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Volume 9
Issue 7
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Remote Sens., Volume 9, Issue 7 (July 2017) – 125 articles

Cover Story (view full-size image): It has long been assumed that South Asia's ancient Indus Civilization was riverine, but many presumed watercourses are no longer visible. For the last 30 years, satellite imagery has been used to map the hydrology of the extensive plains that Indus populations occupied. This paper adopts a seasonal multi-temporal approach to the detection of palaeorivers over this large area (more than 80,000 km2). Twenty-eight years of Landsat 5 data—a total of 1711 multispectral images and 1254 8-day vegetation composites—have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure. The resulting data have allowed the mapping of 8000 km of relic water courses in the Sutlej-Yamuna interfluve, a core area for this Bronze Age civilization. The research also provided insights into the environmental conditions in which Indus urbanism developed and ultimately declined. View the paper
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3939 KiB  
Article
Coastal Waveform Retracking for Jason-2 Altimeter Data Based on Along-Track Echograms around the Tsushima Islands in Japan
by Xifeng Wang and Kaoru Ichikawa
Remote Sens. 2017, 9(7), 762; https://doi.org/10.3390/rs9070762 - 24 Jul 2017
Cited by 27 | Viewed by 6568
Abstract
Although the Brown mathematical model is the standard model for waveform retracking over open oceans, due to heterogeneous surface reflections within altimeter footprints, coastal waveforms usually deviate from open ocean waveform shapes and thus cannot be directly interpreted by the Brown model. Generally, [...] Read more.
Although the Brown mathematical model is the standard model for waveform retracking over open oceans, due to heterogeneous surface reflections within altimeter footprints, coastal waveforms usually deviate from open ocean waveform shapes and thus cannot be directly interpreted by the Brown model. Generally, the two primary sources of heterogeneous surface reflections are land surfaces and bright targets such as calm surface water. The former reduces echo power, while the latter often produces particularly strong echoes. In previous studies, sub-waveform retrackers, which use waveform samples collected from around leading edges in order to avoid trailing edge noise, have been recommended for coastal waveform retracking. In the present study, the peaky-type noise caused by fixed-point bright targets is explicitly detected and masked using the parabolic signature in the sequential along-track waveforms (or, azimuth-range echograms). Moreover, the power deficit of waveform trailing edges caused by weak land reflections is compensated for by estimating the ratio of sea surface area within each annular footprint in order to produce pseudo-homogeneous reflected waveforms suitable for the Brown model. Using this method, altimeter waveforms measured over the Tsushima Islands in Japan by the Ocean Surface Topography Mission (OSTM)/Jason-2 satellite are retracked. Our results show that both the correlation coefficient and root mean square difference between the derived sea surface height anomalies and tide gauge records retain similar values at the open ocean (0.9 and 20 cm) level, even in areas approaching 3 km from coastlines, which is considerably improved from the 10 km correlation coefficient limit of the conventional MLE4 retracker and the 7 km sub-waveform ALES retracker limit. These values, however, depend on the topography of the study areas because the approach distance limit increases (decreases) in areas with complicated (straight) coastlines. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Characteristics of a typical Brown waveform over the open ocean.</p> Full article ">Figure 2
<p>(<b>a</b>) Rescaled and realigned along-track waveforms (echogram) measured by the Jason-2 altimeter over the southern Tsushima Islands (pass 36, cycle 22). The shaded area (black patch) corresponds to land. Each column of the echogram represents an individual waveform at a given latitude and the rescaled power is indicated by the color scale; (<b>b</b>) Example of corrupted waveform measured at the location indicated by the red point in <a href="#remotesensing-09-00762-f002" class="html-fig">Figure 2</a>a (34.20°N). The black line represents the actual waveform and red line represents the fitted waveform using the four parameter Brown theoretical model.</p> Full article ">Figure 3
<p>(<b>a</b>) Ground track (blue line) and footprint (blue circles draw for every 1 s, radius is 10 km) of the Jason-2 altimeter over the Tsushima Islands (pass 36). The shortest distance between the tide gauge (red point) and altimeter ground track is about 6 km; (<b>b</b>) Enlarged local map for the southern section of the pass. The vertices in <a href="#remotesensing-09-00762-f002" class="html-fig">Figure 2</a>a indicate that the reflection points are located 3 km and 5 km apart from the nadir track at 34.23°N and 34.21°N, respectively. These locations, which include semi-closed bays, are marked by circles.</p> Full article ">Figure 4
<p>(<b>a</b>) Schematic showing the geometrical relationship between an altimeter and a strong reflector (bright patch) on the sea surface. The altimeter is closest to the reflector at point <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> with a distance <math display="inline"> <semantics> <mrow> <msub> <mi>d</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>; (<b>b</b>) Parabolic shape in the echogram. The value <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>y</mi> </mrow> </semantics> </math> corresponds to the geographical distance between point <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mi>i</mi> </msub> </mrow> </semantics> </math> of the altimeter with <math display="inline"> <semantics> <mrow> <msub> <mi>y</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> (nearest approach), and <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics> </math> represents the round-trip time difference, <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>t</mi> <mo>=</mo> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>−</mo> <msub> <mi>t</mi> <mn>0</mn> </msub> </mrow> </semantics> </math>.</p> Full article ">Figure 5
<p>Purple points represent the marked pixels in the echogram for the 2% threshold criterion. Cyan points represent the parabolic shape described by Equation (2). The red point is the vertex of the parabola.</p> Full article ">Figure 6
<p>(<b>a</b>) Masked echogram for pass 36 cycle 22 of Jason-2 data south of the Tsushima Islands. All pixel traces along the parabolic shape are masked. The shaded area represents the land; (<b>b</b>) Bright targets masked waveform (dash line) and fitted Brown waveform (solid line) measured at 34.2°N (the same waveform as shown in <a href="#remotesensing-09-00762-f002" class="html-fig">Figure 2</a>b).</p> Full article ">Figure 7
<p>(<b>a</b>) Annular altimeter footprint (drawn for every five gates) for the waveform measured at 34.2°N (corresponding to the red point shown in <a href="#remotesensing-09-00762-f002" class="html-fig">Figure 2</a>a); (<b>b</b>) Compensated waveform (dashed line) and the fitted Brown waveform (solid line) measured at the location shown in (<b>a</b>).</p> Full article ">Figure 8
<p>SSH (<b>a</b>); SWH (<b>b</b>); sigma0 (<b>c</b>) and mispointing angle (<b>d</b>) before (green) and after (red) land compensation south of the Tsushima Islands for cycle 22 of the Jason-2 altimeter.</p> Full article ">Figure 9
<p>RMSD between the results with and without land compensation south of the Tsushima Islands for seven years of Jason-2 altimeter data; for range (<b>a</b>); SWH (<b>b</b>); sigma0 (<b>c</b>) and mispointing angle (<b>d</b>).</p> Full article ">Figure 10
<p>Correlation coefficient (CC) (<b>a</b>) and RMSD (<b>b</b>) for the SSHA derived from tide gauge and altimeter measurements for three different methods. Black is the result for the SGDR product, blue is the result for the ALES product, and red is the result for this study.</p> Full article ">Figure 11
<p>Comparison of SSHA time series derived from ALES retracker (blue) and this study (red) at 34.21°N with tide gauge measurements (green).</p> Full article ">Figure 12
<p>(<b>a</b>) Ground track (blue line) of Jason-2 altimeter pass 36 over northwestern coast of the Tsushima Islands. Points are plotted every 5 km from the coast of Cape Karasaki together with corresponding footprints with a 10 km radius. The section of the pass over Asou Bay is identified by the purple line; (<b>b</b>) Rescaled and realigned along-track waveforms (echogram) measured by the Jason-2 altimeter over the northwestern coast of the Tsushima Islands (pass 36, cycle 22); (<b>c</b>) RMS variation for SSHA derived from altimeter measurements for three different methods. Black is the result of SGDR product, blue is the result of ALES product, and red is the result of this study.</p> Full article ">Figure 13
<p>(<b>a</b>) Ground track (blue line) of Jason-2 altimeter pass 164 over southeast coast of Taiwan. Points are plotted for every 5 km from the coast together with corresponding footprints with 10 km radius; (<b>b</b>) Rescaled and realigned along-track waveforms (echogram) measured by Jason-2 altimeter over Taiwan (pass 164, cycle 47); (<b>c</b>) RMS variation for SSHA derived from altimeter measurements for three different methods. Black is the result for the SGDR product, blue is the result for the ALES product, and red is the result for this study.</p> Full article ">
5510 KiB  
Article
Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake
by Huizeng Liu, Qingquan Li, Tiezhu Shi, Shuibo Hu, Guofeng Wu and Qiming Zhou
Remote Sens. 2017, 9(7), 761; https://doi.org/10.3390/rs9070761 - 23 Jul 2017
Cited by 118 | Viewed by 12374
Abstract
Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (CSPM) is a key parameter describing water quality. This study, using in-situ spectral and CSPM measurements as well as Sentinel [...] Read more.
Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (CSPM) is a key parameter describing water quality. This study, using in-situ spectral and CSPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop CSPM retrieval models and further to estimate the CSPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate CSPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived CSPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of CSPM. The validation results also showed the reliability of these six models, and the estimated CSPM explained 77–93% of the variation of measured CSPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived CSPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The CSPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for CSPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively. Full article
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>True color composite of atmospherically corrected Sentinel 2 MSI images captured on 15 August 2016 (<b>a</b>) and 2 April 2017 (<b>b</b>), respectively, showing Poyang Lake, and the sampling sites in 2010 (×) and 2011(+) are illustrated in (<b>a</b>).</p> Full article ">Figure 2
<p>Remote sensing reflectance (<span class="html-italic">R</span><sub>rs</sub>) spectra and their corresponding suspended particulate matter concentrations (mg/L), and the relative spectral response function of the Sentinel 2 MSI (black dash curve) (<b>a</b>); and the correlation coefficient (r) between the <span class="html-italic">R</span><sub>rs</sub> and suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) (<b>b</b>).</p> Full article ">Figure 3
<p>Suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) retrieval models based on Sentinel 2 MSI B4 (<b>a</b>) and B7 (<b>b</b>).</p> Full article ">Figure 4
<p>Scatter plots of estimated against measured suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) for the validation dataset: Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>). The solid line is the regression line between the estimated and measured values, and the dashed line is 1:1 line.</p> Full article ">Figure 4 Cont.
<p>Scatter plots of estimated against measured suspended particulate matter concentration (<span class="html-italic">C</span><sub>SPM</sub>) for the validation dataset: Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>). The solid line is the regression line between the estimated and measured values, and the dashed line is 1:1 line.</p> Full article ">Figure 5
<p>Suspended particulate matter concentrations (<span class="html-italic">C</span><sub>SPM</sub>) retrieved from Sentinel 2 MSI B4 (<b>a</b>), B7 (<b>b</b>), B8b (<b>c</b>), and MODIS Terra B1 (<b>d</b>) captured on 15 August 2016. The areas in the red rectangle are zoomed in to show the detailed <span class="html-italic">C</span><sub>SPM</sub> variations.</p> Full article ">Figure 6
<p>Scatter plots of <span class="html-italic">C</span><sub>SPM</sub> values derived from Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>) against those from MODIS Terra B1 on 15 August 2016. The solid line is the regression line and the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log transformation (y = log<sub>1.05</sub>(x)).</p> Full article ">Figure 7
<p>The mean and standard deviation (Std) of <span class="html-italic">C</span><sub>SPM</sub> derived from Sentinel 2 MSI B4–B8b and MODIS Terra B1 on 15 August 2016.</p> Full article ">Figure 8
<p>Suspended particulate matter concentrations (<span class="html-italic">C</span><sub>SPM</sub>) retrieved from Sentinel 2 MSI B4 (<b>a</b>), B7 (<b>b</b>), B8b (<b>c</b>), and MODIS Aqua B1 (<b>d</b>) on 2 April 2017. The areas in the red rectangle are zoomed in to show the detailed <span class="html-italic">C</span><sub>SPM</sub> variations.</p> Full article ">Figure 9
<p>Scatter plots of <span class="html-italic">C</span><sub>SPM</sub> values derived from Sentinel 2 MSI B4 (<b>a</b>), B5 (<b>b</b>), B6 (<b>c</b>), B7 (<b>d</b>), B8 (<b>e</b>), and B8b (<b>f</b>) against those from MODIS Aqua on 2 April 2017. The solid line is the regression line and the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log transformation (y = log1.05(x)).</p> Full article ">Figure 10
<p>The mean and standard deviation (Std) of <span class="html-italic">C</span><sub>SPM</sub> derived from Sentinel 2 MSI B4–B8b and MODIS Aqua B1 sensed on 2 April 2017.</p> Full article ">
7256 KiB  
Article
Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data
by Yi’na Hu, Jian Peng, Yanxu Liu, Yueyue Du, Huilei Li and Jiansheng Wu
Remote Sens. 2017, 9(7), 760; https://doi.org/10.3390/rs9070760 - 23 Jul 2017
Cited by 33 | Viewed by 8262
Abstract
Spatial inequality of urban development may cause problems like inequality of living conditions and the lack of sustainability, drawing increasing academic interests and societal concerns. Previous studies based on statistical data can hardly reveal the interior mechanism of spatial inequality due to the [...] Read more.
Spatial inequality of urban development may cause problems like inequality of living conditions and the lack of sustainability, drawing increasing academic interests and societal concerns. Previous studies based on statistical data can hardly reveal the interior mechanism of spatial inequality due to the limitation of statistical units, while the application of remote sensing data, such as nighttime light (NTL) data, provides an effective solution. In this study, based on the DMSP/OLS NTL data, the urbanization type of all towns in the Beijing-Tianjin-Hebei urban agglomeration was analyzed from the aspects of development level and speed. Meanwhile, spatial cluster analysis of development level by local Moran’s I was used to explore spatial inequality, and the trend was discussed by comparing the development characteristics on both sides of the transition line of different development levels (inequality boundary). The results showed that the development level of the whole region increased dramatically as the mean DN value increased by 65.99%, and 83.72% of the towns showed a positive development during 2000–2012. The spatial distribution of urbanization types showed that Beijing and Tianjin were at a high urbanization level with rapid speed of development, with the southern region having a medium development level and the northwestern region lagging behind. The spatial cluster analysis also revealed a gradually intensifying trend of inequality as the number of towns with balanced development reduced by 319 during 2000–2012, while the towns in the high-high areas increased by 99 and those in the low-low areas increased by 229. Moreover, the development speed inside the inequality boundary was obviously higher than that outside, indicating an increasingly serious situation for spatial inequality of urban development in the whole region. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of study area.</p> Full article ">Figure 2
<p>Spatial pattern of nighttime light in Beijing-Tianjin-Hebei urban agglomeration during 2000–2012. (<b>a</b>) The DN value of nighttime light image in 2000; (<b>b</b>) The DN value of nighttime light image in 2012; (<b>c</b>) Area proportion of each level in 2000 and 2012.</p> Full article ">Figure 3
<p>Spatial pattern of development speed in Beijing-Tianjin-Hebei urban agglomeration during 2000–2012.</p> Full article ">Figure 4
<p>Spatial pattern of urbanization types in Beijing-Tianjin-Hebei urban agglomeration during 2000–2012.</p> Full article ">Figure 5
<p>Spatial cluster of urban development in Beijing-Tianjin-Hebei urban agglomeration during 2000–2012.</p> Full article ">Figure 6
<p>Changes of urban development inside and outside the inequality boundary in Beijing-Tianjin-Hebei urban agglomeration during 2000–2012.</p> Full article ">Figure 7
<p>Speed of change in urban development inside and outside the inequality boundary in Beijing-Tianjin-Hebei urban agglomeration during 2000–2012.</p> Full article ">
4597 KiB  
Article
Ku-, X- and C-Band Microwave Backscatter Indices from Saline Snow Covers on Arctic First-Year Sea Ice
by Vishnu Nandan, Torsten Geldsetzer, Mallik Mahmud, John Yackel and Saroat Ramjan
Remote Sens. 2017, 9(7), 757; https://doi.org/10.3390/rs9070757 - 23 Jul 2017
Cited by 12 | Viewed by 7703
Abstract
In this study, we inter-compared observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A Ku-, X- and C-band surface-borne polarimetric microwave scatterometer system was used to measure fully-polarimetric [...] Read more.
In this study, we inter-compared observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A Ku-, X- and C-band surface-borne polarimetric microwave scatterometer system was used to measure fully-polarimetric backscatter from the three snow covers, near-coincident with corresponding in situ snow thermophysical measurements. The study investigated differences in co-polarized backscatter observations from the scatterometer system for all three frequencies, modeled penetration depths, utilized co-pol ratios, and introduced dual-frequency ratios to discriminate dominant polarization-dependent frequencies from these snow covers. Results demonstrate that the measured co-polarized backscatter magnitude increased with decreasing snow thickness for all three frequencies, owing to stronger gradients in snow salinity within thinner snow covers. The innovative dual-frequency ratios suggest greater sensitivity of Ku-band microwaves to snow grain size as snow thickness increases and X-band microwaves to snow salinity changes as snow thickness decreases. C-band demonstrated minimal sensitivity to changes in snow salinities. Our results demonstrate the influence of salinity associated dielectric loss, throughout all layers of the three snow covers, as the governing factor affecting microwave backscatter and penetration from all three frequencies. Our “plot-scale” observations using co-polarized backscatter, co-pol ratios and dual-frequency ratios suggest the future potential to up-scale our multi-frequency approach to a “satellite-scale” approach, towards effective development of snow geophysical and thermodynamic retrieval algorithms on smooth first-year sea ice. Full article
(This article belongs to the Special Issue Microwave Indices from Active and Passive Sensors for Remote Sensing Applications)
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Graphical abstract
Full article ">Figure 1
<p>Map of Resolute Bay region (indicated in green dot) in Resolute Passage in the Canadian Arctic, Nunavut, Canada. Study site location is indicated in red star. Snow covered first-year ice accumulated areas are depicted in light blue, and land in brown. Note: A similar figure with different color scheme can be found in [<a href="#B14-remotesensing-09-00757" class="html-bibr">14</a>].</p> Full article ">Figure 2
<p>Hourly air temperature measured on 19 May 2012, from the on-ice micro-meteorological station. Colored dots represent times of in-situ snow property measurements at ~9:45 a.m. (for 14 cm), ~12:25 p.m. (for 4 cm) and ~9:30 p.m. (for 8 cm) local times. Green vertical lines denote the timing of the scatterometer measurements quasi-coincident with the in-situ snow thermophysical property measurements.</p> Full article ">Figure 3
<p>Surface-based multi-frequency polarimetric microwave scatterometer system: C-band scatterometer (foreground), and UW-Scat (Ku- and X-bands) (background).</p> Full article ">Figure 4
<p>Sample snow cover on first-year sea ice (FYI) located adjacent to the scatterometer scan area: (<b>a</b>) 14 cm; (<b>b</b>) 8 cm; and (<b>c</b>) 4 cm.</p> Full article ">Figure 5
<p>Observed Ku-, X- and C-band backscatter (<math display="inline"> <semantics> <mrow> <msubsup> <mi>σ</mi> <mrow> <mi>V</mi> <mi>V</mi> </mrow> <mn>0</mn> </msubsup> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msubsup> <mi>σ</mi> <mrow> <mi>H</mi> <mi>H</mi> </mrow> <mn>0</mn> </msubsup> </mrow> </semantics> </math> ), from 14 cm, 8 cm and 4 cm snow covers on FYI acquired on 19 May 2012: (<b>a</b>) Ku-band; (<b>b</b>) X-band; and (<b>c</b>) C-band. Scatterometer backscatter trend lines are cubic fits. Colored points represent measurement points with error bars indicating min-max deviation. Vertical black dotted lines partition near-range (NR), mid-range (MR) and far-range (FR) incidence angles.</p> Full article ">Figure 6
<p>Calculated Ku-, X- and C-band co-pol ratios (<math display="inline"> <semantics> <mrow> <msub> <mrow> <mi mathvariant="sans-serif">γ</mi> </mrow> <mrow> <mi>c</mi> <mi>o</mi> </mrow> </msub> </mrow> </semantics> </math>) from 14 cm, 8 cm and 4 cm snow covers on FYI: (<b>a</b>) Ku-band; (<b>b</b>) X-band; and (<b>c</b>) C-band. Co-pol ratio trend lines are cubic fits. Colored points represent measurement points. Vertical black dotted lines partition near-range (NR), mid-range (MR) and far-range (FR) incidence angles.</p> Full article ">Figure 7
<p>Derived (Ku-, X-), (X-, C-) and (Ku-, C-) VV and HH dual-frequency ratios (<math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>) from 14 cm, 8 cm and 4 cm snow covers on FYI: (<b>a</b>,<b>b</b>) Ku- and X-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>,<b>d</b>) X- and C-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>; and (<b>e</b>,<b>f</b>) Ku- and C-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>. Dual-frequency ratio trend lines are cubic fits. Colored points represent dual-frequency ratios from measurement points. Vertical black dotted lines partition near-range (NR), mid-range (MR) and far-range (FR) incidence angles.</p> Full article ">Figure 7 Cont.
<p>Derived (Ku-, X-), (X-, C-) and (Ku-, C-) VV and HH dual-frequency ratios (<math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>) from 14 cm, 8 cm and 4 cm snow covers on FYI: (<b>a</b>,<b>b</b>) Ku- and X-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>,<b>d</b>) X- and C-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>; and (<b>e</b>,<b>f</b>) Ku- and C-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>. Dual-frequency ratio trend lines are cubic fits. Colored points represent dual-frequency ratios from measurement points. Vertical black dotted lines partition near-range (NR), mid-range (MR) and far-range (FR) incidence angles.</p> Full article ">Figure 7 Cont.
<p>Derived (Ku-, X-), (X-, C-) and (Ku-, C-) VV and HH dual-frequency ratios (<math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>) from 14 cm, 8 cm and 4 cm snow covers on FYI: (<b>a</b>,<b>b</b>) Ku- and X-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>; (<b>c</b>,<b>d</b>) X- and C-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>; and (<b>e</b>,<b>f</b>) Ku- and C-band VV and HH <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="sans-serif">γ</mi> <mrow> <mi>D</mi> <mi>F</mi> <mi>R</mi> </mrow> </msub> </mrow> </semantics> </math>. Dual-frequency ratio trend lines are cubic fits. Colored points represent dual-frequency ratios from measurement points. Vertical black dotted lines partition near-range (NR), mid-range (MR) and far-range (FR) incidence angles.</p> Full article ">
10270 KiB  
Article
Saturation Correction for Nighttime Lights Data Based on the Relative NDVI
by Zheng Wang, Fei Yao, Weifeng Li and And Jiansheng Wu
Remote Sens. 2017, 9(7), 759; https://doi.org/10.3390/rs9070759 - 22 Jul 2017
Cited by 8 | Viewed by 6804
Abstract
DMSP/OLS images are widely used as data sources in various domains of study. However, these images have some deficiencies, one of which is digital number (DN) saturation in urban areas, which leads to significant underestimation of light intensity. We propose a new method [...] Read more.
DMSP/OLS images are widely used as data sources in various domains of study. However, these images have some deficiencies, one of which is digital number (DN) saturation in urban areas, which leads to significant underestimation of light intensity. We propose a new method to correct the saturation. With China as the study area, the threshold value of the saturation DN is screened out first. A series of regression analyses are then carried out for the 2006 radiance calibrated nighttime lights (RCNL) image and relative NDVI (RNDVI) to determine a formula for saturation correction. The 2006 stable nighttime lights (SNL) image (F162006) is finally corrected and evaluated. It is concluded that pixels are saturated when the DN is larger than 50, and that the saturation is more serious when the DN is larger. RNDVI, which was derived by subtracting the interpolated NDVI from the real NDVI, is significantly better than the real NDVI for reflecting the degree of human activity. Quadratic functions describe the relationship between DN and RNDVI well. The 2006 SNL image presented more variation within urban cores and stronger correlations with the 2006 RCNL image and Gross Domestic Product after correction. However, RNDVI may also suffer “saturation” when it is lower than −0.4, at which point it is no longer effective at correcting DN saturation. In general, RNDVI is effective, although far from perfect, for saturation correction of the 2006 SNL image, and could be applied to the SNL images for other years. Full article
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<p>Relationship between the mean of the DNs of the 2006 RCNL image and the corresponding DN of the 2006 SNL image F162006 at pixel scale: The original linear relationship is distorted when the DN is above 50 and becomes increasingly distorted the higher the DN is over 50.</p> Full article ">Figure 2
<p>Change curves of the R<sup>2</sup> and the coefficient slope of regressions when pixels with DN ≤ UL are added into the regression gradually: The R<sup>2</sup> decreases sharply when UL &gt; 50, and the coefficient slope gets further away from 1 when UL &gt; 50.</p> Full article ">Figure 3
<p>Distribution rule of DN values of the 2006 RCNL image (dots for DN &lt; 15 and DN &gt; 120 have been omitted in the figure to better show the shape of the curve when the DN is close to 63).</p> Full article ">Figure 4
<p>Distribution rule of the DNs of the 2006 SNL image F162006 (dots for DN &lt; 15 have been omitted in the figure to better show the shape of the curve).</p> Full article ">Figure 5
<p>Flowchart of the proposed method.</p> Full article ">Figure 6
<p>Real NDVI in China in 2006.</p> Full article ">Figure 7
<p>RNDVI in China in 2006.</p> Full article ">Figure 8
<p>Scatter plot for DN − 20 of the 2006 RCNL image and RNDVI.</p> Full article ">Figure 9
<p>Nighttime lights images before and after saturation correction. (<b>a</b>–<b>c</b>) Charts correspond to the 2006 SNL image, corrected image, and RCNL image, respectively.</p> Full article ">Figure 10
<p>DN value curves in the corrected (blue), 2006 SNL (red) and RCNL (green) images extracted by the straight line (from (40.26°N, 116.04°E) to (38.78°N, 117.57°E), Beijing–Tianjin region).</p> Full article ">Figure 11
<p>DN value curves in the corrected (blue), 2006 SNL (red) and RCNL (green) images extracted by the straight line (from (34.19°N, 109.18°E) to (34.40°N, 108.51°E), Xi’an–Xianyang region).</p> Full article ">
22132 KiB  
Article
Two-Step Downscaling of Trmm 3b43 V7 Precipitation in Contrasting Climatic Regions With Sparse Monitoring: The Case of Ecuador in Tropical South America
by Jacinto Ulloa, Daniela Ballari, Lenin Campozano and Esteban Samaniego
Remote Sens. 2017, 9(7), 758; https://doi.org/10.3390/rs9070758 - 22 Jul 2017
Cited by 36 | Viewed by 7041
Abstract
Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of [...] Read more.
Spatial prediction of precipitation with high resolution is a challenging task in regions with strong climate variability and scarce monitoring. For this purpose, the quasi-continuous supply of information from satellite imagery is commonly used to complement in situ data. However, satellite images of precipitation are available at coarse resolutions, and require adequate methods for spatial downscaling and calibration. The objective of this paper is to introduce and evaluate a 2-step spatial downscaling approach for monthly precipitation applied to TRMM 3B43 (from 0 . 25 ∘ ≈ 27 km to 5 km resolution), resulting in 5 downscaled products for the period 01-2001/12-2011. The methodology was evaluated in 3 contrasting climatic regions of Ecuador. In step 1, bilinear resampling was applied over TRMM, and used as a reference product. The second step introduces further variability, and consists of four alternative gauge-satellite merging methods: (1) regression with in situ stations, (2) regression kriging with in situ stations, (3) regression with in situ stations and auxiliary variables, and (4) regression kriging with in situ stations and auxiliary variables. The first 2 methods only use the resampled TRMM data set as an independent variable. The last 2 methods enrich these models with auxiliary environmental factors, incorporating atmospheric and land variables. The results showed that no product outperforms the others in every region. In general, the methods with residual kriging correction outperformed the regression models. Regression kriging with situ data provided the best representation in the Coast, while regression kriging with in situ and auxiliary data generated the best results in the Andes. In the Amazon, no product outperformed the resampled TRMM images, probably due to the low density of in situ stations. These results are relevant to enhance satellite precipitation, depending on the availability of in situ data, auxiliary satellite variables and the particularities of the climatic regions. Full article
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<p>Digital Elevation Map (DEM) of the delimited study area and rain gauge stations network.</p> Full article ">Figure 2
<p>Work flow of the overall downscaling procedure and the 5 resulting products.</p> Full article ">Figure 3
<p>Correlation summary between each auxiliary variable and the observed precipitation data for the time period of study.</p> Full article ">Figure 4
<p>Monthly precipitation [mm] of April 2011 for the five products and the original TRMM data.</p> Full article ">Figure 5
<p>Monthly precipitation [mm] August 2011 for the five products and the original TRMM data.</p> Full article ">Figure 6
<p>Summary of statistical values: (<b>a</b>) Coast region; (<b>b</b>) Andes region; (<b>c</b>) Amazon region.</p> Full article ">Figure 6 Cont.
<p>Summary of statistical values: (<b>a</b>) Coast region; (<b>b</b>) Andes region; (<b>c</b>) Amazon region.</p> Full article ">Figure 7
<p><span class="html-italic">RMSE</span> of the five products over the station points.</p> Full article ">Figure 8
<p><span class="html-italic">PBIAS</span> of the five products over the station points.</p> Full article ">Figure 9
<p><span class="html-italic">R<sup>2</sup></span> of the five products over the station points.</p> Full article ">Figure 10
<p>Monthly precipitation [mm] of April 2014 for the TRMM product and IMERG, both at their native resolution and resampled to 5 km.</p> Full article ">Figure 11
<p>Monthly precipitation [mm] of August 2014 for the TRMM product and IMERG, both at their native resolution and resampled to 5 km.</p> Full article ">Figure 12
<p>5 × 5 moving window correlation between the TRMM product and IMERG resampled to 5 km.</p> Full article ">
15707 KiB  
Article
Passive Radar Array Processing with Non-Uniform Linear Arrays for Ground Target’s Detection and Localization
by Nerea Del-Rey-Maestre, David Mata-Moya, Maria-Pilar Jarabo-Amores, Pedro-Jose Gómez-del-Hoyo, Jose-Luis Bárcena-Humanes and Javier Rosado-Sanz
Remote Sens. 2017, 9(7), 756; https://doi.org/10.3390/rs9070756 - 22 Jul 2017
Cited by 25 | Viewed by 9810
Abstract
The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity [...] Read more.
The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity function domain to exploit integration gain, system resolution and the sparsity of targets in this domain. A modified two-stage detection scheme is described, which is based on a previously-published one by other authors. All of these contributions are validated in a real semiurban scenario, proving the capabilities of detection, the direction of arrival estimation and the tracking of ground targets in the presence of big buildings that generate strong clutter returns. Detection performance is validated through the probability of false alarm and the probability of detection estimation with specified estimation errors. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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<p>Basic operating scheme of a Passive Radar System (PRS).</p> Full article ">Figure 2
<p>Basic architecture of a PRS with one surveillance channel and one reference channel.</p> Full article ">Figure 3
<p>Zero-Doppler cut of the Ambiguity Function (AF) of a simulated Digital Video Broadcasting-Terrestrial (DVB-T) signal.</p> Full article ">Figure 4
<p>Functional block diagram of Improved Detection Techniques for Passive Radars (IDEPAR).</p> Full article ">Figure 5
<p>Televés 4G NOVA antenna.</p> Full article ">Figure 6
<p>Televés 4G NOVA antenna radiation pattern generated using ANSYS HFSS.</p> Full article ">Figure 7
<p>Gain of the Televés 4G NOVA antenna.</p> Full article ">Figure 8
<p>Azimuth plane radiation pattern of the ULA with five Televes 4G NOVA antennas, for 770 MHz, inter-element spacing equal to 315 mm and three steering directions: <math display="inline"> <semantics> <msup> <mn>0</mn> <mo>∘</mo> </msup> </semantics> </math> (broadside), <math display="inline"> <semantics> <msup> <mn>15</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics> </math>. The effect of grating lobes is marked with an ellipse.</p> Full article ">Figure 9
<p>Designed NULA based on Televés 4G NOVA antennas without the radome detailing the inter-element spacing.</p> Full article ">Figure 10
<p>Azimuth plane radiation pattern of the NULA with five Televes 4G NOVA antennas without the radome or reflector, for 770 MHz, and optimized distances, with three steering directions: <math display="inline"> <semantics> <msup> <mn>0</mn> <mo>∘</mo> </msup> </semantics> </math> (broadside), <math display="inline"> <semantics> <msup> <mn>15</mn> <mo>∘</mo> </msup> </semantics> </math> and <math display="inline"> <semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics> </math>.</p> Full article ">Figure 11
<p>Azimuth plane radiation patterns of the ULAs (<span class="html-italic">d</span> = 210 mm and 270 mm) and NULA, with five Televes 4G NOVA antennas without the radome or reflector, for 770 MHz.</p> Full article ">Figure 12
<p>Azimuth plane radiation patterns of the ULAs (<span class="html-italic">d</span> = 210 mm and 270 mm) and NULA, with 11 Televes 4G NOVA antennas without the radome or reflector, for 770 MHz.</p> Full article ">Figure 13
<p>Signals throughout the processing chain previous to the array signal processing stage.</p> Full article ">Figure 14
<p>Two-stage frequency-domain spatial filtering scheme.</p> Full article ">Figure 15
<p>Received power levels provided by the Torrespaña Illuminator of Opportunity (IoO) in the considered radar scenario.</p> Full article ">Figure 16
<p>Radar scenario located at the roof of the Superior Polytechnic School of Alcalá University.</p> Full article ">Figure 17
<p>Azimuth radiation pattern of NULA multiple simultaneous orthogonal beams in the azimuth coverage area <span class="html-italic">φ</span> ∈ [−30°,−30°].</p> Full article ">Figure 18
<p>IDEPAR range coverage in the considered radar scenario. (<b>a</b>) Only one Televés 4G NOVA antenna in the surveillance channel; (<b>b</b>) Televés 4G NOVA antenna array in the surveillance channel.</p> Full article ">Figure 19
<p>CFAR detection performance using a single radiating element (<b>a</b>) and the proposed first array signal processing stage applied to the signals acquired by the designed NULA (<b>b</b>).</p> Full article ">Figure 20
<p>Targets trajectories in the coverage sector (white). The Polytechnic School of University of Alcalá (EPS, <span class="html-italic">Escuela Politécnica Superior</span>) and a big building made of aluminum (IMMPA, <span class="html-italic">Instituto de Medicina Molecular Príncipe de Asturias</span>) are marked in purple.</p> Full article ">Figure 21
<p>Comparison of azimuth radiation patterns of NULA with weights calculated for maximizing the directivity (blue) and for guaranteeing <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>L</mi> <mi>L</mi> <mo>&lt;</mo> <mn>15</mn> </mrow> </semantics> </math> dB (red).</p> Full article ">Figure 22
<p>DoA based on the beamformer output spectrum for detections in the 30th CPI associated with the considered trajectories.</p> Full article ">Figure 23
<p>DoA based on the beamformer output spectrum for targets <math display="inline"> <semantics> <mrow> <mi>T</mi> <mn>1</mn> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>T</mi> <mn>2</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mi>T</mi> <mn>3</mn> </mrow> </semantics> </math>.</p> Full article ">Figure 24
<p>2D estimated trajectories and GPS data in the radar scenario.</p> Full article ">
22422 KiB  
Article
Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution
by Zhongbin Li, Hankui K. Zhang, David P. Roy, Lin Yan, Haiyan Huang and Jian Li
Remote Sens. 2017, 9(7), 755; https://doi.org/10.3390/rs9070755 - 22 Jul 2017
Cited by 30 | Viewed by 11426
Abstract
The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the [...] Read more.
The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use. Full article
(This article belongs to the Special Issue Multisource Remote Sensing Data Fusion and Applications in Vegetation Monitoring)
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<p>Illustration of the 7.5 m row and column shifts that occur between the 15-m panchromatic (gray) and the 30-m reflective band (red) Landsat-8 pixels.</p> Full article ">Figure 2
<p>40 × 33 Landsat-8 30-m image (<b>a</b>) LPAD downscaled to 15-m data (80 × 66) with (<b>b</b>) and without (<b>c</b>) considering the 15-m to 30-m Landsat-8 pixel grid shifts. The Landsat-8 image was acquired 6 November 2016 over a commercial crop field in California (centered on 34.8973°N 117.1505°W) with scene center solar zenith and azimuth of 36.5972° and 159.4612°, respectively.</p> Full article ">Figure 3
<p>New York 256 × 256 20-m pixel true color subsets: (<b>a</b>) Sentinel-2A 20-m red, green, blue bands aggregated from the original Sentinel-2A 10-m bands; (<b>b</b>) Landsat-8 20-m red, green, blue bands nearest neighbor resampled from 30 m; (<b>c</b>) Landsat-8 20-m red, green, blue bands bilinear resampled from 30 m; and (<b>d</b>) Landsat-8 20-m red, green, blue bands cubic convolution resampled from 30 m.</p> Full article ">Figure 3 Cont.
<p>New York 256 × 256 20-m pixel true color subsets: (<b>a</b>) Sentinel-2A 20-m red, green, blue bands aggregated from the original Sentinel-2A 10-m bands; (<b>b</b>) Landsat-8 20-m red, green, blue bands nearest neighbor resampled from 30 m; (<b>c</b>) Landsat-8 20-m red, green, blue bands bilinear resampled from 30 m; and (<b>d</b>) Landsat-8 20-m red, green, blue bands cubic convolution resampled from 30 m.</p> Full article ">Figure 4
<p>New York city true color images showing: (<b>a</b>) Sentinel-2A 20-m image (1500 × 1500 20-m pixels) and sub-set (red box); (<b>b</b>) Sentinel-2A 256 × 256 20-m pixel sub-set; (<b>c</b>) Landsat-8 bilinear resampled to 20 m; (<b>d</b>) Landsat-8 bilinear-based LPAD 20-m data; (<b>e</b>) Landsat-8 cubic convolution resampled to 20 m; (<b>f</b>) Landsat-8 cubic convolution-based LPAD 20-m data.</p> Full article ">Figure 5
<p>Crop field, California, true color images showing: (<b>a</b>) Sentinel-2A 20-m image (3000 × 3000 20-m pixels) and sub-set (red box); (<b>b</b>) Sentinel-2A 256 × 256 20-m pixel sub-set; (<b>c</b>) Landsat-8 bilinear resampled to 20 m; (<b>d</b>) Landsat-8 bilinear-based LPAD 20-m data; (<b>e</b>) Landsat-8 cubic convolution resampled to 20 m; (<b>f</b>) Landsat-8 cubic convolution-based LPAD 20-m data.</p> Full article ">Figure 6
<p>Burned forest area, California, true color images showing: (<b>a</b>) Sentinel-2A 20-m image (3600 × 3600 20-m pixels) and sub-set (red box); (<b>b</b>) Sentinel-2A 256 × 256 20-m pixel sub-set; (<b>c</b>) Landsat-8 bilinear resampled to 20 m; (<b>d</b>) Landsat-8 bilinear-based LPAD 20-m data; (<b>e</b>) Landsat-8 cubic convolution resampled to 20 m; (<b>f</b>) Landsat-8 cubic convolution-based LPAD 20-m data.</p> Full article ">Figure 7
<p>Mountain landslides, New Zealand, true color images showing: (<b>a</b>) Sentinel-2A 20-m image (1500 × 1500 20-m pixels) and sub-set (red box); (<b>b</b>) Sentinel-2A 256 × 256 20-m pixel sub-area; (<b>c</b>) Landsat-8 bilinear resampled to 20 m; (<b>d</b>) Landsat-8 bilinear-based LPAD 20-m data; (<b>e</b>) Landsat-8 cubic convolution resampled to 20 m; (<b>f</b>) Landsat-8 cubic convolution-based LPAD 20-m data.</p> Full article ">Figure 8
<p>Tropical forest, Democratic Republic of the Congo, true color images showing: (<b>a</b>) Sentinel-2A 20-m image (1230 × 1425 20-m pixels) and sub-set (red box); (<b>b</b>) Sentinel-2A 256 × 256 20-m pixel sub-set; (<b>c</b>) Landsat-8 bilinear resampled to 20 m; (<b>d</b>) Landsat-8 bilinear-based LPAD 20-m data; (<b>e</b>) Landsat-8 cubic convolution resampled to 20 m; (<b>f</b>) Landsat-8 cubic convolution-based LPAD 20-m data. The smoke aerosols present in the Sentinel-2A image are evident in the north and west of (<b>a</b>).</p> Full article ">
3578 KiB  
Technical Note
LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya
by Linda See, Juan Carlos Laso Bayas, Dmitry Schepaschenko, Christoph Perger, Christopher Dresel, Victor Maus, Carl Salk, Juergen Weichselbaum, Myroslava Lesiv, Ian McCallum, Inian Moorthy and Steffen Fritz
Remote Sens. 2017, 9(7), 754; https://doi.org/10.3390/rs9070754 - 22 Jul 2017
Cited by 32 | Viewed by 8998
Abstract
Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set [...] Read more.
Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement. Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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<p>The LACO-Wiki system architecture. The client/user accesses the LACO-Wiki portal, which can make use of multiple storage servers to distribute the data and the processing tasks.</p> Full article ">Figure 2
<p>The components of the LACO-Wiki workflow.</p> Full article ">Figure 3
<p>The LACO-Wiki interface (<a href="http://www.laco-wiki.net" target="_blank">http://www.laco-wiki.net</a>) showing the four components of the validation workflow as menu items at the top of the screen.</p> Full article ">Figure 4
<p>Uploading a land cover map in Step 1 of the LACO-Wiki validation workflow.</p> Full article ">Figure 5
<p>Details of a data set in LACO-Wiki including options to share and preview the data set and to generate a sample.</p> Full article ">Figure 6
<p>Details about a sample in LACO-Wiki. This sample can be shared, downloaded or used to create a validation session.</p> Full article ">Figure 7
<p>Creating a validation session in LACO-Wiki based on an existing sample. This involves providing a name, a description, choosing the type of validation (blind, plausibility, enhanced plausibility), whether this is for an online or mobile session, the validation settings, which include additional fields such as a comment box or whether the user should judge the positional accuracy, and what base layers should appear in the validation session, e.g., Google Maps, Bing Maps, etc.</p> Full article ">Figure 8
<p>Example of visual interpretation in LACO-Wiki, showing the final pixel in Sample 2 (see <a href="#sec3dot3-remotesensing-09-00754" class="html-sec">Section 3.3</a>). The imagery can be changed to Bing or the pixel can be viewed directly in Google Earth by pressing the “Download sample (kmz)” button.</p> Full article ">Figure 9
<p>Distribution of the difference in years between the reference year for GlobeLand30 (2010) and the available high resolution imagery in: (<b>a</b>) Sample 1 (<span class="html-italic">n</span> = 147); and (<b>b</b>) Sample 2 (<span class="html-italic">n</span> = 411).</p> Full article ">Figure 10
<p>The agreement of interpreters and the relative importance of very high resolution imagery by class in GlobeLand30.</p> Full article ">Figure 11
<p>Comparison of the mapped area of GlobeLand30 compared to the adjusted areas based on Sample 2 (allowing for choices 1 or 2) showing 95% confidence intervals.</p> Full article ">Figure 12
<p>Agreement between interpreters showing adjusted medians and 95% confidence intervals (<span class="html-italic">n</span> = 679).</p> Full article ">Figure 13
<p>Imagery from: (<b>a</b>) Google Maps in LACO-Wiki for 2017; (<b>b</b>) Bing in LACO-Wiki for 2012; and (<b>c</b>) Google Earth for 2010.</p> Full article ">
12201 KiB  
Article
A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data
by Xinyan Qin, Gongping Wu, Xuhui Ye, Le Huang and Jin Lei
Remote Sens. 2017, 9(7), 753; https://doi.org/10.3390/rs9070753 - 22 Jul 2017
Cited by 51 | Viewed by 9538
Abstract
Overhead high-voltage power lines are key components of power transmission and their monitoring has a very significant influence on security and reliability of power system. Advanced laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of power system scenes. [...] Read more.
Overhead high-voltage power lines are key components of power transmission and their monitoring has a very significant influence on security and reliability of power system. Advanced laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of power system scenes. Nevertheless, power line corridors are found in increasingly complex environments (e.g., mountains and forests), and the multi-loop structure on the same power line tower raises great challenges to process light detection and ranging (LiDAR) data. This paper addresses these challenges by constructing a new collection mode of LiDAR data for power lines using cable inspection robot (CIR). A novel method is proposed to extract and reconstruct power line using CIR LiDAR data, which has two advantages: (1) rapidly extracts power line point by position and orientation system (POS) extraction model; and (2) better solves pseudo-line during reconstruction of power line by structured partition. The proposed method mainly includes four steps: CIR LiDAR data generation, POS-based crude extraction, voxel-based accurate extraction and power line reconstruction. The feasibility and validity of the proposed method are verified by test site experiment and actual line experiment, demonstrating a fast and reliable solution to accurately reconstruct power line. Full article
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<p>The mechanical structure of self-developed CIR.</p> Full article ">Figure 2
<p>Schematic diagram of collection data using CIR LiDAR system.</p> Full article ">Figure 3
<p>The working principle of CIR LiDAR.</p> Full article ">Figure 4
<p>Key components of CIR LiDAR system.</p> Full article ">Figure 5
<p>Flow diagram of the power line extraction and reconstruction using CIR LiDAR data.</p> Full article ">Figure 6
<p>Schematic diagram of quasi-scene of CIR LiDAR.</p> Full article ">Figure 7
<p>The flow chart of data processing of CIR LiDAR.</p> Full article ">Figure 8
<p>Schematic diagram of double-loop two-bundle power lines.</p> Full article ">Figure 9
<p>The construction process of OCP and ECS.</p> Full article ">Figure 10
<p>The POS-based extraction model.</p> Full article ">Figure 11
<p>Additional sag of the ground wire.</p> Full article ">Figure 12
<p>Flow chart of voxel-based denoising.</p> Full article ">Figure 13
<p>Flow chart of single power line clustering.</p> Full article ">Figure 14
<p>Photo of the test site.</p> Full article ">Figure 15
<p>Point cloud of quasi-scene.</p> Full article ">Figure 16
<p>POS-based crude extraction of test site.</p> Full article ">Figure 17
<p>Testing site data projected to the OCP: (<b>a</b>) point cloud of traveling ground wire; and (<b>b</b>) all power line point clouds.</p> Full article ">Figure 18
<p>Clustering result of Lines 1 and 2.</p> Full article ">Figure 19
<p>Fitting models of all power lines.</p> Full article ">Figure 20
<p>Photo of the tower and power lines.</p> Full article ">Figure 21
<p>The construction data of the tower.</p> Full article ">Figure 22
<p>The quasi-scene point cloud of the actual lines.</p> Full article ">Figure 23
<p>The POS-based crude extraction of the actual power lines.</p> Full article ">Figure 24
<p>The experimental data cloud projected to OCP.</p> Full article ">Figure 25
<p>The fitting model of the five power lines.</p> Full article ">
7776 KiB  
Article
Minimizing the Residual Topography Effect on Interferograms to Improve DInSAR Results: Estimating Land Subsidence in Port-Said City, Egypt
by Ahmed Gaber, Noura Darwish and Magaly Koch
Remote Sens. 2017, 9(7), 752; https://doi.org/10.3390/rs9070752 - 21 Jul 2017
Cited by 25 | Viewed by 7431
Abstract
The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard [...] Read more.
The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise. Full article
(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands)
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<p>The top map shows the average thickness of Holocene sediments in the northern delta (modified after Stanley and Clemente, [<a href="#B26-remotesensing-09-00752" class="html-bibr">26</a>]) and the bottom map shows the regional structural setting of the northern Nile Delta (modified after Mosconi et al. [<a href="#B42-remotesensing-09-00752" class="html-bibr">42</a>]).</p> Full article ">Figure 2
<p>Land subsidence rate using repeated GPS observations for six stations over the Nile Delta (Modified after Hoda et al. [<a href="#B30-remotesensing-09-00752" class="html-bibr">30</a>]).</p> Full article ">Figure 3
<p>High spatial resolution WorldView-2 image of Port-Said and Port-Fuad.</p> Full article ">Figure 4
<p>Two benchmarks (BM) were used as reference points for the 347 GCPs.</p> Full article ">Figure 5
<p>Applied processing workflow in this study using the DInSAR/SBAS module.</p> Full article ">Figure 6
<p>Plots show: time versus relative position (<b>top</b>); and time versus perpendicular baseline (<b>bottom</b>). Selected acquisitions are displayed as green points and the super master as yellow.</p> Full article ">Figure 7
<p>(<b>a</b>) Wrapped interferogram phase with strong phase ramp (mostly in range direction) originated from the orbit inaccuracies, together with a large atmospheric artifacts (master; 14 November 2008 and slave; 28 May 2009); (<b>b</b>) wrapped interferogram phase in high coherent area with small atmospheric artifacts (master; 14 November 2008 and slave; 12 November 2007); and (<b>c</b>) well unwrapped phase in high coherent areas (master; 14 November 2008 and slave; 4 April 2007).</p> Full article ">Figure 8
<p>(<b>a</b>) Unwrapped interferogram before the refinement and re-flattening steps (master; 14 November 2008 and slave; 12 November 2007); and (<b>b</b>) well-unwrapped interferogram after the refinement and re-flattening steps.</p> Full article ">Figure 9
<p>Statistic of the estimated residual topography: (<b>a</b>) using 0 wavelet numbers for the ALOS/PRISM DEM, where the average residual topography becomes almost 0 m and the standard deviation is significantly reduced and equal to 9.5 m; (<b>b</b>) using 0 wavelet for the SRTM DEM, where the average residual topography becomes −52.5 m and standard deviation equal to 35.1 m; and (<b>c</b>) using wavelet number equal to 2 for the SRTM, where the average residual topography becomes −1.5 m and high standard deviation equal to 25.03 m.</p> Full article ">Figure 10
<p>The velocity maps: (<b>a</b>) before; and (<b>b</b>) after the atmospheric artifacts filtering was applied during the second estimation of the displacement. A strong displacement (brown) and the uplift (purple) can be noted here. These displacement maps were improved significantly compared to the obtained maps using the first estimation.</p> Full article ">Figure 11
<p>(<b>a</b>) Statistics histograms of velocity precision; and (<b>b</b>) the corresponding height precision.</p> Full article ">Figure 12
<p>Comparison of elevation values obtained from satellite derived DEMs at 16 GCPs; (<b>a</b>) SRTM vs. ALOS/PRISM; and (<b>b</b>) ALOS/PRISM vs. total station measurements.</p> Full article ">Figure 13
<p>Comparison of: (<b>a</b>) the SRTM derived DEM; and (<b>b</b>) the newly generated DEM from ALOS/PRISM of Port-Said City.</p> Full article ">Figure 14
<p>Vertical displacement in Port-Said City from: 2007/2008 (<b>top</b>); 2007/2009 (<b>middle</b>); and 2007/2010 (<b>bottom</b>). The sequence shows that the subsidence rate along the different years is not constant.</p> Full article ">Figure 15
<p>The average vertical ground motion velocities from 2007 to 2010 of pixels with coherence threshold ≥0.3.</p> Full article ">Figure 16
<p>The average vertical ground motion velocities from 2007 to 2010 of pixels with coherence threshold ≥0.4.</p> Full article ">Figure 17
<p>Land-Subsidence map showing the locations of the affected buildings.</p> Full article ">
5097 KiB  
Article
Influence of Ecological Factors on Estimation of Impervious Surface Area Using Landsat 8 Imagery
by Yuqiu Jia, Lina Tang and Lin Wang
Remote Sens. 2017, 9(7), 751; https://doi.org/10.3390/rs9070751 - 21 Jul 2017
Cited by 15 | Viewed by 6091
Abstract
Estimation of impervious surface area is important to the study of urban environments and social development, but surface characteristics, as well as the temporal, spectral, and spatial resolutions of remote sensing images, influence the estimation accuracy. To investigate the effects of regional environmental [...] Read more.
Estimation of impervious surface area is important to the study of urban environments and social development, but surface characteristics, as well as the temporal, spectral, and spatial resolutions of remote sensing images, influence the estimation accuracy. To investigate the effects of regional environmental characteristics on the estimation of impervious surface area, we divided China into seven sub-regions based on climate, soil type, feature complexity, and vegetation phenology: arid and semi-arid areas, Huang-Huai-Hai winter wheat production areas, typical temperate regions, the Pearl River Delta, the middle and lower reaches of the Yangtze River, typical tropical and subtropical regions, and the Qinghai Tibet Plateau. Impervious surface area was estimated from Landsat 8 images of five typical cities, including Yinchuan, Shijiazhuang, Shenyang, Ningbo, and Kunming. Using the linear spectral unmixing method, impervious and permeable surface areas were determined at the pixel-scale based on end-member proportions. We calculated the producer’s accuracy, user’s accuracy, and overall accuracy to assess the estimation accuracy, and compared the accuracies among images acquired from different seasons and locations. In tropical and subtropical regions, vegetation canopies can confound the identification of impervious surfaces and, thus, images acquired in winter, early spring, and autumn are most suitable; estimations in the Pearl River Delta, the middle and lower reaches of the Yangtze River are influenced by soil, vegetation phenology, vegetation canopy, and water, and images acquired in spring, summer, and autumn provide the best results; in typical temperate areas, images acquired from spring to autumn are most effective for estimations; in winter wheat-growing areas, images acquired throughout the year are suitable; and in arid and semi-arid areas, summer and early autumn, during which vegetation is abundant, are the optimal seasons for estimations. Knowledge of optimal time frames, multi-source data, and intelligent algorithms should be integrated to reduce spectral confusion and improve the estimation of impervious surface area from Landsat 8 OLI imagery. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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<p>Spectral curves of impervious surfaces and winter wheat in Shijiazhuang from images acquired on (<b>a</b>) 1 January 2015 and (<b>b</b>) 23 April 2015. High and low albedo are the main types of impervious surfaces. Each curve was plotted using the surface reflectance means of more than 100 pixels.</p> Full article ">Figure 2
<p>Spectral curves of impervious surfaces and desert soil in Yinchuan. The image was taken on 1 September 2015. High and low albedo are the main types of impervious surfaces. Desert soils 1 and 2 represent the two main soil types.</p> Full article ">Figure 3
<p>Ecological regionalization of impervious surfaces for remote sensing.</p> Full article ">Figure 4
<p>Classification of mixed pixels based on end-member contributions. The square represents a 30 m × 30 m pixel.</p> Full article ">Figure 5
<p>Impervious surface area in five cites based on images taken during different seasons. From blue to red, the percentage of impervious surfaces gradually increase.</p> Full article ">Figure 6
<p>Number of pixels representing impervious surfaces based on a 0.1 interval: (<b>a</b>) Kunming, (<b>b</b>) Ningbo; (<b>c</b>) Sheyang; (<b>d</b>) Shijiazhuang; and (<b>e</b>) Yinchuan.</p> Full article ">Figure 7
<p>Impervious surface classification for five cities at different time points. Red indicates the impervious surface class; gray indicates permeable surfaces; and blue indicates water.</p> Full article ">Figure 8
<p>Distribution of samples. Images are displayed in RGB color space, where R, G, and B represent NIR, red, and green, respectively.</p> Full article ">Figure 9
<p>Examples of impervious surfaces that are misclassified. Landsat 8 images are displayed under the band combination of R = NIR, G = red, and B = green. In grayscale images, the brighter the color, the higher the percentage of the impervious surface fraction, vegetation fraction, and soil fraction. In class maps, the impervious surface class is red, the permeable class is gray, and water is blue. (<b>a</b>) In Kunming (28 May 2015), most of the mixed pixels (blue oval) between vegetation and impervious surface were not classified as impervious surfaces; (<b>b</b>) in Ningbo (23 March 2015), bare land (green oval) was misclassified; and (<b>c</b>) in Yinchuan (1 September 2015), bare sand soil (yellow circles) was misclassified.</p> Full article ">
5929 KiB  
Article
Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images
by Rongyong Huang, Kefu Yu, Yinghui Wang, Jikun Wang, Lin Mu and Wenhuan Wang
Remote Sens. 2017, 9(7), 750; https://doi.org/10.3390/rs9070750 - 21 Jul 2017
Cited by 38 | Viewed by 7367
Abstract
Shallow water depth measurements using multispectral images are crucial for marine surveying and mapping. At present, relevant studies either depend on the use of other auxiliary data (such as field water depths or water column data) or contain too many unknown variables, thus [...] Read more.
Shallow water depth measurements using multispectral images are crucial for marine surveying and mapping. At present, relevant studies either depend on the use of other auxiliary data (such as field water depths or water column data) or contain too many unknown variables, thus making these studies suitable only for images that contain enough visible wavebands. To solve this problem, a Quasi-Analytical Algorithm (QAA) approach is proposed in this paper for estimating the water depths around Weizhou Island by developing a QAA to estimate the diffuse attenuation coefficients and simplifying the parameterization of the bathymetric model. The approach contains an initialization sub-approach and a novel global adjustment sub-approach. It is not only independent of other auxiliary data but also greatly reduces the number of unknowns. Experimental results finally demonstrated that the Root Mean Square Errors (RMSEs) were 1.01 m and 0.77 m for the ZY-3 image and the WorldView-3 (WV-3) image, respectively, so the approach is competitive to other QAA bathymetric methods. Besides, the global adjustment sub-approach was also seen to be superior to common smoothing filters: if the Signal-to-Noise Ratio (SNR) is as low as 42, i.e., ZY-3, it can smooth the water depths and improve the accuracies, otherwise can avoid the over-smoothing of water depths. Full article
(This article belongs to the Section Ocean Remote Sensing)
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<p>The position of Weizhou Island and one of the corresponding satellite image (ZY-3).</p> Full article ">Figure 2
<p>The bathymetric model used by Hu et al. [<a href="#B37-remotesensing-09-00750" class="html-bibr">37</a>]: <math display="inline"> <semantics> <mi>B</mi> </semantics> </math> is bottom albedo and <math display="inline"> <semantics> <mrow> <msub> <mi>r</mi> <mi>N</mi> </msub> <mo stretchy="false">(</mo> <mi>λ</mi> <mo stretchy="false">)</mo> </mrow> </semantics> </math> is reflectance spectral shape normalized at the reference wavelength.</p> Full article ">Figure 3
<p>The semi-analytic bathymetric model used by Eugenio et al. [<a href="#B38-remotesensing-09-00750" class="html-bibr">38</a>]: <math display="inline"> <semantics> <mi>a</mi> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>b</mi> <mi>b</mi> </msub> </mrow> </semantics> </math> are the attenuation coefficients and the backscattering coefficient, respectively; <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>w</mi> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>ϕ</mi> </msub> </mrow> </semantics> </math>, and <math display="inline"> <semantics> <mrow> <msub> <mi>a</mi> <mi>g</mi> </msub> </mrow> </semantics> </math> are the attenuation coefficients for water, phytoplankton, and gelbstoff, respectively; <math display="inline"> <semantics> <mrow> <msub> <mi>b</mi> <mi>w</mi> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi>b</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics> </math> are the backscattering coefficients for water and particles, respectively; <math display="inline"> <semantics> <mi>u</mi> </semantics> </math> is the backscattering diffuse attenuation ratio; <math display="inline"> <semantics> <mi>ε</mi> </semantics> </math> is an empirical parameter to account the effect of changing angle on the effective scattering; and <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mi>N</mi> </msub> </mrow> </semantics> </math> is the 550-nm-normalized sand-albedo shape.</p> Full article ">Figure 4
<p>The flowchart of the implementation of the proposed bathymetric method.</p> Full article ">Figure 5
<p>The experimental multispectral images: (<b>a</b>) the ZY-3 multispectral image; and (<b>b</b>) the WV-3 multispectral image. Based on the division represented by the straight line, the western region of the WV-3 image may be seriously influenced by clouds; therefore, we only study the eastern region of the WV-3 image in our experiments.</p> Full article ">Figure 6
<p>The position distribution of the measured water depths: the red triangles are the stations where the water depths were measured, these measured water depths are used as water depth check points for the accuracy assessment.</p> Full article ">Figure 7
<p>The atmospheric correction approach.</p> Full article ">Figure 8
<p>The two parts of the updated semi-analytic model.</p> Full article ">Figure 9
<p>The water depths estimated by the ZY-3 image: (<b>Top</b>) before the global adjustment; and (<b>Bottom</b>) after the global adjustment.</p> Full article ">Figure 10
<p>The water depths estimated by the WV-3 image: (<b>Top</b>) before the global adjustment; and (<b>Bottom</b>) after the global adjustment.</p> Full article ">Figure 11
<p>The comparisons between the estimated and measured water depths: (<b>a</b>) ZY-3; and (<b>b</b>) WV-3.</p> Full article ">Figure 12
<p>The Comparisons between the water depths estimated from the ZY-3 and the WV-3 images.</p> Full article ">Figure 13
<p>The water depths along a transect of the two images: the dotted boxes mark some possible large outliers of the water depths of the ZY-3 or the WV-3 image before the global adjustment; the dotted circle marks the place where the local variations and the differences of the water depths estimated from the two images become large.</p> Full article ">
5369 KiB  
Article
Mapping of Aedes albopictus Abundance at a Local Scale in Italy
by Frédéric Baldacchino, Matteo Marcantonio, Mattia Manica, Giovanni Marini, Roberto Zorer, Luca Delucchi, Daniele Arnoldi, Fabrizio Montarsi, Gioia Capelli, Annapaola Rizzoli and Roberto Rosà
Remote Sens. 2017, 9(7), 749; https://doi.org/10.3390/rs9070749 - 21 Jul 2017
Cited by 20 | Viewed by 7190
Abstract
Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its [...] Read more.
Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its simulated abundance at a local scale to better assess the transmission risk of mosquito-borne pathogens and optimize mosquito control strategy. During 2014–2015, we sampled adult mosquitoes using 72 BG-Sentinel traps per year in the provinces of Belluno and Trento, Italy. We found that the sum of Ae. albopictus females collected during eight trap nights from June to September was positively related to the mean temperature of the warmest quarter and the percentage of artificial areas in a 250 m buffer around the sampling locations. Maps of Ae. albopictus abundance simulated from the most parsimonious model in the study area showed the largest populations in highly artificial areas with the highest summer temperatures, but with a high uncertainty due to the variability of the trapping collections. Vector abundance maps at a local scale should be promoted to support stakeholders and policy-makers in optimizing vector surveillance and control. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Applications for the Detection of Biological Invasions)
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<p>Trapping locations with BG-sentinel traps baited with BG lures and CO<sub>2</sub>. The different colours indicate the year(s) of sampling (2014 and/or 2015) for each sampling location (Microsoft Bing Aerial map).</p> Full article ">Figure 2
<p>Mean number of <span class="html-italic">Aedes albopictus</span> females collected per trap night in 2014 and 2015 with BG-sentinel traps baited with BG lures and CO<sub>2</sub>.</p> Full article ">Figure 3
<p>The coloured lines represent the simulated abundance (applying the most parsimonious model) of <span class="html-italic">Ae. albopictus</span> females collected in eight trap nights between June and September with respect to the proportion of artificial areas, while varying the temperature of the warmest quarter across its range in the study area (the curves take into account the two explanatory fixed effects, but not the trap random effect included in the negative binomial mixed models). In the background, we reported (as points) the total abundance of adult females observed in each sampling location as a function of the artificial area in a 250 m buffer. The y axis was limited at 300 adult females for graphical reasons.</p> Full article ">Figure 4
<p>Map (datum: ETRS89 European Terrestrial Reference System; projection: LAEA Lambert Azimuthal Equal Area) of the simulated abundance of <span class="html-italic">Aedes albopictus</span> females (sum of eight trap nights from June to September) in the provinces of Belluno and Trento (Italy) based on the most parsimonious model including the mean temperature of the warmest quarter and the percentage of artificial areas in 250 m pixels as predictors. The colour ranges from purple, corresponding to 0 specimens per trap per year, to red-berry, corresponding to 112 specimens per trap per year.</p> Full article ">Figure 5
<p>Map (datum: ETRS89 European Terrestrial Reference System; projection: LAEA Lambert Azimuthal Equal Area) of the simulation 95% Confidence Interval (CI) of the abundance of <span class="html-italic">Aedes albopictus</span> females (sum of eight trap nights from June to September) in the provinces of Belluno and Trento (Italy).</p> Full article ">
21624 KiB  
Article
Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements
by Thierry Erudel, Sophie Fabre, Thomas Houet, Florence Mazier and Xavier Briottet
Remote Sens. 2017, 9(7), 748; https://doi.org/10.3390/rs9070748 - 20 Jul 2017
Cited by 37 | Viewed by 7732
Abstract
This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation [...] Read more.
This study aims to evaluate three classes of methods to discriminate between 13 peatland vegetation types using reflectance data. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. On one hand, it is assumed that the same vegetation type spectral signatures have similarities. Consequently, they can be compared to a reference spectral database. To catch those similarities, several similarities criteria (related to distances (Euclidean distance, Manhattan distance, Canberra distance) or spectral shapes (Spectral Angle Mapper) or probabilistic behaviour (Spectral Information Divergence)) and several mathematical transformations of spectral signatures enhancing absorption features (such as the first derivative or the second derivative, the normalized spectral signature, the continuum removal, the continuum removal derivative reflectance, the log transformation) were investigated. Furthermore, those similarity measures were applied on spectral ranges which characterize specific biophysical properties. On the other hand, we suppose that specific biophysical properties/components may help to discriminate between vegetation types applying supervised classification such as Random Forest (RF), Support Vector Machines (SVM), Regularized Logistic Regression (RLR), Partial Least Squares-Discriminant Analysis (PLS-DA). Biophysical components can be used in a local way considering vegetation spectral indices or in a global way considering spectral ranges and transformed spectral signatures, as explained above. RLR classifier applied on spectral vegetation indices (training size = 25%) was able to achieve 77.21% overall accuracy in discriminating peatland vegetation types. It was also able to discriminate between 83.95% vegetation types considering specific spectral range [[range-phrase = –]3501350 n m ], first derivative of spectral signatures and training size = 25%. Conversely, similarity criterion was able to achieve 81.70% overall accuracy using the Canberra distance computed on the full spectral range [[range-phrase = –]3502500 n m ]. The results of this study suggest that RLR classifier and similarity criteria are promising to map the different vegetation types with high ecological values despite vegetation heterogeneity and mixture. Full article
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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<p>Location of the in situ spectroradiometer measurements—True color composite made from hyperspectral (HySpex) aerial imageries acquired on the 09/12/2014 (R = 639.98 <inline-formula> <mml:math id="mm253" display="block"> <mml:semantics> <mml:mrow> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>, G = 549.06 <inline-formula> <mml:math id="mm254" display="block"> <mml:semantics> <mml:mrow> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>, B = 461.79 <inline-formula> <mml:math id="mm255" display="block"> <mml:semantics> <mml:mrow> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>).</p> Full article ">Figure 2
<p>Mean spectral reflectances of the 13 vegetation types and the U.S. Standard atmospheric transmittance.</p> Full article ">Figure 3
<p>Flowchart showing the different methods used to classify the vegetation types.</p> Full article ">Figure 4
<p>Median spectra, spectrum of mean reflectances, spectrum of median reflectances of <italic>Eleocharis quinqueflora</italic> (ELQU).</p> Full article ">Figure 5
<p>Frequency distribution of the Kruskal-Wallis test for the 129 spectral indices for paired species across the 13 vegetation types. The horizontal red line stands for 75% of all 78 possible combinations of the 13 vegetation types.</p> Full article ">Figure 6
<p>Mean spectral reflectance of the 13 vegetation types. Dashed lines represent the wavelengths used by Water Index (WI).</p> Full article ">Figure 7
<p>Mean first derivative spectral signatures of the 13 vegetation types on [695–730 <inline-formula> <mml:math id="mm256" display="block"> <mml:semantics> <mml:mrow> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>]. The green dashed line represents the wavelength used by the Boochs2 index.</p> Full article ">Figure 8
<p>(<bold>Left</bold>) spectral signatures of AQ_B (blue) and AQ_C (dark slate gray). Red dashed lines are the wavelengths used by the Normalized Difference Water Index (NDWI) [860,1240] index; (<bold>Right</bold>) NDWI [860,1240] values for each vegetation type, H is the Hellinger distance.</p> Full article ">Figure 9
<p>(<bold>Left</bold>) spectral signatures of Sphagnum sp. <bold>(SPHA)!</bold> (black) and AQ_A (green). Red dashed lines are WI wavelengths; (<bold>Right</bold>) WI values for each vegetation type, H is the Hellinger distance.</p> Full article ">Figure 10
<p>(<bold>Left</bold>) spectral signatures of SPHA (black) and AQ_A (green). Red dashed lines are Optimised Soil-Adjust Vegetation Index (OSAVI) [800,670] wavelengths; (<bold>Right</bold>) OSAVI [800,670] values for each vegetation type, H is the Hellinger distance.</p> Full article ">Figure 11
<p>(<bold>Left</bold>) spectral signatures of SPHA (black) and Calluna vulgaris (CAVU) (gray); (<bold>Right</bold>) F_WP values for each vegetation type, H is the Hellinger distance.</p> Full article ">Figure 12
<p>(<bold>Left</bold>) spectral signatures of CAVU (gray) and Salix sp. (SALI) (cyan); (<bold>Right</bold>) map of CARTER[695,420] and Normalized Difference Infrared Index (NDII) values for each vegetation type, H is the Hellinger distance.</p> Full article ">Figure 13
<p>(<bold>Left</bold>) spectral signatures of CA_HV (pink) and PI_CV (magenta); (<bold>Right</bold>) map of Optimised Soil-Adjust Vegetation Index (OSAVI) [800,670] and GITELSON values for each vegetation type, H is the Hellinger distance value.</p> Full article ">Figure 14
<p>Vegetation types identification accuracies (overall accuracy) with indices.</p> Full article ">Figure 15
<p>Vegetation type identification accuracies with the training size = 25%.</p> Full article ">Figure 16
<p>Vegetation type identification accuracies on [350–1350 <inline-formula> <mml:math id="mm257" display="block"> <mml:semantics> <mml:mrow> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>].</p> Full article ">Figure 17
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Sphagnum</italic> sp. (SPHA).</p> Full article ">Figure 18
<p>Mean reflectance (<inline-formula> <mml:math id="mm226" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm227" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Sphagnum</italic> sp. (SPHA).</p> Full article ">Figure 19
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Calluna vulgaris</italic> (CAVU).</p> Full article ">Figure 20
<p>Mean reflectance (<inline-formula> <mml:math id="mm228" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm229" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Calluna vulgaris</italic> (CAVU).</p> Full article ">Figure 21
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Eleocharis quinqueflora</italic> (ELQU).</p> Full article ">Figure 22
<p>Mean reflectance (<inline-formula> <mml:math id="mm230" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm231" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Eleocharis quinqueflora</italic> (ELQU).</p> Full article ">Figure 23
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Pinguicula</italic> sp. (PING).</p> Full article ">Figure 24
<p>Mean reflectance (<inline-formula> <mml:math id="mm232" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm233" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Pinguicula</italic> sp. (PING).</p> Full article ">Figure 25
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Menyanthes trifoliata</italic> (METR).</p> Full article ">Figure 26
<p>Mean reflectance (<inline-formula> <mml:math id="mm234" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm235" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Menyanthes trifoliata</italic> (METR).</p> Full article ">Figure 27
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Juniperus communis</italic> (JUCO).</p> Full article ">Figure 28
<p>Mean reflectance (<inline-formula> <mml:math id="mm236" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm237" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Juniperus communis</italic> (JUCO).</p> Full article ">Figure 29
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Rhododendron ferrugineum</italic> (RHFR).</p> Full article ">Figure 30
<p>Mean reflectance (<inline-formula> <mml:math id="mm238" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm239" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Rhododendron ferrugineum</italic> (RHFR).</p> Full article ">Figure 31
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Salix</italic> sp. (SALI).</p> Full article ">Figure 32
<p>Mean reflectance (<inline-formula> <mml:math id="mm240" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm241" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Salix</italic> sp. (SALI).</p> Full article ">Figure 33
<p>Location of the in situ spectroradiometer measurements for the plots of Aquatic type a (AQ_A).</p> Full article ">Figure 34
<p>Mean reflectance (<inline-formula> <mml:math id="mm242" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm243" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of Aquatic type a (AQ_A).</p> Full article ">Figure 35
<p>Location of the in situ spectroradiometer measurements for the plots of Aquatic type b (AQ_B).</p> Full article ">Figure 36
<p>Mean reflectance (<inline-formula> <mml:math id="mm245" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm246" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of Aquatic type b (AQ_B).</p> Full article ">Figure 37
<p>Location of the in situ spectroradiometer measurements for the plots of Aquatic type c (AQ_C).</p> Full article ">Figure 38
<p>Mean reflectance (<inline-formula> <mml:math id="mm247" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm248" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of Aquatic type c (AQ_C).</p> Full article ">Figure 39
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Carex</italic> sp. homogeneous vegetation (CA_HV).</p> Full article ">Figure 40
<p>Mean reflectance (<inline-formula> <mml:math id="mm249" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm250" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Carex</italic> sp. homogeneous vegetation (CA_HV).</p> Full article ">Figure 41
<p>Location of the in situ spectroradiometer measurements for the plots of <italic>Pinguicula</italic> sp. combined vegetation (PI_CV).</p> Full article ">Figure 42
<p>Mean reflectance (<inline-formula> <mml:math id="mm251" display="block"> <mml:semantics> <mml:mi>μ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) and standard deviation (<inline-formula> <mml:math id="mm252" display="block"> <mml:semantics> <mml:mi>σ</mml:mi> </mml:semantics> </mml:math> </inline-formula>) of <italic>Pinguicula</italic> sp. combined vegetation (PI_CV).</p> Full article ">
6847 KiB  
Article
Aerosol Optical Properties and Associated Direct Radiative Forcing over the Yangtze River Basin during 2001–2015
by Lijie He, Lunche Wang, Aiwen Lin, Ming Zhang, Muhammad Bilal and Minghui Tao
Remote Sens. 2017, 9(7), 746; https://doi.org/10.3390/rs9070746 - 20 Jul 2017
Cited by 36 | Viewed by 5932
Abstract
The spatiotemporal variation of aerosol optical depth at 550 nm (AOD550), Ångström exponent at 470–660 nm (AE470–660), water vapor content (WVC), and shortwave (SW) instantaneous aerosol direct radiative effects (IADRE) at the top-of-atmosphere (TOA) in clear skies obtained from [...] Read more.
The spatiotemporal variation of aerosol optical depth at 550 nm (AOD550), Ångström exponent at 470–660 nm (AE470–660), water vapor content (WVC), and shortwave (SW) instantaneous aerosol direct radiative effects (IADRE) at the top-of-atmosphere (TOA) in clear skies obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth’s Radiant Energy System (CERES) are quantitatively analyzed over the Yangtze River Basin (YRB) in China during 2001–2015. The annual and seasonal frequency distributions of AE470–660 and AOD550 reveal the dominance of fine aerosol particles over YRB. The regional average AOD550 is 0.49 ± 0.31, with high value in spring (0.58 ± 0.35) and low value in winter (0.42 ± 0.29). The higher AOD550 (≥0.6) is observed in midstream and downstream regions of YRB and Sichuan Basin due to local anthropogenic emissions and long-distance transport of dust particles, while lower AOD550 (≤0.3) is in high mountains of upstream regions. The IADRE is estimated using a linear relationship between SW upward flux and coincident AOD550 from CERES and MODIS at the satellite passing time. The regional average IADRE is −35.60 ± 6.71 Wm−2, with high value (−40.71 ± 6.86 Wm−2) in summer and low value (−29.19 ± 7.04 Wm−2) in winter, suggesting a significant cooling effect at TOA. The IADRE at TOA is lower over Yangtze River Delta (YRD) (≤−30 Wm−2) and higher in midstream region of YRB, Sichuan Basin and the source area of YRB (≥−45 Wm−2). The correlation coefficient between the 15-year monthly IADRE and AOD550 values is 0.63, which confirms the consistent spatiotemporal variation patterns over most of the YRB. However, a good agreement between IADRE and AOD is not observed in YRD and the source area of YRB, which is probably due to the combined effects of aerosol and surface properties. Full article
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Graphical abstract
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<p>The location of Yangtze River Basin (YRB) over China. The urban (Chengdu) and rural (Dongtan and Changde) China Aerosol Remote Sensing Network (CARSNET) stations are marked with red point and red square, respectively.</p> Full article ">Figure 2
<p>The flow diagram for calculating the Shortwave (SW) instantaneous aerosol direct radiative effect (IADRE) at the top-of-atmosphere (TOA). The IADRE is estimated by linear fitting the concurrent Clouds and the Earth’s Radiant Energy System (CERES) SW TOA fluxes and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth at 550nm (AOD<sub>550</sub>) selected from the CERES_SSF_FM1_MODIS_Ed3A product in each 1° × 1° grid cell over YRB for the period 2001–2015.</p> Full article ">Figure 3
<p>Scatter plots of MODIS (DB) AOD<sub>550</sub> and CARSNET AOD<sub>550</sub> over: (<b>a</b>) Chengdu (2007–2010); (<b>b</b>) Changde (2007–2011); and (<b>c</b>) Dongtan (2009–2011). The red solid line indicates the regression line and the blue dash line indicates 1:1 line.</p> Full article ">Figure 4
<p>Variations of monthly averaged AOD<sub>550</sub> (<b>a</b>), Ångström exponent at 470–660 nm (AE<sub>470–660</sub> (<b>b</b>)), and water vapor content (WVC (<b>c</b>)) over YRB during 2001–2015. The error bars represent the standard deviations of AOD<sub>550</sub>, AE<sub>470</sub><sub>–</sub><sub>660</sub> and WVC in each month.</p> Full article ">Figure 5
<p>Monthly and seasonal variations of regional average AOD<sub>550</sub> (<b>a</b>,<b>b</b>), AE<sub>470–660</sub> (<b>c</b>,<b>d</b>), and WVC (<b>e</b>,<b>f</b>) over YRB for the period 2001–2015. The error bars represent the standard deviations of AOD<sub>550</sub>, AE<sub>470–660</sub> and WVC in each month or season.</p> Full article ">Figure 6
<p>Spatial distribution of annual mean AOD<sub>550</sub> over YRB for the period 2001–2015.</p> Full article ">Figure 7
<p>Spatial distribution of seasonal mean AOD<sub>550</sub> values over YRB for the period January 2001–December 2015.</p> Full article ">Figure 8
<p>Frequency distribution of annual mean AOD<sub>550</sub> (<b>a</b>), AE<sub>470–660</sub> (<b>b</b>) and WVC (<b>c</b>) values over YRB during 2001–2015.</p> Full article ">Figure 9
<p>Frequency distribution of seasonal mean AOD<sub>550</sub> (<b>a</b>), AE<sub>470–660</sub> (<b>b</b>), and WVC (<b>c</b>) values over YRB during 2001–2015.</p> Full article ">Figure 10
<p>Annual variation (<b>a</b>) and linear regression (<b>b</b>) of AOD<sub>550</sub> and SW TOA IADRE over YRB for the period 2001–2015.</p> Full article ">Figure 11
<p>Monthly (<b>a</b>) and seasonal (<b>b</b>) variation of SW TOA IADRE over YRB for the period 2001–2015. The error bars represent the standard deviations of IADRE in each month or season.</p> Full article ">Figure 12
<p>Annual spatial distribution of SW TOA IADRE over YRB during 2001–2015.</p> Full article ">Figure 13
<p>Seasonal spatial distribution of SW TOA IADRE over YRB during 2001–2015.</p> Full article ">Figure 14
<p>Calculation of the monthly IADRE efficiency using the median values of IADRE and AOD<sub>550</sub> over YRB for the period 2001–2015.</p> Full article ">Figure 15
<p>Monthly (<b>a</b>) and seasonal (<b>b</b>) variation of IADRE efficiency over YRB during 2001–2015. The bars indicate the relative error in each month or season.</p> Full article ">
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Article
Evaluation of the U.S. Geological Survey Landsat Burned Area Essential Climate Variable across the Conterminous U.S. Using Commercial High-Resolution Imagery
by Melanie K. Vanderhoof, Nicole Brunner, Yen-Ju G. Beal and Todd J. Hawbaker
Remote Sens. 2017, 9(7), 743; https://doi.org/10.3390/rs9070743 - 20 Jul 2017
Cited by 17 | Viewed by 6391
Abstract
The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of [...] Read more.
The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation. Full article
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
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<p>(<b>A</b>) The distribution of area classified as burned by the Burned Area Essential Climate Variable (BAECV) product between 1984 and 2015, and (<b>B</b>) the distribution of the high-resolution images used to validate the BAECV relative to a modified version of the U.S. Environmental Protection Agency Level I ecoregions. High-resolution images containing at least one burned area (<span class="html-italic">n =</span> 143) are shown in relation to images containing no burned areas (<span class="html-italic">n</span> = 143), burned area points are shown on top so some not burned image locations are not visible.</p> Full article ">Figure 2
<p>The correlation between the amount of burned area identified by the reference dataset and the amount of burned area identified by the Burned Area Essential Climate Variable (BAECV) across the conterminous U.S. (CONUS) where each point represents a high-resolution image. RMSE: root mean square error.</p> Full article ">Figure 3
<p>An example of differences in the spatial characteristics for a fire as shown by (<b>A</b>) a Worldview-2 image in natural color composite, and as mapped by (<b>B</b>) Worldview-2, (<b>C</b>) the Landsat Burned Area Essential Climate Variable (BAECV) product, and (<b>D</b>) the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 burned area dataset.</p> Full article ">Figure 4
<p>Comparisons between the reference dataset and the Landsat Burned Area Essential Climate Variable (BAECV) to demonstrate the visual effect of error rates. (<b>A</b>) The QuickBird-2 image was collected on 13 August 2003, the Landsat image was collected seven days prior. Error of omission and commission was 20% and 42%, respectively. (<b>B</b>) The four QuickBird-2 images were collected on 31 August 2005, the Landsat image was collected two days prior. Error of omission and commission was 19% and 65%, respectively. (<b>C</b>) The GeoEye-1 image was collected on 15 September 2009, and the Landsat image was collected six days later. Error of omission and commission was 28% and 39%, respectively. The high-resolution images are shown in false color where live vegetation is red.</p> Full article ">Figure 5
<p>A comparison of: (<b>A</b>) the RapidEye-3 image (DOY 122, 2015); (<b>B</b>) the Landsat ETM+ image (DOY 120, 2015); (<b>C</b>) the Landsat ETM+ image using band combinations 7, 4 and 2 for red, green and blue, respectively; (<b>D</b>) burned area as classified by RapidEye-3; and (<b>E</b>) burned area as classified by the BAECV. The blue circles show a recent burned area agreed upon by the high-resolution imagery and Landsat BAECV. The yellow circles show an incorrect error of omission due to a new fire occurring in the two-day gap between the high-resolution and Landsat collection dates. The purple circles show the more common commission errors due to the BAECV classifying fields with minimal vegetation as burned. Moderate Resolution Imaging Spectroradiometer (MODIS) active fire points identified the fire in the blue circle (DOY 119) but no other fires in the image extent shown.</p> Full article ">Figure 6
<p>Examples of the persistence of burn evidence in agricultural areas in eastern Kansas from (<b>A</b>) DOY 65, (<b>B</b>) DOY 81, (<b>C</b>) DOY 94 and (<b>D</b>) DOY 104, 2016 (four images across 39 days) using imagery from RapidEye satellites. Although the burned area, marked by the red rectangle, is clearly visible at DOY 81, it rapidly becomes difficult to distinguish a fading burn from freshly tilled or bare soil using aerial imagery, alone.</p> Full article ">Figure 7
<p>Two examples demonstrating the role of vegetation recovery in creating false errors due to a date gap between the high-resolution imagery and the Burned Area Essential Climate Variable (BAECV): (<b>A</b>) an example of mapping a burned area in New Mexico at a site dominated by shrub-scrub and grassland (p33r34); and (<b>B</b>) an example of mapping a burned area in California at a site dominated by mixed forest (p43r33). The high-resolution images are shown in false color where live vegetation is red.</p> Full article ">
3357 KiB  
Article
Expansion of Industrial Plantations Continues to Threaten Malayan Tiger Habitat
by Varada S. Shevade, Peter V. Potapov, Nancy L. Harris and Tatiana V. Loboda
Remote Sens. 2017, 9(7), 747; https://doi.org/10.3390/rs9070747 - 19 Jul 2017
Cited by 17 | Viewed by 13633
Abstract
Southeast Asia has some of the highest deforestation rates globally, with Malaysia being identified as a deforestation hotspot. The Malayan tiger, a critically endangered subspecies of the tiger endemic to Peninsular Malaysia, is threatened by habitat loss and fragmentation. In this study, we [...] Read more.
Southeast Asia has some of the highest deforestation rates globally, with Malaysia being identified as a deforestation hotspot. The Malayan tiger, a critically endangered subspecies of the tiger endemic to Peninsular Malaysia, is threatened by habitat loss and fragmentation. In this study, we estimate the natural forest loss and conversion to plantations in Peninsular Malaysia and specifically in its tiger habitat between 1988 and 2012 using the Landsat data archive. We estimate a total loss of 1.35 Mha of natural forest area within Peninsular Malaysia over the entire study period, with 0.83 Mha lost within the tiger habitat. Nearly half (48%) of the natural forest loss area represents conversion to tree plantations. The annual area of new plantation establishment from natural forest conversion increased from 20 thousand ha year−1 during 1988–2000 to 34 thousand ha year−1 during 2001–2012. Large-scale industrial plantations, primarily those of oil palm, as well as recently cleared land, constitute 80% of forest converted to plantations since 1988. We conclude that industrial plantation expansion has been a persistent threat to natural forests within the Malayan tiger habitat. Expanding oil palm plantations dominate forest conversions while those for rubber are an emerging threat. Full article
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<p>Study area in Peninsular Malaysia showing the Tiger Conservation Landscapes (TCL) [<a href="#B36-remotesensing-09-00747" class="html-bibr">36</a>], percent tree cover for year 2000 [<a href="#B24-remotesensing-09-00747" class="html-bibr">24</a>], and protected areas from the World Database on Protected Areas (WDPA) [<a href="#B37-remotesensing-09-00747" class="html-bibr">37</a>].</p> Full article ">Figure 2
<p>Methodology used to map natural forest, natural forest loss, and plantation expansion.</p> Full article ">Figure 3
<p>Mapped natural forest remaining in 2013, tiger conservation landscape (TCL), and natural forest loss between 1988–2000 (pre-2000) and 2001–2012 (post-2000).</p> Full article ">Figure 4
<p>Sample-based area (thousand ha) estimates for yearly natural forest and habitat loss and annual rates of total natural forest loss for pre-2000 and post-2000 periods. Yearly losses are depicted as solid lines, while mean annual rates of loss for the pre-2000 and post-2000 periods are average rates of loss for the respective periods, depicted as constant dashed lines.</p> Full article ">Figure 5
<p>(<b>a</b>) Percent of total tiger habitat loss pre-2000 (1988–2000) and post-2000 (2001–2012) within different plantation types by 2014 (<b>b</b>) Sample-based area of habitat loss between 2001 and 2012 within plantations (in 2014) by plantation type.</p> Full article ">
5491 KiB  
Article
Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy
by Matthew Maimaitiyiming, Abduwasit Ghulam, Arianna Bozzolo, Joseph L. Wilkins and Misha T. Kwasniewski
Remote Sens. 2017, 9(7), 745; https://doi.org/10.3390/rs9070745 - 19 Jul 2017
Cited by 108 | Viewed by 11355
Abstract
Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); [...] Read more.
Early detection of water stress is critical for precision farming for improving crop productivity and fruit quality. To investigate varying rootstock and irrigation interactions in an open agricultural ecosystem, different irrigation treatments were implemented in a vineyard experimental site either: (i) nonirrigated (NIR); (ii) with full replacement of evapotranspiration (FIR); or (iii) intermediate irrigation (INT, 50% replacement of evapotranspiration). In the summers 2014 and 2015, we collected leaf reflectance factor spectra of the vineyard using field spectroscopy along with grapevine physiological parameters. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate the normalized difference spectral index (NDSI) along with 26 various indices from the literature. Then, the relationship between the indices and plant physiological parameters were examined and the strongest relationships were determined. We found that newly-identified NDSIs always performed better than the indices from the literature, and stomatal conductance (Gs) was the plant physiological parameter that showed the highest correlation with NDSI(R603,R558) calculated using leaf reflectance factor spectra (R2 = 0.720). Additionally, the best NDSI(R685,R415) for non-photochemical quenching (NPQ) was determined (R2 = 0.681). Gs resulted in being a proxy of water stress. Therefore, the partial least squares regression (PLSR) method was utilized to develop a predictive model for Gs. Our results showed that the PLSR model was inferior to the NDSI in Gs estimation (R2 = 0.680). The variable importance in the projection (VIP) was then employed to investigate the most important wavelengths that were most effective in determining Gs. The VIP analysis confirmed that the yellow band improves the prediction ability of hyperspectral reflectance factor data in Gs estimation. The findings of this study demonstrate the potential of hyperspectral spectroscopy data in motoring plant stress response. Full article
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<p>Overview of the vineyard used for the experiment in the present study (source: Google Earth). NIR, nonirrigated; FIR, full replacement of evapotranspiration; INT, 50% replacement of evapotranspiration.</p> Full article ">Figure 2
<p>Daily average minimum (Tmin, °C), maximum air temperature (Tmax, °C) and amount of daily precipitation (mm) events. Dark arrows indicate the dates on which field measurements were conducted and gray arrows indicate the start and end dates of irrigation.</p> Full article ">Figure 3
<p>Coefficients of determination (<span class="html-italic">R</span><sup>2</sup>) between grapevine physiological parameters and NDSI (<span class="html-italic">R<sub>i</sub></span>,<span class="html-italic">R<sub>j</sub></span>) for the calibration dataset (<span class="html-italic">n</span> = 169). (<b>a</b>) Stomatal conductance (<span class="html-italic">G<sub>s</sub></span>) and NDSI using full spectral range (350–2500 nm); (<b>b</b>) stomatal conductance (<span class="html-italic">G<sub>s</sub></span>) and NDSI; (<b>c</b>) steady-state fluorescence (<span class="html-italic">F<sub>s</sub></span>) and NDSI; (<b>d</b>) maximum fluorescence yield (<span class="html-italic">F<sub>m’</sub></span>) and NDSI; (<b>e</b>) non-photochemical quenching (<span class="html-italic">NPQ</span>) and NDSI.</p> Full article ">Figure 4
<p>Scatter plots of predicted and measured stomatal conductance (<span class="html-italic">G<sub>s</sub></span>) and non-photochemical quenching (<span class="html-italic">NPQ</span>) values for the best NDSI models in <a href="#remotesensing-09-00745-t004" class="html-table">Table 4</a>. (<b>a</b>) NDSI(<span class="html-italic">R</span><sub>603</sub>,<span class="html-italic">R</span><sub>558</sub>) model and (<b>b</b>) NDSI(<span class="html-italic">R</span><sub>685</sub>,<span class="html-italic">R</span><sub>415</sub>) model. The <span class="html-italic">R</span><sup>2</sup> and <span class="html-italic">RMSE<sub>val</sub></span> are for the validation dataset (<span class="html-italic">n</span> = 42).</p> Full article ">Figure 5
<p>Variable importance in the projection (VIP) of the partial least squares regression (PLSR) predictive model for stomatal conductance (<span class="html-italic">G<sub>s</sub></span>).</p> Full article ">Figure 6
<p>Scatter plot of predicted and measured stomatal conductance (<span class="html-italic">G<sub>s</sub></span>) values for the best partial least squares regression (PLSR) predictive model. The <span class="html-italic">R</span><sup>2</sup> and <span class="html-italic">RMSE<sub>val</sub></span> are for the independent validation dataset (<span class="html-italic">n</span> = 42).</p> Full article ">Figure 7
<p>Mean values of NDSI(<span class="html-italic">R</span><sub>601</sub>,<span class="html-italic">R</span><sub>557</sub>) (<b>a</b>) and rNDSI(B4,B3) (<b>b</b>) for stomatal conductance (<span class="html-italic">G<sub>s</sub></span>, mol H<sub>2</sub>O m<sup>−2</sup> s<sup>−1</sup>) measured on 19 August 2014 (DOY 231). ANOVA of each index was carried out, and different letters on the bars indicate significant differences according to the HSD Tukey’s test at <span class="html-italic">p</span> &lt; 0.05. Error bars represent pooled RMSE of the ANOVA test.</p> Full article ">Figure 8
<p>Mean reflectance factor spectra collected on 19 August 2014 for the different irrigation treatments.</p> Full article ">
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Article
Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO2 and CO2
by Sam J. Silva and A. F. Arellano
Remote Sens. 2017, 9(7), 744; https://doi.org/10.3390/rs9070744 - 19 Jul 2017
Cited by 37 | Viewed by 9196
Abstract
We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO2 and CO2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA [...] Read more.
We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO2 and CO2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA Greenhouse Gases Observing Satellite to estimate atmospheric enhancements of these co-emitted species based on their spatiotemporal variability (spread, σ) within 14 regions dominated by combustion emissions. We find that patterns in σXCO/σXCO2 and σXCO/σXNO2 are able to distinguish between combustion types across the globe. These patterns show distinct groupings for biomass burning and the developing/developed status of a region that are not well represented in global emissions inventories. We show here that such multi-species analyses can provide constraints on emission inventories, and be useful in monitoring trends and understanding regional-scale combustion. Full article
(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)
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<p>Annual mean retrieved concentrations of XCO<sub>2</sub> (<b>A</b>), XCO (<b>B</b>), and XNO<sub>2</sub> (<b>C</b>) for the year 2010 used in this work. Panel (<b>D</b>) shows the urban extents as defined by the Anthromes dataset. Megacity locations are shown as black points, and the boxes represent the combustion regions selected for this analysis.</p> Full article ">Figure 2
<p>Regional-scale combustion signatures derived from: (<b>A</b>) spread of XCO<sub>2</sub>, XCO and XNO<sub>2</sub> satellite retrievals, and (<b>B</b>) emission estimates from EDGAR4.2 database with GFEDv3 fire emissions. Note that (<b>A</b>) and (<b>B</b>) have different units. Light line bars correspond to the range of our estimates in (see text for error calculation using boot-strap method).</p> Full article ">Figure 3
<p>Ratios of CO/CO<sub>2</sub> plotted against the CO/NO<sub>2</sub> ratio, derived from the satellite retrievals (top), and the EDGARv4.2 emissions inventory (bottom). The colors represent the dominant combustion category of a given region.</p> Full article ">Figure 4
<p>Temporal (<b>A</b>) and spatial (<b>B</b>) pattern of large-scale biomass burning signatures (XCO, XNO<sub>2</sub>, XCO<sub>2</sub>) over the southern Amazon and Southeast Asia for year 2010 fire season (lines correspond to two-term Gaussian model fits). Estimates of CO emissions (in Tg) from GFEDv3 and retrievals of FRP from MODIS (in GW) are plotted for comparison. Numbers 1 to 3 correspond to the peaks in FRP for both time and space.</p> Full article ">Figure 5
<p>WBDI percent fossil-fuel energy use versus combustible (<b>a</b>) and renewable and waste consumption (<b>b</b>) across 46 megacities as a function of XCO and XNO<sub>2</sub> spread for the year 2010.</p> Full article ">
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Article
Feasibility of GNSS-R Ice Sheet Altimetry in Greenland Using TDS-1
by Antonio Rius, Estel Cardellach, Fran Fabra, Weiqiang Li, Serni Ribó and Manuel Hernández-Pajares
Remote Sens. 2017, 9(7), 742; https://doi.org/10.3390/rs9070742 - 19 Jul 2017
Cited by 44 | Viewed by 7162
Abstract
Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of [...] Read more.
Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of the measurements. Here we perform a study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission. TDS-1 was primarily designed to provide sea state information such as sea surface roughness or wind, but not altimetric products. The data have been analyzed with altimetric methodologies, already tested in aircraft based experiments, to extract signal delay observables to be used to infer properties of the Greenland ice sheet cover. The penetration depth of the GNSS signals into ice has also been considered. The large scale topographic signal obtained is consistent with the one obtained with ICEsat GLAS sensor, with differences likely to be related to L-band signal penetration into the ice and the along-track variations in structure and morphology of the firn and ice volumes The main conclusion derived from this work is that GNSS-R also provides potentially valuable measurements of the ice sheet cover. Thus, this methodology has the potential to complement our understanding of the ice firn and its evolution. Full article
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<p>Map of the region considered in this study, containing the southern part of Greenland. The black contour levels over Greenland have been extracted from the National Snow and Ice Data Center (NSIDC) GLAS/ICESat 1 km laser altimetry digital elevation model of Greenland [<a href="#B9-remotesensing-09-00742" class="html-bibr">9</a>]. The green contour lines have been obtained from the Earth Gravitational Model (EGM) 2008 [<a href="#B18-remotesensing-09-00742" class="html-bibr">18</a>]. The two transects correspond to the specular points selected in this study, crossing different Benson facies [<a href="#B19-remotesensing-09-00742" class="html-bibr">19</a>]. The blue trace corresponds to 26 January 2015, and the red one to the day after. There are two points that divide, from South to North, each trace in three sections, sea, topographic step, and ice, with different prevailing scattering mechanisms. Within these tracks the peak-to-peak variations of the heights with respect WGS84 are on the order of ten meters for the WGM2008 levels, while they are of two kilometers for the (NSIDC) GLAS/ICESat levels.</p> Full article ">Figure 2
<p>The normalized power <math display="inline"> <semantics> <mrow> <msub> <mi>P</mi> <mi mathvariant="normal">n</mi> </msub> <mrow> <mo>(</mo> <mi>τ</mi> <mo>,</mo> <mo>Δ</mo> <mi>f</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> of a simulated TDS-1 one-second sea DDM, computed with typical values encountered in the TDS-1 situation. The origin of the coordinate system has been assigned to the point corresponding to the observed delay <math display="inline"> <semantics> <msup> <mi>τ</mi> <mi>obs</mi> </msup> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mo>Δ</mo> <mi>f</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>. The TDS-1 resolution is indicated by the small red box. The larger red rectangle indicates the TDS-1 correlation pixels used in this altimetric study. The beginning and end of the waveform are indicated with the labels <math display="inline"> <semantics> <msubsup> <mi>τ</mi> <mn>1</mn> <mi>window</mi> </msubsup> </semantics> </math> and <math display="inline"> <semantics> <msubsup> <mi>τ</mi> <mrow> <mn>128</mn> </mrow> <mi>window</mi> </msubsup> </semantics> </math>. The point <math display="inline"> <semantics> <msubsup> <mi>τ</mi> <mrow> <mi>TDS</mi> <mo>−</mo> <mn>1</mn> </mrow> <mi>ref</mi> </msubsup> </semantics> </math> corresponds to the TDS-1 <span class="html-italic">a priori</span> reference and <math display="inline"> <semantics> <msup> <mi>τ</mi> <mi>ref</mi> </msup> </semantics> </math> is the corresponding <span class="html-italic">a priori</span> value considered in this study.</p> Full article ">Figure 3
<p>The modeled delays due to tidal <math display="inline"> <semantics> <msub> <mi>τ</mi> <mrow> <mi>t</mi> <mi>i</mi> <mi>d</mi> <mi>e</mi> <mi>s</mi> </mrow> </msub> </semantics> </math> (green), ionospheric <math display="inline"> <semantics> <msub> <mi>τ</mi> <mi mathvariant="normal">I</mi> </msub> </semantics> </math> (blue) and tropospheric effects <math display="inline"> <semantics> <msub> <mi>τ</mi> <mi mathvariant="normal">T</mi> </msub> </semantics> </math> (red), are represented for the two transects. (<b>a</b>) 26 January 2015. (<b>b</b>) 27 January 2015.</p> Full article ">Figure 4
<p>Two panels showing the time evolution of the power waveforms, one for each track. The left-hand figure in each panel contains the waveform widths <math display="inline"> <semantics> <msub> <mi>τ</mi> <mi>width</mi> </msub> </semantics> </math>, defined at their 3-dB level. The vertical red line corresponds to the width of unscattered GPS C/A code signals. In the middle figure of each panel we show the modeled delay <math display="inline"> <semantics> <msup> <mi>τ</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> <mi>l</mi> </mrow> </msup> </semantics> </math> as expected with respect to a reflection off a point on the reference WGS-84 ellipsoid. The model has assumed surface topographies provided by the EGM 2008 geoid over the sea and by NSIDC GLAS/ICESat over Greenland. In the right figure of each panel we present all the waveforms 2D image. (<b>a</b>) 26 January 2015. (<b>b</b>) 27 January 2015.</p> Full article ">Figure 5
<p>The observed delays <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msup> <mi>τ</mi> <mi>sea</mi> </msup> </mrow> </semantics> </math> and their standard deviation <math display="inline"> <semantics> <msub> <mi>σ</mi> <mrow> <mi>δ</mi> <mi>τ</mi> </mrow> </msub> </semantics> </math> obtained from the sea waveforms. Superimposed on the values <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msup> <mi>τ</mi> <mi>sea</mi> </msup> </mrow> </semantics> </math> we include a running mean used to derive <math display="inline"> <semantics> <msub> <mi>σ</mi> <mrow> <mi>δ</mi> <mi>τ</mi> </mrow> </msub> </semantics> </math>. The trend in the observed delays is attributed in this study to effects of TDS-1 clock inaccuracies. (<b>a</b>) 26 January 2015. (<b>b</b>) 27 January 2015.</p> Full article ">Figure 6
<p>The differential delays <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mi>τ</mi> </mrow> </semantics> </math> and their standard deviation <math display="inline"> <semantics> <msub> <mi>σ</mi> <mrow> <mi>δ</mi> <mi>τ</mi> </mrow> </msub> </semantics> </math> obtained from the whole data set, after correcting for the trend derived from the sea waveforms. Superimposed to the values <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msup> <mi>τ</mi> <mi>sea</mi> </msup> </mrow> </semantics> </math> we include a running mean used to derive <math display="inline"> <semantics> <msub> <mi>σ</mi> <mrow> <mi>δ</mi> <mi>τ</mi> </mrow> </msub> </semantics> </math>. (<b>a</b>) 26 January 2015. (<b>b</b>) 27 January 2015.</p> Full article ">Figure 7
<p>In each column we have represented from bottom to top (1) the topography associated to each track <math display="inline"> <semantics> <msub> <mi>H</mi> <mrow> <mi>T</mi> <mi>o</mi> <mi>p</mi> <mi>o</mi> </mrow> </msub> </semantics> </math>, (2) the estimate of the <span class="html-italic">apparent</span> the height relative to the <span class="html-italic">a priori</span> model <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msup> <mi>h</mi> <mi>apparent</mi> </msup> </mrow> </semantics> </math>, and (3) its standard deviation <math display="inline"> <semantics> <msub> <mi>σ</mi> <mrow> <mi>δ</mi> <mi>h</mi> </mrow> </msub> </semantics> </math>. (<b>a</b>) 26 January 2015. (<b>b</b>) 27 January 2015.</p> Full article ">Figure 8
<p>Refractive index as a function of ice depth, as reported in Table 4 of Ref [<a href="#B45-remotesensing-09-00742" class="html-bibr">45</a>], estimated from North Greenland Eemian Ice Drilling (NEEM) camp data.</p> Full article ">Figure 9
<p>(<b>a</b>) Estimates of <math display="inline"> <semantics> <mrow> <mi>δ</mi> <msup> <mi>h</mi> <mi>eff</mi> </msup> </mrow> </semantics> </math> as a function of the latitude of the specular point for the 26 January 2015 track (blue), and the 27 January 2015 (red). (<b>b</b>) Section of the Greenland Map, courtesy of University of Georgia/Thomas Mote, supplied by the National Snow and Ice Data Center, University of Colorado, showing the number of melting days [<a href="#B48-remotesensing-09-00742" class="html-bibr">48</a>], indicated by the color scale, for the period January–October 2016. The two traces correspond to the approximate location of the specular points considered.</p> Full article ">
12616 KiB  
Article
High Precision DEM Generation Algorithm Based on InSAR Multi-Look Iteration
by Xiaoming Gao, Yaolin Liu, Tao Li and Danqin Wu
Remote Sens. 2017, 9(7), 741; https://doi.org/10.3390/rs9070741 - 18 Jul 2017
Cited by 24 | Viewed by 6975
Abstract
Interferometric Synthetic Aperture Radar (InSAR) is one of the most sufficient technologies to provide global digital elevation modeling (DEM). It unwraps the interferometric phase to provide observations for phase-to-height conversion. However, the phase gradient has great influence on the phase unwrapping quality, thereby [...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) is one of the most sufficient technologies to provide global digital elevation modeling (DEM). It unwraps the interferometric phase to provide observations for phase-to-height conversion. However, the phase gradient has great influence on the phase unwrapping quality, thereby affecting the topographic mapping accuracy. Multi-look processing can improve the reliability of phase unwrapping by reducing the noise phase gradient. Nevertheless, it reduces the spatial resolution while increasing the height phase gradient, thus lowering the reliability of height values. In this paper, we propose a multi-look iteration algorithm to suppress the noise and maintain the reliability of height values. First, we use a large number of looks (NL) to suppress the noise phase gradient and obtain a coarse DEM. Then taking this coarse DEM as a reference, we remove the topographic phase from the interferogram with a small NL, which will reduce height phase gradient and ensure the accuracy of phase unwrapping. Finally, we obtain DEM products with high precision and fine resolution. We validate the proposed algorithm using both simulated and real data, and obtain DEM products in a greatly undulated region using TanDEM-X data. Results show that the proposed method is capable of providing DEM with resolution of 4 m and accuracy of 1.73 m. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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<p>Relationship between pixel spacing and the probability density distribution function of elevation gradients in (<b>a</b>) the range direction and (<b>b</b>) the azimuth direction. Lines are the pixel spacing with different multi-look numbers.</p> Full article ">Figure 2
<p>(<b>a</b>–<b>c</b>) Noise phase probability density functions with different coherences and number of looks. The coherence is 0.7, 0.8, and 0.9, respectively. The multi-look numbers of the blue, red, and black lines are 64, 16, and 4, respectively.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>c</b>) Probability density function of interferometric noise phase gradient with different coherence and number of looks. The coherence is 0.7, 0.8, and 0.9, respectively. The multi-look numbers of the blue, red, and black lines are 64, 16, and 4.</p> Full article ">Figure 3 Cont.
<p>(<b>a</b>–<b>c</b>) Probability density function of interferometric noise phase gradient with different coherence and number of looks. The coherence is 0.7, 0.8, and 0.9, respectively. The multi-look numbers of the blue, red, and black lines are 64, 16, and 4.</p> Full article ">Figure 4
<p>Relationship among the number of looks, the maximum detectable phase gradient, and the noise phase gradient.</p> Full article ">Figure 5
<p>(<b>a</b>) is DEM simulated using fractional Brownian motion, the white rectangle corresponds to (<b>b</b>) the spatial coverage of SLC image.</p> Full article ">Figure 6
<p>Relationship between number of looks and the mean values of absolute height phase gradient, absolute noise phase gradient, and absolute phase gradient.</p> Full article ">Figure 7
<p>(<b>a</b>–<b>c</b>) Relationship between the height accuracy and the mean values of absolute phase gradient, absolute noise phase gradient and absolute phase gradient.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>c</b>) Histogram of elevation error. In (<b>a</b>–<b>c</b>) the simulated elevation differences are 2000 m, 2600 m, 3000 m, respectively.</p> Full article ">Figure 9
<p>(<b>a</b>) Location map of the study area. The red triangle is Litang County, Ganzi Tibetan Autonomous Prefecture, and (<b>b</b>) the corresponding SRTM DEM. The red dots are ICESat data and the two green triangles are the ground control points.</p> Full article ">Figure 10
<p>Elevation error map (w.r.t SRTM) with NLs of (<b>a</b>) 8 × 8, (<b>b</b>) 4 × 4, and (<b>c</b>) 2 × 2. We zoom in the rectangular regions of A and B and show the corresponding results in (<b>d</b>,<b>e</b>). White regions within the error map are pixels with coherence lower than 0.9.</p> Full article ">
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Article
How Reliable Is Structure from Motion (SfM) over Time and between Observers? A Case Study Using Coral Reef Bommies
by Vincent Raoult, Sarah Reid-Anderson, Andreas Ferri and Jane E. Williamson
Remote Sens. 2017, 9(7), 740; https://doi.org/10.3390/rs9070740 - 18 Jul 2017
Cited by 36 | Viewed by 8018
Abstract
Recent efforts to monitor the health of coral reefs have highlighted the benefits of using structure from motion-based assessments, and despite increasing use of this technique in ecology and geomorphology, no study has attempted to quantify the precision of this technique over time [...] Read more.
Recent efforts to monitor the health of coral reefs have highlighted the benefits of using structure from motion-based assessments, and despite increasing use of this technique in ecology and geomorphology, no study has attempted to quantify the precision of this technique over time and across different observers. This study determined whether 3D models of an ecologically relevant reef structure, the coral bommie, could be constructed using structure from motion and be reliably used to measure bommie volume and surface area between different observers and over time. We also determined whether the number of images used to construct a model had an impact on the final measurements. Three dimensional models were constructed of over twenty coral bommies from Heron Island, a coral cay at the southern end of the Great Barrier Reef. This study did not detect any significant observer effect, and there were no significant differences in measurements over four sampling days. The mean measurement error across all bommies and between observers was 15 ± 2% for volume measurements and 12 ± 1% for surface area measurements. There was no relationship between the number of pictures taken for a reconstruction and the measurements from that model, however, more photographs were necessary to be able to reconstruct complete coral bommies larger than 1 m3. These results suggest that structure from motion is a viable tool for ongoing monitoring of ecologically-significant coral reefs, especially to establish effects of disturbances, provided the measurement error is considered. Full article
(This article belongs to the Section Ocean Remote Sensing)
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<p>Simplified measurement pipeline with Agisoft Photoscan Professional, from image alignment to volume and surface area measurements. The model can be viewed and manipulated online at <a href="https://skfb.ly/6n6Hy" target="_blank">https://skfb.ly/6n6Hy</a>.</p> Full article ">Figure 2
<p>Measurements resulting from successive manual cropping of (<b>A</b>) volume and (<b>B</b>) surface area of a single mesh constructed once for three bommies.</p> Full article ">Figure 3
<p>Mean (<b>A</b>) volume and (<b>B</b>) surface area ± 95% CI for the bommies used to compare observer effects. Bommies sorted according to mean volume and surface area, respectively.</p> Full article ">Figure 4
<p>Mean (<b>A</b>) volume and (<b>B</b>) surface area ± 95% CI for three bommies measured four separate times over five days.</p> Full article ">Figure 5
<p>Relationship between the number of photographs taken and the deviation from the mean (<b>A</b>) volume and (<b>B</b>) surface area of measured bommies (<span class="html-italic">n</span> = 9). The shaded area represents 95% confidence interval of linear regression (black line).</p> Full article ">Figure 6
<p>Summary of explained and unexplained measurement error for volume and surface area measurements of structures using structure from motion.</p> Full article ">
8806 KiB  
Article
Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy)
by Romy Schlögel, Benni Thiebes, Marco Mulas, Giovanni Cuozzo, Claudia Notarnicola, Stefan Schneiderbauer, Mattia Crespi, Augusto Mazzoni, Volkmar Mair and Alessandro Corsini
Remote Sens. 2017, 9(7), 739; https://doi.org/10.3390/rs9070739 - 18 Jul 2017
Cited by 28 | Viewed by 8338
Abstract
From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in [...] Read more.
From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in recent years. Multi-temporal interferometry can assist in monitoring landslides on the regional and slope scale and thereby assist in assessing related hazards and risks. Our study focuses on the Corvara landslides in the Italian Alps, a complex earthflow with spatially varying displacement patterns. We used radar imagery provided by the COSMO-SkyMed constellation and carried out a validation of the derived time-series data with differential GPS data. Movement rates were assessed using the Permanent Scatterers based Multi-Temporal Interferometry applied to 16 artificial Corner Reflectors installed on the source, track and accumulation zones of the landslide. The overall movement trends were well covered by Permanent Scatterers based Multi-Temporal Interferometry, however, fast acceleration phases and movements along the satellite track could not be assessed with adequate accuracy due to intrinsic limitations of the technique. Overall, despite the intrinsic limitations, Multi-Temporal Interferometry proved to be a promising method to monitor landslides characterized by a linear and relatively slow movement rates. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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<p>Corvara landslide: (<b>a</b>) Landslide location; (<b>b</b>) geometric features of the X-Band Corner Reflectors (CR) and detail of the trihedral faces; (<b>c</b>) permanent Global Position System (GPS) monitoring site with X-band CR; (<b>d</b>) outlines of the landslide-affected area, monitoring system and mean annual displacements (September 2013–September 2015) measured by DGPS (background: 2.5 m DTM and 2011 orthophoto); and (<b>e</b>) field impressions from a highly active section north of point 51.</p> Full article ">Figure 2
<p>Flowchart of the methodology, divided into: (i) SAR image processing; and (ii) results interpretation and validation using ground-based GPS observations.</p> Full article ">Figure 3
<p>Maps showing: the topographic distortions over the study site (<b>A</b>); the estimated interferometric coherence across the landslide area (<b>B</b>); and the annual velocities observed both within and outside the landslide boundaries (<b>C</b>).</p> Full article ">Figure 4
<p>Plot of GPS-3D displacements <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <msub> <mi>U</mi> <mi mathvariant="normal">G</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics> </math> during the period of acquisition according to a negative LOS unit vector <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msup> <mi>n</mi> <mo>′</mo> </msup> <msub> <mrow/> <mrow> <mi>LOS</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> on the x-axis to represent realistically its orientation in the (X, Y, Z) reference frame. According to Equation (3), <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msup> <mi>n</mi> <mo>′</mo> </msup> <msub> <mrow/> <mrow> <mi>LOS</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> = 100 mm for visualization purposes (UCSMP, 2016).</p> Full article ">Figure 4 Cont.
<p>Plot of GPS-3D displacements <math display="inline"> <semantics> <mrow> <mo stretchy="false">(</mo> <msub> <mi>U</mi> <mi mathvariant="normal">G</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics> </math> during the period of acquisition according to a negative LOS unit vector <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msup> <mi>n</mi> <mo>′</mo> </msup> <msub> <mrow/> <mrow> <mi>LOS</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> on the x-axis to represent realistically its orientation in the (X, Y, Z) reference frame. According to Equation (3), <math display="inline"> <semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msup> <mi>n</mi> <mo>′</mo> </msup> <msub> <mrow/> <mrow> <mi>LOS</mi> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math> = 100 mm for visualization purposes (UCSMP, 2016).</p> Full article ">Figure 5
<p>Cumulative displacements retrieved by PS-MTI (red) compared with GPS validation surveys (blue): CR4; CR6 CR8 and CR11 are part of the track zone; CR13 and CR53 are part of the transit zone; and CR23, CR25, CR28, CR49, CR57 and CR58 characterize the depletion zone. Note that, due to the large displacements recorded, the diagram of CR53 is out of scale respect to the others.</p> Full article ">Figure 6
<p>Evaluation of PS-MTI and GPS-LOS cumulative displacement time-series: (<b>a</b>) relative offset; and (<b>b</b>) absolute offset. The red shaded area represents the median GPS error computed for each monitoring point. Boxplots represent the 1st and 3rd quartiles (limiting the bottom and top of the boxes) and the median (2nd quartile) is depicted by the black horizontal line. Whiskers represent the difference between the 1st quartile and 1.5 times the Inter Quartile Range (IQR), bottom whisker, and the 3rd quartile minus 1.5 times the IQR, top whisker.</p> Full article ">Figure 7
<p>Differential displacements retrieved by PS-MTI (red) compared with GPS validation surveys (blue). The blue shaded areas represent the positioning standard deviation calculated for every GPS acquisition. Note that, due to the large displacements recorded, the diagram of CR53 is out of scale respect to the others.</p> Full article ">Figure 8
<p>Evaluation of PS-MTI and GPS-LOS differential displacement time-series: (<b>a</b>) relative offset; and (<b>b</b>) absolute offset. The red shaded area represents the median GPS error computed for each monitoring point. Boxplots represent the 1st and 3rd quartiles (limiting the top and bottom of the boxes) and the median (2nd quartile) is depicted by the black horizontal line. Whiskers represent the difference between the 1st quartile and 1.5 times the IQR, bottom whisker, and the 3rd quartile minus 1.5 times the IQR, top whisker.</p> Full article ">
146 KiB  
Erratum
Erratum: Chance, E.W., et al. Identifying Irrigated Areas in the Snake River Plain, Idaho: Evaluating Performance across Compositing Algorithms, Spectral Indices, and Sensors. Remote Sens. 2017, 9, 546
by Eric W. Chance, Kelly M. Cobourn, Valerie A. Thomas, Blaine C. Dawson and Alejandro N. Flores
Remote Sens. 2017, 9(7), 738; https://doi.org/10.3390/rs9070738 - 18 Jul 2017
Viewed by 3231
Abstract
In the published paper [1], the title and Appendix Tables A4, A5, A7, and A8 contain typographical errors. The correct title and table captions are as follows: [...]
Full article
72641 KiB  
Article
Mathematical Modeling and Accuracy Testing of WorldView-2 Level-1B Stereo Pairs without Ground Control Points
by Jiang Ye, Xu Lin and Tao Xu
Remote Sens. 2017, 9(7), 737; https://doi.org/10.3390/rs9070737 - 17 Jul 2017
Cited by 8 | Viewed by 8239
Abstract
With very high resolution satellite (VHRS) imagery of 0.5 m, WorldView-2 (WV02) satellite images have been widely used in the field of surveying and mapping. However, for the specific WV02 satellite image geometric orientation model, there is a lack of detailed research and [...] Read more.
With very high resolution satellite (VHRS) imagery of 0.5 m, WorldView-2 (WV02) satellite images have been widely used in the field of surveying and mapping. However, for the specific WV02 satellite image geometric orientation model, there is a lack of detailed research and explanation. This paper elaborates the construction process of the WV02 satellite rigorous sensor model (RSM), which considers the velocity aberration, the optical path delay and the atmospheric refraction. We create a new physical inverse model based on a double-iterative method. Through this inverse method, we establish the virtual control grid in the object space to calculate the rational function model (RFM) coefficients. In the RFM coefficient calculation process, we apply the correcting characteristic value method (CCVM) and least squares (LS) method to compare the two experiments’ accuracies. We apply two stereo pairs of WV02 Level 1B products in Qinghai, China to verify the algorithm and test image positioning accuracy. Under the no-control conditions, the monolithic horizontal mean square error (RMSE) of the rational polynomial coefficient (RPC) is 3.8 m. This result is 13.7% higher than the original RPC positioning accuracy provided by commercial vendors. The stereo pair horizontal positioning accuracy of both the physical and RPC models is 5.0 m circular error 90% (CE90). This result is in accordance with the WV02 satellite images nominal positioning accuracy. This paper provides a new method to improve the positioning accuracy of the WV02 satellite image RPC model without GCPs. Full article
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<p>Raw and level-1B image sensor geometry: (<b>a</b>) raw image; and (<b>b</b>) using a virtual scan line to generate the level-1B image with smooth ephemeris and attitude.</p> Full article ">Figure 2
<p>Study area location and stereo pair composition (gray indicates the study area).</p> Full article ">Figure 3
<p>Ground control points (GCPs) distribution in the test area.</p> Full article ">Figure 4
<p>Spatial coordinate conversion process.</p> Full article ">Figure 5
<p>Physical sensor model geolocation: (<b>a</b>) the positioning principle with a single image; and (<b>b</b>) the physical sensor model direct location process.</p> Full article ">Figure 6
<p>WV02 satellite physical model refinement.</p> Full article ">Figure 7
<p>WV02 satellite physical model refinement: (<b>a</b>) the deviation of atmospheric refraction from the direction of the satellite’s sight; and (<b>b</b>) an exponential function to fit the atmospheric refractive error <span class="html-italic">d</span>, where <span class="html-italic">β</span> is the mean off-nadir view angle of the satellite.</p> Full article ">Figure 8
<p>Physical direct and reverse models.</p> Full article ">Figure 9
<p>The physical reverse model.</p> Full article ">Figure 10
<p>The WV02 rational polynomial coefficient (RPC) generation workflow.</p> Full article ">Figure 11
<p>WV02 rigorous sensor model (RSM) location errors: (<b>a</b>) monolithic image; and (<b>b</b>) stereo pair’s location errors.</p> Full article ">Figure 12
<p>WV02 rational function model (RFM) location errors: (<b>a</b>) single image; and (<b>b</b>) stereo pair.</p> Full article ">Figure 13
<p>The difference between the RSM and RFM geolocations of the virtual checkpoints: (<b>a</b>) Correcting Characteristic Value Method (CCVM); and (<b>b</b>) Least Squares (LS) (image is 15JUL24042129, with a height plane of 3408 m).</p> Full article ">
4551 KiB  
Article
A Burned Area Mapping Algorithm for Chinese FengYun-3 MERSI Satellite Data
by Tianchan Shan, Changlin Wang, Fang Chen, Qinchun Wu, Bin Li, Bo Yu, Zeeshan Shirazi, Zhengyang Lin and Wei Wu
Remote Sens. 2017, 9(7), 736; https://doi.org/10.3390/rs9070736 - 16 Jul 2017
Cited by 11 | Viewed by 6395
Abstract
Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager [...] Read more.
Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites. Full article
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<p>Graphical Abstract.</p> Full article ">Figure 2
<p>Two study areas and Landsat 8 OLI datasets acquired for validation.</p> Full article ">Figure 3
<p>The spectral properties of burned areas in the time series data (after removing the cloud).</p> Full article ">Figure 4
<p>Flowchart for the selection of training pixels. There are a total of 16 steps. (1) Analysis of vegetation indices (VIs) time series (steps 1–6) (<a href="#sec2dot3dot1-remotesensing-09-00736" class="html-sec">Section 2.3.1</a>); (2) Temporal texture analysis (steps 6 and 7) (<a href="#sec2dot3dot2-remotesensing-09-00736" class="html-sec">Section 2.3.2</a>); and (3) Selection of training pixels (steps 9–16) (<a href="#sec2dot3dot3-remotesensing-09-00736" class="html-sec">Section 2.3.3</a>).</p> Full article ">Figure 5
<p>Flowchart for the classification of the burned pixels.</p> Full article ">Figure 6
<p>Comparison of burned area results obtained using different methods (study area: America).</p> Full article ">Figure 7
<p>Comparison of burned area results obtained using different methods (study area: Canada).</p> Full article ">
9165 KiB  
Article
Large-Scale, Multi-Temporal Remote Sensing of Palaeo-River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation
by Hector A. Orengo and Cameron A. Petrie
Remote Sens. 2017, 9(7), 735; https://doi.org/10.3390/rs9070735 - 16 Jul 2017
Cited by 84 | Viewed by 15787
Abstract
Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied [...] Read more.
Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing-based reconstructions of palaeo-river networks have commonly employed single or limited time-span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure. The use of multi-temporal data has allowed us to overcome seasonal cultivation patterns and long-term visibility issues related to recent crop selection, extensive irrigation and land-use patterns. The application of this approach on the Sutlej-Yamuna interfluve (northwest India), a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo-river network comprising more than 8000 km of palaeo-channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated. Full article
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Graphical abstract
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Seasonal variability of bimensal averages of long-term vegetation indices (<b>a</b>–<b>f</b>). Note the higher visibility of palaeo-rivers in the rainy months: January–April (<b>a</b>,<b>b</b>); and July–August (<b>d</b>).</p> Full article ">Figure 3
<p>Image resulting of the application of Seasonal Multi-Temporal Vegetation Indices (SMTVI) for the rainy months as an RGB Composite of the 1984–2012 EVI averages (R: July–August; G: Mary–April; and B: January–February).</p> Full article ">Figure 4
<p>Normalised Difference Vegetation Seasonality Index (NDVSI) of the study area where the greenest areas indicate a higher seasonal variation of vegetation. Ghaggar-Hakra palaeo-channel has been highlighted with a dashed line.</p> Full article ">Figure 5
<p>Workflow followed for the generation of the different outputs described in the text and their recommended application according to seasonal vegetation variability.</p> Full article ">Figure 6
<p>Results of the interpretation of both SMTVI and seasonal multi-temporal spectral decomposition techniques for the reconstruction of the palaeo-hydrological network of the Sutlej-Yamuna interfluve.</p> Full article ">Figure 7
<p>Palaeo-rivers detected in the study area by previous Remote Sensing-based studies.</p> Full article ">Figure 8
<p>(<b>Top</b>) RGB (TCT axes 1, 4 and 2) composite of a multi-temporal seasonal mean of dry months Tasselled Cap Transformation. Note the higher albedo in the zoomed areas ((<b>a</b>,<b>b</b>) Sentinel 2 RGB natural colour composite of bands 4, 3 and 2) from Nohar to Bhadra.</p> Full article ">Figure 9
<p>Comparison of visibility potential between different seasonal conditions and techniques illustrating the need to combine different approaches for the detection of palaeo-rivers.</p> Full article ">
2055 KiB  
Article
Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
by Lin Pan, Xingxing Li, Xiaohong Zhang, Xin Li, Cuixian Lu, Qile Zhao and Jingnan Liu
Remote Sens. 2017, 9(7), 734; https://doi.org/10.3390/rs9070734 - 15 Jul 2017
Cited by 33 | Viewed by 6024
Abstract
The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free [...] Read more.
The joint use of multi-frequency signals brings new prospects for precise positioning and has become a trend in Global Navigation Satellite System (GNSS) development. However, a new type of inter-frequency clock bias (IFCB), namely the difference between satellite clocks computed with different ionospheric-free carrier phase combinations, was noticed. Consequently, the B1/B3 precise point positioning (PPP) cannot directly use the current B1/B2 clock products. Datasets from 35 globally distributed stations are employed to investigate the IFCB. For new generation BeiDou Navigation Satellite System (BDS) satellites, namely BDS-3 satellites, the IFCB between B1/B2a and B1/B3 satellite clocks, between B1/B2b and B1/B3 satellite clocks, between B1C/B2a and B1C/B3 satellite clocks, and between B1C/B2b and B1C/B3 satellite clocks is analyzed, and no significant IFCB variations can be observed. The IFCB between B1/B2 and B1/B3 satellite clocks for BDS-2 satellites varies with time, and the IFCB variations are generally confined to peak amplitudes of about 5 cm. The IFCB of BDS-2 satellites exhibits periodic signal, and the accuracy of prediction for IFCB, namely the root mean square (RMS) statistic of the difference between predicted and estimated IFCB values, is 1.2 cm. A triple-frequency PPP model with consideration of IFCB is developed. Compared with B1/B2-based PPP, the positioning accuracy of triple-frequency PPP with BDS-2 satellites can be improved by 12%, 25% and 10% in east, north and vertical directions, respectively. Full article
(This article belongs to the Special Issue Multi-Constellation Global Navigation Satellite Systems: Methods and Applications)
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Graphical abstract
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<p>Ground tracks of 14 BDS-2 satellites and 4 BDS-3 satellites on 13 July 2016. This figure is plotted by the Matlab mapping package M_Map [<a href="#B34-remotesensing-09-00734" class="html-bibr">34</a>].</p> Full article ">Figure 2
<p>Geographical distribution of 35 stations. This figure is plotted by the Matlab mapping package M_Map [<a href="#B34-remotesensing-09-00734" class="html-bibr">34</a>]. The green points refer to MGEX stations. The blue and yellow points refer to iGMAS stations. The black points refer to the stations with the capability of tracking the new navigation signals.</p> Full article ">Figure 3
<p>Time series of triple-frequency carrier phase combinations at station SGG1 on 13 July 2016.</p> Full article ">Figure 4
<p>RMS statistics of low-frequency and high-frequency components in the triple-frequency carrier phase combination time series for each BDS satellite at station SGG1.</p> Full article ">Figure 5
<p>Dependence of carrier phase multipath and noise errors on satellite elevation angles for triple-frequency carrier phase combination at stations SGG1 and XIA1.</p> Full article ">Figure 6
<p>Time series of the IFCB estimates for a period of 32 days from 15 August to 15 September 2016.</p> Full article ">Figure 7
<p>Average values of sum of weights at an epoch over 32 days.</p> Full article ">Figure 8
<p>Cross-correlations between IFCB time series of two days.</p> Full article ">Figure 9
<p>Residuals obtained by subtracting IFCB time series of the first day from those of the day after a week for MEO satellites and of the second day for GEO and IGSO satellites.</p> Full article ">Figure 10
<p>PPP positioning errors for three different cases at JFNG on 12 September 2016.</p> Full article ">Figure 11
<p>Phase observation residuals for triple-frequency PPP at JFNG on 12 September 2016.</p> Full article ">
2976 KiB  
Article
Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery
by Daniel Kępski, Bartłomiej Luks, Krzysztof Migała, Tomasz Wawrzyniak, Sebastian Westermann and Bronisław Wojtuń
Remote Sens. 2017, 9(7), 733; https://doi.org/10.3390/rs9070733 - 15 Jul 2017
Cited by 28 | Viewed by 9245
Abstract
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation [...] Read more.
Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials. Full article
(This article belongs to the Special Issue Snow Remote Sensing)
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<p>Location of the study area. (<b>a</b>) Overview with denoted extent of the camera view and the polygon used as the classification mask. (<b>b</b>) Zoom to the classification area with superimposed vegetation map. The detailed characteristics of specific plant communities are described in Wojtuń et al. 2013 [<a href="#B42-remotesensing-09-00733" class="html-bibr">42</a>].</p> Full article ">Figure 2
<p>Time-lapse camera set: (<b>a</b>) Internal view; (<b>b</b>) Placement near the Fugleberget summit; (<b>c</b>) View from the camera (16 June 2016) with marked location of all the Ground Control Points (GCP) used in the orthorectification process.</p> Full article ">Figure 3
<p>Workflow for the images obtained from the time-lapse camera on the example of picture from 4 May 2016: (<b>a</b>) raw photo from the camera; (<b>b</b>) picture after the orthorectification process; (<b>c</b>) picture superimposed on reference scene. Marked characteristic terrain features recognized on both pictures by SURF algorithm (“ptsOriginal”—features on the reference scene; “ptsDistorted”—the same features identified on the input image (4 May 2016)) and used in alignment process compensating the camera movements; (<b>d</b>) picture embedded in geographical space (Landsat 8 scene from 28 April 2016 in the background) with marked GCPs used in georeferencing process; (<b>e</b>) Results of pixel classification into snow/snow-free surfaces using threshold value in blue band, clipped to the study area; (<b>f</b>) processed picture with superimposed the land cover map used in spatial analysis.</p> Full article ">Figure 4
<p>Classification example for 5 June 2016. (<b>a</b>) Orthoimage zoomed to classification area; (<b>b</b>) Classification result with threshold value in blue band set to 140; (<b>c</b>) Frequency histogram of RGB values with characteristic bimodal distribution—note that the distance between modal values is largest for the blue band.</p> Full article ">Figure 5
<p>Results of weekly (<b>a</b>) snow depth and (<b>b</b>) snow water equivalent manual measurements in the Fuglebekken catchment in 2013/14, 2014/15 and 2015/16 in boxplot format [<a href="#B68-remotesensing-09-00733" class="html-bibr">68</a>].</p> Full article ">Figure 6
<p>Evolution of SCE in Fuglebekken catchment obtained from the time-lapse camera images in spring (<b>a</b>) 2014, (<b>b</b>) 2015 and (<b>c</b>) 2016; each blue point represents a classified image, daily meteorological data are shown in the background.</p> Full article ">Figure 7
<p>Snow cover distribution in the Fuglebekken catchment based on the 2014–2016 data set in specific melting stages: early (~72% snow coverage), advanced (~33%) and late (~4%). (<b>a</b>) Results of SCE superimposition for three years. (<b>b</b>) Surface percentage of snow coverage during the three stages.</p> Full article ">Figure 8
<p>Relationship between date of snowmelt on manual soundings sites and SWE measured there during the maximum accumulation time that occurred: (<b>a</b>) 28 April in 2014, (<b>b</b>) 20 April in 2015 and (<b>c</b>) 2 April in 2016. Red horizontal line indicate the date of advanced stage occurrence (~33% SCE), blue line is a linear regression.</p> Full article ">Figure 9
<p>Evolution of snow coverage on various types of land cover in Fuglebekken catchment at approximately 5 day intervals in spring: (<b>a</b>) 2014, (<b>b</b>) 2015 and (<b>c</b>) 2016. Blue bars represent SCE in the whole study area. (<b>d</b>) The share of individual land cover types in the study area. Two tundra vegetation types covering the lowest percentage of study area were omitted in spatial analysis: ornitocoprophilus (O) tundra (0.5%) and geophytic initial (GI) tundra (0.2%).</p> Full article ">
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