Special Issue Global Navigation Satellite Systems for Earth Observing System)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>GNSS-R reflection reference surfaces and the corresponding specular reflection points. Where <span class="html-italic">S</span> and <span class="html-italic">S’</span> are the specular reflection points on geoid and ocean tidal surface, respectively. The purple arrow represents the positioning correction by the ocean tidal correction positioning (OTCP) method.</p> Full article ">Figure 2
<p>The GNSS-R specular reflection point positioning geometry [<a href="#B13-remotesensing-11-01626" class="html-bibr">13</a>].</p> Full article ">Figure 3
<p>Tidal height modulos and the corresponding tidal positioning corrections of the tracks.</p> Full article ">Figure 4
<p>Tidal height modulos and the corresponding tidal positioning corrections of the tracks with the fitting of the two.</p> Full article ">Figure 5
<p>Tidal height (red curve) and its gradient modulo (blue curve) of the specular points by the OTCP method on a deep sea-offshore track.</p> Full article ">Figure 6
<p>Tidal height gradient modulos and the corresponding tidal positioning correction gradient modulos of the offshore segments.</p> Full article ">Figure 7
<p>Tidal height gradient modulos and the corresponding tidal positioning correction gradient modulos of the deep sea segments.</p> Full article ">Figure 8
<p>Tidal height gradient modulos, the corresponding tidal positioning correction gradient modulos and their fittings of the offshore and the deep sea segments.</p> Full article ">
20 pages, 2363 KiB  
Article
Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)
by Giulio Nils Caroletti, Roberto Coscarelli and Tommaso Caloiero
Remote Sens. 2019, 11(13), 1625; https://doi.org/10.3390/rs11131625 - 9 Jul 2019
Cited by 30 | Viewed by 4407
Abstract
Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output [...] Read more.
Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951–1980 and 79 series for the period 1981–2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981–2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981–2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations. Full article
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<p>Digital Elevation Model (DEM) map of Calabria with the location of the 79-stations rain gauge, labeled with the code number (see <a href="#app1-remotesensing-11-01625" class="html-app">Appendix A</a> <a href="#remotesensing-11-01625-t0A1" class="html-table">Table A1</a> for details on rain gauge codes and names).</p> Full article ">Figure 2
<p>Adimensional error of mean (x-axis) and standard deviation (y-axis) of datasets versus validation data: (<b>a</b>) for the 1951–1980 time period, in which 73 stations out of 79 were available; (<b>b</b>) for the 1981–2010 time period, during which all 79 stations were used.</p> Full article ">Figure 3
<p>Change in dataset performance for mean error (x-axis) and standard deviation error (y-axis) from the 1951–1980 to the 1981–2010 time period for the 13 ENSEMBLES models and the E-OBS dataset.</p> Full article ">Figure 4
<p>Pearson correlation coefficient between monthly rain gauge precipitation and gridded dataset precipitation: (<b>a</b>) for all months in the 1951–2010 time period (13 ENSEMBLES models + E-OBS); (<b>b</b>) for all months in the 1981-2010 period (13 ENSEMBLES models + E-OBS + CHIRPS).</p> Full article ">Figure 5
<p>Pearson correlation coefficient between seasonal rain gauge precipitation and gridded dataset precipitation: (<b>a</b>) winter, (<b>c</b>) spring, (<b>e</b>) summer and (<b>g</b>) fall months in the 1951–2010 time period (13 ENSEMBLES models + E-OBS); (<b>b</b>) winter (<b>d</b>) spring, (<b>f</b>) summer and (<b>h</b>) fall months in the 1981–2010 period (13 ENSEMBLES models + E-OBS + CHIRPS).</p> Full article ">Figure 6
<p>QQ-plots for gridded data with Pearson correlation values with rain gauge above 0.9. Monthly precipitation versus rain gauge data at all stations, from 1951 to 2010: (<b>a</b>) for E-OBS reanalysis; (<b>b</b>) for ECH-HIR model data; (<b>c</b>) for ARP-HIR model data; (<b>d</b>) for BCM-HIR model data; (<b>e</b>) for ECH-RCA model data; (<b>f</b>) for HCH-RCA model data; (<b>g</b>) for ECH-REM model data; (<b>h</b>) for ECH-RMO model data.</p> Full article ">Figure 7
<p>As <a href="#remotesensing-11-01625-f006" class="html-fig">Figure 6</a>, but for CHIRPS data, from 1981 to 2010.</p> Full article ">
24 pages, 7935 KiB  
Article
A New Method for Characterizing NOAA-20/S-NPP VIIRS Thermal Emissive Bands Response Versus Scan Using On-Orbit Pitch Maneuver Data
by Wenhui Wang, Changyong Cao and Slawomir Blonski
Remote Sens. 2019, 11(13), 1624; https://doi.org/10.3390/rs11131624 - 9 Jul 2019
Cited by 7 | Viewed by 3568
Abstract
The on-orbit calibration of Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Emissive Bands (TEB), onboard the National Oceanic and Atmospheric Administration-20 (NOAA-20) and the Suomi National Polar-orbiting Partnership (S-NPP) satellites, have been stable during nominal operations. However, larger than expected scan angle/scene temperature [...] Read more.
The on-orbit calibration of Visible Infrared Imaging Radiometer Suite (VIIRS) Thermal Emissive Bands (TEB), onboard the National Oceanic and Atmospheric Administration-20 (NOAA-20) and the Suomi National Polar-orbiting Partnership (S-NPP) satellites, have been stable during nominal operations. However, larger than expected scan angle/scene temperature dependent biases, relative to the co-located Cross-track Infrared Sounder (CrIS) observations, were observed in the NOAA-20 longwave infrared (LWIR) bands. The Response Versus Scan (RVS) effect—the variation of instrument reflectance of source radiance with scan angle, is a significant contributor to VIIRS calibration. TEB RVS is characterized using prelaunch test data and verified on-orbit using pitch maneuver data. This study presents a new method that characterizes VIIRS on-orbit TEB RVS at both Earth View (EV) and Space View (SV) scan angles simultaneously. This method was compared with an existing on-orbit RVS method (the Wu et al. method), which derives RVS at EV scan angles using pitch maneuver data and extrapolates SV RVS from EV. The new method derived on-orbit RVS differ from prelaunch values up to 1.0% at the beginning of scan in the NOAA-20 LWIR bands, and ~0.5% in S-NPP M15. VIIRS–CrIS inter-comparison results indicates that the new method derived on-orbit RVS can effectively minimize LWIR scan angle/scene temperature dependent biases, with scan averaged biases reduced from 0.40K to 0.15K for NOAA-20 LWIR bands, and from 0.24K to 0.08K for S-NPP M15. The Wu et al. method can also reduce the scan angle dependent biases, but at the expense of increasing the scene temperature dependent biases. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Diagram of Visible Infrared Imaging Radiometer Suite (VIIRS) scan pattern. Scan angles and angle of incidences (AOIs) at space view (SV), blackbody (OBCBB), the beginning of Earth View (EV) scan (EVBOS), nadir, the end of EV scan (EVEOS), and AOI<sub>min</sub> are also given.</p> Full article ">Figure 2
<p>S-NPP and NOAA-20 band averaged Response Versus Scan (RVS) as functions of AOI for half angle mirror (HAM)-A (solid lines) and HAM-B (dash lines). The SV, EVBOS, EVEOS, and OBCBB AOIs are also marked.</p> Full article ">Figure 3
<p>NOAA-20 VIIRS–CrIS BT differences at 10 scene temperatures and as function of CrIS FOR position and VIIRS scan angle. Under each scene temperature, the sizes of the filled circles are proportional to the number of data samples used.</p> Full article ">Figure 4
<p>S-NPP VIIRS–CrIS BT differences at 10 scene temperatures and as function of CrIS FOR position and VIIRS scan angle. Under each scene temperature, the sizes of the filled circles are proportional to the number of data samples used.</p> Full article ">Figure 5
<p>An image of NOAA-20 M15 EV counts during the beginning of pitch maneuverer (January 31, 2018, 13:42:23–13:48:04 UTC). The bright feature that occupies most of the image is the Earth. The dark portion of the image in the bottom shows the deep space data for on-orbit RVS characterization. The striping at the beginning and the end of scan are fill values due to bowtie deletion.</p> Full article ">Figure 6
<p>The Wang et al. method derived NOAA-20 raw (pixel level) on-orbit RVS data as function of frame. For each band, detector level raw RVS are illustrated by different colors ranging from blue (HAM-A, first detector) to dark red (HAM-B, last detector).</p> Full article ">Figure 7
<p>NOAA-20 bands I5, M13, and M15–M16 detector level on-orbit RVS derived using the Wang et al. method, after smoothed using 2<sup>nd</sup> order polynomials.</p> Full article ">Figure 8
<p>Comparison of band-averaged NOAA-20 (left panel) and S-NPP (right panel) prelaunch and the Wang et al. method derived on-orbit RVS for HAM-A (solid lines) and HAM-B (dash lines).</p> Full article ">Figure 9
<p>Comparison of band-averaged NOAA-20 (left panel) and S-NPP (right panel) prelaunch and the Wu et al. method derived on-orbit RVS for HAM-A (solid lines) and HAM-B (dash lines). .</p> Full article ">Figure 10
<p>NOAA-20 VIIRS–CrIS BT differences at 10 scene temperatures and as functions of CrIS FOR position and VIIRS scan angle. VIIRS M15–M16, M13, and I5 SDRs from March 18, 2019 were reprocessed using the Wu et al. method derived on-orbit RVS. Under each scene temperature, the sizes of the filled circles are proportional to the number of data samples used.</p> Full article ">Figure 11
<p>NOAA-20 VIIRS–CrIS BT differences at 10 scene temperatures and as function of CrIS FOR position and VIIRS scan angle. VIIRS M15–M16, M13, and I5 SDRs from March 18, 2019 were reprocessed using the Wang et al. method derived on-orbit RVS. Under each scene temperature, the sizes of the filled circles are proportional to the number of data samples used.</p> Full article ">Figure 12
<p>S-NPP VIIRS–CrIS BT differences at 10 scene temperatures and as function of CrIS FOR position and VIIRS scan angle. VIIRS M15–M16, M13, and I5 SDRs from March 18, 2019 were reprocessed using the Wang et al. method derived on-orbit RVS. Under each scene temperature, the sizes of the filled circles are proportional to the number of data samples used.</p> Full article ">Figure 13
<p>NOAA-20 VIIRS–CrIS BT differences as function of CrIS scene temperature. Scene temperature dependent biases for the reprocessed VIIRS SDRs (the Wang et al. method derived on-orbit RVS) were plotted in the foreground in solid lines; biases for the NOAA operational processing (prelaunch RVS) were plotted in the background using dash lines and pale colors.</p> Full article ">
20 pages, 5343 KiB  
Article
Automatic Identification of Shrub-Encroached Grassland in the Mongolian Plateau Based on UAS Remote Sensing
by Yu Dong, Huimin Yan, Na Wang, Mei Huang and Yunfeng Hu
Remote Sens. 2019, 11(13), 1623; https://doi.org/10.3390/rs11131623 - 9 Jul 2019
Cited by 13 | Viewed by 3587
Abstract
Recently, the increasing shrub-encroached grassland in the Mongolian Plateau partly indicates grassland quality decline and degradation. Accurate shrub identification and regional difference analysis in shrub-encroached grassland are significant for ecological degradation research. Object-oriented filter (OOF) and digital surface model (DSM)-digital terrain model (DTM) [...] Read more.
Recently, the increasing shrub-encroached grassland in the Mongolian Plateau partly indicates grassland quality decline and degradation. Accurate shrub identification and regional difference analysis in shrub-encroached grassland are significant for ecological degradation research. Object-oriented filter (OOF) and digital surface model (DSM)-digital terrain model (DTM) analyses were combined to establish a high-accuracy automatic shrub identification algorithm (CODA), which made full use of remote sensing products by unmanned aircraft systems (UASs). The results show that: (1) The overall accuracy of CODA in the Grain for Green test area is 89.96%, which is higher than that of OOF (84.52%) and DSM-DTM (78.44%), mainly due to the effective elimination of interference factors (such as shrub-like highland, well-grown grassland in terrain-depression area, etc.) by CODA. (2) The accuracy (87.5%) of CODA in the typical steppe test area is lower than that (92.5%) in the desert steppe test area, which may be related to the higher community structure complexity of typical steppe. Besides, the shrub density is smaller, and the regional difference is more massive in the typical steppe test area. (3) The ground sampling distance for best CODA accuracy in the Grain for Green test area is about 15 cm, while it is below 3 cm in the typical and desert steppe test area. Full article
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<p>Experimental areas: (<b>a</b>) locations of the Grass for Green Test Area (GGA), the Typical Steppe Test Area (TSA) and the Desert Steppe Test Area (DSA); (<b>b</b>,<b>c</b>) unmanned aircraft system (UAS) image and photo of the GGA; (<b>d</b>,<b>e</b>) UAS image and photo of the TSA; (<b>f</b>,<b>g</b>) UAS image and photo of the DSA.</p> Full article ">Figure 2
<p>Flow chart of the combining object-oriented filter and digital surface model (DSM) - digital terrain model (DTM) algorithm (CODA) for shrub identification (where Open Op. refers to open operation).</p> Full article ">Figure 3
<p>Object-oriented filter (OOF) based shrub identification approach: (<b>a</b>) multiresolution segmentation result; (<b>b</b>) ExG-ExR features of objects; (<b>c</b>) threshold classification result; (<b>d</b>) identified Shrub Area.</p> Full article ">Figure 4
<p>DSM-DTM based shrub identification approach: (<b>a</b>) DSM; (<b>b</b>) ground DTM; (<b>c</b>) fill DTM; (<b>d</b>) shrub area.</p> Full article ">Figure 5
<p>Shrub Identification Results in A Sample Area by: (<b>a</b>) OOF; (<b>b</b>) DSM-DTM; (<b>c</b>) CODA.</p> Full article ">Figure 6
<p>Accuracy analysis in the GGA: (<b>a</b>) accuracy of each region; (<b>b</b>) the relationship between accuracy and shrub count.</p> Full article ">Figure 7
<p>Shrub counts of each region in the (<b>a</b>) TSA; (<b>b</b>) DSA.</p> Full article ">Figure 8
<p>Shrub identification accuracy and information entropy of images at different resolution in (<b>a</b>) the GGA; (<b>b</b>) the TSA; (<b>c</b>) the DSA. (<b>d</b>) A sample region of the GGA in different resolutions.</p> Full article ">Figure 9
<p>Why is the accuracy of CODA is higher than that of OOF and DSM-DTM?</p> Full article ">Figure 10
<p>ExG-ExR directional heterogeneity of (<b>a</b>) the ideal model; (<b>b</b>) a practical situation of shrub-encroached grassland.</p> Full article ">
27 pages, 15535 KiB  
Article
A Lightweight Hyperspectral Image Anomaly Detector for Real-Time Mission
by Ning Ma, Ximing Yu, Yu Peng and Shaojun Wang
Remote Sens. 2019, 11(13), 1622; https://doi.org/10.3390/rs11131622 - 8 Jul 2019
Cited by 12 | Viewed by 3460
Abstract
In real-time onboard hyperspectral-image(HSI) anomalous targets detection, processing speed and accuracy are equivalently desirable which is hard to satisfy at the same time. To improve detection accuracy, deep learning based HSI anomaly detectors (ADs) are widely studied. However, their large scale network results [...] Read more.
In real-time onboard hyperspectral-image(HSI) anomalous targets detection, processing speed and accuracy are equivalently desirable which is hard to satisfy at the same time. To improve detection accuracy, deep learning based HSI anomaly detectors (ADs) are widely studied. However, their large scale network results in a massive computational burden. In this paper, to improve the detection throughput without sacrificing the accuracy, a pruning–quantization–anomaly–detector (P-Q-AD) is proposed by building an underlying constraint formulation to make a trade-off between accuracy and throughput. To solve this formulation, multi-objective optimization with nondominated sorting genetic algorithm II (NSGA-II) is employed to shrink the network. As a result, the redundant neurons are removed. A mixed precision network is implemented with a delicate customized fixed-point data expression to further improve the efficiency. In the experiments, the proposed P-Q-AD is implemented on two real HSI data sets and compared with three types of detectors. The results show that the performance of the proposed approach is no worse than those comparison detectors in terms of the receiver operating characteristic curve (ROC) and area under curve (AUC) value. For the onboard mission, the proposed P-Q-AD reaches over 4.5 × speedup with less than 0.5 % AUC loss compared with the floating-based detector. The pruning and the quantization approach in this paper can be referenced for designing the anomalous targets detectors for high efficiency. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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<p>Lifetime of the main multispectral imagers sensors and the hyperspectral-image (HSI) sensor in past and plan [<a href="#B3-remotesensing-11-01622" class="html-bibr">3</a>].</p> Full article ">Figure 2
<p>The colour image and the ground truth image of Sanjose data set.</p> Full article ">Figure 3
<p>The colour image and the ground truth image of Cloverdale data set.</p> Full article ">Figure 4
<p>The colour image and the ground truth image of Louisiana data set.</p> Full article ">Figure 5
<p>The colour image of Sandiego airport HSI dataset.</p> Full article ">Figure 6
<p>The colour image and the ground truth image of Sandiego data set.</p> Full article ">Figure 7
<p>The colour image and the ground truth image of Los Angeles data set.</p> Full article ">Figure 8
<p>An stacked-auto-encoder (SAE) based HSI anomaly detector (AD).</p> Full article ">Figure 9
<p>The structure of a common deep learning based HSI AD.</p> Full article ">Figure 10
<p>The location relationship between pixel under test (PUT) and surrounding pixels.</p> Full article ">Figure 11
<p>The computation amount and the algorithm-hardware-cost-factor (AHCF) with different <span class="html-italic">R</span>. (<b>a</b>) Computation amount of two different algorithms. (<b>b</b>) A small algorithm implemented by the operations with less hardware resources. (<b>c</b>) A big algorithm implemented by the operations with less hardware resources. (<b>d</b>) A small algorithm implemented by the operations with huge hardware resources. (<b>e</b>) A big algorithm implemented by the operations with huge hardware resources.</p> Full article ">Figure 12
<p>The analysis of throughput and the AHCF in device. (<b>a</b>) The implementation of a big <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>C</mi> <mi>R</mi> </mrow> </msub> </semantics></math> algorithm in an abundant resources device. (<b>b</b>) The implementation of a small <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>C</mi> <mi>R</mi> </mrow> </msub> </semantics></math> algorithm in an abundant resources device. (<b>c</b>) The implementation of a big <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>C</mi> <mi>R</mi> </mrow> </msub> </semantics></math> algorithm in an insufficient resources device. (<b>d</b>) The implementation of a small <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>C</mi> <mi>R</mi> </mrow> </msub> </semantics></math> algorithm in an insufficient resources device.</p> Full article ">Figure 13
<p>The lookup table (LUT) consumption of the multiply in different bits number.</p> Full article ">Figure 14
<p>The flowchart of the proposed pruning–quantization–anomaly–detector (P-Q-AD) for onboard HSI AD.</p> Full article ">Figure 15
<p>The basic flow of the NSGA-II.</p> Full article ">Figure 16
<p>The ZCU102 platform.</p> Full article ">Figure 17
<p>The detection results for Louisiana data set. (<b>a</b>) The anomaly score image of Louisiana by LRXD; (<b>b</b>) The anomaly score image of Louisiana by collaborative representation based anomaly detector (CRD); (<b>c</b>) The anomaly score image of Louisiana by floating point precision without pruning anomaly detectors (Floating-AD); (<b>d</b>) The anomaly score image of Louisiana by floating point precision with pruning anomaly detectors (Floating-AD)P-Floating-AD; (<b>e</b>) The anomaly score image of Louisiana by P-Fixed-AD; (<b>f</b>) The anomaly score image of Louisiana by P-Q-AD.</p> Full article ">Figure 18
<p>The receiver operating characteristic (ROC) curve for Louisiana dataset.</p> Full article ">Figure 19
<p>The detection results for Sandiego data set.</p> Full article ">Figure 20
<p>The ROC curve for Sandiego data set.</p> Full article ">Figure 21
<p>The detection results for Los Angeles data set.</p> Full article ">Figure 22
<p>The ROC curve for the Los Angeles data set.</p> Full article ">Figure 23
<p>The computational resources consumption by different detectors. To make those detectors reach its maximum parallel for high detection speed, the detectors are designed as high as possible until one of the resources consumption reaches the limitation of the field-programmable gate arrays (FPGA) chip for each detector.</p> Full article ">
19 pages, 2871 KiB  
Article
An Effectiveness Evaluation Model for Satellite Observation and Data-Downlink Scheduling Considering Weather Uncertainties
by Siyue Zhang, Yiyong Xiao, Pei Yang, Yinglai Liu, Wenbing Chang and Shenghan Zhou
Remote Sens. 2019, 11(13), 1621; https://doi.org/10.3390/rs11131621 - 8 Jul 2019
Cited by 15 | Viewed by 4555
Abstract
Low Earth orbit (LEO) satellites play an important role in human space activities, and market demands for commercial uses of LEO satellites have been increasing rapidly in recent years. LEO satellites mainly consist of Earth observation satellites (EOSs), the major commercial applications of [...] Read more.
Low Earth orbit (LEO) satellites play an important role in human space activities, and market demands for commercial uses of LEO satellites have been increasing rapidly in recent years. LEO satellites mainly consist of Earth observation satellites (EOSs), the major commercial applications of which are various sorts of Earth observations, such as map making, crop growth assessment, and disaster surveillance. However, the success rates of observation tasks are influenced considerably by uncertainties in local weather conditions, inadequate sunlight, observation dip angle, and other practical factors. The available time windows (ATWs) suitable for observing given types of targets and for transmitting data back to ground receiver stations are relatively narrow. In order to utilize limited satellite resources efficiently and maximize their commercial benefits, it is necessary to evaluate the overall effectiveness of satellites and planned tasks considering various factors. In this paper, we propose a method for determining the ATWs considering the influence of sunlight angle, elevation angle, and the type of sensor equipped on the satellite. After that, we develop a satellite effectiveness evaluation (SEE) model for satellite observation and data-downlink scheduling (SODS) based on the Availability–Capacity–Profitability (ACP) framework, which is designed to evaluate the overall performance of satellites from the perspective of time resource utilization, the success rate of tasks, and profit return. The effects of weather uncertainties on the tasks’ success are considered in the SEE model, and the model can be applied to support the decision-makers on optimizing and improving task arrangements for EOSs. Finally, a case study is presented to demonstrate the effectiveness of the proposed method and verify the ACP-based SEE model. The obtained ATWs by the proposed method are compared with those by the Systems Tool Kit (STK), and the correctness of the method is thus validated. Full article
(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)
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<p>The two-body motion model used in this study. <span class="html-italic">r</span> is the position vector.</p> Full article ">Figure 2
<p>Satellite orbital parameters.</p> Full article ">Figure 3
<p>Diagram of the orbit of the satellite about the Earth. <span class="html-italic">E</span> is the eccentric anomaly and <span class="html-italic">P</span> and <span class="html-italic">A</span> are perigee and apogee, respectively.</p> Full article ">Figure 4
<p>Satellite ground coverage. <span class="html-italic">h</span> is the instantaneous height of the satellite <span class="html-italic">S</span> at time <span class="html-italic">t, S’</span> is the sub-point of the satellite on the Earth’s surface,<math display="inline"><semantics> <mi>σ</mi> </semantics></math> is the minimum viewing angle, and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mi>σ</mi> </msub> </mrow> </semantics></math> is the corresponding coverage angle.</p> Full article ">Figure 5
<p>The sub-solar point. <span class="html-italic">S’</span> is the sub-satellite point, which is defined as the intersection of the line connecting the satellite and the center of the Earth with the Earth’s surface. <span class="html-italic">G</span> is the sub-solar point. <span class="html-italic">z</span> represents the zenith distance between the sub-solar point and the sub-satellite point.</p> Full article ">Figure 6
<p>The time window of a low Earth orbit (LEO) satellite.</p> Full article ">Figure 7
<p>Example of orbits of a low Earth orbit (LEO) satellite. (<b>a</b>) 3D graphic; (<b>b</b>) 2D graphic.</p> Full article ">Figure 8
<p>Example of time window scheduling.</p> Full article ">
23 pages, 486 KiB  
Article
Analytical Relationship between Two-Band Spectral Vegetation Indices Measured at Multiple Sensors on a Parametric Representation of Soil Isoline Equations
by Kenta Taniguchi, Kenta Obata and Hiroki Yoshioka
Remote Sens. 2019, 11(13), 1620; https://doi.org/10.3390/rs11131620 - 8 Jul 2019
Cited by 2 | Viewed by 3569
Abstract
Differences between the wavelength band specifications of distinct sensors introduce systematic differences into the values of a spectral vegetation index (VI). Such relative errors must be minimized algorithmically after data acquisition, based on a relationship between the measurements. This study introduces a technique [...] Read more.
Differences between the wavelength band specifications of distinct sensors introduce systematic differences into the values of a spectral vegetation index (VI). Such relative errors must be minimized algorithmically after data acquisition, based on a relationship between the measurements. This study introduces a technique for deriving the analytical relationship between the VIs from two sensors. The derivation proceeds using a parametric form of the soil isoline equations, which relate the reflectances of two different wavelengths. First, the derivation steps are explained conceptually. Next, the conceptual steps are cast in a practical derivation by assuming a general form of the two-band VI. Finally, the derived expressions are demonstrated numerically using a coupled leaf and canopy radiative transfer model. The results confirm that the derived expression reduced the original differences between the VI values obtained from the two sensors, indicating the validity of the derived expressions. The derived expressions and numerical results suggested that the relationship between the VIs measured at different wavelengths varied with the soil reflectance spectrum beneath the vegetation canopy. These results indicate that caution is required when retrieving intersensor VI relationships over regions consisting of soil surfaces having distinctive spectra. Full article
(This article belongs to the Section Biogeosciences Remote Sensing)
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<p>Flowchart of the derivation steps used to obtain the intersensor vegetation index (VI) relationships based on the soil isoline equations.</p> Full article ">Figure 2
<p>Plots of the soil isolines (lines) represented by Equation (<a href="#FD4-remotesensing-11-01620" class="html-disp-formula">4</a>) and numerically simulated reflectance spectra (symbols) in the red and near-infra red (NIR) reflectance space in the range of <math display="inline"><semantics> <mrow> <mo>(</mo> <mo>−</mo> <mn>0.05</mn> <mo>≤</mo> <msubsup> <mi>ρ</mi> <mi>n</mi> <mo>′</mo> </msubsup> <mo>≤</mo> <mn>0.3</mn> <mo>)</mo> </mrow> </semantics></math>. The three types of soil brightness for simulation are wet, intermediate, and dry conditions, which correspond to circles, asterisks, and square marks, respectively. The soil isoline was truncated at the fourth-order term (<math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>). The reflectance spectra simulated at a constant soil brightness are denoted by the same mark.</p> Full article ">Figure 3
<p>Plots of the four coefficients represented by <math display="inline"><semantics> <mrow> <msup> <mi>ψ</mi> <mrow> <mi>xy</mi> <mo>∗</mo> </mrow> </msup> <mrow> <mo>(</mo> <msub> <mi>G</mi> <mi>ε</mi> </msub> <mo>)</mo> </mrow> </mrow> </semantics></math> for (gray cross) a residual list <math display="inline"><semantics> <msub> <mi>G</mi> <mi>ε</mi> </msub> </semantics></math> and (a black line) residual free (<math display="inline"><semantics> <mrow> <msub> <mi>G</mi> <mi>ε</mi> </msub> <mo>=</mo> <mn mathvariant="bold">0</mn> </mrow> </semantics></math>) for NDVI relations under (left) case 1, (center) case 2, and (right) case 3 conditions.</p> Full article ">Figure 4
<p>Differences of <math display="inline"><semantics> <msubsup> <mi>δ</mi> <mrow> <mo>(</mo> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>N</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mi>xy</mi> </msubsup> </semantics></math> in Equation (<a href="#FD32-remotesensing-11-01620" class="html-disp-formula">32</a>) for a sensor pair for case 1. The four panels located at the upper left, upper right, lower left, and lower right correspond to <math display="inline"><semantics> <msup> <mi>δ</mi> <mi>UD</mi> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mi>δ</mi> <mi>UU</mi> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mi>δ</mi> <mi>DD</mi> </msup> </semantics></math>, and <math display="inline"><semantics> <msup> <mi>δ</mi> <mi>DU</mi> </msup> </semantics></math>, respectively. The individual panels show results for <math display="inline"><semantics> <msup> <mi>δ</mi> <mi>xy</mi> </msup> </semantics></math> with truncation combinations (<math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>N</mi> <mn>2</mn> </msub> </mrow> </semantics></math>) of (gray) (1,2), (red) (2,1), and (green) (2,2), with solid and dashed lines for leaf area indices (LAIs) of 1.0 and 3.0, respectively.</p> Full article ">Figure 5
<p>Plots of differences before (<math display="inline"><semantics> <msub> <mi>ε</mi> <mi>v</mi> </msub> </semantics></math>) and after translation (<math display="inline"><semantics> <msub> <mover> <mi>ε</mi> <mo>¯</mo> </mover> <mi>v</mi> </msub> </semantics></math>) for the NDVI as a function of the LAI. Two types of soil spectra, wet and dry, are shown in solid and dotted lines. The gray lines represent the variables defined by Equation (<a href="#FD33-remotesensing-11-01620" class="html-disp-formula">33</a>) and corresponding to the left axis, and the red and green colors corresponding to the left axis represent the variables defined by Equation (<a href="#FD34-remotesensing-11-01620" class="html-disp-formula">34</a>) with <math display="inline"><semantics> <msub> <mi>N</mi> <mn>2</mn> </msub> </semantics></math> values of one and two, respectively. The results obtained from different orders of the truncation terms are organized in columns. From left to right, the columns present the results of the first-, second-, and third-order approximations. The results for the cases of three sensor pairs are presented across the rows.</p> Full article ">Figure 6
<p>Bar plot of root mean square error (RMSE) in log10 scale of NDVI translations obtained from using polynomials of different orders in the soil isoline approximations between 0 to 4 for <math display="inline"><semantics> <msub> <mi>N</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>N</mi> <mn>2</mn> </msub> </semantics></math> for the sensor-pair of case 1. Dashed lines display the improvement of symmetric order cases.</p> Full article ">Figure 7
<p>The plots presented in <a href="#remotesensing-11-01620-f005" class="html-fig">Figure 5</a> were calculated for the following VIs: SAVI, EVI2, and DVI. The left column presents the results of the SAVI calculated using a first-order term. The second, third, and right-hand columns present results of the SAVI, EVI2, and DVI, respectively, calculated up to the third-order term. The influence of the truncation order for the SAVI was assessed by comparing the left and center columns. The differences between the SAVI, EVI, and DVI cases were assessed by comparing the other three columns.</p> Full article ">Figure 8
<p>Color plots of normalized root mean square error (RMSE) for NDVI translation error in a sensor-pair for case 1. Results for three Cab values of 20, 40, and 80 in a leaf and four leaf angle distributions (LADs—planophile, electophile, electophafile, and uniform) are displayed in logarithmic scale on a basis value of 10.</p> Full article ">
19 pages, 4755 KiB  
Article
DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data
by Ya’nan Zhou, Jiancheng Luo, Li Feng and Xiaocheng Zhou
Remote Sens. 2019, 11(13), 1619; https://doi.org/10.3390/rs11131619 - 8 Jul 2019
Cited by 29 | Viewed by 4760
Abstract
Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture [...] Read more.
Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Mapping)
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<p>Flowchart of deep convolutional network (DCN)-based spatial features for improving time-series crop classification.</p> Full article ">Figure 2
<p>The study area in northern Hunan Province, China. The right-hand section provides an overview of the SAR data (R: 23 May 2017 VH polarization, G: 26 October 2017 VH polarization, B: 3 August 2017 VV polarization) and roadmaps and sample spots of the field survey.</p> Full article ">Figure 3
<p>Farmland parcels and field survey samples for crop classification. Green polygons denote parcels, yellow points denote samples, and the red line denotes the roadmap used when sampling.</p> Full article ">Figure 4
<p>Sample augmentation for the LSTM-based classification.</p> Full article ">Figure 5
<p>The C–L (concatenation first and LSTM later) structure for organizing and combing time-series features.</p> Full article ">Figure 6
<p>The L–C (LSTM first and concatenation later) structure for organizing and combing time-series features.</p> Full article ">Figure 7
<p>Classification accuracies along with epochs for the five combinations.</p> Full article ">Figure 8
<p>Distance matrixes of crops using VGG16-V1, ResNet50-R1, and DenseNet121-D1 features.</p> Full article ">Figure 9
<p>Results of time-series crop classification for Hunan Province using the optimal experiment setting.</p> Full article ">Figure 9 Cont.
<p>Results of time-series crop classification for Hunan Province using the optimal experiment setting.</p> Full article ">
17 pages, 8376 KiB  
Article
Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation
by Wen Zhuo, Jianxi Huang, Li Li, Xiaodong Zhang, Hongyuan Ma, Xinran Gao, Hai Huang, Baodong Xu and Xiangming Xiao
Remote Sens. 2019, 11(13), 1618; https://doi.org/10.3390/rs11131618 - 8 Jul 2019
Cited by 81 | Viewed by 7983
Abstract
Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth [...] Read more.
Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing for Agricultural Monitoring in the Big Data Era)
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<p>Study area. (Every county has 2 measured sites; each site has 5 sample points).</p> Full article ">Figure 2
<p>Calendar of the field experiment, remote sensing data acquisition dates and general growth stages of winter wheat.</p> Full article ">Figure 3
<p>Flowchart for the winter wheat yield estimation using the EnKF-based assimilation algorithm.</p> Full article ">Figure 4
<p>Maps of soil moisture in the study area retrieved from the water cloud model (<b>a</b>) 1 April, (<b>b</b>) 7 May and (<b>c</b>) 1 Jun. (<b>d</b>) Retrieved soil moisture content of 22 measured sites in Hengshui city over 3 time periods. (Every county has 2 measured sites).</p> Full article ">Figure 5
<p>Validation of the retrieved soil moisture from the water cloud model; (<b>a</b>) 1 April, (<b>b</b>) 7 May, and (<b>c</b>) 1 June. Note: 55 sample points were used for validation.</p> Full article ">Figure 6
<p>Comparison of the SM curves simulated by WOFOST model with and without the EnKF-based assimilation method (a sample point in Shenzhou county).</p> Full article ">Figure 7
<p>Regional winter wheat yield estimation without and with the EnKF assimilation method ((<b>a</b>) without assimilation; (<b>b</b>) with EnKF assimilation).</p> Full article ">Figure 8
<p>Winter wheat yield estimation (<b>a</b>) without assimilation and (<b>b</b>) with EnKF assimilation.</p> Full article ">
22 pages, 7072 KiB  
Article
Deep Feature Fusion with Integration of Residual Connection and Attention Model for Classification of VHR Remote Sensing Images
by Jicheng Wang, Li Shen, Wenfan Qiao, Yanshuai Dai and Zhilin Li
Remote Sens. 2019, 11(13), 1617; https://doi.org/10.3390/rs11131617 - 8 Jul 2019
Cited by 28 | Viewed by 5894
Abstract
The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown [...] Read more.
The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied. Full article
(This article belongs to the Special Issue Convolutional Neural Networks Applications in Remote Sensing)
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<p>Network architectures of (<b>a</b>) convolutional neural network (CNN) and (<b>b</b>) fully convolutional network (FCN) for classification tasks.</p> Full article ">Figure 2
<p>Schematic diagram of proposed method. Dashed arrows indicate the flow of loss in the training stage.</p> Full article ">Figure 3
<p>Framework of proposed multiconnection residual network containing several residual units (ResNet). Three types of residual connections are added to ResNet, illustrated by dashed arrows: 1. residual connections between residual blocks in the encoder part; 2. residual connections in ResU between the encoder and decoder; 3. residual connections in ResU and residual network containing transposed residual units (Trans-ResU) in the decoder part. ResU, residual unit; Trans-ResU, transposed residual unit.</p> Full article ">Figure 4
<p>Framework of proposed class-specific attention model. Note that only two scales of the original input image are used to illustrate the model, for convenience.</p> Full article ">Figure 5
<p>Sample image and corresponding ground truth map in Massachusetts building dataset.</p> Full article ">Figure 6
<p>Review of International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset.</p> Full article ">Figure 7
<p>Bar chart of F1 scores of various methods for Massachusetts building dataset.</p> Full article ">Figure 8
<p>Qualitative comparison of various methods for Massachusetts building dataset. The first and second columns are image samples to be classified and corresponding ground truth maps, respectively. The other five columns are classification results of various methods; the last two columns are the results of our proposed methods.</p> Full article ">Figure 9
<p>Bar chart of F1 scores and OA of various methods for ISPRS Potsdam dataset.</p> Full article ">Figure 10
<p>Qualitative comparison of various methods for ISPRS Potsdam dataset. The first and second columns are the image samples to be classified and corresponding ground truth maps, respectively. The other five columns are classification results of various methods; the last two columns are the results of our proposed methods.</p> Full article ">Figure 11
<p>(<b>a</b>) Image samples, (<b>b</b>) corresponding ground truth maps, and (<b>c</b>) extraction results by the proposed method mcResNet-csAM in the Massachusetts building dataset.</p> Full article ">Figure 12
<p>(<b>a</b>) Image samples, (<b>b</b>) corresponding ground truth maps, and (<b>c</b>) extraction results by the proposed method mcResNet-csAM in the ISPRS Potsdam dataset.</p> Full article ">
16 pages, 8899 KiB  
Article
Validation of 7 Years in-Flight HY-2A Calibration Microwave Radiometer Products Using Numerical Weather Model and Radiosondes
by Zhilu Wu, Jungang Wang, Yanxiong Liu, Xiufeng He, Yang Liu and Wenxue Xu
Remote Sens. 2019, 11(13), 1616; https://doi.org/10.3390/rs11131616 - 8 Jul 2019
Cited by 5 | Viewed by 5131
Abstract
Haiyang-2A (HY-2A) has been working in-flight for over seven years, and the accuracy of HY-2A calibration microwave radiometer (CMR) data is extremely important for the wet troposphere delay correction (WTC) in sea surface height (SSH) determination. We present a comprehensive evaluation of the [...] Read more.
Haiyang-2A (HY-2A) has been working in-flight for over seven years, and the accuracy of HY-2A calibration microwave radiometer (CMR) data is extremely important for the wet troposphere delay correction (WTC) in sea surface height (SSH) determination. We present a comprehensive evaluation of the HY-2A CMR observation using the numerical weather model (NWM) for all the data available period from October 2011 to February 2018, including the WTC and the precipitable water vapor (PWV). The ERA(ECMWF Re-Analysis)-Interim products from European Centre for Medium-Range Weather Forecasts (ECMWF) are used for the validation of HY-2A WTC and PWV products. In general, a global agreement of root-mean-square (RMS) of 2.3 cm in WTC and 3.6 mm in PWV are demonstrated between HY-2A observation and ERA-Interim products. Systematic biases are revealed where before 2014 there was a positive WTC/PWV bias and after that, a negative one. Spatially, HY-2A CMR products show a larger bias in polar regions compared with mid-latitude regions and tropical regions and agree better in the Antarctic than in the Arctic with NWM. Moreover, HY-2A CMR products have larger biases in the coastal area, which are all caused by the brightness temperature (TB) contamination from land or sea ice. Temporally, the WTC/PWV biases increase from October 2011 to March 2014 with a systematic bias over 1 cm in WTC and 2 mm in PWV, and the maximum RMS values of 4.62 cm in WTC and 7.61 mm in PWV occur in August 2013, which is because of the unsuitable retrieval coefficients and systematic TB measurements biases from 37 GHz band. After April 2014, the TB bias is corrected, HY-2A CMR products agree very well with NWM from April 2014 to May 2017 with the average RMS of 1.68 cm in WTC and 2.65 mm in PWV. However, since June 2017, TB measurements from the 18.7 GHz band become unstable, which led to the huge differences between HY-2A CMR products and the NWM with an average RMS of 2.62 cm in WTC and 4.33 mm in PWV. HY-2A CMR shows high accuracy when three bands work normally and further calibration for HY-2A CMR is in urgent need. Furtherly, 137 global coastal radiosonde stations were used to validate HY-2A CMR. The validation based on radiosonde data shows the same variation trend in time of HY-2A CMR compared to the results from ECMWF, which verifies the results from ECMWF. Full article
(This article belongs to the Special Issue Global Navigation Satellite Systems for Earth Observing System)
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<p>Global Radiosonde stations distribution, the red triangles represent radiosonde stations.</p> Full article ">Figure 2
<p>Global content and changing rate distribution of precipitable water vapor (PWV)/wet troposphere delay correction (WTC). (<b>a</b>) Global PWV distribution; (<b>b</b>) PWV changing rate; (<b>c</b>) global WTC distribution; (<b>d</b>) WTC changing rate.</p> Full article ">Figure 3
<p><b>Figure 3</b>. The distribution of HY-2A WTC/PWV outlier points in time (<b>a</b>), and space (<b>b</b>), the color in the bar of (<b>b</b>) represents the time of outlier points.</p> Full article ">Figure 4
<p>Spatial distribution of HY-2A WTC/PWV outliers: (<b>a</b>) WTC; (<b>b</b>) PWV. The color represents the difference between HY-2A and the numerical weather model (NWM).</p> Full article ">Figure 5
<p>Scatter diagrams of HY-2A and NWM: (<b>a</b>) WTC; (<b>b</b>) PWV. The red line is fitted line between HY-2A CMR products and NWM, the dotted red line represents simulated line y = x, the accuracy assessment is in the bottom-right and correlation of two sets in the up-left. The color from dark to light describes the absolute bias between HY-2A CMR and NWM.</p> Full article ">Figure 6
<p>HY-2A CMR products accuracy assessment in latitude: (<b>a</b>) WTC; (<b>b</b>) PWV. The left axis is the average ratio of root-mean-square (RMS) and observation values, the blue line represents relative RMS error. The right axis is the mean values, standard deviation (STD) and RMS, whereas the solid red line describes mean values, the long dotted red line describes RMS and the short-dotted line represents STD.</p> Full article ">Figure 7
<p>Global distribution of the WTC/PWV differences between HY-2A CMR products and NWM; (<b>a</b>,<b>b</b>) describe mean value and STD of WTC differences, respectively; (<b>c</b>,<b>d</b>) describe mean value and STD of PWV differences, respectively.</p> Full article ">Figure 8
<p>HY-2A CMR products accuracy with respect to (w.r.t.) time: (<b>a</b>) WTC; and (<b>b</b>) PWV. The green circle represents the mean value, the purple hexagonal represents STD and dark pentagram represents RMS.</p> Full article ">Figure 9
<p>Daily average brightness temperature (TB) of HY-2A CMR for the three bands: Upper: 18.7 GHz; middle: 23.8 GHz; bottom: 37 GHz.</p> Full article ">Figure 10
<p>The global distribution of WTC differences between HY-2A CMR and NWM; (<b>a</b>–<b>h</b>) from 2011 to 2018, respectively. Since there are less than three months in the years 2011 and 2018, the resolution is relatively lower.</p> Full article ">Figure 10 Cont.
<p>The global distribution of WTC differences between HY-2A CMR and NWM; (<b>a</b>–<b>h</b>) from 2011 to 2018, respectively. Since there are less than three months in the years 2011 and 2018, the resolution is relatively lower.</p> Full article ">Figure 11
<p>Global distribution of PWV differences between HY-2A CMR and NWM; (<b>a</b>–<b>h</b>) from 2011 to 2018, respectively. The lower resolution of 2011 and 2018 is because of less data.</p> Full article ">Figure 11 Cont.
<p>Global distribution of PWV differences between HY-2A CMR and NWM; (<b>a</b>–<b>h</b>) from 2011 to 2018, respectively. The lower resolution of 2011 and 2018 is because of less data.</p> Full article ">Figure 12
<p>Validation for HY-2A CMR based on radiosonde data: (<b>a</b>) WTC; (<b>b</b>) PWV.</p> Full article ">
27 pages, 8701 KiB  
Article
Urban Landscape Change Analysis Using Local Climate Zones and Object-Based Classification in the Salt Lake Metro Region, Utah, USA
by Jed Collins and Iryna Dronova
Remote Sens. 2019, 11(13), 1615; https://doi.org/10.3390/rs11131615 - 8 Jul 2019
Cited by 26 | Viewed by 7463
Abstract
Urban areas globally are vulnerable to warming climate trends exacerbated by their growing populations and heat island effects. The Local Climate Zone (LCZ) typology has become a popular framework for characterizing urban microclimates in different regions using various classification methods, including a widely [...] Read more.
Urban areas globally are vulnerable to warming climate trends exacerbated by their growing populations and heat island effects. The Local Climate Zone (LCZ) typology has become a popular framework for characterizing urban microclimates in different regions using various classification methods, including a widely adopted pixel-based protocol by the World Urban Database and Access Portal Tools (WUDAPT) Project. However, few studies to date have explored the potential of object-based image analysis (OBIA) to facilitate classification of LCZs given their inherent complexity, and few studies have further used the LCZ framework to analyze land cover changes in urban areas over time. This study classified LCZs in the Salt Lake Metro Region, Utah, USA for 1993 and 2017 using a supervised object-based analysis of Landsat satellite imagery and assessed their change during this time frame. The overall accuracy, measured for the most recent classification period (2017), was equal to 64% across 12 LCZs, with most of the error resulting from similarities among highly developed LCZs and non-developed classes with sparse or low-stature vegetation. The observed 1993–2017 changes in LCZs indicated a regional tendency towards primarily suburban, open low-rise development, and large low-rise and paved classes. However, despite the potential for local cooling with landscape transitions likely to increase vegetation cover and irrigation compared to pre-development conditions, summer averages of Landsat-derived top-of-atmosphere brightness temperatures showed a pronounced warming between 1992–1994 and 2016–2018 across the study region, with a 0.1–2.9 °C increase among individual LCZs. Our results indicate that future applications of LCZs towards urban change analyses should develop a stronger understanding of LCZ microclimate sensitivity to changes in size and configuration of urban neighborhoods and regions. Furthermore, while OBIA is promising for capturing the heterogeneous and multi-scale nature of LCZs, its applications could be strengthened by adopting more generalizable approaches for LCZ-relevant segmentation and validation, and by incorporating active remote sensing data to account for the 3D complexity of urban areas. Full article
(This article belongs to the Section Urban Remote Sensing)
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<p>Study area in Salt Lake County, Utah, USA.</p> Full article ">Figure 2
<p>Outline of the main analysis workflow.</p> Full article ">Figure 3
<p>Change in within-object variance and its rate of change with segmentation scale parameter (using near-infrared band as example).</p> Full article ">Figure 4
<p>Local climate zone distributions mapped via Random Forest algorithm for 1993 and 2017.</p> Full article ">Figure 5
<p>Major transitions among Salt Lake Valley’s Local Climate Zones from 1993 to 2017.</p> Full article ">Figure 6
<p>Changes in spatial pattern indicators of different LCZs from 1993 to 2017: (<b>a</b>) mean proximity index, and (<b>b</b>) mean patch size.</p> Full article ">Figure 7
<p>Mean Landsat land surface temperature (LST) values for mapped LCZs in the Salt Lake City, UT, USA study area in 1993 and 2017: (<b>a</b>) annual average LST, and (<b>b</b>) normalized annual average LST. Error bars show standard deviation of temperature metrics within LCZs.</p> Full article ">Figure A1
<p>Examples of spatial patterns of different LCZs from high-resolution reference data.</p> Full article ">Figure A2
<p>Assessment of different accuracy measures via bootstrapping.</p> Full article ">Figure A3
<p>Different patterns of annual-average Landsat land surface temperature across the study area between 1993 and 2017.</p> Full article ">
23 pages, 1195 KiB  
Article
Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth
by Utsav B. Gewali, Sildomar T. Monteiro and Eli Saber
Remote Sens. 2019, 11(13), 1614; https://doi.org/10.3390/rs11131614 - 8 Jul 2019
Cited by 20 | Viewed by 4629
Abstract
An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however [...] Read more.
An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset. Full article
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<p>Multitask Gaussian process.</p> Full article ">Figure 2
<p>Pipeline to generate synthetic dataset.</p> Full article ">Figure 3
<p>Performance as a function of training set size. (<b>a</b>–<b>c</b>) are from Algae dataset, (<b>d</b>,<b>e</b>) are from National Ecological Observatory Network (NEON) dataset, and (<b>f</b>–<b>h</b>) are from SPARC dataset.</p> Full article ">Figure 4
<p>Mean predictive <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> as function of training set size (x-axis) and illumination variations (y-axis) evaluated on simulated dataset. The x-axis of the plots is training set size and the y-axis of the plots is <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>v</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 5
<p>Gaussian processes (GP)-exponential spectral angle mapper (ESAM) and GP-squared exponential (SE) trained on the NEON dataset applied to the test hyperspectral image.</p> Full article ">Figure 6
<p>Mean predictive <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> of multitask GP as function of training set size (x-axis) and illumination variations (y-axis) evaluated on simulated dataset. The x-axis of the plots is training set size and the y-axis of the plots is <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>v</mi> <mi>a</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">
50 pages, 7469 KiB  
Article
Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake
by Caitlin A.L. Riddick, Peter D. Hunter, José Antonio Domínguez Gómez, Victor Martinez-Vicente, Mátyás Présing, Hajnalka Horváth, Attila W. Kovács, Lajos Vörös, Eszter Zsigmond and Andrew N. Tyler
Remote Sens. 2019, 11(13), 1613; https://doi.org/10.3390/rs11131613 - 7 Jul 2019
Cited by 18 | Viewed by 5648
Abstract
To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for [...] Read more.
To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for retrieval of PC concentrations in the highly optically complex waters of Lake Balaton (Hungary). MEdium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere radiances were first atmospherically corrected using the Self-Contained Atmospheric Parameters Estimation for MERIS data v.B2 (SCAPE-M_B2). Overall, the Simis05 semi-analytical algorithm outperformed more complex inversion algorithms, providing accurate estimates of PC up to ±7 days from the time of satellite overpass during summer cyanobacteria blooms (RMSElog < 0.33). Same-day retrieval of PC also showed good agreement with cyanobacteria biomass (R2 > 0.66, p < 0.001). In-depth analysis of the Simis05 algorithm using in situ measurements of inherent optical properties (IOPs) revealed that the Simis05 model overestimated the phytoplankton absorption coefficient [aph(λ)] by a factor of ~2. However, these errors were compensated for by underestimation of the mass-specific chlorophyll absorption coefficient [a*chla(λ)]. This study reinforces the need for further validation of algorithms over a range of optical water types in the context of the recently launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3. Full article
(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)
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<p>Map of Lake Balaton, indicating the routine monitoring stations (Balaton Limnological Institute, BLI; Central Transdanubian (Regional) Inspectorate for Environmental Protection, KdKVI), and the 35 stations from the August 2010 field campaign (August 2010).</p> Full article ">Figure 2
<p>Mean in situ HyperSAS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) spectra measured in August 2010 in Lake Balaton (n = 30), showing the variability by basin from west (Keszthely) to east (Siófok).</p> Full article ">Figure 3
<p>Inter-annual variations in (<b>a</b>,<b>b</b>) Chl<span class="html-italic">-a</span>, (<b>c</b>,<b>d</b>) phytoplankton biomass, (<b>e</b>,<b>f</b>) PC and (<b>g</b>,<b>h</b>) cyanobacteria biomass at Keszthely (westernmost basin) and Tihany or Siofók (easternmost basin; data from BLI routine monitoring).</p> Full article ">Figure 4
<p>Validation scatter plots of MERIS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) atmospherically corrected with SCAPE-M_B2 as a function of in situ <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) measured by the HyperSAS for each of the 12 MERIS bands (<b>a</b>)–(<b>i</b>). <span class="html-italic">In situ</span> HyperSAS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) was simulated to each MERIS band using the spectral response functions, assuming a Gaussian distribution. Scatterplots include all stations with <span class="html-italic">in situ</span> data (n = 30), with same day matchups shown in red (n = 7). Statistics correspond to the same day matchups only.</p> Full article ">Figure 5
<p>MERIS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) spectra atmospherically corrected with SCAPE-M_B2 and in situ HyperSAS radiometry <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) data for stations 1–15. MERIS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) are the mean of a 3×3 pixel window with error bars indicating standard deviation. HyperSAS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) are shown as a range, with lines indicating the minimum to maximum <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) recorded at each station.</p> Full article ">Figure 6
<p>MERIS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) spectra atmospherically corrected with SCAPE-M_B2 and in situ HyperSAS radiometry <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) data for stations 16–30. MERIS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) are the mean of a 3×3 pixel window with error bars indicating standard deviation. HyperSAS <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) are shown as a range, with lines indicating the minimum to maximum <span class="html-italic">R<sub>rs</sub></span>(<span class="html-italic">λ</span>) recorded at each station. Same-day matchups include stations 16–22 only, shown in red (n = 7). Note the different ranges for y-axis values.</p> Full article ">Figure 7
<p>Algorithm performance for phycocyanin (PC) retrieval from MERIS data over Lake Balaton from 2010 to 2011 using (<b>a</b>) Dekker93, (<b>b</b>) Schalles00, (<b>c</b>) Simis05, (<b>d</b>) Hunter10_Duan12, (<b>e</b>) Mishra13, (<b>f</b>) Qi14, (<b>g</b>) Li15 and (<b>h</b>) Liu18. Matchups are ±1 day of in situ measurements (n = 22). Note the different axis scale for Qi14 due to high retrieved PC values &gt;1000 mg m<sup>−3</sup>. Negative retrieved PC values are not shown in log scale for Schalles00, Mishra13 and Li15. For algorithm details see <a href="#remotesensing-11-01613-t001" class="html-table">Table 1</a> and <a href="#app1-remotesensing-11-01613" class="html-app">Appendix A</a>.</p> Full article ">Figure 8
<p>Gons05 retrieval of Chl<span class="html-italic">-a</span> from MERIS at matchups with measured Chl<span class="html-italic">-a</span> within (<b>a</b>) ±1 day, (<b>b</b>) ±3 days and (<b>c</b>) ±7 days. Retrieved values shown are mean pixel values within two standard deviations. Linear regression results shown are for all data. Dashed line is 1:1.</p> Full article ">Figure 9
<p>Gons05 retrieval of Chl<span class="html-italic">-a</span> from MERIS at matchups with measured Chl<span class="html-italic">-a</span> within (<b>a</b>) ±1 day, (<b>b</b>) ±3 days and (<b>c</b>) ±7 days, including only those matchups used for retrieval of PC using Simis05. Retrieved values shown are mean pixel values within two standard deviations. Linear regression results shown are for all data. Dashed line is 1:1.</p> Full article ">Figure 10
<p>Gons05 retrieval of Chl<span class="html-italic">-a</span> from MERIS at matchups with phytoplankton biomass from (<b>a</b>) ±1 day, (<b>b</b>) ±3 days and (<b>c</b>) ±7 days. Retrieved values shown are mean pixel values within two standard deviations. Linear regression results are shown for all data.</p> Full article ">Figure 11
<p>Simis05 retrieval of PC from MERIS at matchups with measured PC from (<b>a</b>) ±1 day, (<b>b</b>) ±3 days and (<b>c</b>) ±7 days. Retrieved values shown are mean pixel values within two standard deviations. Linear regression results shown are for all data. Dashed line is 1:1.</p> Full article ">Figure 12
<p>Simis05 retrieval of PC from MERIS at matchups with cyanobacteria biomass from (<b>a</b>) ±1 day, (<b>b</b>) ±3 days and (<b>c</b>) ±7 days. Retrieved values shown are mean pixel values within two standard deviations. Linear regression results are shown for all data.</p> Full article ">Figure 13
<p>Time series of estimated PC from MERIS (Simis05 algorithm) for May–September 2008.</p> Full article ">Figure 14
<p>Retrieval of (<b>a</b>) <span class="html-italic">a<sub>ph</sub></span>(665) and (<b>b</b>) <span class="html-italic">a<sub>ph</sub></span>(620) using Simis05 for 22 August 2010 with matchups ± 4 days (n = 29).</p> Full article ">Figure 15
<p>(<b>a</b>) Spectra of backscattering at three wavelengths as measured by the BB3 during the August 2010 field campaign (n = 35) and (<b>b</b>) MERIS-retrieved <span class="html-italic">b<sub>b</sub></span>(778.75) using Simis05 with matchups of in situ measured <span class="html-italic">b<sub>b</sub></span>(650) ± 4 days (n = 29).</p> Full article ">Figure 16
<p>MERIS-retrieved concentrations of Chl<span class="html-italic">-a</span> (<b>a</b>–<b>d</b>) and PC (<b>e</b>–<b>f</b>) over the time series of in situ measurements of Chl<span class="html-italic">-a</span> (<b>a</b>–<b>b</b>), phytoplankton biomass (<b>c</b>–<b>d</b>) and cyanobacteria biomass (<b>e</b>–<b>f</b>) at Keszthely (westernmost basin) and Tihany (easternmost basin).</p> Full article ">Figure A1
<p>Validation plots of MERIS retrieved <span class="html-italic">a<sub>ph</sub></span>(<span class="html-italic">λ</span>) from the Mishra13 (<b>a</b>,<b>d</b>), Li15 (<b>b</b>,<b>e</b>) and Simis05 (<b>c</b>,<b>f</b>) models for matchups within 1 day of satellite overpass.</p> Full article ">Figure A2
<p>Validation plots of MERIS retrieved <span class="html-italic">b<sub>b</sub></span>(<span class="html-italic">λ</span>) from the Mishra13 (<b>a</b>,<b>c</b>,<b>e</b>) and Li15 (<b>b</b>,<b>d</b>,<b>f</b>) bio-optical models, and the Simis05 semi-analytical model (<b>g</b>) for matchups within 1 day of satellite overpass (n = 16). Note that negative retrieved values are not shown in log scale.</p> Full article ">
29 pages, 14562 KiB  
Article
Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System
by Vincent Mariathasan, Enrico Bezuidenhoudt and K. Raymond Olympio
Remote Sens. 2019, 11(13), 1612; https://doi.org/10.3390/rs11131612 - 7 Jul 2019
Cited by 17 | Viewed by 6705
Abstract
In recent years, with more open data platforms and tools available to store and process satellite imagery, Earth Observation data have become widely accessible and usable especially for countries previously not in the possession of tasking rights to satellites and the needed processing [...] Read more.
In recent years, with more open data platforms and tools available to store and process satellite imagery, Earth Observation data have become widely accessible and usable especially for countries previously not in the possession of tasking rights to satellites and the needed processing capacity. Due to its ideal scanning and acquisition conditions for low cloud coverage imagery, Namibia aims to make use of this new development and integrate Earth Observation data into its national monitoring system of sustainable development goals (SDG). The purpose of this study is to assess the potential of open source tools and global datasets to estimate the national SDG indicators on Change of water-related ecosystems (6.6.1), Rural population with access to roads (9.1.1), Forest coverage (15.1.1) and Land degradation (15.3.1). The results are set into perspective of existing information in each particular sector. The study shows that, in the absence of in-situ measurements or data collected through surveys, the Earth Observation-based results represent a high potential to supplement the national statistics for Namibia or to serve as primary data sources once validated through ground-truthing. Furthermore, examples are given for the limitations of the assessed Earth Observation solutions in the context of Namibia. Hence, the study also serves as valuable input for discussions on a consensus on national definitions and standards by all stakeholders responsible for releasing official statistics. Full article
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
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<p>Comparison GSW 5-year average for Namibia with different shapefiles.</p> Full article ">Figure 2
<p>Comparison Boundaries of the United States Department of State Large Scale International Boundary (USDOS LSIB) vs. the Namibia Statistics Agency (NSA) shapefile at Walvis Bay (Erongo Region).</p> Full article ">Figure 3
<p>Comparison A: Hardap Dam GSW for September 2015 (orange) with Sentinel-2A Level 1C Normalized Difference Water Index (NDWI) (blue &gt;0.5) from 22 September 2015 and overlap (grey).</p> Full article ">Figure 4
<p>Comparison B: Oshana GSW for September 2015 (orange) with Sentinel-2A L1C NDWI (blue &gt;0.5) from 18 September 2015.</p> Full article ">Figure 5
<p>Comparison C: Okavango at Divundu GSW for September 2015 (orange) with Sentinel-2A L1C NDWI (blue &gt;0.5) from 18 September 2015 and overlap (grey).</p> Full article ">Figure 6
<p>Regional Pattern and Trends in Permanent Surface Water.</p> Full article ">Figure 7
<p>Annual Permanent Surface Water—Northern Regions.</p> Full article ">Figure 8
<p>Monthly Average Surface Water and Water Level at Zambezi Region and River.</p> Full article ">Figure 9
<p>Average monthly surface water and flow at Zambezi Region and River.</p> Full article ">Figure 10
<p>Comparison of surface water and water flows annual peaks between 2008 and 2015.</p> Full article ">Figure 11
<p>Comparison of surface water in April 2009 (blue) and April 2015 (purple) in the Zambezi Region.</p> Full article ">Figure 12
<p>(<b>a</b>) Comparison of OpenStreetMap to Namibia Official Roads, (<b>b</b>) zoom on one area showing OSM road as fence.</p> Full article ">Figure 13
<p>Rural Omaheke Dwelling Units 2011—within 2 km road buffer zone.</p> Full article ">Figure 14
<p>Rural population ratio estimates per region.</p> Full article ">Figure 15
<p>Comparison Land Degradation Map between 2001 and 2015. (<b>a</b>) Own calculation, (<b>b</b>) bush encroachment areas in 1999 [<a href="#B60-remotesensing-11-01612" class="html-bibr">60</a>], and (<b>c</b>) bush encroachment extent in 2015 [<a href="#B13-remotesensing-11-01612" class="html-bibr">13</a>].</p> Full article ">Figure 15 Cont.
<p>Comparison Land Degradation Map between 2001 and 2015. (<b>a</b>) Own calculation, (<b>b</b>) bush encroachment areas in 1999 [<a href="#B60-remotesensing-11-01612" class="html-bibr">60</a>], and (<b>c</b>) bush encroachment extent in 2015 [<a href="#B13-remotesensing-11-01612" class="html-bibr">13</a>].</p> Full article ">Figure 16
<p>Comparison Land Degradation Map between 2001 and 2015 with (<b>a</b>) own calculation with Trends.Earth, and (<b>b</b>) bush encroachment areas in Otjozondjupa 2016 [<a href="#B61-remotesensing-11-01612" class="html-bibr">61</a>].</p> Full article ">
22 pages, 3682 KiB  
Article
Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model
by Anxin Ding, Ziti Jiao, Yadong Dong, Xiaoning Zhang, Jouni I. Peltoniemi, Linlu Mei, Jing Guo, Siyang Yin, Lei Cui, Yaxuan Chang and Rui Xie
Remote Sens. 2019, 11(13), 1611; https://doi.org/10.3390/rs11131611 - 6 Jul 2019
Cited by 15 | Viewed by 4998
Abstract
The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and [...] Read more.
The original kernel-driven bidirectional reflectance distribution function (BRDF) models were developed based on soil-vegetation systems. To further improve the ability of the models to characterize the snow surface scattering properties, a snow kernel was derived from the asymptotic radiative transfer (ART) model and used in the kernel-driven BRDF model framework. However, there is a need to further evaluate the influence of using this snow kernel to improve the original kernel-driven models in snow albedo retrieval applications. The aim of this study is to perform such an evaluation using a variety of snow BRDF data. The RossThick-Roujean (RTR) model is used as a framework for taking in the new snow kernel (hereafter named the RTS model) since the Roujean geometric-optical (GO) kernel captures a neglectable hotspot effect and represents a more prominent dome-shaped BRDF, especially at a small solar zenith angle (SZA). We obtained the following results: (1) The RTR model has difficulties in reconstructing the snow BRDF shape, especially at large SZAs, which tends to underestimate the reflectance in the forward direction and overestimate reflectance in the backward direction for various data sources. In comparison, the RTS model performs very well in fitting snow BRDF data and shows high accuracy for all data. (2) The RTR model retrieved snow albedos at SZAs = 30°–70° are underestimated by 0.71% and 0.69% in the red and near-infrared (NIR) bands, respectively, compared with the simulation results of the bicontinuous photon tracking (bic-PT) model, which serve as “real” values. However, the albedo retrieved by the RTS model is significantly improved and generally agrees well with the simulation results of the bic-PT model, although the improved model still somewhat underestimates the albedo by 0.01% in the red band and overestimates the albedo by 0.05% in the NIR band, respectively, at SZAs = 30°–70°, which may be negligible. (3) The albedo derived by these two models shows a high correlation (R2 > 0.9) between the field-measured and Polarization and Directionality of the Earth’s Reflectances (POLDER) data, especially for the black-sky albedo. However, the albedo derived using the RTR model is significantly underestimated compared with the RTS model. The RTR model underestimates the black-sky albedo (white-sky albedo) retrievals by 0.62% (1.51%) and 0.93% (2.08%) in the red and NIR bands, respectively, for the field-measured data. The shortwave black-sky and white-sky albedos derived using the RTR model for the POLDER data are underestimated by 1.43% and 1.54%, respectively, compared with the RTS model. These results indicate that the snow kernel in the kernel-driven BRDF model frame is more accurate in snow albedo retrievals and has the potential for application in the field of the regional and global energy budget. Full article
(This article belongs to the Special Issue Remote Sensing for Quantifying Spatial and Temporal Variability of Snow and Snow Processes)
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Figure 1
<p>The solar zenith angle (SZA) distribution of the Polarization and Directionality of the Earth’s Reflectances (POLDER) and field-measured data.</p> Full article ">Figure 2
<p>The simulated data of the bicontinuous photon tracking (bic-PT) model (red dots) and the reconstructed bidirectional reflectance distribution function (BRDF) shapes by the RossThick-Snow (RTS) model (green) and RossThick-Roujean (RTR) model (black) in the principal plane (PP) of the red band (670 nm), where (<b>a</b>), (<b>b</b>) and (<b>c</b>) represent the SZAs equal to 0°, 40° and 70°, respectively. The equivalent grain radius is 0.1 mm, the b parameter is 1, and the snow density is 0.1 g/cm<sup>3</sup>.</p> Full article ">Figure 3
<p>The density plots comparing the simulated data with the RossThick-Roujean (RTR)model (blue dots) and RossThick-Snow (RTS)model (red dots) in the 670 nm (<b>a</b>) and 865 nm (<b>b</b>) bands.</p> Full article ">Figure 4
<p>The spectral albedo simulated by the bic-PT model (black) and the spectral albedo retrieved by the RTS (blue) and RTR models (red), where (<b>a</b>), (<b>b</b>) and (<b>c</b>) represent SZAs of 0°, 40° and 70°, respectively. The equivalent grain radius is 0.1 mm, the b parameter is 1, and the snow density is 0.1 g/cm<sup>3</sup>.</p> Full article ">Figure 5
<p>The comparison of albedos retrieved by these two kernel-driven models with the albedo simulated by the bic-PT model, and the two wavelengths are 670 nm (<b>a</b>) and 865 nm (<b>b</b>). The central dotted lines are 1:1 lines, the outer dotted lines are 0.02 offset lines, and the outermost dotted lines are 0.05 offset lines.</p> Full article ">Figure 6
<p>The comparison of the albedos retrieved by the two kernel-driven models with the “validation” albedo, and the two wavelengths are 670 nm (<b>a</b>) and 865 nm (<b>b</b>). The central dotted lines are 1:1 lines, the outer dotted lines are 0.02 offset lines, and the outermost dotted lines are 0.05 offset lines.</p> Full article ">Figure 7
<p>The angle distribution of the “validation” data (<b>a</b>) and three typical azimuthal samplings (<b>b</b>), (<b>c</b>) and (<b>d</b>) (i.e., ID = 0, 4 and 9), where the red dots represent the sun’s direction, and the black dots represent the view directions.</p> Full article ">Figure 8
<p>The field-measured data (red dots) and reconstructed BRDF shapes using the RTS model (green) and RTR model (black) in the PP and red band (670 nm), where (<b>a</b>) and (<b>b</b>) represent the cases, and the SZAs equal 50° and 70°, respectively.</p> Full article ">Figure 9
<p>The density plots comparing simulated data with the RTR model (blue dots) and RTS model (red dots) in the 670 nm (<b>a</b>) and 865 nm (<b>b</b>) bands.</p> Full article ">Figure 10
<p>The comparison of black-sky albedo (BSA) (red dots) and white-sky-albedo (WSA) (blue dots) retrieved by the two kernel-driven bands in the red (<b>a</b>) and near-infrared (NIR) bands (<b>b</b>) (i.e., 670 nm and 865 nm). The central dotted lines are 1:1 lines, the outer dotted lines are 0.02 offset lines, and the outermost dotted lines are 0.05 offset lines.</p> Full article ">Figure 11
<p>The field-measured data (red dots) and reconstructed BRDF shapes using the RTS model (green) and RTR model (black) in the PP and red band (670 nm), where (<b>a</b>) and (<b>b</b>) represent the cases, and the average SZAs equal 68° and 69°, respectively.</p> Full article ">Figure 12
<p>The comparison of BSA (blue) and WSA (red) retrieved by these two kernel-driven models in 6 spectral channels (i.e., 490, 565, 670, 765, 865 and 1020 nm). Where (<b>a</b>) and (<b>b</b>) represent the bias and relative error (RE) of snow albedo retrieved by these two models as a function of the wavelength. We further compare the difference between these two models for the POLDER shortwave albedos derived by using Equation (1). The results are shown in <a href="#remotesensing-11-01611-f013" class="html-fig">Figure 13</a> and are generally consistent with the results of the narrowband albedo. The shortwave albedo retrieved by the two models also has a high correlation (R<sup>2</sup> = ~0.95), especially in the BSA. However, the shortwave albedo retrieved by the RTR model shows a significant difference relative to the RTS model (P &lt; 0.05), with their differences, which are 1.43% and 1.54%, respectively. Some points are beyond the range of ±0.02, accounting for 17.01% and 15.10% in the BSA and WSA, respectively. This difference is most probably attributed to their ability in fitting POLDER multiangle observations because the potential uncertainties resulting from various factors are completely consistent between the two models.</p> Full article ">Figure 13
<p>The comparison of the broadband BSA (<b>a</b>) and WSA (<b>b</b>) retrieved by these two kernel-driven models. The thick dashed lines are 1:1 lines, the thin dashed lines are 0.02 and 0.05 offset lines deviating from the 1:1 lines, respectively.</p> Full article ">
21 pages, 8688 KiB  
Article
The Reduction Method of Bathymetric Datasets that Preserves True Geodata
by Marta Wlodarczyk-Sielicka, Andrzej Stateczny and Jacek Lubczonek
Remote Sens. 2019, 11(13), 1610; https://doi.org/10.3390/rs11131610 - 6 Jul 2019
Cited by 9 | Viewed by 4106
Abstract
Water areas occupy over 70 percent of the Earth’s surface and are constantly subject to research and analysis. Often, hydrographic remote sensors are used for such research, which allow for the collection of information on the shape of the water area bottom and [...] Read more.
Water areas occupy over 70 percent of the Earth’s surface and are constantly subject to research and analysis. Often, hydrographic remote sensors are used for such research, which allow for the collection of information on the shape of the water area bottom and the objects located on it. Information about the quality and reliability of the depth data is important, especially during coastal modelling. In-shore areas are liable to continuous transformations and they must be monitored and analyzed. Presently, bathymetric geodata are usually collected via modern hydrographic systems and comprise very large data point sequences that must then be connected using long and laborious processing sequences including reduction. As existing bathymetric data reduction methods utilize interpolated values, there is a clear requirement to search for new solutions. Considering the accuracy of bathymetric maps, a new method is presented here that allows real geodata to be maintained, specifically position and depth. This study presents a description of a developed method for reducing geodata while maintaining true survey values. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Environment)
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<p>Clustering using an artificial neural network (ANN).</p> Full article ">Figure 2
<p>Alternative test surfaces considered in this analysis.</p> Full article ">Figure 3
<p>The research process used in this analysis.</p> Full article ">Figure 4
<p>Examples of reduced dataset number one for the following parameters: (<b>a</b>) threshold: 18; range between max and min depth: 0.5; number of clusters: 4 and constant <span class="html-italic">C</span>: 1; (<b>b</b>) threshold: 18; range between max and min depth: 0.5; number of clusters: 4 and constant <span class="html-italic">C</span>: 25; (<b>c</b>) threshold: 18; range between max and min depth: 0.5; number of clusters: 100 and constant <span class="html-italic">C</span>: 1; (<b>d</b>) threshold: 18; range between max and min depth: 0.5; number of clusters: 100 and constant <span class="html-italic">C</span>: 25.</p> Full article ">Figure 5
<p>The bathymetric geodata reduction method used in this study.</p> Full article ">Figure 6
<p>Graph of the cluster size function.</p> Full article ">Figure 7
<p>Depth points generated from test surface No. 1 at a scale of 1:1000. (<b>a</b>) Resolution based on our new method; (<b>b</b>) method number one; (<b>c</b>) method number two.</p> Full article ">Figure 8
<p>Surfaces obtained at a 1:500 scale using test surface No. 1. (<b>a</b>) Reference; (<b>b</b>) our method; (<b>c</b>) method number one; (<b>d</b>) method number two.</p> Full article ">Figure 9
<p>Isobaths at a 1:500 scale using test dataset number one. (<b>a</b>) Our method; (<b>b</b>) method number one; (<b>c</b>) method number two.</p> Full article ">Figure 10
<p>Maximum errors obtained for test dataset number one.</p> Full article ">Figure 11
<p>Mean errors obtained for test dataset number one.</p> Full article ">Figure 12
<p>The points reduced in this study with our new method based on test surface one.</p> Full article ">Figure 13
<p>Surfaces from the Babina area for scale: (<b>a</b>) 1: 500, (<b>b</b>) 1:1000, and (<b>c</b>) 1:2000.</p> Full article ">Figure 14
<p>Surfaces from the Debicki canal for scale: (<b>a</b>) 1:500, (<b>b</b>) 1:1000, and (<b>c</b>) 1:2000.</p> Full article ">
16 pages, 2735 KiB  
Article
The Inter-Calibration of the DSCOVR EPIC Imager with Aqua-MODIS and NPP-VIIRS
by David Doelling, Conor Haney, Rajendra Bhatt, Benjamin Scarino and Arun Gopalan
Remote Sens. 2019, 11(13), 1609; https://doi.org/10.3390/rs11131609 - 6 Jul 2019
Cited by 9 | Viewed by 3378
Abstract
The Deep Space Climate Observatory (DSCOVR) through the earth polychromatic imaging camera (EPIC) continuously observes the illuminated disk from the Lagrange-1 point. The EPIC sensor was designed to monitor the diurnal variation of ozone, clouds, aerosols, and vegetation, especially those features that benefit [...] Read more.
The Deep Space Climate Observatory (DSCOVR) through the earth polychromatic imaging camera (EPIC) continuously observes the illuminated disk from the Lagrange-1 point. The EPIC sensor was designed to monitor the diurnal variation of ozone, clouds, aerosols, and vegetation, especially those features that benefit from observation near-backscatter conditions. The EPIC sensor does not contain any onboard calibration systems. This study describes the inter-calibration of EPIC channels 5 (0.44 µm), 6 (0.55 µm), 7 (0.68 µm), and 10 (0.78 µm) with respect to Aqua-MODIS and NPP-VIIRS. The calibration is transferred using coincident ray-matched reflectance pairs over all-sky tropical ocean (ATO) and deep convective cloud (DCC) targets. A robust and automated image-alignment technique based on feature matching was formulated to improve the navigation quality of the EPIC images. The EPIC V02 dataset exhibits improved navigation over V01. As the visible channels display similar spatial features, a single visible channel can be used to co-register the remaining visible bands. The VIIRS-referenced EPIC ATO and DCC ray-matched calibration coefficients are within 0.3%. The EPIC four-year calibration trends based on VIIRS are within 0.15%/year. The MODIS-based EPIC calibration coefficients were compared against the Geogdzhayev and Marshak 2018 published calibration coefficients and were found to be within 1.6%. Full article
(This article belongs to the Special Issue Remote Sensing: 10th Anniversary)
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Figure 1

Figure 1
<p>The (<b>a</b>) all-sky tropical ocean ray-matching (ATO-RM) and (<b>b</b>) (DCC-RM E7 V02 radiance and M5 reflectance pairs for November 2016. (<b>c</b>) and (<b>d</b>) same as (<b>a</b>) and (<b>b</b>) except for E7 and A1. The associated statistics are given in the lower right corner. The SLOPE (black line), OFFSET, and STDerr% are the slope (1/cnt s<sup>−1</sup>), offset (cnt s<sup>−1</sup>), and standard error in (%) of the linear regression. NUM refers to the number of EPIC and VIIRS ray-matched pairs. FOR[0.0] (red line) is the linear regression forced through the origin (1/cnt s<sup>−1</sup>).</p> Full article ">Figure 2
<p>The V02 E7/M5 ATO-RM (<b>a</b>) and DCC-RM (<b>b</b>) monthly force fit reflectance gains and temporal linear trends (black line). STDerr% is the standard error of the temporal linear regression in %. MEAN is the monthly reflectance gain average. SLP(%/yr) is the linear regression trend in %/year.</p> Full article ">Figure 3
<p>The EPIC (E), Aqua-MODIS (A) and SNPP-VIIRS moderate (M) and imagery (I) imager band normalized spectral response functions. Deep convective cloud (black dots) and clear-sky ocean (blue dots) SCIAMACHY reflectance observations at the SCIAMACHY spectral resolution are also shown. Each plot represents the MODIS and VIIRS band pairings for the E5, E6, E7, and E10 EPIC bands, respectively.</p> Full article ">Figure 4
<p>The E7/A1 and E7/M5 (0.65 µm) V01 (v1) and V02 (v2) navigation correction shift position 0.25° latitude (y-offset) by 0.25° longitude (x-offset) frequency plots. The center white or black box represents no spatial shift or perfect navigation.</p> Full article ">Figure 5
<p>The same as <a href="#remotesensing-11-01609-f004" class="html-fig">Figure 4</a>, except for V01 (v1) top row and V02 (v2) bottom row. From left to right EPIC E10 (0.78 µm)/NPP-VIIRS M5(0.65 µm), E10 (0.78 µm)/M7(0.86 µm), E9 (0.780 µm)/M5 (0.65 µm), and E9 (0.78 µm)/M7 (0.86 µm).</p> Full article ">Figure 6
<p>The (<b>a</b>) EPIC E7 V02, (<b>b</b>) V02 with navigation correction (V02-NAV), and (<b>c</b>) R06 radiance and NPP-VIIRS I1 (0.65 µm) ATO-RM reflectance pairs for 19 September 2016. The SLOPE (black line), OFFSET, and STDerr% are the slope (1/cnt s<sup>−1</sup>), offset (cnt s<sup>−1</sup>), and standard error in (%) of the linear regression. NUM refers to the number of EPIC and VIIRS ray-matched pairs. FOR[0.0] (red line) is the linear regression forced through the origin (1/cnt s<sup>−1</sup>).</p> Full article ">
21 pages, 1543 KiB  
Article
Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO2 Emissions from Space
by Tim Hill and Ray Nassar
Remote Sens. 2019, 11(13), 1608; https://doi.org/10.3390/rs11131608 - 6 Jul 2019
Cited by 28 | Viewed by 5336
Abstract
The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based [...] Read more.
The observational requirements for space-based quantification of anthropogenic CO 2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO2 emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO 2 monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO 2 emission monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)
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Figure 1
<p>Model and simulated column-averaged CO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> mole fraction (XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math>) observations relative to a constant 400 ppm background for 3 source emission rates and 4 pixel sizes. The first column shows the simulated Gaussian plume at 50 m resolution for each power plant emission rate against a clean background. Columns 2, 3, 4, and 5 show the simulated observations averaged to pixels of size 2 × 2 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, 4 × 4 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, 7 × 7 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, and 10 × 10 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, respectively. The top row shows the simulated observations for a source with emission rate 6 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>, the middle row shows observations for a 13 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source, and the bottom row shows observations for a 25 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source.</p> Full article ">Figure 2
<p>Simulated and noisy XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> observations relative to a constant 400 ppm background as in <a href="#remotesensing-11-01608-f001" class="html-fig">Figure 1</a>. Column 2 shows the noisy observations for square 4 × 4 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> pixels. Columns 3 and 4 show the noisy observations for rectangular 7 × 2.3 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> pixels with the long side oriented across the plume (column 3) and along the plume (column 4).</p> Full article ">Figure 3
<p>Simulated images of XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> concentrations from a 13 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source relative to a constant 400 ppm background for six different wind directions. Angles are measured counter clockwise from the positive <span class="html-italic">x</span>-axis, or eastward. Panel labels identify the angle used for each panel.</p> Full article ">Figure 4
<p>Simulated noisy XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> observations relative to a 400 ppm background for 4 × 4 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> pixels after rotating to align the plumes and interpolating the rotated grids back onto a regular grid aligned with the plume. Columns 1 and 3 show the images at their 4 km × 4 km pixel resolution immediately after rotating to align the plumes. Columns 2 and 4 show the images after interpolating onto a higher resolution (50 m) regular grid using a nearest neighbour method.</p> Full article ">Figure 5
<p>Emission rate estimates for all 12 combinations of pixel sizes and emission rates for an ensemble of 30 simulations. The green line is the true simulated emission rate, the orange line in each box is the median emission estimate, the black boxes show the range between the lower and upper quartiles (the interquartile range, IQR), and the whiskers (capped black lines) extend from the smallest datum within 1.5 times the IQR below the box to the largest datum within 1.5 times the IQR above the box. Outliers outside this range are shown as open circles. (<b>a</b>) Emission rate estimates for a 6 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source. (<b>b</b>) Emission rate estimates for a 12 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source. (<b>c</b>) Emission rate estimates for a 25 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source.</p> Full article ">Figure 6
<p>Simulated and model XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> observations relative to a constant 400 ppm background with topography, footprint, albedo, and stability class biases. Panels are as in <a href="#remotesensing-11-01608-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 7
<p>Emission rate estimates for all 12 combinations of pixel sizes and emission rates for an ensemble of 30 simulations as in <a href="#remotesensing-11-01608-f005" class="html-fig">Figure 5</a>, with topography, footprint, albedo, and stability class biases.</p> Full article ">Figure 8
<p>Emission rate estimates for all 12 combinations of pixel sizes and emission rates for an ensemble of 30 simulations as in <a href="#remotesensing-11-01608-f005" class="html-fig">Figure 5</a>, with a simulated wind at 45<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math> and a modelled direction of 35<math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>.</p> Full article ">Figure 9
<p>Emission estimates from an ensemble of 30 simulations for square pixels (4 × 4 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>), and rectangular pixels with the long side oriented both perpendicular to (7 × 2.3 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>) and along (2.3 × 7 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>) the wind, as in <a href="#remotesensing-11-01608-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 10
<p>XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> observations relative to a 400 ppm background for one instance of the 30 member ensemble. (<b>a</b>) Average observations from 6 simulated images at varying wind directions for a 13 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source. (<b>b</b>) Fitted relative XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> enhancements from the least squares fit. The label identifies that the estimated emission rate was 12.602 Mt CO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> for this individual ensemble member.</p> Full article ">Figure 11
<p>Simulated images of XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> relative to a 400 ppm background as in <a href="#remotesensing-11-01608-f004" class="html-fig">Figure 4</a>, for a 6 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source.</p> Full article ">Figure 12
<p>XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> observations relative to a 400 ppm background for one instance of the 30 member ensemble for a 6 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> source. (<b>a</b>) Average observations from 6 simulated images at varying wind directions. (<b>b</b>) Fitted relative XCO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> enhancements from the least squares fit. The label identifies that the estimated emission rate was 6.382 Mt yr<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> for this ensemble member.</p> Full article ">Figure 13
<p>Weekly emission scale factors for 50 highest emitting US power plants, data from 2016–2018. Error bars show the standard deviation in the binned weekly relative emissions.</p> Full article ">Figure 14
<p>Quantile-quantile plot of relative daily emission data with squared correlation coefficient <math display="inline"><semantics> <msup> <mi>r</mi> <mn>2</mn> </msup> </semantics></math>. Red line shows the expected distribution if the residuals are perfectly normally distributed. (<b>a</b>) Relative emissions with no weekly factor adjustment. (<b>b</b>) Relative emissions with weekly cycle removed.</p> Full article ">Figure 15
<p>Hourly scale factors for 50 highest emitting US power plants, using 2018 data. Error bars show the standard deviation in the binned hourly relative emissions.</p> Full article ">Figure 16
<p>2D histogram of relative hourly power plant emissions for 2018. (<b>a</b>) Relative emission data from all 50 plants, with no adjustments. (<b>b</b>) Difference between emissions with weekly and hourly factors removed and mean emissions, normalized by the mean emissions.</p> Full article ">Figure 17
<p>Quantile-quantile plot of relative hourly emission data with squared correlation coefficient <math display="inline"><semantics> <msup> <mi>r</mi> <mn>2</mn> </msup> </semantics></math>. Red line shows the expected distribution if the residuals are perfectly normally distributed. (<b>a</b>) Relative emissions with no hourly factor adjustment. (<b>b</b>) Relative emissions with hourly and weekly cycles removed.</p> Full article ">
19 pages, 6924 KiB  
Article
Experimental Investigation of Ocean Wave Measurement Using Short-Range K-Band Radar: Dock-Based and Boat-Based Wind Wave Measurements
by Jian Cui, Ralf Bachmayer, Brad de Young and Weimin Huang
Remote Sens. 2019, 11(13), 1607; https://doi.org/10.3390/rs11131607 - 6 Jul 2019
Cited by 6 | Viewed by 4408
Abstract
In this paper, an ocean wave measurement technique and a newly developed short-range K-band radar are tested. In previous work, the technique and its feasibility were studied based on numerical simulations and wave tank experiments, while its performance at sea was still unknown. [...] Read more.
In this paper, an ocean wave measurement technique and a newly developed short-range K-band radar are tested. In previous work, the technique and its feasibility were studied based on numerical simulations and wave tank experiments, while its performance at sea was still unknown. Surface current, Stokes drift, and wave breaking can greatly complicate interpreting radar backscatters. The feasibility of the technique needed to be further investigated with sea experiments. Experiments were carried out at a stationary site and from a moving platform. The short-range K-band radar transmitted continuous wave and received backscatters at low-grazing angles. The Bragg-scattering from the radar’s effective footprint dominated the backscatters. The Doppler shift frequency of the Bragg-scattering was attributed to the phase velocity of Bragg waves and modulated by the surface motions induced by current, Stokes drift, platform, and gravity waves. These sources of the Doppler shift frequency were analyzed, and the components induced by wind waves were successfully retrieved and converted into wave spectra that were consistent with the measurements of wave rider buoy. The experimental investigation further validated the feasibility of using short-range K-band radar to measure ocean waves. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Two effective footprints. The radar is located at the red point (0, 0). The arrow points to the transmission direction. The sea surface is flat except for Bragg waves generated by an 8 m/s wind blowing towards the radar.</p> Full article ">Figure 2
<p>Two Bragg peaks in Doppler power spectrum. If <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 0 m/s, two peaks present at −15.8 Hz and 15.8 Hz for receding and advancing Bragg waves, respectively, otherwise two peaks shift along the frequency axis. Here if <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>c</mi> </msub> </mrow> </semantics></math> = 0.5 m/s, two peaks shift right 80 Hz.</p> Full article ">Figure 3
<p>Three rotational motions and three linear motions of the K-band radar. The right-hand coordinate system <span class="html-italic">xyz</span> is built with the origin at the radar. The x-axis is parallel to the mean sea surface and follows the radar direction. Roll, pitch and yaw are rotational motions around <span class="html-italic">x</span>, <span class="html-italic">y</span>, and <span class="html-italic">z</span>-axes, respectively; surge, sway, and heave are linear motions along <span class="html-italic">x</span>, <span class="html-italic">y</span>, and <span class="html-italic">z</span>-axes, respectively. The center of the effective footprint is marked with the red point.</p> Full article ">Figure 4
<p>Processing flow for the IF output of the K-band radar. DSF: Doppler shift frequency.</p> Full article ">Figure 5
<p>(<b>a</b>) Bird view of the dock (from Google Maps). <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>w</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>r</mi> </msub> </mrow> </semantics></math> are the wind direction and the radar looking direction, respectively. (<b>b</b>) Sea surface observed along the radar beam.</p> Full article ">Figure 6
<p>Normalized power spectrogram of a 100-s IF output. The sampling rate is 1600 Hz. The resolutions of time and frequency are 0.1 s and 2 Hz, respectively.</p> Full article ">Figure 7
<p>DSF extraction and sea spikes identification. 75.8 Hz equates to twice the standard deviation of the extracted DSF.</p> Full article ">Figure 8
<p>Sea spike removal using moving average and the removal of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>d</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>The wave spectrum measured with a TRIAXYS Wave Buoy. It was located at (Latitude: 47°27.708′ N, Longitude: 53°6.498′ W) and operated by Marine Institute of Memorial University of Newfoundland. More information is available at SmartAtlantic.ca.</p> Full article ">Figure 10
<p>Amplitude spectrum of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>w</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Wind wave spectra measured by buoy and radar.</p> Full article ">Figure 12
<p>(<b>a</b>) Radar and auxiliary sensors on the boat. (<b>b</b>) Location of the experiment.</p> Full article ">Figure 13
<p>Sea surface observed at the bow of the boat. Some raindrops partially obscured the camera lens.</p> Full article ">Figure 14
<p>Normalized power spectrogram of a 30-s IF output of the K-band radar. The sampling rate is 1600 Hz. The resolutions of time and frequency are 0.1 s and 2 Hz, respectively.</p> Full article ">Figure 15
<p>DSF extraction from the processed spectrogram and sea spikes identification.</p> Full article ">Figure 16
<p>Removal of DSF <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>p</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 17
<p>Amplitude spectrum of <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>w</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>Wind wave spectra measured by buoy and radar.</p> Full article ">
12 pages, 2407 KiB  
Technical Note
Adjustment of Transceiver Lever Arm Offset and Sound Speed Bias for GNSS-Acoustic Positioning
by Guanxu Chen, Yang Liu, Yanxiong Liu, Ziwen Tian and Menghao Li
Remote Sens. 2019, 11(13), 1606; https://doi.org/10.3390/rs11131606 - 5 Jul 2019
Cited by 28 | Viewed by 6385
Abstract
Global Navigation Satellite System—Acoustic (GNSS-A) positioning is the main technique for seafloor geodetic positioning. A transceiver lever arm offset and sound velocity bias in seawater are the main systematic errors of the GNSS-A positioning technique. Based on data from a sea trial in [...] Read more.
Global Navigation Satellite System—Acoustic (GNSS-A) positioning is the main technique for seafloor geodetic positioning. A transceiver lever arm offset and sound velocity bias in seawater are the main systematic errors of the GNSS-A positioning technique. Based on data from a sea trial in shallow water, this paper studies the functional model of GNSS-A positioning. The impact of the two systematic errors on seafloor positioning is analysed and corresponding processing methods are proposed. The results show that the offset in the lever arm measurement should be parameterised in the observation equation. Given the high correlation between the vertical lever arm offset and the vertical coordinate of the seafloor station, a sample search method was introduced to fix the vertical offset correction. If the calibration of the sound velocity profiler cannot be ensured, the correction parameter of the sound velocity bias should be solved. According to the refined functional model and corrections, the position of a seafloor station in shallow water can be determined with a precision of better than 1 cm. Full article
(This article belongs to the Special Issue Global Navigation Satellite Systems for Earth Observing System)
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Figure 1

Figure 1
<p>Principle of seafloor GNSS-Acoustic positioning.</p> Full article ">Figure 2
<p>The section of the ship’s 3-D sailing track diagram (the blue points, which are scattered and do not display the regular geometry, denote the 3-D track position of the ship; the red points, which present clearly circle and cross tracks, are the projections of blue points to the average height plane; the depth of the dot colour represents the distance from the perspective).</p> Full article ">Figure 3
<p>The sound velocity profiles.</p> Full article ">Figure 4
<p>The correlation coefficient of the parameters.</p> Full article ">Figure 5
<p>The relation between the vertical lever arm and the mean square error of unit weight.</p> Full article ">Figure 6
<p>Time series of residual errors for the three models.</p> Full article ">
17 pages, 4157 KiB  
Article
A Priori Solar Radiation Pressure Model for BeiDou-3 MEO Satellites
by Xingyuan Yan, Chenchen Liu, Guanwen Huang, Qin Zhang, Le Wang, Zhiwei Qin and Shichao Xie
Remote Sens. 2019, 11(13), 1605; https://doi.org/10.3390/rs11131605 - 5 Jul 2019
Cited by 29 | Viewed by 4474
Abstract
Due to the cuboid satellite body of BeiDou-3 satellites, the accuracy of their orbit showed a trend of systematic variation with the sun-satellite-earth angle (ε) using the Extend CODE Orbit Model (ECOM1). Therefore, an a priori cuboid box-wing model (named the cuboid model) [...] Read more.
Due to the cuboid satellite body of BeiDou-3 satellites, the accuracy of their orbit showed a trend of systematic variation with the sun-satellite-earth angle (ε) using the Extend CODE Orbit Model (ECOM1). Therefore, an a priori cuboid box-wing model (named the cuboid model) is necessary to compensate ECOM1. Considering that the body-dimensions and optical properties of the BeiDou-3 satellites used to construct the box-wing model have not yet been fully released, the adjustable box-wing model (ABW) was used for precise orbit determination (POD). The a priori cuboid box-wing model was directly estimated by the precision radiation accelerations, obtained from ABW POD. When using ECOM1 model, for 14 < β < 40°, a linear systematic variation of D0 related to the elevation of the sun above the orbital plane (β-angle) with a slope of 0.048 nm/s2/°, was found for C30. After adding the cuboid model to assist ECOM1 (named Cuboid + ECOM1), the slope was reduced to 0.005 nm/s2/°, and for C20 satellite, the standard deviation (STD) of D0 was improved, from 1.28 to 0.85 nm/s2 (34%). For satellite laser ranging (SLR) validation, when using the ECOM1 model, the systematic variation with the ε angle was about 14 cm for C20 and C30. After using the Cuboid + ECOM1 model, the variation was significantly reduced to about 5 cm. For C20 and C21, compared with the ECOM1 model, the root mean square (RMS) of the ECOM2 and Cuboid + ECOM1 model was improved by about 0.54 (10.3%) and 0.43 cm (8.7%). For C29 and C30, the RMS of ECOM2 and Cuboid + ECOM1 model was improved for about 0.7 (10.9%) and 1.6 cm (25.6%). Finally, the RMS of the SLR residuals of 4.37 to 4.88 cm was achieved for BeiDou-3 POD. Full article
(This article belongs to the Special Issue Global Navigation Satellite Systems for Earth Observing System)
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<p>Distributions of precise orbit determination (POD). Magenta points are international GNSS Monitoring and Assessment System (iGMAS) stations, which can track all BeiDou-2/3 satellites. The others are Multi-GNSS Experiment (MGEX) stations, and the orange point can track BeiDou-3 satellites.</p> Full article ">Figure 2
<p>The number of observations of BeiDou-2 and BeiDou-3 satellites (4 April 2019). Brown bars are numbers for BeiDou-2 Inclined Geosynchronous Orbit (IGSO) and Medium Earth Orbit (MEO) satellites. Green bars are for BeiDou-3 MEO satellites.</p> Full article ">Figure 3
<p>Comparison of the D0 variation between the BeiDou-2 and BeiDou-3 satellites. (<b>a</b>–<b>d</b>) are the D0 variation of C09, C12, C20, and C30, respectively.</p> Full article ">Figure 4
<p>D—(green scatters) and B—(red scatters) solar radiation accelerations derived from the adjustable box-wing (ABW) model. (<b>a</b>–<b>c</b>) are the D—accelerations for C30, C20, and C12, respectively; and (<b>d</b>–<b>f</b>) are the B—accelerations for C30, C20, and C12, respectively.</p> Full article ">Figure 5
<p>Estimations of the cuboid parameters. The black and green squares are for <math display="inline"><semantics> <mrow> <msubsup> <mi>a</mi> <mi>C</mi> <mrow> <mi>α</mi> <mi>δ</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>a</mi> <mi>C</mi> <mi>ρ</mi> </msubsup> </mrow> </semantics></math>, the red and purple dots are for <math display="inline"><semantics> <mrow> <msubsup> <mi>a</mi> <mi>S</mi> <mrow> <mi>α</mi> <mi>δ</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>a</mi> <mi>S</mi> <mi>ρ</mi> </msubsup> </mrow> </semantics></math>, and the blue and brown stars are <math display="inline"><semantics> <mrow> <msubsup> <mi>a</mi> <mi>A</mi> <mrow> <mi>α</mi> <mi>δ</mi> </mrow> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>a</mi> <mi>A</mi> <mi>ρ</mi> </msubsup> </mrow> </semantics></math>. The left figures are for the estimations for C20 (<b>a</b>), and the right ones are for C30 (<b>b</b>).</p> Full article ">Figure 6
<p>Estimations of the D0 parameters. The black, red, and blue dots are for the empirical ECOM2, ECOM1, and Cuboid + ECOM1 models, separately. The top figure (<b>a</b>) is for C20, and the bottom figure (<b>b</b>) is for C30.</p> Full article ">Figure 7
<p>One-way satellite laser ranging (SLR) residual of the BeiDou-3 satellites. (<b>a</b>,<b>d</b>) are the ECOM1 results of C20 and C30, (<b>b</b>,<b>e</b>) are the ECOM2 results of C20 and C30, and (<b>c</b>,<b>f</b>) are the Cuboid + ECOM1 results of C20 and C30. The blue line is the cubic polynomial fitting curve of the SLR residuals.</p> Full article ">Figure 8
<p>Residuals of the satellite clock offset, fitted by the quadratic polynomial. (<b>a</b>,<b>d</b>) are the ECOM1 results of C30 and C20, (<b>b</b>,<b>e</b>) are the ECOM2 results of C30 and C20, and (<b>c</b>,<b>f</b>) are the Cuboid + ECOM1 results of C30 and C20, respectively. The blue line is the cubic polynomial fitting curve of the residuals.</p> Full article ">
22 pages, 27173 KiB  
Article
An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images
by Chu He, Peizhang Fang, Zhi Zhang, Dehui Xiong and Mingsheng Liao
Remote Sens. 2019, 11(13), 1604; https://doi.org/10.3390/rs11131604 - 5 Jul 2019
Cited by 18 | Viewed by 4209
Abstract
Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three [...] Read more.
Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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<p>Framework of several segmentation methods.</p> Full article ">Figure 2
<p>Structure of generative model.</p> Full article ">Figure 3
<p>Improvement in encoder-decoder model.</p> Full article ">Figure 4
<p>The iteration in CRF layer.</p> Full article ">Figure 5
<p>Structure of discriminative model.</p> Full article ">Figure 6
<p>Flowchart of training and testing process.</p> Full article ">Figure 7
<p>The experimental data 1. (<b>a</b>) Pauli SAR image; (<b>b</b>) The ground truth.</p> Full article ">Figure 8
<p>The experimental data 2. (<b>a</b>) Remote sensing image; (<b>b</b>) The ground truth.</p> Full article ">Figure 9
<p>Segmentation results 1.</p> Full article ">Figure 10
<p>Segmentation results 2.</p> Full article ">Figure 11
<p>Segmentation results 3.</p> Full article ">Figure 12
<p>Segmentation results 4.</p> Full article ">Figure 13
<p>Statistics on the evaluation indexes of Data Set 2.</p> Full article ">
2 pages, 416 KiB  
Correction
Correction: Z. Liu and Y. Liu. Does Anthropogenic Land Use Change Play a Role in Changes of Precipitation Frequency and Intensity over the Loess Plateau of China? Remote Sens. 2018, 10, 1818
by Zhengjia Liu and Yansui Liu
Remote Sens. 2019, 11(13), 1603; https://doi.org/10.3390/rs11131603 - 5 Jul 2019
Cited by 2 | Viewed by 2652
Abstract
The original version of the paper [...] Full article
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<p>Major land use changes, with overall change area being greater than 0.5% of the entire study area during the periods 1990–1999 (<b>a</b>) and 2000–2010 (<b>b</b>). C2F represents conversion from croplands to forests; C2G represents conversion from croplands to grasslands; G2C represents conversion from grasslands to croplands; G2F represents conversion from grasslands to forests; and B2G represents conversion from barren or sparsely vegetated land to grasslands.</p> Full article ">
30 pages, 8557 KiB  
Article
Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots
by Christoph Gollob, Tim Ritter, Clemens Wassermann and Arne Nothdurft
Remote Sens. 2019, 11(13), 1602; https://doi.org/10.3390/rs11131602 - 5 Jul 2019
Cited by 51 | Viewed by 5818
Abstract
This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic [...] Read more.
This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forests)
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Figure 1
<p>Scan settings. Numbers below scanner positions (marked as triangles) denote scan variants in which each respective scanner position was used. For the most intensive scan variant, variant 1, the distance between each neighboring scanner position (triangle) was constantly 15 m. The Styrofoam balls as reference targets (filled circles) were located in two concentric squares. The outer square had a side length of 14.1 m and the distance of each reference target to the center was 10 m. The inner square had a side length of 7.1 m and the distance of each reference target to the center was 5 m. In addition, the two squares were rotated by 45°. In scan variants without a central scan (2, 4, 6, 8), an extra reference target was placed at the center location.</p> Full article ">Figure 2
<p>Distribution of detection rates for different scan variants, lower dbh thresholds, and plot radii. Black squares represent average detection rates over all 23 sample plots and for a specific setting.</p> Full article ">Figure 3
<p>Commission rates for different scan variants, lower dbh thresholds, and plot radii. Black squares represent mean detection rates.</p> Full article ">Figure 4
<p>Overall accuracies for different scan variants, lower dbh thresholds, and plot radii. Black squares represent mean detection rates.</p> Full article ">Figure 5
<p>Overall accuracy achieved with scan variant 3 evaluated separately for lower dbh thresholds of 5, 10, and 15 cm and a fine grid of maximum radii. Solid black line indicates average overall accuracy of 23 sample plots. Gray shaded area indicates 95% interval for the 23 plots.</p> Full article ">Figure A1
<p>dbh RMSE in cm for different scan variants, lower dbh thresholds, and plot radii. Black squares represent mean dbh RMSE.</p> Full article ">Figure A2
<p>dbh RMSE in % for different scan variants, lower dbh thresholds, and plot radii. Black squares represent mean dbh RMSE.</p> Full article ">Figure A3
<p>dbh bias in cm for different scan variants, lower dbh thresholds, and plot radii. Black squares represent mean dbh bias.</p> Full article ">Figure A4
<p>dbh bias in % for different scan variants, lower dbh thresholds, and plot radii. Black squares represent mean dbh bias.</p> Full article ">Figure A5
<p>Overall accuracy over plot radius. Bold black line indicates mean. Gray area indicates 95% confidence envelope. Bold numbers on the right mark scan variant.</p> Full article ">Figure A6
<p>Overall accuracy over plot radius. Bold black line indicates mean. Gray area indicates 95% confidence envelope. Bold numbers on the right mark scan variant.</p> Full article ">Figure A7
<p>Detection rate over number of stems per hectare. Bold red line indicates logistic response curve. Points indicate observations.</p> Full article ">
20 pages, 11517 KiB  
Article
High-Resolution Lightning Detection and Possible Relationship with Rainfall Events over the Central Mediterranean Area
by Guido Paliaga, Carlo Donadio, Marina Bernardi and Francesco Faccini
Remote Sens. 2019, 11(13), 1601; https://doi.org/10.3390/rs11131601 - 5 Jul 2019
Cited by 23 | Viewed by 3916
Abstract
Lightning activity is usually associated with precipitations events and represents a possible indicator of climate change, even contributing to its increase with the production of NOx gases. The study of lightning activity on long temporal periods is crucial for fields related to atmospheric [...] Read more.
Lightning activity is usually associated with precipitations events and represents a possible indicator of climate change, even contributing to its increase with the production of NOx gases. The study of lightning activity on long temporal periods is crucial for fields related to atmospheric phenomena from intense rain-related hazard processes to long-term climate changes. This study focuses on 19 years of lightning-activity data, recorded from Italian Lightning Detection Network SIRF, part of the European network EUCLID (European Cooperation for Lightning Detection). Preliminary analysis was dedicated to the spatial and temporal assessment of lightning through detection in the Central Mediterranean area, focusing on yearly and monthly data. Temporal and spatial features have been analyzed, measuring clustering through the application of global Moran’s I statistics and spatial local autocorrelation; a Mann–Kendall trend test was performed on monthly series aggregating the original data on a 5 × 5 km cell. A local statistically significant trend emerged from the analysis, suggesting possible linkage between surface warming and lightning activity. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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<p>(<b>A</b>) Studied area and annual stroke mean per square km calculated for the 2000–2018 period; (<b>B</b>) higher densities along: 1. the central Liguria coastline, 2. (blue line), the Tyrrhenian coastline, 3. the northern Adriatic Sea, 4. Northeast Italy, 5. the Capodistrian peninsula, 6. Provence in France, 7. (red line), the Central Italian Alps, 8. (red line) the Apennines, and 9. inner Corsica. Other cited areas: 10. Tyrrhenian Sea, 11. Sicily, 12. Sardinia, 13. northern Atlas, and 14. the western Balkan peninsula.</p> Full article ">Figure 2
<p><b>(A</b>) Minimum, maximum, and mean monthly cloud–ground stroke counts. (<b>B</b>) Yearly counts of strokes and global Moran’s <span class="html-italic">I</span> values; mean for total period and linear trend. (<b>C</b>) Monthly extreme values for the total period.</p> Full article ">Figure 3
<p>Monthly counts of cloud–ground strokes and global Moran’s <span class="html-italic">I</span> values, the mean for the total period, and the linear trend in the total measurement period.</p> Full article ">Figure 4
<p>Spatial Mann–Kendall trend test for the monthly series in the 2000–2018 period with 90%, 95%, and 99% confidence level. 1: January; 2: February; 3: March; 4: April; 5: May; 6: June; 7: July; 8: August; 9: September; 10: October; 11: November; 12: December.</p> Full article ">Figure 5
<p>Cloud–ground strokes per square km in August for the entire period. Blue open circles indicate main flood events.</p> Full article ">Figure 6
<p>Cloud–ground strokes per square km in October for the entire period. Blue open circles indicate main flood events.</p> Full article ">Figure 7
<p>Cloud–ground strokes per square km in November for the entire period. Blue open circles indicate main flood events.</p> Full article ">Figure 8
<p>Monthly percentage of cloud–ground strokes between sea, land, and coastal areas (10 km depth buffer zone) for the studied period.</p> Full article ">Figure 9
<p>Monthly mean stroke density on sea, land, and coastal areas for the studied period.</p> Full article ">Figure 10
<p>Local autocorrelation at a distance of D = 30 km for October in the 2000–2018 period at 90%, 95%, and 99% confidence levels.</p> Full article ">
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