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Remote Sens., Volume 8, Issue 2 (February 2016) – 83 articles

Cover Story (view full-size image): Coral reefs are in decline worldwide and face stresses at all scales, from coastal development and overexploitation to global warming and ocean acidification. Monitoring reef status by manual surveys provides data at localised scales which is not cost-effective for the regional and global scales at which reefs are threatened. Satellite imagery has now been leveraged for reef applications for over 40 years with a multitude of sensor and algorithm developments. We review the historical and state-of-the-art achievements and remaining challenges, over the range of monitoring objectives from the physical and biological composition of the reef to the ocean environment in which they occur. Of increasing importance is the aim to go beyond basic maps, to concepts relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threats and ecosystem services. View this paper
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2818 KiB  
Article
Remote Sensing of Soil Alkalinity and Salinity in the Wuyu’er-Shuangyang River Basin, Northeast China
by Lin Bai, Cuizhen Wang, Shuying Zang, Yuhong Zhang, Qiannan Hao and Yuexiang Wu
Remote Sens. 2016, 8(2), 163; https://doi.org/10.3390/rs8020163 - 20 Feb 2016
Cited by 57 | Viewed by 11109
Abstract
The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity [...] Read more.
The Songnen Plain of the Northeast China is one of the three largest soda saline-alkali regions worldwide. To better understand soil alkalinization and salinization in this important agricultural region, it is vital to explore the distribution and variation of soil alkalinity and salinity in space and time. This study examined soil properties and identified the variables to extract soil alkalinity and salinity via physico-chemical, statistical, spectral, and image analysis. The physico-chemical and statistical results suggested that alkaline soils, coming from the main solute Na2CO3 and NaHCO3 in parent rocks, characterized the study area. The pH and electric conductivity (EC ) were correlated with both narrow band and broad band reflectance. For soil pH, the sensitive bands were in short wavelength (VIS) and the band with the highest correlation was 475 nm (r = 0.84). For soil EC, the sensitive bands were also in VIS and the band with the highest correlation was 354 nm (r = 0.84). With the stepwise regression, it was found that the pH was sensitive to reflectance of OLI band 2 and band 6, while the EC was only sensitive to band 1. The R2Adj (0.73 and 0.72) and root mean square error (RMSE) (0.98 and 1.07 dS/m) indicated that, the two stepwise regression models could estimate soil alkalinity and salinity with a considerable accuracy. Spatial distributions of soil alkalinity and salinity were mapped from the OLI image with the RMSE of 1.01 and 0.64 dS/m, respectively. Soil alkalinity was related to salinity but most soils in the study area were non-saline soils. The area of alkaline soils was 44.46% of the basin. Highly alkaline soils were close to the Zhalong wetland and downstream of rivers, which could become a severe concern for crop productivity in this area. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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<p>The Wuyu’er-Shuangyang River Basin in the Songnen Plain, Northeast China.</p> Full article ">Figure 2
<p>Soil types of the study area and distributions of soil sample sites.</p> Full article ">Figure 3
<p>Average soil spectra at 1 nm interval for soil samples with seven pH-EC levels.</p> Full article ">Figure 4
<p>Correlation coefficient curves between pH, EC, and soil spectra at 1nm interval.</p> Full article ">Figure 5
<p>Spatial distribution of soil alkalinity across the basin.</p> Full article ">Figure 6
<p>Distributions of soil salinity across the basin.</p> Full article ">
3525 KiB  
Article
Comparing and Combining Remotely Sensed Land Surface Temperature Products for Improved Hydrological Applications
by Robert M. Parinussa, Venkat Lakshmi, Fiona Johnson and Ashish Sharma
Remote Sens. 2016, 8(2), 162; https://doi.org/10.3390/rs8020162 - 20 Feb 2016
Cited by 22 | Viewed by 6641
Abstract
Land surface temperature (LST) is an important variable that provides a valuable connection between the energy and water budget and is strongly linked to land surface hydrology. Space-borne remote sensing provides a consistent means for regularly observing LST using thermal infrared (TIR) and [...] Read more.
Land surface temperature (LST) is an important variable that provides a valuable connection between the energy and water budget and is strongly linked to land surface hydrology. Space-borne remote sensing provides a consistent means for regularly observing LST using thermal infrared (TIR) and passive microwave observations each with unique strengths and weaknesses. The spatial resolution of TIR based LST observations is around 1 km, a major advantage when compared to passive microwave observations (around 10 km). However, a major advantage of passive microwaves is their cloud penetrating capability making them all-weather sensors whereas TIR observations are routinely masked under the presence of clouds and aerosols. In this study, a relatively simple combination approach that benefits from the cloud penetrating capacity of passive microwave sensors was proposed. In the first step, TIR and passive microwave LST products were compared over Australia for both anomalies and raw timeseries. A very high agreement was shown over the vast majority of the country with R2 typically ranging from 0.50 to 0.75 for the anomalies and from 0.80 to 1.00 for the raw timeseries. Then, the scalability of the passive microwave based LST product was examined and a pixel based merging approach through linear scaling was proposed. The individual and merged LST products were further compared against independent LST from the re-analysis model outputs. This comparison revealed that the TIR based LST product agrees best with the re-analysis data (R2 0.26 for anomalies and R2 0.76 for raw data), followed by the passive microwave LST product (R2 0.16 for anomalies and R2 0.66 for raw data) and the combined LST product (R2 0.18 for anomalies and R2 0.62 for raw data). It should be noted that the drop in performance comes with an increased revisit frequency of approximately 20% compared to the revised frequency of the TIR alone. Additionally, this comparison against re-analysis data was subdivided over Australia’s major climate zones and revealed that the relative agreement between the individual and combined LST products against the re-analysis data is consistent over these climate zones. These results are also consistent for both the anomalies and the raw time series. Finally, two examples were provided that demonstrate the proposed merging approach including an example for the Hunter Valley floods along Australia’s central coast that experienced significant flooding in April 2015. Full article
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<p>An example of the MODIS Land Surface Temperature product on 3 April 2015 (01:30 PM) that demonstrates cloud obstruction (e.g., WA, SA, NSW and ACT) which affects the availability of this Land Surface Temperature product. Note that the gray shading is an area that was not observed by the MODIS sensor on that particular day. Also, this figure presents the abbreviations for the Australian states that will be further used for reference purposes.</p> Full article ">Figure 2
<p>The high agreement between the anomalies Land Surface Temperature products from MODIS and AMSR2 expressed in R<sup>2</sup> (<b>a</b>) for day time observations; SE (<b>b</b>) for day time observations; R<sup>2</sup> (<b>c</b>) for night time observations and SE (<b>d</b>) for night time observations.</p> Full article ">Figure 3
<p>Histograms of the anomalies that show the agreement between the Land Surface Temperature products from MODIS and AMSR2 expressed in R<sup>2</sup> (<b>a</b>) for day time observations, SE (<b>b</b>) for day time observations, R<sup>2</sup> (<b>c</b>) for night time observations and SE (<b>d</b>) for night time observations.</p> Full article ">Figure 4
<p>Pixel based scaling parameters (slope and offset) for MODIS Land Surface Temperature and AMSR2 (T<sub>b, 37V</sub>) observations. The slope (<b>a</b>); offset (<b>b</b>) and the sample sizes (<b>c</b>) for day-time observations and the slope (<b>d</b>); offset (<b>e</b>) and the sample sizes (<b>f</b>) for night-time observations.</p> Full article ">Figure 5
<p>R<sup>2</sup> between the anomalies from MERRA and (<b>a</b>) MODIS; (<b>b</b>) the merged MODIS-AMSR2 Land Surface Temperature product and (<b>c</b>) AMSR2, as well as the percentage of gained samples through the addition of AMSR2 observations (<b>d</b>).</p> Full article ">Figure 6
<p>Histograms that show the agreement between the anomalies from MERRA Land Surface Temperature and the remotely sensed Land Surface Temperature products from MODIS (<b>a</b>); AMSR2 (<b>b</b>) and through the presented combination approach (<b>c</b>) expressed in R<sup>2</sup>.</p> Full article ">Figure 7
<p>Australia’s major climate zones according to the Köppen-Geiger climate classification. Several sub categories of the more detailed classification were merged into these four major classes.</p> Full article ">Figure 8
<p>An example of the individual MODIS LST product for 3 April 2015 (<b>a</b>); the combined Land Surface Temperature product (<b>b</b>) and a spatial map (<b>c</b>) that demonstrates the sensors that were used in the combined Land Surface Temperature product. The Land Surface Temperature product in the gray shading is based on MODIS whereas the cyan regions are based on scaled AMSR2 observations.</p> Full article ">Figure 9
<p>A recent flooding example for the Hunter Valley along Australia’s central coast demonstrating the combination approach and its usefulness for warning systems. This area experienced severe flooding after receiving significant amounts of precipitation on 21 and 22 April 2015. This timeseries demonstrates the successful scaling of AMSR2 observations resulting in an increasing number of Land Surface Temperture observations. (<b>a</b>) LST products during the day; (<b>b</b>) LST products during the night; (<b>c</b>) precipitation from the Australian Water Availability Project.</p> Full article ">
5505 KiB  
Article
Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data
by Sarah J. Graves, Gregory P. Asner, Roberta E. Martin, Christopher B. Anderson, Matthew S. Colgan, Leila Kalantari and Stephanie A. Bohlman
Remote Sens. 2016, 8(2), 161; https://doi.org/10.3390/rs8020161 - 19 Feb 2016
Cited by 65 | Viewed by 9134
Abstract
Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced [...] Read more.
Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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<p>Images of the Azuero Peninsula study site. (<b>a</b>) Carnegie Airborne Observatory (CAO) true-color image of 23,000 ha; (<b>b</b>) Location of the study site; (<b>c</b>) Agricultural tree cover shown in a true-color image of the CAO image with 2-m spatial resolution; (<b>d</b>) Typical tree cover on agricultural land.</p> Full article ">Figure 2
<p>Effect of imbalanced data on SVM decision plane. (<b>a</b>) Visualization of the SVM decision plane when two classes are of balanced size; (<b>b</b>) the decision plane when data size is imbalanced with a large number of misclassifications of the minority class.</p> Full article ">Figure 3
<p>Model and species-level accuracy metrics of a 21-class SVM model (<b>a</b>) Model-level metrics of percent overall accuracy and F-score; (<b>b</b>) species-level F-score. Both plots show values across 30 model iterations. Full species names for each species code are given in <a href="#remotesensing-08-00161-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 4
<p>Species-level recall and precision percent accuracy across 30 model iterations. Full species names for each species code are given in <a href="#remotesensing-08-00161-t002" class="html-table">Table 2</a>. Precision is equivalent to user accuracy, recall is equivalent to producer accuracy. High precision relative to recall means the species has lower comission errors than omission errors (see S5 for example confusion matrix).</p> Full article ">Figure 5
<p>Relationship between class sample size and model prediction bias. Line shows a linear model with 95% confidence interval around the mean. Histogram on the top (number of pixels) and left (prediction bias) show distributions of large and small species-classes based on a 1000 pixel threshold. Dashed blue lines show mean values of size and bias for all data, and light and dark grey dashed lines show means for small and large groups, respectively. Full species names for each species code are given in <a href="#remotesensing-08-00161-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 6
<p>Model-level accuracy comparison of 4 SVM models that differ in input data. Model accuracy was measured with a 3-fold cross validation method (30 model iterations). The Even, Imbalanced, and Weighted models were run on a 400 crown subset of the full 890 crowns. Ten random subsets were done for a total of 300 model iterations. Paired t-tests between Imbalanced and Weighted model variations showed significant differences between variations for the F-score and bias metrics.</p> Full article ">Figure 7
<p>Predicted species of agricultural tree crowns on subset of the landscape. Crown predictions are overlaid on true-color 2-meter resolution image. Polygons are colored by their predicted species. Full species names for each species code are given in <a href="#remotesensing-08-00161-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 8
<p>Predicted and error-adjusted canopy area and relative species abundance (percent) of 200,000 individual tree crowns across the 23,000 ha landscape. Predicted area was calculated as the total crown area for each species from the 21-class SVM classification model. Error-adjusted area accounts for differences in species prediction errors as calculated in Equation (3) (see Olofsson <span class="html-italic">et al.</span> [<a href="#B51-remotesensing-08-00161" class="html-bibr">51</a>] for full methods). Points show the relative species abundance based on the predicted area (black circles) and the error-adjusted area (white triangles). Full species names for each species code are given in <a href="#remotesensing-08-00161-t002" class="html-table">Table 2</a>.</p> Full article ">
10795 KiB  
Article
Dynamic Mapping of Evapotranspiration Using an Energy Balance-Based Model over an Andean Páramo Catchment of Southern Ecuador
by Galo Carrillo-Rojas, Brenner Silva, Mario Córdova, Rolando Célleri and Jörg Bendix
Remote Sens. 2016, 8(2), 160; https://doi.org/10.3390/rs8020160 - 19 Feb 2016
Cited by 41 | Viewed by 7969
Abstract
Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the [...] Read more.
Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the páramo. To overcome this lack of knowledge, we implemented the energy-balance model METRIC with Landsat 7 ETM+ and MODIS-Terra imagery for a páramo catchment. The implementation contemplated adjustments for complex terrain in order to obtain daily, monthly and annual ET maps (between 2013 and 2014). In addition, we compared our results to the global ET product MOD16. Finally, a rigorous validation of the outputs was conducted with residual ET from the water balance. ET retrievals from METRIC (Landsat-based) showed good agreement with the validation-related ET at monthly and annual steps (mean bias error <8 mm·month−1 and annual deviation <17%). However, METRIC (MODIS-based) outputs and the MOD16 product were revealed to be unsuitable for our study due to the low spatial resolution. At last, the plausibility of METRIC to obtain spatial ET retrievals using higher resolution satellite data is demonstrated, which constitutes the first contribution to the understanding of spatially-explicit ET over an alpine catchment in the neo-tropical Andes. Full article
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Full article ">Figure 1
<p>Study area and the quinoas catchment.</p> Full article ">Figure 2
<p>Views of the Quinoas catchment: (<b>A</b>) tussock grass and wetlands, upper valley; (<b>B</b>) Polylepis forests/water body, upper valley; (<b>C</b>) Pinus and evergreen native forest, mid valley; and (<b>D</b>) evergreen vegetation, low valley (photos by Andrés Abril, October 2015).</p> Full article ">Figure 3
<p>Quinoas automatic weather stations climographs (January 2013 to December 2014).</p> Full article ">Figure 4
<p>Reference ET fraction (<span class="html-italic">ET<sub>r</sub>f</span>) for METRIC<sub>L</sub> and METRIC<sub>M</sub> for the vegetated testing plots in the study area (median, minimum and maximum).</p> Full article ">Figure 5
<p>METRIC<sub>L</sub> ET map for 19 October 2013 (cloud-free day).</p> Full article ">Figure 6
<p>METRIC<sub>L</sub>, METRIC<sub>M</sub> and MOD16 ET map comparison for May 2013 (<b>a1</b>,<b>b1</b>,<b>c1</b>); November 2013 (<b>a2</b>,<b>b2</b>,<b>c2</b>) and May 2014 (<b>a3</b>,<b>b3</b>,<b>c3</b>).</p> Full article ">Figure 7
<p>Modeled and observed radiation for 2013–2014 (hourly means of the three AWSs) and Earth declination angle.</p> Full article ">Figure 8
<p>Monthly comparison of METRIC<sub>L</sub>, METRIC<sub>M</sub>, MOD16 ET in the testing plots. Statistical values of <span class="html-italic">R</span><sup>2</sup>, mean bias error (MBE) and RMSE for the METRIC retrievals are shown, as well as the average rainfall from the AWSs. Error bars show SD.</p> Full article ">Figure 9
<p>Aggregated precipitation, runoff and residual ET in comparison with METRIC<sub>L</sub>, METRIC<sub>M</sub> and MOD16 ET. Noticeable runoff increments are highlighted in red color.</p> Full article ">
13126 KiB  
Article
A Comparative Study of Cross-Product NDVI Dynamics in the Kilimanjaro Region—A Matter of Sensor, Degradation Calibration, and Significance
by Florian Detsch, Insa Otte, Tim Appelhans and Thomas Nauss
Remote Sens. 2016, 8(2), 159; https://doi.org/10.3390/rs8020159 - 19 Feb 2016
Cited by 27 | Viewed by 8900
Abstract
While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In [...] Read more.
While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In order to assess the extent of such differences in highly heterogeneous terrain, we analyze and compare intra-annual seasonal fluctuations and long-term monotonic trends (2003–2012) in the Kilimanjaro region, Tanzania. The considered NDVI datasets include the Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra and Aqua, Collections 5 and 6, and the 3rd Generation Global Inventory Modeling and Mapping Studies (GIMMS) product. The degree of agreement in seasonal fluctuations is assessed by calculating a pairwise Index of Association (IOAs), whereas long-term trends are derived from the trend-free pre-whitened Mann–Kendall test. On the seasonal scale, the two Terra-MODIS products (and, accordingly, the two Aqua-MODIS products) are best associated with each other, indicating that the seasonal signal remained largely unaffected by the new Collection 6 calibration approach. On the long-term scale, we find that the negative impacts of band ageing on Terra-MODIS NDVI have been accounted for in Collection 6, which now distinctly outweighs Aqua-MODIS in terms of greening trends. GIMMS NDVI, by contrast, fails to capture small-scale seasonal and trend patterns that are characteristic for the highly fragmented landscape which is likely owing to the coarse spatial resolution. As a short digression, we also demonstrate that the amount of false discoveries in the determined trend fraction is distinctly higher for p < 0.05 ( 52.6 % ) than for p < 0.001 ( 2.2 % ) which should point the way for any future studies focusing on the reliable deduction of long-term monotonic trends. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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<p>(<b>a</b>) Spatial coverage of the 1/12-degree GIMMS grid (white dashed) including consecutive pixel numbering superimposed upon an aerial image of the study area (Map data: Google, TerraMetrics [<a href="#B47-remotesensing-08-00159" class="html-bibr">47</a>]); (<b>b</b>) spatial pattern of IOA<sub>s</sub> between NDVI<sub>3g</sub> and MODIS CMG-based NDVI<sub>Aqua-C5</sub> (<a href="#sec2dot2dot1-remotesensing-08-00159" class="html-sec">Section 2.2.1</a>); (<b>c</b>) raw time series (2003–2012) of NDVI<sub>3g</sub> (purple) and MODIS CMG-based NDVI<sub>Aqua-C5</sub> (orange) including 10%–90% quantile range calculated from all 250-m NDVI<sub>Aqua-C5</sub> pixels falling inside each 8-km GIMMS pixel (light orange). The underlying coordinate reference system (CRS) in (<b>a</b>) and (<b>b</b>) is EPSG:4326 and the digital elevation model (black solid) is based on toposheets at scale 1:50000 digitized by Ong’injo <span class="html-italic">et al.</span> [<a href="#B48-remotesensing-08-00159" class="html-bibr">48</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Bing Maps aerial image of the study area (downloaded via OpenStreetMap [<a href="#B49-remotesensing-08-00159" class="html-bibr">49</a>]); spatial patterns of “significant” values of τ (2003–2012) for (<b>b</b>) NDVI<sub>3g</sub>; (<b>c</b>) NDVI<sub>Terra-C5</sub>; (<b>d</b>) NDVI<sub>Aqua-C5</sub>; (<b>e</b>) NDVI<sub>Terra-C6</sub>; and (<b>f</b>) NDVI<sub>Aqua-C6</sub>. Also included are the 1-d Kernel density estimates for the 250-m MODIS products originating from Collections 5 (connecting <b>c</b> and <b>d</b>) and 6 (connecting <b>e</b> and <b>f</b>). The underlying CRS and additional data are the same as in <a href="#remotesensing-08-00159-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>MD<sub>τ</sub> calculated from “significant” values of τ (<math display="inline"> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </math>) for each pair of MODIS NDVI products.</p> Full article ">Figure 4
<p>Spatial patterns of “conclusive” values of τ (2003–2012) for (<b>a</b>) NDVI<sub>Terra-C5</sub>; (<b>b</b>) NDVI<sub>Aqua-C5</sub>; (<b>c</b>) NDVI<sub>Terra-C6</sub>; and (<b>d</b>) NDVI<sub>Aqua-C6</sub>. Also included are the 1-d Kernel density estimates for the 250-m MODIS products originating from Collections 5 (connecting <b>a</b> and <b>b</b>) and 6 (connecting <b>c</b> and <b>d</b>). The underlying CRS and additional data are the same as in <a href="#remotesensing-08-00159-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 5
<p>MD<sub>τ</sub> calculated from “conclusive” values of τ (<math display="inline"> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.001</mn> </mrow> </math>) for each pair of MODIS NDVI products.</p> Full article ">Figure 6
<p>FDR depending on the <span class="html-italic">Power</span> of the applied test and the trend prevalence (<span class="html-italic">P</span>) under real conditions for (<b>a</b>) “significant” trends (<math display="inline"> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </math>); and (<b>b</b>) “conclusive” trends (<math display="inline"> <mrow> <mi>p</mi> <mo>&lt;</mo> <mn>0.001</mn> </mrow> </math>).</p> Full article ">
6170 KiB  
Article
Evaluation of VIIRS and MODIS Thermal Emissive Band Calibration Stability Using Ground Target
by Sriharsha Madhavan, Jake Brinkmann, Brian N. Wenny, Aisheng Wu and Xiaoxiong Xiong
Remote Sens. 2016, 8(2), 158; https://doi.org/10.3390/rs8020158 - 19 Feb 2016
Cited by 18 | Viewed by 7076
Abstract
The S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, a polar orbiting Earth remote sensing instrument built using a strong MODIS background, employs a similarly designed on-board calibrating source—a V-grooved blackbody for the Thermal Emissive Bands (TEB). The central wavelengths of most VIIRS [...] Read more.
The S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, a polar orbiting Earth remote sensing instrument built using a strong MODIS background, employs a similarly designed on-board calibrating source—a V-grooved blackbody for the Thermal Emissive Bands (TEB). The central wavelengths of most VIIRS TEBs are very close to those of MODIS with the exception of the 10.7 µm channel. To ensure the long term continuity of climate data records derived using VIIRS and MODIS TEB, it is necessary to assess any systematic differences between the two instruments, including scenes with temperatures significantly lower than blackbody operating temperatures at approximately 290 K. Previous work performed by the MODIS Characterization Support Team (MCST) at NASA/GSFC used the frequent observations of the Dome Concordia site located in Antarctica to evaluate the calibration stability and consistency of Terra and Aqua MODIS over the mission lifetime. The near-surface temperature measurements from an automatic weather station (AWS) provide a direct reference useful for tracking the stability and determining the relative bias between the two MODIS instruments. In this study, the same technique is applied to the VIIRS TEB and the results are compared with those from the matched MODIS TEB. The results of this study show a small negative bias when comparing the matching VIIRS and Aqua MODIS TEB, implying a higher brightness temperature for S-VIIRS at the cold end. Statistically no significant drift is observed for VIIRS TEB performance over the first 3.5 years of the mission. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Instrument setup with on-board calibrators (<b>a</b>) MODIS; (<b>b</b>) VIIRS [<a href="#B1-remotesensing-08-00158" class="html-bibr">1</a>,<a href="#B2-remotesensing-08-00158" class="html-bibr">2</a>].</p> Full article ">Figure 2
<p>V-grooved BlackBody controlled using various thermistors (<b>a</b>) MODIS; (<b>b</b>) VIIRS [<a href="#B6-remotesensing-08-00158" class="html-bibr">6</a>].</p> Full article ">Figure 3
<p>Relative Spectral Response of the VIIRS TEBs overlaid with spectral radiance of the BlackBody at 290 K [<a href="#B7-remotesensing-08-00158" class="html-bibr">7</a>].</p> Full article ">Figure 4
<p>S-VIIRS Band M15 image illustrating the Antarctic EV location comprising the Dome C site.</p> Full article ">Figure 5
<p>Lifetime temperature observations over Dome C using AWS, T- and A- MODIS band 31.</p> Full article ">Figure 6
<p>Long term Brightness Temperature difference trends (Sensor-AWS) (<b>a</b>) M12/B20; (<b>b</b>) M13/B22; (<b>c</b>) M14/B29; (<b>d</b>) M15/B31; (<b>e</b>) M16/B32.</p> Full article ">Figure 6 Cont.
<p>Long term Brightness Temperature difference trends (Sensor-AWS) (<b>a</b>) M12/B20; (<b>b</b>) M13/B22; (<b>c</b>) M14/B29; (<b>d</b>) M15/B31; (<b>e</b>) M16/B32.</p> Full article ">Figure 6 Cont.
<p>Long term Brightness Temperature difference trends (Sensor-AWS) (<b>a</b>) M12/B20; (<b>b</b>) M13/B22; (<b>c</b>) M14/B29; (<b>d</b>) M15/B31; (<b>e</b>) M16/B32.</p> Full article ">Figure 7
<p>The 3.5-year relative bias trend between S-VIIRS and A-MODIS. (<b>a</b>) M12/B20; (<b>b</b>) M13/B22; (<b>c</b>) M14/B29; (<b>d</b>) M15/B31; (<b>e</b>) M16/B32.</p> Full article ">Figure 7 Cont.
<p>The 3.5-year relative bias trend between S-VIIRS and A-MODIS. (<b>a</b>) M12/B20; (<b>b</b>) M13/B22; (<b>c</b>) M14/B29; (<b>d</b>) M15/B31; (<b>e</b>) M16/B32.</p> Full article ">Figure 7 Cont.
<p>The 3.5-year relative bias trend between S-VIIRS and A-MODIS. (<b>a</b>) M12/B20; (<b>b</b>) M13/B22; (<b>c</b>) M14/B29; (<b>d</b>) M15/B31; (<b>e</b>) M16/B32.</p> Full article ">Figure 8
<p>RSR Curves for matching TEB for VIIRS and MODIS.</p> Full article ">Figure 9
<p>Relative bias trends between S-VIIRS and T-MODIS. (<b>a</b>) M15/B31; (<b>b</b>) M16/B32; (<b>c</b>) M14/B29.</p> Full article ">Figure 9 Cont.
<p>Relative bias trends between S-VIIRS and T-MODIS. (<b>a</b>) M15/B31; (<b>b</b>) M16/B32; (<b>c</b>) M14/B29.</p> Full article ">
4238 KiB  
Article
The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification
by Bei Zhao, Yanfei Zhong, Liangpei Zhang and Bo Huang
Remote Sens. 2016, 8(2), 157; https://doi.org/10.3390/rs8020157 - 19 Feb 2016
Cited by 91 | Viewed by 7729
Abstract
High spatial resolution (HSR) image scene classification is aimed at bridging the semantic gap between low-level features and high-level semantic concepts, which is a challenging task due to the complex distribution of ground objects in HSR images. Scene classification based on the bag-of-visual-words [...] Read more.
High spatial resolution (HSR) image scene classification is aimed at bridging the semantic gap between low-level features and high-level semantic concepts, which is a challenging task due to the complex distribution of ground objects in HSR images. Scene classification based on the bag-of-visual-words (BOVW) model is one of the most successful ways to acquire the high-level semantic concepts. However, the BOVW model assigns local low-level features to their closest visual words in the “visual vocabulary” (the codebook obtained by k-means clustering), which discards too many useful details of the low-level features in HSR images. In this paper, a feature coding method under the Fisher kernel (FK) coding framework is introduced to extend the BOVW model by characterizing the low-level features with a gradient vector instead of the count statistics in the BOVW model, which results in a significant decrease in the codebook size and an acceleration of the codebook learning process. By considering the differences in the distributions of the ground objects in different regions of the images, local FK (LFK) is proposed for the HSR image scene classification method. The experimental results show that the proposed scene classification methods under the FK coding framework can greatly reduce the computational cost, and can obtain a better scene classification accuracy than the methods based on the traditional BOVW model. Full article
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<p>FK coding framework for the representation of HSR imagery.</p> Full article ">Figure 2
<p>Procedure of the FK-O scene classification method.</p> Full article ">Figure 3
<p>Procedure of the FK-S scene classification method.</p> Full article ">Figure 4
<p>Image segmentation by chessboard segmentation with different numbers of regions.</p> Full article ">Figure 5
<p>Graphical model representations. (<b>a</b>) GMM; (<b>b</b>) LGMM.</p> Full article ">Figure 6
<p>UCM dataset. (<b>a</b>–<b>u</b>) agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts.</p> Full article ">Figure 7
<p>Google dataset. (<b>a</b>–<b>l</b>) meadow, pond, harbor, industrial, park, river, residential, overpass, agriculture, commercial, water, and idle land.</p> Full article ">Figure 8
<p>Wuhan IKONOS dataset. (<b>a</b>–<b>h</b>) dense residential, idle, industrial, medium residential, parking lot, commercial, vegetation, and water.</p> Full article ">Figure 9
<p>Large image annotation using the Wuhan IKONOS dataset. (<b>a</b>) false-color image of the large image with 6150 × 8250 pixels; (<b>b</b>) annotated large image.</p> Full article ">Figure 10
<p>Classification performance with different patch sizes and spacing. The top and bottom rows show the classification accuracies when varying the patch spacing from four to ten pixels, with the patch size as 8 × 8 pixels, and when varying the patch size from 8 × 8 to 16 × 16 pixels, with the patch spacing as 50% of the size, respectively.</p> Full article ">Figure 11
<p>Confusion matrices obtained by the FK-S scene classification method for the three datasets. (<b>a</b>) UCM dataset; (<b>b</b>) Google dataset; (<b>c</b>) Wuhan IKONOS dataset.</p> Full article ">Figure 11 Cont.
<p>Confusion matrices obtained by the FK-S scene classification method for the three datasets. (<b>a</b>) UCM dataset; (<b>b</b>) Google dataset; (<b>c</b>) Wuhan IKONOS dataset.</p> Full article ">Figure 12
<p>Accuracies of the FK-O and FK-S scene classification methods with different numbers of Gaussians. (<b>a</b>) UCM dataset; (<b>b</b>) Google dataset; (<b>c</b>) Wuhan IKONOS dataset.</p> Full article ">Figure 13
<p>Accuracies of the FK-S scene classification method with different numbers of regions. (<b>a</b>) UCM dataset; (<b>b</b>) Google dataset; (<b>c</b>) Wuhan IKONOS dataset.</p> Full article ">
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Article
Impacts of Re-Vegetation on Surface Soil Moisture over the Chinese Loess Plateau Based on Remote Sensing Datasets
by Qiao Jiao, Rui Li, Fei Wang, Xingmin Mu, Pengfei Li and Chunchun An
Remote Sens. 2016, 8(2), 156; https://doi.org/10.3390/rs8020156 - 19 Feb 2016
Cited by 37 | Viewed by 7349
Abstract
A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not [...] Read more.
A large-scale re-vegetation supported by the Grain for Green Project (GGP) has greatly changed local eco-hydrological systems, with an impact on soil moisture conditions for the Chinese Loess Plateau. It is important to know how, exactly, re-vegetation influences soil moisture conditions, which not only crucially constrain growth and distribution of vegetation, and hence, further re-vegetation, but also determine the degree of soil desiccation and, thus, erosion risk in the region. In this study, three eco-environmental factors, which are Soil Water Index (SWI), the Normalized Difference Vegetation Index (NDVI), and precipitation, were used to investigate the response of soil moisture in the one-meter layer of top soil to the re-vegetation during the GGP. SWI was estimated based on the backscatter coefficient produced by the European Remote Sensing Satellite (ERS-1/2) and Meteorological Operational satellite program (MetOp), while NDVI was derived from SPOT imageries. Two separate periods, which are 1998–2000 and 2008–2010, were selected to examine the spatiotemporal pattern of the chosen eco-environmental factors. It has been shown that the amount of precipitation in 1998–2000 was close to that of 2008–2010 (the difference being 13.10 mm). From 1998–2000 to 2008–2010, the average annual NDVI increased for 80.99%, while the SWI decreased for 72.64% of the area on the Loess Plateau. The average NDVI over the Loess Plateau increased rapidly by 17.76% after the 10-year GGP project. However, the average SWI decreased by 4.37% for two-thirds of the area. More specifically, 57.65% of the area on the Loess Plateau experienced an increased NDVI and decreased SWI, 23.34% of the area had an increased NDVI and SWI. NDVI and SWI decreased simultaneously for 14.99% of the area, and the decreased NDVI and increased SWI occurred at the same time for 4.02% of the area. These results indicate that re-vegetation, human activities, and climate change have impacts on soil moisture. However, re-vegetation, which consumes a large quantity of soil water, may be the major factor for soil moisture change in most areas of the Loess Plateau. It is, therefore, suggested that Soil Moisture Content (SMC) should be kept in mind when carrying out re-vegetation in China’s arid and semi-arid regions. Full article
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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<p>Location of the study site and distribution of weather stations.</p> Full article ">Figure 2
<p>The spatial differences of mean annual precipitation (<b>a</b>); the precipitation for the three seasons (spring (<b>b</b>); summer (<b>c</b>) autumn (<b>d</b>)) between the two periods on the Loess Plateau.</p> Full article ">Figure 2 Cont.
<p>The spatial differences of mean annual precipitation (<b>a</b>); the precipitation for the three seasons (spring (<b>b</b>); summer (<b>c</b>) autumn (<b>d</b>)) between the two periods on the Loess Plateau.</p> Full article ">Figure 3
<p>The spatial differences of mean annual NDVI (<b>a</b>), the NDVI for the three seasons (spring (<b>b</b>); summer (<b>c</b>); and autumn (<b>d</b>)) between the two chosen periods on the Loess Plateau.</p> Full article ">Figure 4
<p>The spatial differences of mean annual SWI (<b>a</b>); the SWI for the three seasons (spring (<b>b</b>); summer (<b>c</b>); and autumn (<b>d</b>)) between 1998–2000 and 2008–2010 on the Loess Plateau.</p> Full article ">Figure 5
<p>Changes in the SWI and NDVI on the Loess Plateau. ΔSWI/ΔNDVI represents the difference of mean annual SWI/NDVI in 1998–2000 and 2008–2010.</p> Full article ">Figure 6
<p>Spatial distribution of area change in the SWI and NDVI (<b>a</b>); SWI, NDVI, and precipitation (<b>b</b>) on the Loess Plateau.</p> Full article ">
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Article
Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection
by Zhongwen Hu, Qingquan Li, Qian Zhang and Guofeng Wu
Remote Sens. 2016, 8(2), 155; https://doi.org/10.3390/rs8020155 - 18 Feb 2016
Cited by 23 | Viewed by 7942
Abstract
The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature [...] Read more.
The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions. Full article
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<p>The flowchart of the proposed method.</p> Full article ">Figure 2
<p>Block-based feature extraction and multi-scale representation.</p> Full article ">Figure 3
<p>Harris corner point detection and refinement. (<b>a</b>) Original Harris corner points; (<b>b</b>) Refined results (<span class="html-italic">R</span> = 25, <span class="html-italic">N</span> = 10); (<b>c</b>) Refined results (<span class="html-italic">R</span> = 25, <span class="html-italic">N</span> = 15).</p> Full article ">Figure 4
<p>The flowchart for calculating the multiple built-up indexes.</p> Full article ">Figure 5
<p>The built-up index image obtained by normalizing the distance image of the corner responses. (<b>a</b>) The index image obtained using the original distances of the corner responses; (<b>b</b>) The index image obtained using the stretched distances of the corner responses (β = 0.1).</p> Full article ">Figure 6
<p>An illustration of block offset and data fusion. (<b>a</b>) Offset = 0; (<b>b</b>) Offset = w/2; (<b>c</b>) Fused blocks.</p> Full article ">Figure 7
<p>The experimental data set and the corresponding reference built-up areas. (<b>a</b>) Multi-spectral QuickBird image with 0.6 m/pixel; (<b>b</b>) TerraSAR image with 3 m/pixel.</p> Full article ">Figure 8
<p>The built-up indexes and final results obtained with (<b>a</b>) offset = 0; (<b>b</b>) offset = w/2; (<b>c</b>) the fused result; and (<b>d</b>) the precision-recall curves. The boundaries of the built-up areas are not smoothed.</p> Full article ">Figure 9
<p>The built-up indexes and the corresponding final results obtained at different scales with the Pantex and BASI procedures. The final results are those with the best F-measure values. (<b>a</b>–<b>d</b>) The built-up indexes and final results of the proposed method with s = 0, 1, 2 and 5 respectively; (<b>e</b>,<b>f</b>) The built-up indexes and final results of the Pantex and BASI.</p> Full article ">Figure 10
<p>Quantitative evaluation of the performances at different scales.</p> Full article ">Figure 11
<p>Built-up indexes obtained using different image features and the corresponding results. (<b>a</b>–<b>d</b>) The index image and the final results using spectral, textural, structural and corner response, respectively; (<b>e</b>) The proposed minMBI index image and the final result.</p> Full article ">Figure 12
<p>Quantitative evaluation of the performances using different image features.</p> Full article ">Figure 13
<p>Corner point detection and refinement using a high spatial resolution SAR image. (<b>a</b>) Original Harris corner points and the zoomed view in the rectangle area; (<b>b</b>) Refined Harris corner points and the zoomed view in the rectangle area.</p> Full article ">Figure 14
<p>Comparison with Pantex and BASI using a TerraSAR image. (<b>a</b>) Proposed method built-up index (<b>b</b>) Pantex built-up index; (<b>c</b>) BASI built-up index; (<b>d</b>–<b>f</b>) The final results using the proposed method, Pantex and BASI, respectively; (<b>g</b>) The <span class="html-italic">P-R</span> curves of Pantex, BASI, and the proposed method.</p> Full article ">Figure 15
<p>Experiments on the ZY-3 image. (<b>a</b>) The test image; (<b>b</b>) The proposed method; (<b>c</b>) The Pantex index; (<b>d</b>) The BASI index; (<b>e</b>–<b>g</b>) The details of the proposed method, Pantex, and BASI in the rectangle of (<b>a</b>).</p> Full article ">Figure 16
<p>Built-up area detection using different images and different scenes with the proposed method. (<b>a</b>,<b>b</b>) Built-up area detection over hilly areas using QuickBird images with a resolution of 0.6 m/pixel; 750 <span class="html-italic">×</span> 750 and 2961 <span class="html-italic">×</span> 3108 pixels; (<b>c</b>,<b>d</b>) Built-up area detection over mountainous areas using QuickBird images with a resolution of 2.4 m/pixel, 750 <span class="html-italic">×</span> 750 and 750 <span class="html-italic">×</span> 750 pixels; (<b>e</b>–<b>h</b>) The detection results over flat areas using QuickBird (2.4 m/pixel, 900 <span class="html-italic">×</span> 900 pixels), aerial (0.32 m/pixel, 2001 <span class="html-italic">×</span> 2001 pixels, and 1 m/pixel, 40 0 <span class="html-italic">×</span> 400 pixels), and SPOT-5 (2.5 m/pixel, 400 <span class="html-italic">×</span> 400 pixels) images, respectively.</p> Full article ">Figure 17
<p>Built-up area detection over settlements and industrial areas. The Harris corner points are drawn in green.</p> Full article ">
6414 KiB  
Article
Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series
by Sylvain Ferrant, Vincent Bustillo, Enguerrand Burel, Jordy Salmon-Monviola, Martin Claverie, Nathalie Jarosz, Tiangang Yin, Vincent Rivalland, Gérard Dedieu, Valerie Demarez, Eric Ceschia, Anne Probst, Ahmad Al-Bitar, Yann Kerr, Jean-Luc Probst, Patrick Durand and Simon Gascoin
Remote Sens. 2016, 8(2), 154; https://doi.org/10.3390/rs8020154 - 17 Feb 2016
Cited by 20 | Viewed by 9542
Abstract
Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from [...] Read more.
Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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<p>Study site location in southwest France and satellite time series acquisitions details. Four 8 meters resolution Leaf Area Index (LAI) products are shown: high and low LAI correspond to, respectively, well developed wheat and first growing stage sunflower in April; low LAI value for the dry wheat and high LAI for the maturity stage of sunflower in June 2007. The next year (2008), opposite location of crops are observed, implied by the sunflower and winter wheat crop succession. The area used to test the methodology corresponds to the underlined crop field in red. It corresponds to a homogeneous area in term of crop cover: sunflower in 2006, 2008 and 2010, and winter wheat in 2007 and 2009.</p> Full article ">Figure 2
<p>Details of the Topography Nitrogen Transfer and Transformation (TNT2) model spatial input (<b>a</b>) with “<span class="html-italic">a-priori</span>” Soil Water Holding Capacity (SWHC) parameters derived from soil map and soil survey and the Topographic saturation index (TSI) value (Equation (1)) derived from Digital Elevation Model; (<b>b</b>) Simulation of maximum LAI (LAX) for the wheat (2007) and the sunflower (2008) with “<span class="html-italic">a-priori</span>” soil parameters; (<b>c</b>) LAX derived from satellite series and aerial photography of the sunflower crop cover in July 2008. The red polygon highlight the simulations and observations for the year 2008.</p> Full article ">Figure 2 Cont.
<p>Details of the Topography Nitrogen Transfer and Transformation (TNT2) model spatial input (<b>a</b>) with “<span class="html-italic">a-priori</span>” Soil Water Holding Capacity (SWHC) parameters derived from soil map and soil survey and the Topographic saturation index (TSI) value (Equation (1)) derived from Digital Elevation Model; (<b>b</b>) Simulation of maximum LAI (LAX) for the wheat (2007) and the sunflower (2008) with “<span class="html-italic">a-priori</span>” soil parameters; (<b>c</b>) LAX derived from satellite series and aerial photography of the sunflower crop cover in July 2008. The red polygon highlight the simulations and observations for the year 2008.</p> Full article ">Figure 3
<p>Sunflower maximum of LAI (LAX) and biomass (BiomaX in t/ha) sensitivity to soil parameters for the year 2006: retention/drainage porosity ratio (mic/mac) and soil depth, for a pixel located in upstream area (<b>a</b>); and downstream area (<b>b</b>). Each blue circle corresponds to a TNT2 simulation.</p> Full article ">Figure 4
<p>Sobol indices of each sensitivity surfaces of LAX for each pixel and each year (line). S<sub>depth</sub> and S<sub>micmac</sub> (respectively, Equations (1) and (2)) represent the sobol sensitivity of LAX to the soil depth parameter (<span class="html-italic">depth</span>, first column) and S<sub>micmac</sub> the sensitivity to the retention porosity parameter (<span class="html-italic">micmac</span>, micro–macro porosity ratio). The higher the value is, the higher the sensitivity is.</p> Full article ">Figure 5
<p>Observed, synthetic and simulated LAX (in column) for each year (in line). The fourth column represents the best numerical solution of SWHC for each year selected from the best fit between synthetic LAX (second column) and observed LAX (first column). The third column shows simulated LAX after having reset soil parameters using the best numerical solution of the fourth column. Numbers in the boxes represent the correlation coefficient and the RMSE between synthetic and observed LAX (second column) and simulated and observed LAX (third column) for each year.</p> Full article ">Figure 6
<p>The optimal SWHC derived by minimizing the distance between observed and synthetic LAX over (<b>a</b>) four years (2007–2010); (<b>b</b>) two years for winter wheat (2007 and 2009); and (<b>c</b>) two years in Sunflower (2008 and 2010) are presented. The RMSE computed between observed and simulated LAX is presented for each method (<b>d</b>–<b>f</b>). RMSE around 1 corresponds to 25% of the maximum LAI (max(LAX) = 4) observed for the studied crops.</p> Full article ">Figure 7
<p>Sunflower development simulated for the year 2006 after reinitializing soil parameters: (<b>a</b>); Simulation of the biomass on 14 July 2006 with the relationship between LAI this day and Biomass (<b>b</b>); The corresponding evapotranspiration for this day (<b>c</b>); Biomass of each pixel is shown as a function of cumulative evapotranspiration from January to July (<b>d</b>); The corresponding N content in crop on 4 July (<b>e</b>); High heterogeneity between biomass and nitrogen content in sunflower is simulated because the nitrogen stress factors do not influence biomass growth in these simulations (<b>f</b>).</p> Full article ">
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Article
Quantifying the Daytime and Night-Time Urban Heat Island in Birmingham, UK: A Comparison of Satellite Derived Land Surface Temperature and High Resolution Air Temperature Observations
by Juliana Antunes Azevedo, Lee Chapman and Catherine L. Muller
Remote Sens. 2016, 8(2), 153; https://doi.org/10.3390/rs8020153 - 17 Feb 2016
Cited by 151 | Viewed by 16185
Abstract
The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an [...] Read more.
The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an ongoing limitation for studies. The availability of remote sensing data has therefore helped fill a scientific need by providing high resolution temperature data of our cities. However, satellite-mounted sensors measure land surface temperatures (LST) and not canopy air temperatures with the latter being the key parameter in UHI investigations. Fortunately, such data is becoming increasingly available via urban meteorological networks, which now provide an opportunity to quantify and compare surface and canopy UHI on an unprecedented scale. For the first time, this study uses high resolution air temperature data from the Birmingham Urban Climate Laboratory urban meteorological network and MODIS LST to quantify and identify the spatial pattern of the daytime and night-time UHI in Birmingham, UK (a city with an approximate population of 1 million). This analysis is performed under a range of atmospheric stability classes and investigates the relationship between surface and canopy UHI in the city. A significant finding of this work is that it demonstrates, using observations, that the distribution of the surface UHI appears to be clearly linked to landuse, whereas for canopy UHI, advective processes appear to play an increasingly important role. Strong relationships were found between air temperatures and LST during both the day and night at a neighbourhood scale, but even with the use of higher resolution urban meteorological datasets, relationships at the city scale are still limited. Full article
(This article belongs to the Special Issue The Application of Thermal Urban Remote Sensing to Understand and Monitor Urban Climates)
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<p>Birmingham—UK (<b>a</b>) Land use classes in Birmingham [<a href="#B37-remotesensing-08-00153" class="html-bibr">37</a>]; (<b>b</b>) Variation of altitude and location of laces mentioned in the text.</p> Full article ">Figure 2
<p>Daytime UHI<sub>surface</sub> intensity, for Pasquill-Gifford Stability Classes (<b>B</b>); (<b>C</b>) and (<b>D</b>); and Average for June, July, August 2013 and prevailing wind direction for the period. Based on MODIS Aqua LST product.</p> Full article ">Figure 3
<p>Daytime UHI<sub>canopy</sub> intensity, for Pasquill-Gifford Stability Classes (<b>A</b>); (<b>B</b>); (<b>C</b>) and (<b>D</b>); and Average for June, July, August 2013 and prevailing wind direction for the period. Based on the BUCL dataset.</p> Full article ">Figure 4
<p>Night-time UHI<sub>surface</sub> intensity, for Pasquill-Gifford Stability Classes (<b>F</b>) and (<b>G</b>), and Average for June, July, August 2013 and prevailing wind direction for the period. Based on MODIS Aqua LST product.</p> Full article ">Figure 5
<p>Night-time UHI<sub>canopy</sub> intensity, for Pasquill-Gifford Stability Classes (<b>D</b>); (<b>E</b>); (<b>F</b>) and (<b>G</b>); and Average for June, July, August 2013 and prevailing wind direction for the period. Based on the BUCL dataset.</p> Full article ">Figure 6
<p>LST and T<sub>air</sub> daytime comparison at 13:30. (<b>a</b>) LST—T<sub>air</sub> difference (MODIS—BUCL); (<b>b</b>) <span class="html-italic">R</span><sup>2</sup> values at sensors and weather stations location; (<b>c</b>) <span class="html-italic">R</span><sup>2</sup> values for city scale (all sensors and weather stations).</p> Full article ">Figure 7
<p>LST and T<sub>air</sub> night-time comparison at 1:30. (<b>a</b>) LST—T<sub>air</sub> difference (MODIS—BUCL); (<b>b</b>) <span class="html-italic">R</span><sup>2</sup> values at sensors and weather stations location; (<b>c</b>) <span class="html-italic">R</span><sup>2</sup> values for city scale (all sensors and weather stations).</p> Full article ">
13842 KiB  
Article
Satellite-Based Thermophysical Analysis of Volcaniclastic Deposits: A Terrestrial Analog for Mantled Lava Flows on Mars
by Mark A. Price, Michael S. Ramsey and David A. Crown
Remote Sens. 2016, 8(2), 152; https://doi.org/10.3390/rs8020152 - 17 Feb 2016
Cited by 5 | Viewed by 6274
Abstract
Orbital thermal infrared (TIR) remote sensing is an important tool for characterizing geologic surfaces on Earth and Mars. However, deposition of material from volcanic or eolian activity results in bedrock surfaces becoming significantly mantled over time, hindering the accuracy of TIR compositional analysis. [...] Read more.
Orbital thermal infrared (TIR) remote sensing is an important tool for characterizing geologic surfaces on Earth and Mars. However, deposition of material from volcanic or eolian activity results in bedrock surfaces becoming significantly mantled over time, hindering the accuracy of TIR compositional analysis. Moreover, interplay between particle size, albedo, composition and surface roughness add complexity to these interpretations. Apparent Thermal Inertia (ATI) is the measure of the resistance to temperature change and has been used to determine parameters such as grain/block size, density/mantling, and the presence of subsurface soil moisture/ice. Our objective is to document the quantitative relationship between ATI derived from orbital visible/near infrared (VNIR) and thermal infrared (TIR) data and tephra fall mantling of the Mono Craters and Domes (MCD) in California, which were chosen as an analog for partially mantled flows observed at Arsia Mons volcano on Mars. The ATI data were created from two images collected ~12 h apart by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. The results were validated with a quantitative framework developed using fieldwork that was conducted at 13 pre-chosen sites. These sites ranged in grain size from ash-sized to meter-scale blocks and were all rhyolitic in composition. Block size and mantling were directly correlated with ATI. Areas with ATI under 2.3 × 10−2 were well-mantled with average grain size below 4 cm; whereas values greater than 3.0 × 10−2 corresponded to mantle-free surfaces. Correlation was less accurate where checkerboard-style mixing between mantled and non-mantled surfaces occurred below the pixel scale as well as in locations where strong shadowing occurred. However, the results validate that the approach is viable for a large majority of mantled surfaces on Earth and Mars. This is relevant for determining the volcanic history of Mars, for example. Accurate identification of non-mantled lava surfaces within an apparently well-mantled flow field on either planet provides locations to extract important mineralogical constraints on the individual flows using TIR data. Full article
(This article belongs to the Special Issue Volcano Remote Sensing)
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<p>THEMIS IR global composite (100 pixel/degree) of a portion of the southern Arsia Mons flow field. (<b>A</b>) Day; (<b>B</b>) Night. Locator pins identify common examples of the four different thermophysical behaviors exhibited by individual lava flows over the diurnal cycle: (i) unchanging temperature, always cool; (ii) unchanging temperature, always warm; (iii) changing temperature; warm during the day and cool at night, indicative of a low thermal inertia surface; (iv) changing temperature, cool during the day and warm at night, indicative of a high thermal inertia surface. Inset is a MOLA-derived shaded relief image with the study area denoted by the white box.</p> Full article ">Figure 2
<p>High-resolution visible satellite image from Digital Globe (within Google Earth) the Mono Craters and Domes with North Coulee (NC) outlined in white. Other features include: Mono Lake (ML), Panum Crater (PC), Northwest Coulee (NWC), South Coulee (SC), and the informally-named Upper Dome (UD). Inset shows the state of California with the study area (MCD) denoted by the black box.</p> Full article ">Figure 3
<p>Relative apparent thermal inertia image of North Coulee (outlined in black) derived from 10 July 2011 ASTER data (created by Ramsey and Crown, 2010) [<a href="#B4-remotesensing-08-00152" class="html-bibr">4</a>]. The colorized scale image is overlain on a Digital Globe (within Google Earth) high-resolution color image for detail. This image was used as a guide for later fieldwork and the numbered pins refer to visited field sites. The white box denotes the area shown in <a href="#remotesensing-08-00152-f006" class="html-fig">Figure 6</a>. Data courtesy of NASA/GSFC/METI/Japan Space Systems. U.S./Japan ASTER Science Team.</p> Full article ">Figure 4
<p>ASTER data products used to compile the apparent thermal inertia image. (<b>A</b>) Atmospherically-corrected, daytime surface kinetic temperature (ASTER level 2 AST_08 product) acquired on 10 July 2011 at 11:50:47 PDT; (<b>B</b>) Atmospherically-corrected nighttime surface kinetic temperature (ASTER level 2 AST_08 product) acquired on 10 July 2011 at 22:54:33 PDT; (<b>C</b>) Visible/Near Infrared surface albedo acquired at the same time as (<b>A</b>); (<b>D</b>) Temperature difference product (day − night).</p> Full article ">Figure 5
<p>Apparent thermal inertia image derived for this study from the data shown in <a href="#remotesensing-08-00152-f004" class="html-fig">Figure 4</a>. June Lake (<b>lower left</b>) and northwest-facing steep slopes have very high ATI values, whereas large, non-vegetated pumice deposits and old fire scars (<b>upper right</b>) (exposing the underlying pumice) have the lowest ATI values. Note that North Coulee has a range of ATI values 942 consistent with the preliminary work of Ramsey and Crown (2010) (see <a href="#remotesensing-08-00152-f003" class="html-fig">Figure 3</a>).</p> Full article ">Figure 6
<p>ATI draped over a high-resolution Digital Globe image (within Google Earth) of the northeast lobe of North Coulee. Blue dots indicate proposed field sites prior to the fieldwork being conducted. The numbered pins denote visited sites with associated field data. Note the very different ATI behavior on the northwest versus the southeast facing slopes. Figure created in ArcGIS 10 using Bing Maps layer.</p> Full article ">Figure 7
<p>Field photographs of Site 1 (<b>upper row</b>) and Site 2 (<b>lower row</b>) with context images shown in the left hand column and close-up images in the right hand column. Site 1 is a large pumice plain at the base of the coulee (black circle shows an approximate 30 cm high pine tree sapling for scale). The particle size for this site is 1 cm or less. Site 2 is a mantled location on the coulee (white circle shows an approximate 12 cm camera case for scale). Note the bimodal distribution of particle sizes at this site with the largest particles exceeding 12 cm.</p> Full article ">Figure 8
<p>Field photographs of Site 3 (<b>upper row</b>) and Site 4 (<b>lower row</b>) with context images shown in the left hand column and close-up images in the right hand column. Site 3 has a strongly bimodal particle size consisting of mantling similar to Site 2 as well as a non-mantled primary lava flow surface (blocks). Site 4 is a completely non-mantled location on the coulee. The block size distribution measured along a transect for this site is shown in <a href="#remotesensing-08-00152-f009" class="html-fig">Figure 9</a>.</p> Full article ">Figure 9
<p>Block size distributions for the non-mantled Site 4 and Site 12. The average block size for each is denoted with the arrows.</p> Full article ">Figure 10
<p>ASTER TIR band 12 (9.1 micrometers) surface emissivity (Level 2, AST_05) versus ATI for various the field sites. The lack of correlation is a direct result of very similar composition (pumiceous rhyolite lava and air fall pumice) reflected in the emissivity with very different particle sizes (pyroclastic air fall to blocks) reflected in the ATI. Error bars denote the standard error associated with the AST_05 product and the composite error in the derived ATI.</p> Full article ">Figure 11
<p>Average pixel-integrated particle size map of the North Coulee 972 flow derived from extensive field measurements and related back to the calculated ATI, with blue (&lt;1 cm), cyan (1 cm–3 cm); green (3 cm–500 cm); yellow (500 cm–1.0 m); and red (&gt;1 m). The approximate flow boundary is shown by the black outline. Map created from ATI image.</p> Full article ">
3683 KiB  
Article
Mapping Urban Land Use by Using Landsat Images and Open Social Data
by Tengyun Hu, Jun Yang, Xuecao Li and Peng Gong
Remote Sens. 2016, 8(2), 151; https://doi.org/10.3390/rs8020151 - 17 Feb 2016
Cited by 324 | Viewed by 36169
Abstract
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large [...] Read more.
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government. Full article
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions along the Urban-Rural Gradient)
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<p>Map of the study area. The white line shows the boundary of the administrative area of Beijing, China. The built-up areas are at the center of the satellite image and are indicated by a red boundary.</p> Full article ">Figure 2
<p>The proposed flowchart for mapping detailed land use.</p> Full article ">Figure 3
<p>Distribution of urban land use parcels. (<b>A</b>) Overall pattern; (<b>B</b>) and (<b>C</b>) are zoomed-in views (red frame) of the original road networks and the segmented land parcels.</p> Full article ">Figure 4
<p>Training samples for nine subclasses of land use in the built-up regions: (<b>A</b>) cottage; (<b>B</b>) community; (<b>C</b>) retail place; (<b>D</b>) service building; (<b>E</b>) industrial land; (<b>F</b>) medical place; (<b>G</b>) educational/research place; (<b>H</b>) administrative office; (<b>I</b>) public service.</p> Full article ">Figure 5
<p>Normalized Kernel density maps of Points of Interest (POI) data: (<b>A</b>) residential; (<b>B</b>) marketing and recreation; (<b>C</b>) service building; (<b>D</b>) hotel and restaurant; (<b>E</b>) industrial; (<b>F</b>) medical; (<b>G</b>) educational; (<b>H</b>) institutional infrastructure; (<b>I</b>) government and social organization; and (<b>J</b>) transportation land.</p> Full article ">Figure 6
<p>Detailed land use map of the Beijing area in 2013. (<b>A</b>) Overview map of the Level I land use; (<b>B</b>) zoomed-in view of the official land use map in Beijing; (<b>C</b>) detailed map of Level II land use types with the same extent of (<b>B</b>).</p> Full article ">Figure 7
<p>Map of the standard deviation of the similarities of parcels. The 2nd to the 5th ring roads are shown by orange lines.</p> Full article ">
3587 KiB  
Article
The Potential of Autonomous Ship-Borne Hyperspectral Radiometers for the Validation of Ocean Color Radiometry Data
by Vittorio E. Brando, Jenny L. Lovell, Edward A. King, David Boadle, Roger Scott and Thomas Schroeder
Remote Sens. 2016, 8(2), 150; https://doi.org/10.3390/rs8020150 - 16 Feb 2016
Cited by 44 | Viewed by 8232
Abstract
Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia’s ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral [...] Read more.
Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia’s ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral data can be re-sampled for comparison with current and future sensor wavebands. The continuous spectral acquisition along the ship track enables spatial resampling to match satellite footprint. This study reports spectral comparisons of the radiometer data with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua for contrasting water types in tropical waters off northern Australia based on the standard NIR atmospheric correction implemented in SeaDAS. Consistent match-ups are shown for transects of up to 50 km over a range of reflectance values. The MODIS and VIIRS satellite reflectance data consistently underestimated the in situ spectra in the blue with a bias relative to the “dynamic above water radiance and irradiance collector” (DALEC) at 443 nm ranging from 9.8 × 10−4 to 3.1 × 10−3 sr−1. Automated acquisition has produced good quality data under standard operating and maintenance procedures. A sensitivity analysis explored the effects of some assumptions in the data reduction methods, indicating the need for a comprehensive investigation and quantification of each source of uncertainty in the estimate of the DALEC reflectances. Deployment on a Research Vessel provides the potential for the radiometric data to be combined with other sampling and observational activities to contribute to algorithm development in the wider bio-optical research community. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>The “dynamic above water radiance and irradiance collector” (DALEC) hyperspectral radiometer: (<b>a</b>) Instrument schematics and measurement geometry (courtesy of <span class="html-italic">in situ</span> Marine Optics) (<b>b</b>) instrument mounted on RV Solander.</p> Full article ">Figure 2
<p>Study site. (<b>a</b>) Northern Australia location map for DALEC data used in this study, the green box indicates the position of image (<b>b</b>) while the red box indicates the position of image (<b>c</b>). True color images are from Moderate Resolution Imaging Spectroradiometer (MODIS) acquired on 12 April 2015 at 05:54 UTC (<b>b</b>) and on 24 May 2015 at 04:25 UTC (<b>c</b>). Contrasting dots overlaying the red and green transects identify segments of DALEC data used in matchups, as described in <a href="#sec4-remotesensing-08-00150" class="html-sec">Section 4</a> and <a href="#remotesensing-08-00150-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 3
<p>Example of DALEC processing sequence at Scott Reef on 12 April 2015 for 193 spectra spanning approximately 1 km. (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mi>u</mi> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>E</mi> <mi>d</mi> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mrow> <mi>s</mi> <mi>k</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> on two axes (<math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mrow> <mi>s</mi> <mi>k</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics> </math> in red on the right axis); (<b>c</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>L</mi> <mi>w</mi> </msub> </mrow> </semantics> </math>; (<b>d</b>) instantaneous <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math>; (<b>e</b>) instantaneous <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> after similarity spectrum correction with 5th and 25th percentile spectra indicated in red; (<b>f</b>) average and standard deviation of the aggregated <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> (<span class="html-italic">i.e.</span>, of the 5–25 percentile range of the spectra).</p> Full article ">Figure 4
<p>Spectral comparison at 1km scale between DALEC, VIIRS and MODIS acquired at Scott Reef on 12 April 2015. (<b>a</b>) DALEC, VIIRS and MODIS mean and standard deviation (DALEC standard deviation shown as dashed lines); (<b>b</b>) Transect plot of DALEC (+), VIIRS (triangle) and MODIS (diamond) at three spectral bands: the blue (443 nm), green (DALEC and VIIRS, 551 nm; MODIS 547 nm) and red (DALEC and VIIRS, 671 nm; MODIS 667 nm); (<b>c</b>) DALEC and VIIRS data; (<b>d</b>) DALEC and MODIS data. Black line is 1:1 in both (<b>c</b>) and (<b>d</b>). In all cases, error bars on DALEC data represent the standard deviation over the aggregation period and error bars on satellite data indicate the standard deviation over a 3x3 neighborhood of pixels.</p> Full article ">Figure 5
<p>Spectrally resolved match-up summary statistics. (<b>a</b>) VIIRS MAPD; (<b>b</b>) MODIS MAPD; (<b>c</b>) VIIRS RMSD; (<b>d</b>) MODIS RMSD; (<b>e</b>) VIIRS bias; (<b>f</b>) MODIS bias; (<b>g</b>) VIIRS R<sup>2</sup>; (<b>h</b>) MODIS R<sup>2</sup>. MAPD has not been included for the NIR channels on as the very small signal magnitudes result in spurious values of relative difference. The very small range of data values in NIR at Scott Reef (12 April 2015) produced spurious correlation results, so these have not been plotted.</p> Full article ">Figure 6
<p>Spectral comparison between DALEC, MODIS and VIIRS near the Integrated Marine Observing System (IMOS) Darwin mooring located in in the Beagle Gulf. (<b>a</b>) 24 May 2015; (<b>b</b>) 6 August 2015. DALEC mean and standard deviation (dashed lines) over the aggregation period are shown. Error bars on satellite data indicate the standard deviation over a 3x3 neighborhood of pixels.</p> Full article ">Figure 7
<p>Spectral comparison in the Timor Sea for the dates shown and transects illustrated in <a href="#remotesensing-08-00150-f002" class="html-fig">Figure 2</a>c, (<b>a</b>) VIIRS data; (<b>b</b>) MODIS data. DALEC data are aggregated to approximately 1km scale. Error bars on DALEC data represent the standard deviation over the aggregation period and error bars on satellite data indicate the standard deviation over a 3x3 neighborhood of pixels.</p> Full article ">Figure 8
<p>Sensitivity to processing parameters. (<b>a</b>) Mean Absolute Percent Difference (MAPD) and bias between <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> calculated with wind speed of 1 ms<sup>−1</sup> (black lines) and 5 ms<sup>−1</sup> (red lines) relative to <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> calculated with wind speed of 3 ms<sup>−1</sup>; (<b>b</b>) MAPD and bias between <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> calculated with M15 and M99; (<b>c</b>) MAPD and bias between <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> calculated with M15 and M99 classified by sun zenith angle; (<b>d</b>) MAPD and bias between <math display="inline"> <semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics> </math> calculated with M15 and M99 classified by optical water types (OWTs). In all cases MAPD is shown as solid lines and left axis while Bias is shown as dashed lines and right axis; c. and d. use log scale for MAPD.</p> Full article ">
4160 KiB  
Article
Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?
by Elizabeth A. Becker, Karin A. Forney, Paul C. Fiedler, Jay Barlow, Susan J. Chivers, Christopher A. Edwards, Andrew M. Moore and Jessica V. Redfern
Remote Sens. 2016, 8(2), 149; https://doi.org/10.3390/rs8020149 - 16 Feb 2016
Cited by 77 | Viewed by 13416
Abstract
Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean [...] Read more.
Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS) to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE), observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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<p>Completed transects for the Southwest Fisheries Science Center systematic ship surveys conducted between 1991 and 2009 in the California Current Ecosystem study area used for this study. The green lines show on-effort transect coverage in Beaufort sea states of 0-5 for (<b>a</b>) surveys conducted late July through early December in 1991, 1993, 1996, 2001, 2005, and 2008 off the US west coast; and (<b>b</b>) a smaller scale survey conducted September through December of 2009.</p> Full article ">Figure 2
<p>Predicted densities and uncertainty measures from the habitat-based density models built with measured data (top panels) and ROMS output (bottom panels), for (<b>a</b>) striped dolphin, (<b>b</b>) short-beaked common dolphin, (<b>c</b>) long-beaked common dolphin, (<b>d</b>) common bottlenose dolphin, (<b>e</b>) Risso’s dolphin, (<b>f</b>) Pacific white-sided dolphin, (<b>g</b>) northern right whale dolphin, (<b>h</b>) Dall’s porpoise, (<b>i</b>) fin whale, (<b>j</b>) blue whale, and (<b>k</b>) humpback whale. Panels show the multi-year average (Avg) density, and 90% confidence limits (L90% and U90%). Predictions are shown for the study area (1,141,800 km<sup>2</sup>). Density ranges were selected to encompass all values within the confidence limits. Black dots show actual sighting locations from the ship surveys.</p> Full article ">Figure 2 Cont.
<p>Predicted densities and uncertainty measures from the habitat-based density models built with measured data (top panels) and ROMS output (bottom panels), for (<b>a</b>) striped dolphin, (<b>b</b>) short-beaked common dolphin, (<b>c</b>) long-beaked common dolphin, (<b>d</b>) common bottlenose dolphin, (<b>e</b>) Risso’s dolphin, (<b>f</b>) Pacific white-sided dolphin, (<b>g</b>) northern right whale dolphin, (<b>h</b>) Dall’s porpoise, (<b>i</b>) fin whale, (<b>j</b>) blue whale, and (<b>k</b>) humpback whale. Panels show the multi-year average (Avg) density, and 90% confidence limits (L90% and U90%). Predictions are shown for the study area (1,141,800 km<sup>2</sup>). Density ranges were selected to encompass all values within the confidence limits. Black dots show actual sighting locations from the ship surveys.</p> Full article ">Figure 2 Cont.
<p>Predicted densities and uncertainty measures from the habitat-based density models built with measured data (top panels) and ROMS output (bottom panels), for (<b>a</b>) striped dolphin, (<b>b</b>) short-beaked common dolphin, (<b>c</b>) long-beaked common dolphin, (<b>d</b>) common bottlenose dolphin, (<b>e</b>) Risso’s dolphin, (<b>f</b>) Pacific white-sided dolphin, (<b>g</b>) northern right whale dolphin, (<b>h</b>) Dall’s porpoise, (<b>i</b>) fin whale, (<b>j</b>) blue whale, and (<b>k</b>) humpback whale. Panels show the multi-year average (Avg) density, and 90% confidence limits (L90% and U90%). Predictions are shown for the study area (1,141,800 km<sup>2</sup>). Density ranges were selected to encompass all values within the confidence limits. Black dots show actual sighting locations from the ship surveys.</p> Full article ">Figure 2 Cont.
<p>Predicted densities and uncertainty measures from the habitat-based density models built with measured data (top panels) and ROMS output (bottom panels), for (<b>a</b>) striped dolphin, (<b>b</b>) short-beaked common dolphin, (<b>c</b>) long-beaked common dolphin, (<b>d</b>) common bottlenose dolphin, (<b>e</b>) Risso’s dolphin, (<b>f</b>) Pacific white-sided dolphin, (<b>g</b>) northern right whale dolphin, (<b>h</b>) Dall’s porpoise, (<b>i</b>) fin whale, (<b>j</b>) blue whale, and (<b>k</b>) humpback whale. Panels show the multi-year average (Avg) density, and 90% confidence limits (L90% and U90%). Predictions are shown for the study area (1,141,800 km<sup>2</sup>). Density ranges were selected to encompass all values within the confidence limits. Black dots show actual sighting locations from the ship surveys.</p> Full article ">Figure 2 Cont.
<p>Predicted densities and uncertainty measures from the habitat-based density models built with measured data (top panels) and ROMS output (bottom panels), for (<b>a</b>) striped dolphin, (<b>b</b>) short-beaked common dolphin, (<b>c</b>) long-beaked common dolphin, (<b>d</b>) common bottlenose dolphin, (<b>e</b>) Risso’s dolphin, (<b>f</b>) Pacific white-sided dolphin, (<b>g</b>) northern right whale dolphin, (<b>h</b>) Dall’s porpoise, (<b>i</b>) fin whale, (<b>j</b>) blue whale, and (<b>k</b>) humpback whale. Panels show the multi-year average (Avg) density, and 90% confidence limits (L90% and U90%). Predictions are shown for the study area (1,141,800 km<sup>2</sup>). Density ranges were selected to encompass all values within the confidence limits. Black dots show actual sighting locations from the ship surveys.</p> Full article ">Figure 2 Cont.
<p>Predicted densities and uncertainty measures from the habitat-based density models built with measured data (top panels) and ROMS output (bottom panels), for (<b>a</b>) striped dolphin, (<b>b</b>) short-beaked common dolphin, (<b>c</b>) long-beaked common dolphin, (<b>d</b>) common bottlenose dolphin, (<b>e</b>) Risso’s dolphin, (<b>f</b>) Pacific white-sided dolphin, (<b>g</b>) northern right whale dolphin, (<b>h</b>) Dall’s porpoise, (<b>i</b>) fin whale, (<b>j</b>) blue whale, and (<b>k</b>) humpback whale. Panels show the multi-year average (Avg) density, and 90% confidence limits (L90% and U90%). Predictions are shown for the study area (1,141,800 km<sup>2</sup>). Density ranges were selected to encompass all values within the confidence limits. Black dots show actual sighting locations from the ship surveys.</p> Full article ">
5485 KiB  
Article
IKONOS Image-Based Extraction of the Distribution Area of Stellera chamaejasme L. in Qilian County of Qinghai Province, China
by Jingzhong Li, Yongmei Liu, Chonghui Mo, Lei Wang, Guowei Pang and Mingming Cao
Remote Sens. 2016, 8(2), 148; https://doi.org/10.3390/rs8020148 - 16 Feb 2016
Cited by 14 | Viewed by 6575
Abstract
Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in [...] Read more.
Stellera chamaejasme L. (S. chamaejasme) is one of the primary toxic grass species (poisonous plants) distributed in the alpine meadows of Qinghai Province, China. In this study, according to the distinctive phenological characteristics of S. chamaejasme, the spectral differences between S. chamaejasme in the full-bloom stage and other pasture grasses were analyzed and the red, blue, and near-infrared bands of IKONOS image were determined as the diagnostic bands of S. chamaejasme recognition. Feature indexes related to S. chamaejasme were established using the diagnostic bands, and \(NDVI_{blue} = (\rho_{nir} − \rho_{blue})/(\rho_{nir} + \rho_{blue})\) obtained as S. chamaejasme sensitive index based on the linear regression analysis between the indexes derived from field spectra and the actual cover fraction of S. chamaejasme communities. The distribution area of S. chamaejasme was extracted by using the index \(NDVI_{blue}\) derived from IKONOS multispectral image in Qilian County of Qinghai Province, China and the verified result reached an overall accuracy of 90.71%. The study indicated that high resolution multispectral satellite images (such as IKONOS images) had significant potential in remote sensing recognition of toxic grass species. Full article
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<p>Study area located in Qilian County of Qinghai Province, China.</p> Full article ">Figure 2
<p>Flow chart of the methodology.</p> Full article ">Figure 3
<p>Coverage Interpretation of <span class="html-italic">S. chamaejasme</span> quadrat (<b>a</b>) original quadrat photo, (<b>b</b>) photo after cutting and correction, (<b>c</b>) coverage interpretation.</p> Full article ">Figure 4
<p>Spectral curve smoothing based on the Savitzky–Golay approach. (<b>a</b>) Original spectral curve; (<b>b</b>) smoothed spectral curve.</p> Full article ">Figure 5
<p>Distribution of the <span class="html-italic">S. chamaejasme</span> community in the study area, in Baishiya Village, Ebao Town, Qilian County, China (38°02′33.77″N, 100°32′01.82″E). The average coverage of the <span class="html-italic">S. chamaejasme</span> community in an alpine meadow is about 60%.</p> Full article ">Figure 6
<p>Reflectance spectra (<b>a</b>) and first-order differential spectra (<b>b</b>) of the white flowers, leaves of <span class="html-italic">S. chamaejasme</span>, and leaves of pasture grasses.</p> Full article ">Figure 7
<p>Reflection spectra (<b>a</b>) and the first-order differential spectra (<b>b</b>) of the <span class="html-italic">S.</span> <span class="html-italic">chamaejasme</span> communities with different coverage and pasture community.</p> Full article ">Figure 8
<p>Correlation analysis between the spectral reflectance and coverage of <span class="html-italic">S. chamaejasme</span> community quadrats.</p> Full article ">Figure 9
<p>Linear regression analysis between coverage of <span class="html-italic">S. chamaejasme</span> community and feature indexes (<span class="html-italic">n</span> = 38). (<b>a</b>–<b>i</b>) Corresponds to the indexes in Equations (1)–(9).</p> Full article ">Figure 9 Cont.
<p>Linear regression analysis between coverage of <span class="html-italic">S. chamaejasme</span> community and feature indexes (<span class="html-italic">n</span> = 38). (<b>a</b>–<b>i</b>) Corresponds to the indexes in Equations (1)–(9).</p> Full article ">Figure 10
<p>Distribution map of <span class="html-italic">S. chamaejasme</span> in the study area.</p> Full article ">
12352 KiB  
Article
Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing
by Zifeng Hu, Delu Pan, Xianqiang He and Yan Bai
Remote Sens. 2016, 8(2), 147; https://doi.org/10.3390/rs8020147 - 16 Feb 2016
Cited by 36 | Viewed by 7343
Abstract
Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution [...] Read more.
Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes. Full article
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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<p>Bathymetry (in meter) of the two studied regions, NJSW and SKCW, (black rectangular boxes) and their surrounding waters in the Yellow and East China Seas. “A” and “B” red points denote the tide gauge stations of “Dafeng-Harbor” and “Daeheuksando”, respectively. The black lines correspond to cross-front transects in each region.</p> Full article ">Figure 2
<p>Hourly GOCI-derived TSM images on 5 April 2011. The black areas are the regions masked by the atmospheric correct processing in GDPS due to the cloud coverage (in the shelf) or very high reflectance at the near infrared wavelength (along the coasts). The corresponding observation time (GMT+8) is labeled in each image. The averaged TSM in NJSW (<b>Box A</b>) and SKCW (<b>Box B</b>) (<a href="#remotesensing-08-00147-f002" class="html-fig">Figure 2</a>a) and the tidal elevation from tide tables are shown in the bottom-right plot.</p> Full article ">Figure 3
<p>Hourly front patterns derived from GOCI TSM images on 5 April 2011. The black areas are the regions masked by the atmospheric correct processing in GDPS due to the cloud coverage (in the shelf) or very high reflectance at the near infrared wavelength (along the coasts). The corresponding observation time (GMT+8) is labeled in each image. The averaged TSM–JSD in NJSW (<b>Box A</b>) and SKCW (<b>Box B</b>) (in the subplot at 8:30) and tidal elevation from tide tables are shown in the bottom-right plot.</p> Full article ">Figure 4
<p>(<b>a</b>) temporal variability of the TSM–JSD along the transect A in NJSW (<a href="#remotesensing-08-00147-f001" class="html-fig">Figure 1</a>) with three relatively strong fronts marked as A1, A2, and A3; (<b>b</b>) hourly variability of the position of fronts A1, A2, and A3, overlaid with tide height at the corresponding gauge station A; (<b>c</b>) temporal variability of the TSM–JSD along the transect B in SKCW (<a href="#remotesensing-08-00147-f001" class="html-fig">Figure 1</a>) with three relatively strong fronts marked as B1, B2, and B3; (<b>d</b>) Hourly variability of the position of fronts B1, B2, and B3, overlaid with tide height at the corresponding gauge station B.</p> Full article ">Figure 5
<p>Hourly GOCI-derived TSM images on 12 May 2015. The black areas are the regions masked by the atmospheric correct processing in GDPS due to the cloud coverage (in the shelf) or very high reflectance at the near infrared wavelength (along the coasts). The corresponding observation time (GMT+8) is labeled in each image. The averaged TSM in NJSW (<b>Box A in</b> <b>subplot at 8:30</b>) and the tidal elevation from tide tables are shown in the bottom-right plot.</p> Full article ">Figure 6
<p>Hourly front patterns derived from GOCI TSM images on 12 May 2015. The black areas are the regions masked by the atmospheric correct processing in GDPS due to the cloud coverage (in the shelf) or very high reflectance at the near infrared wavelength (along the coasts). The corresponding observation time (GMT+8) is labeled in each image. The averaged TSM–JSD in NJSW (<b>Box A in</b> <b>subplot at 8:30</b>) and tidal elevation from tide tables at the tide gauge stations is shown in the bottom-right plot.</p> Full article ">Figure 7
<p>(<b>a</b>) temporal variability of the TSM–JSD along the transect A in NJSW with three relatively strong fronts marked as C1, C2, and C3. (<b>b</b>) hourly variability of the position of fronts C1, C2, and C3, overlaid with tide height at the corresponding gauge station A.</p> Full article ">Figure 8
<p>Hourly sea surface currents derived from GOCI TSM images on 5 April 2011. The corresponding observation time (GMT+8) is labeled in each image. The tidal elevation from tide tables at the tide gauge stations is shown in the bottom-right plot.</p> Full article ">
9452 KiB  
Article
The Combined Use of Airborne Remote Sensing Techniques within a GIS Environment for the Seismic Vulnerability Assessment of Urban Areas: An Operational Application
by Antonio Costanzo, Antonio Montuori, Juan Pablo Silva, Malvina Silvestri, Massimo Musacchio, Fawzi Doumaz, Salvatore Stramondo and Maria Fabrizia Buongiorno
Remote Sens. 2016, 8(2), 146; https://doi.org/10.3390/rs8020146 - 16 Feb 2016
Cited by 16 | Viewed by 7947
Abstract
The knowledge of the topographic features, the building properties, and the road infrastructure settings are relevant operational tasks for managing post-crisis events, restoration activities, and for supporting search and rescue operations. Within such a framework, airborne remote sensing tools have demonstrated to be [...] Read more.
The knowledge of the topographic features, the building properties, and the road infrastructure settings are relevant operational tasks for managing post-crisis events, restoration activities, and for supporting search and rescue operations. Within such a framework, airborne remote sensing tools have demonstrated to be powerful instruments, whose joint use can provide meaningful analyses to support the risk assessment of urban environments. Based on this rationale, in this study, the operational benefits obtained by combining airborne LiDAR and hyperspectral measurements are shown. Terrain and surface digital models are gathered by using LiDAR data. Information about roads and roof materials are provided through the supervised classification of hyperspectral images. The objective is to combine such products within a geographic information system (GIS) providing value-added maps to be used for the seismic vulnerability assessment of urban environments. Experimental results are gathered for the city of Cosenza, Italy. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Electronic Conference on Remote Sensing (ECRS-1))
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<p>(<b>a</b>) Map of the peak ground acceleration values (PGA, g) with 10% probability of being exceeded in 50 years (modified by [<a href="#B15-remotesensing-08-00146" class="html-bibr">15</a>]). (<b>b</b>) Map of the epicenters referred to the historical earthquakes, which produced damage to the built-up area of Cosenza.</p> Full article ">Figure 2
<p>Classification LiDAR-based point clouds of the Cosenza urban area.</p> Full article ">Figure 3
<p>Sketch of a RGB hyperspectral image, related to the Cosenza urban area.</p> Full article ">Figure 4
<p>Block scheme of the methodology proposed to integrate airborne LiDAR and hyperspectral measurements.</p> Full article ">Figure 5
<p>Intermediate products by airborne LiDAR data: (<b>a</b>) DTM; (<b>b</b>) DSM<b>;</b> (<b>c</b>) map of built areas; and (<b>d</b>) 3D view of the buildings projected on the DTM.</p> Full article ">Figure 6
<p>SAM-based supervised classification analyses of Cosenza. (<b>a</b>) Land-cover and land-use map; and (<b>b</b>) construction materials map of the building roofs.</p> Full article ">Figure 7
<p>Features of urban roads for their typological description <span class="html-italic">versus</span> building collapse.</p> Full article ">Figure 8
<p>LiDAR-based topographic assessment analysis according to EC8. (<b>a</b>) DTM; (<b>b</b>) morphological classification according to the modified Weiss procedure; (<b>c</b>) 2D map; and (<b>d</b>) 3D view of the topographic amplification classes based on the indications of EC8 code.</p> Full article ">Figure 9
<p>Classification of the buildings in the urban area of Cosenza based on the volumes’ (upper panel) 2D map, and (lower panel) 3D view.</p> Full article ">Figure 10
<p>Classification of the buildings in the urban area of Cosenza based on the roof material: (upper panel) 2D map and (lower panel) 3D view.</p> Full article ">Figure 11
<p>Risk map of the blockage of the roads for a selected area of Cosenza.</p> Full article ">
4758 KiB  
Article
Assessing the Effects of Suomi NPP VIIRS M15/M16 Detector Radiometric Stability and Relative Spectral Response Variation on Striping
by Zhuo Wang and Changyong Cao
Remote Sens. 2016, 8(2), 145; https://doi.org/10.3390/rs8020145 - 15 Feb 2016
Cited by 48 | Viewed by 6674
Abstract
Modern satellite radiometers have many detectors with different relative spectral response (RSR). Effect of RSR differences on striping and the root cause of striping in sensor data record (SDR) radiance and brightness temperature products have not been well studied. A previous study used [...] Read more.
Modern satellite radiometers have many detectors with different relative spectral response (RSR). Effect of RSR differences on striping and the root cause of striping in sensor data record (SDR) radiance and brightness temperature products have not been well studied. A previous study used MODTRAN radiative transfer model (RTM) to analyze striping. In this study, we make efforts to find the possible root causes of striping. Line-by-Line RTM (LBLRTM) is used to evaluate the effect of RSR difference on striping and the atmospheric dependency for VIIRS bands M15 and M16. The results show that previous study using MODTRAN is repeatable: the striping is related to the difference between band-averaged and detector-level RSR, and the BT difference has some atmospheric dependency. We also analyzed VIIRS earth view (EV) data with several striping index methods. Since the EV data is complex, we further analyze the onboard calibration data. Analysis of Variance (ANOVA) test shows that the noise along track direction is the major reason for striping. We also found evidence of correlation between solar diffuser (SD) and blackbody (BB) for detector 1 in M15. Digital Count Restoration (DCR) and detector instability are possibly related to the striping in SD and EV data, but further analysis is needed. These findings can potentially lead to further SDR processing improvements. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Detector-level and band-averaged relative spectral response (RSR) in M15. Note: M16A and M16B figures are similar, not shown here.</p> Full article ">Figure 2
<p>Effective temperature difference between detector level and band averaged RSR for six line-by-line radiative transfer model (LBLRTM) atmospheres in M15 (<b>a</b>); M16 (<b>b</b>, the average of M16A and M16B); and M15 − M16 (<b>c</b>).</p> Full article ">Figure 3
<p>The magnitude of brightness temperature difference (from Equation (4)) between using detector level and band averaged RSR for six LBLRTM atmospheres.</p> Full article ">Figure 4
<p>The cumulative histogram for Bay of Bengal over tropical region on 19 June 2013 in M15 − M16 (<b>left</b>), as well as M15 and M16 (<b>right</b>).</p> Full article ">Figure 5
<p>Cumulative histogram over the Gulf of Alaska on 20 May 2014 in M15 − M16 (<b>left</b>), as well as M15 and M16 (<b>right</b>).</p> Full article ">Figure 6
<p>The magnitude of brightness temperature variation among 16 detectors for Visible Infrared Imaging Radiometer Suite (VIIRS) observation data and LBLRTM over tropical (<b>top</b>) and polar (<b>bottom</b>) region for six cases.</p> Full article ">Figure 7
<p>Comparison of the relative magnitude (<span class="html-italic">i.e.</span>, ratio of brightness temperature variation magnitude to temperature range) over tropical and polar region.</p> Full article ">Figure 8
<p>Comparison of effective temperature difference between LBLRTM and the VIIRS observation for tropical and polar cases. Note: VIIRS observation uses the average of several cases to represent the tropical and polar case.</p> Full article ">Figure 9
<p>Variations of BB−SV (<span class="html-italic">i.e.</span>, dBB; <b>top</b>) and SD−SV (<span class="html-italic">i.e.</span>, dSD; <b>bottom</b>) along track and along scan for detector 1 in M15.</p> Full article ">Figure 10
<p>The distribution of BB−SV (<span class="html-italic">i.e.</span>, dBB; <b>left</b>) and SD−SV (<span class="html-italic">i.e.</span>, dSD; <b>right</b>) for detector 1 in M15. Color represents the value of data.</p> Full article ">Figure 11
<p>The variation of dBB with 48 samples for one scan (<b>top</b>) and the variation of dBB with scan for detector 1 and 1st sample along track (<b>bottom</b>) in M15 for three continuous granules (t0755_e0756, t0756_e0758, and t0758_e0759) on 30 June 2015. Dashed line refers to the averaged value.</p> Full article ">Figure 12
<p>Two way Analysis of Variance (ANOVA) test ratio <span class="html-italic">versus</span> 16 detectors for dBB ratio (<b>left panels</b>) and dSD ratio (<b>right panels</b>) along tracks and along scans in M15 (<b>top panels</b>) and M16 (<b>bottom panels</b>).</p> Full article ">Figure 13
<p>Detector dependent SD radiance for Half Angle Mirror (HAM) side A (<b>top</b>) and ratio of SD radiance between HAM sides A and B (<b>bottom</b>) in M15 on 30 June 2015. Each line represents a detector. Note: use 48-sample averaged value for each scan.</p> Full article ">Figure 14
<p>V ratios (defined in Equation (16)) from ANOVA tests for SD radiance HAM side ratio for 16 detectors in M15 (<b>top</b>) and M16 (<b>bottom</b>) for two granules on 30 June 2015.</p> Full article ">Figure 15
<p>The cumulative histogram for SD radiance (<math display="inline"> <semantics> <mrow> <mi>W</mi> <mo>/</mo> <mrow> <mo>(</mo> <mrow> <msup> <mi>m</mi> <mn>2</mn> </msup> <mo>⋅</mo> <mi>S</mi> <mi>r</mi> <mo>⋅</mo> <mi>μ</mi> <mi>m</mi> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math>) over the Bay of Bengal in M15 (<b>left</b>) and M16 (<b>right</b>) for granule d20130619_t0746 on 19 June 2013.</p> Full article ">Figure 16
<p>Linear correlation between dBB and dSD (<b>left</b>) and the cumulative histogram of 48-sample averaged dBB (<b>right</b>) for detector 1 in HAM side A and band M15 over a granule of the Bay of Bengal in case1.</p> Full article ">
2622 KiB  
Article
Examining the Spectral Separability of Prosopis glandulosa from Co-Existent Species Using Field Spectral Measurement and Guided Regularized Random Forest
by Nyasha Mureriwa, Elhadi Adam, Anshuman Sahu and Solomon Tesfamichael
Remote Sens. 2016, 8(2), 144; https://doi.org/10.3390/rs8020144 - 15 Feb 2016
Cited by 25 | Viewed by 6826
Abstract
The invasive taxa of Prosopis is rated the world’s top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data [...] Read more.
The invasive taxa of Prosopis is rated the world’s top 100 unwanted species, and a lack of spatial data about the invasion dynamics has made the current control and monitoring methods unsuccessful. This study thus tests the use of in situ spectroscopy data with a newly-developed algorithm, guided regularized random forest (GRRF), to spectrally discriminate Prosopis from coexistent acacia species (Acacia karroo, Acacia mellifera and Ziziphus mucronata) in the arid environment of South Africa. Results show that GRRF was able to reduce the high dimensionality of the spectroscopy data and select key wavelengths (n = 11) for discriminating amongst the species. These wavelengths are located at 356.3 nm, 468.5 nm, 531.1 nm, 665.2 nm, 1262.3 nm, 1354.1 nm, 1361.7 nm, 1376.9 nm, 1407.1 nm, 1410.9 nm and 1414.6 nm. The use of these selected wavelengths increases the overall classification accuracy from 79.19% and a Kappa value of 0.7201 when using all wavelengths to 88.59% and a Kappa of 0.8524 when the selected wavelengths were used. Based on our relatively high accuracies and ease of use, it is worth considering the GRRF method for reducing the high dimensionality of spectroscopy data. However, this assertion should receive considerable additional testing and comparison before it is accepted as a substitute for reliable high dimensionality reduction. Full article
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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<p>A true-color composite WorldView2 image showing the location of the study area and some of the field samples.</p> Full article ">Figure 2
<p>Images and spectra of Prosopis glandulosa and its co-existent species.</p> Full article ">Figure 3
<p>Flowchart describing the random forest (RF) and guided regularized random forest (GRRF) models used in this study.</p> Full article ">Figure 4
<p>The importance of wavelengths as measured by the traditional RF using the mean decrease in the <span class="html-italic">Gini index</span>. The most important variables are those with the highest mean index.</p> Full article ">Figure 5
<p>Wavelengths selected by GRRF based on the importance scores as measured by the traditional RF.</p> Full article ">
2845 KiB  
Article
Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas
by Narumasa Tsutsumida, Alexis Comber, Kirsten Barrett, Izuru Saizen and Ernan Rustiadi
Remote Sens. 2016, 8(2), 143; https://doi.org/10.3390/rs8020143 - 15 Feb 2016
Cited by 21 | Viewed by 7566
Abstract
Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. [...] Read more.
Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches. Full article
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<p>Study area.</p> Full article ">Figure 2
<p>A flowchart of this research.</p> Full article ">Figure 3
<p>Annual impervious surface area (ISA) proportions estimated by Model <span class="html-italic">ISAsub</span>.</p> Full article ">Figure 4
<p>Relative importance measures of variables for Model <span class="html-italic">ISAsub</span>.</p> Full article ">Figure 5
<p>The relationship between observed and predicted proportions of ISA under Model <span class="html-italic">ISAsub</span>.</p> Full article ">Figure 6
<p>Per-pixel based annual ISA maps. ISA estimated by Model <span class="html-italic">ISAp25</span>, <span class="html-italic">ISAp50</span>, and <span class="html-italic">ISAp75</span> are overlayed.</p> Full article ">Figure 7
<p>Estimation of total ISA by sub-pixel classification (Model <span class="html-italic">ISAsub</span>) and per-pixel classification (Model <span class="html-italic">ISAp25</span>, <span class="html-italic">ISAp50</span>, and <span class="html-italic">ISAp75</span>) and their linear trends during the period 2001–2013.</p> Full article ">Figure 8
<p>ISA maps in 2005 and 2010 estimated from (<b>a</b>) Model <span class="html-italic">ISAsub</span>; and (<b>b</b>) Landsat-based classification (source from Rustiadi <span class="html-italic">et al.</span> (2012)); and (<b>c</b>) change in ISAs from 2005 to 2010.</p> Full article ">Figure 8 Cont.
<p>ISA maps in 2005 and 2010 estimated from (<b>a</b>) Model <span class="html-italic">ISAsub</span>; and (<b>b</b>) Landsat-based classification (source from Rustiadi <span class="html-italic">et al.</span> (2012)); and (<b>c</b>) change in ISAs from 2005 to 2010.</p> Full article ">
5884 KiB  
Article
The Added Value of Stratified Topographic Correction of Multispectral Images
by Ion Sola, María González-Audícana and Jesús Álvarez-Mozos
Remote Sens. 2016, 8(2), 131; https://doi.org/10.3390/rs8020131 - 15 Feb 2016
Cited by 8 | Viewed by 4795
Abstract
Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the [...] Read more.
Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results. Full article
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<p>Land cover information (level 3) and SPOT 5 scene of the study site.</p> Full article ">Figure 2
<p>Detail zone with (<b>a</b>) Uncorrected scene; (<b>b</b>) SCS+C-corrected scene with no stratification; (<b>c</b>) SE-corrected scene with no stratification; (<b>d</b>) SCS+C-corrected scene with LC-8 stratification; (<b>e</b>) SE-corrected scene with LC-8 stratification; (<b>f</b>) SCS+C-corrected scene with NDVI-8 stratification; (<b>g</b>) SE-corrected scene with NDVI-8 stratification.</p> Full article ">Figure 3
<p><span class="html-italic">c</span><sub>λ</sub> coefficient obtained for each spectral band on the different stratification approaches evaluated. Circle sizes represent the proportion of each stratum in the study area.</p> Full article ">Figure 4
<p>Correlation coefficient between cosγ<span class="html-italic"><sub>i</sub></span> and the reflectance of spectral bands for each stratum. Circle sizes represent the proportion of each stratum in the study area.</p> Full article ">Figure 5
<p>SD of cosγ<span class="html-italic"><sub>i</sub></span> for each stratum. Circle sizes represent the proportion of each stratum in the study area.</p> Full article ">Figure 6
<p>Correlation coefficient of the regression between cosγ<span class="html-italic"><sub>i</sub></span> and the reflectance of each spectral band for the uncorrected image (NO-CORR.) and the different strategies tested on SCS + C correction (<b>a</b>) and SE correction (<b>b</b>).</p> Full article ">Figure 7
<p>Scatterplot of the spectral reflectance of band 3 (NIR) for forest strata <span class="html-italic">versus</span> the cosine of solar incidence angle.</p> Full article ">Figure 8
<p>Radiometric stability of land covers represented by the area-weighted average of % change in land cover reflectance after SCS+C correction (<b>a</b>) and SE correction (<b>b</b>).</p> Full article ">Figure 9
<p>Mean intraclass IQR reduction. Measured as the area-weighted average of IQR reduction for eight land covers.</p> Full article ">Figure 10
<p>Intraclass IQR reduction of broad-leaved forest and agricultural areas for each spectral band.</p> Full article ">Figure 11
<p>Reflectance difference between sunlit and shaded slopes for conifer forests.</p> Full article ">
6639 KiB  
Article
JPSS-1 VIIRS Pre-Launch Response Versus Scan Angle Testing and Performance
by David Moyer, Jeff McIntire, Hassan Oudrari, James McCarthy, Xiaoxiong Xiong and Frank De Luccia
Remote Sens. 2016, 8(2), 141; https://doi.org/10.3390/rs8020141 - 12 Feb 2016
Cited by 33 | Viewed by 5741
Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft, with launch dates of October 2011 and December 2016 respectively, are cross-track scanners with an angular swath of [...] Read more.
The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board both the Suomi National Polar-orbiting Partnership (S-NPP) and the first Joint Polar Satellite System (JPSS-1) spacecraft, with launch dates of October 2011 and December 2016 respectively, are cross-track scanners with an angular swath of ±56.06°. A four-mirror Rotating Telescope Assembly (RTA) is used for scanning combined with a Half Angle Mirror (HAM) that directs light exiting from the RTA into the aft-optics. It has 14 Reflective Solar Bands (RSBs), seven Thermal Emissive Bands (TEBs) and a panchromatic Day Night Band (DNB). There are three internal calibration targets, the Solar Diffuser, the BlackBody and the Space View, that have fixed scan angles within the internal cavity of VIIRS. VIIRS has calibration requirements of 2% on RSB reflectance and as tight as 0.4% on TEB radiance that requires the sensor’s gain change across the scan or Response Versus Scan angle (RVS) to be well quantified. A flow down of the top level calibration requirements put constraints on the characterization of the RVS to 0.2%–0.3% but there are no specified limitations on the magnitude of response change across scan. The RVS change across scan angle can vary significantly between bands with the RSBs having smaller changes of ~2% and some TEBs having ~10% variation. Within a band, the RVS has both detector and HAM side dependencies that vary across scan. Errors in the RVS characterization will contribute to image banding and striping artifacts if their magnitudes are above the noise level of the detectors. The RVS was characterized pre-launch for both S-NPP and JPSS-1 VIIRS and a comparison of the RVS curves between these two sensors will be discussed. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Example of the Rotating Telescope Assembly (RTA) and Half Angle Mirror (HAM) Orientations at three different scan angles. The left most diagram corresponds to the Space View (SV) scan angle, the middle is the NADIR scan angle and the right most is the scan angle where the HAM Angle of Incidence (AOI) is at a minimum.</p> Full article ">Figure 2
<p>Band M1 DN Response to the SIS-100 Source during the RVS Test (the orange points correspond to the RVS data used during processing).</p> Full article ">Figure 3
<p>Band M15 DN Response to the LABB Source during the RVS Test (the orange points correspond to the RVS data used during processing).</p> Full article ">Figure 4
<p>Band M1 HAM side A RVS and Fits for all Detectors.</p> Full article ">Figure 5
<p>Band I2 HAM side A RVS and Fits for all Detectors.</p> Full article ">Figure 6
<p>Band M9 HAM side A RVS and Fits for all Detectors.</p> Full article ">Figure 7
<p>DNB HAM side A RVS and Fits for all Detectors.</p> Full article ">Figure 8
<p>Band-averaged RVS for JPSS-1 VIIRS from the SV AOI to the end of scan AOI for bands M1–M4 and DNB.</p> Full article ">Figure 9
<p>Band-averaged RVS for JPSS-1 VIIRS from the SV AOI to the end of scan AOI for bands I1, I2 and M5–M7.</p> Full article ">Figure 10
<p>Band-averaged RVS for JPSS-1 VIIRS from the SV AOI to the end of scan AOI for bands I3 and M8-M11.</p> Full article ">Figure 11
<p>Band-averaged RVS for the Suomi National Polar-orbiting Partnership (S-NPP) VIIRS RSBs from the SV AOI to the end of scan AOI for bands M1–M4.</p> Full article ">Figure 12
<p>Band-averaged RVS for S-NPP VIIRS RSBs from the SV AOI to the end of scan AOI for bands I1, I2 and M5–M7.</p> Full article ">Figure 13
<p>Band-averaged RVS for S-NPP VIIRS RSBs from the SV AOI to the end of scan AOI for bands I3 and M8–M11.</p> Full article ">Figure 14
<p>Band M14 HAM side A RVS Response and Fits for all Detectors.</p> Full article ">Figure 15
<p>Band M12 HAM side A RVS Response and Fits for all Detectors.</p> Full article ">Figure 16
<p>Band-averaged RVS for JPSS-1 VIIRS all TEBs from the SV AOI through to the end of scan AOI for the Mid-wave Wavelength Infrared (MWIR) bands.</p> Full article ">Figure 17
<p>Band-averaged RVS for JPSS-1 VIIRS all TEBs from the SV AOI through to the end of scan AOI for the Long Wavelength Infrared (LWIR) bands.</p> Full article ">Figure 18
<p>Band-averaged RVS for S-NPP VIIRS all TEBs from the SV AOI through to the end of scan AOI for the MWIR bands.</p> Full article ">Figure 19
<p>Band-averaged RVS for S-NPP VIIRS all TEBs from the SV AOI through to the end of scan AOI for the LWIR bands.</p> Full article ">
7512 KiB  
Article
Evaluation of Simplified Polarimetric Decomposition for Soil Moisture Retrieval over Vegetated Agricultural Fields
by Hongquan Wang, Ramata Magagi, Kalifa Goita, Thomas Jagdhuber and Irena Hajnsek
Remote Sens. 2016, 8(2), 142; https://doi.org/10.3390/rs8020142 - 10 Feb 2016
Cited by 36 | Viewed by 7167
Abstract
This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It [...] Read more.
This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected in the framework of SMAP (Soil Moisture Active Passive) Validation Experiment 2012 (SMAPVEX12). The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m3/m3 and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition. Full article
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<p>The SMAPVEX12 study site with delineation of UAVSAR swath (gray color image of T<sub>11</sub> in dB) and corresponding land cover.</p> Full article ">Figure 2
<p>Temporal evolution of the measured volumetric soil moisture along with daily precipitation amount and the availability of UAVSAR acquisitions (black arrows).</p> Full article ">Figure 3
<p>Temporal variation of vegetation (<b>a</b>) height, (<b>b</b>) wet biomass and (<b>c</b>) vegetation water content.</p> Full article ">Figure 3 Cont.
<p>Temporal variation of vegetation (<b>a</b>) height, (<b>b</b>) wet biomass and (<b>c</b>) vegetation water content.</p> Full article ">Figure 4
<p>The simplified polarimetric-based soil moisture retrieval over agricultural fields. The dashed box indicates the process for removing the volume scattering component.</p> Full article ">Figure 5
<p>RGB color composition of the normalized three scattering mechanisms for Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012). Dihedral scattering power in red, volume scattering power in green and surface scattering power in blue. The incidence angle in the range direction varies from 25° to 65°. (<b>d</b>) Classification map of the considered five crop types (the areas in white color are covered by other crops and the forested site).</p> Full article ">Figure 5 Cont.
<p>RGB color composition of the normalized three scattering mechanisms for Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012). Dihedral scattering power in red, volume scattering power in green and surface scattering power in blue. The incidence angle in the range direction varies from 25° to 65°. (<b>d</b>) Classification map of the considered five crop types (the areas in white color are covered by other crops and the forested site).</p> Full article ">Figure 6
<p>Temporal variation of normalized scattering powers of (<b>a</b>) surface scattering, (<b>b</b>) dihedral scattering and (<b>c</b>) volume scattering component for different crop types. The points (corresponding to right y-axis) shows the height of different crop types.</p> Full article ">Figure 7
<p>Correlation analysis of vegetation water content and volume scattering component for Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 8
<p>Correlation analysis of vegetation water content and surface scattering component for Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 June 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 8 Cont.
<p>Correlation analysis of vegetation water content and surface scattering component for Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 June 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 9
<p>Distribution of fields on entropy/α plane before the removal of the volume component on Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 9 Cont.
<p>Distribution of fields on entropy/α plane before the removal of the volume component on Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 10
<p>Distribution of fields on entropy/α plane after the removal of the volume component on Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 11
<p>Distribution of canola fields on entropy/α plane after the removal of volume scattering component on Julian day 185 (3 July 2012) for three vegetation orientations (vertical, horizontal and random).</p> Full article ">Figure 12
<p>Percentage of dominant surface scattering case after removing volume scattering component, for different crop types.</p> Full article ">Figure 13
<p>Comparison between simulated and measured β on Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 14
<p>Spatial distribution of the retrieved soil moisture on Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 15
<p>The retrieval rate by using surface scattering component.</p> Full article ">Figure 16
<p>Comparison between the retrieved and measured soil moisture on Julian day (<b>a</b>) 169 (17 June 2012); (<b>b</b>) 185 (3 July 2012); (<b>c</b>) 199 (17 July 2012).</p> Full article ">Figure 17
<p>Comparison between the retrieved and measured soil moisture during the agricultural campaign for (<b>a</b>) Canola; (<b>b</b>) Corn; (<b>c</b>) Pasture; (<b>d</b>) Soybean; (<b>e</b>) Wheat. The retrieved and measured soil moisture are averaged for each crop type, and the discontinuities in the curves correspond to unsuccessful retrieval periods.</p> Full article ">Figure 17 Cont.
<p>Comparison between the retrieved and measured soil moisture during the agricultural campaign for (<b>a</b>) Canola; (<b>b</b>) Corn; (<b>c</b>) Pasture; (<b>d</b>) Soybean; (<b>e</b>) Wheat. The retrieved and measured soil moisture are averaged for each crop type, and the discontinuities in the curves correspond to unsuccessful retrieval periods.</p> Full article ">
2735 KiB  
Article
L-Band Polarimetric Target Decomposition of Mangroves of the Rufiji Delta, Tanzania
by Ian Brown, Simon Mwansasu and Lars-Ove Westerberg
Remote Sens. 2016, 8(2), 140; https://doi.org/10.3390/rs8020140 - 9 Feb 2016
Cited by 24 | Viewed by 8442
Abstract
The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest [...] Read more.
The mangroves of the Rufiji Delta are an important habitat and resource. The mangrove forest reserve is home to an indigenous population and has been under pressure from an influx of migrants from the landward side of the delta. Timely and effective forest management is needed to preserve the delta and mangrove forest. Here, we investigate the potential of polarimetric target decomposition for mangrove forest monitoring and analysis. Using three ALOS PALSAR images, we show that L-band polarimetry is capable of mapping mangrove dynamics and is sensitive to stand structure and the hydro-geomorphology of stands. Entropy-alpha-anisotropy and incoherent target decompositions provided valuable measures of scattering behavior related to forest structure. Little difference was found between Yamaguchi and Arii decompositions, despite the conceptual differences between these models. Using these models, we were able to differentiate the scattering behavior of the four main species found in the delta, though classification was impractical due to the lack of pure stands. Scattering differences related to season were attributed primarily to differences in ground moisture or inundation. This is the first time mangrove species have been identified by their scattering behavior in L-band polarimetric data. These results suggest higher resolution L-band quad-polarized imagery, such as from PALSAR-2, may be a powerful tool for mangrove species mapping. Full article
(This article belongs to the Special Issue Remote Sensing of Mangroves: Observation and Monitoring)
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<p>Map of the Rufiji Delta mangrove forests based on Landsat-5 TM data from 12 June 2008. The footprints of the satellite imagery and field sites are marked. The nominal division between the northern and central delta is indicated with a red line. The area of mangrove in the northern delta matches closely the mapping based on data from 2010 [<a href="#B3-remotesensing-08-00140" class="html-bibr">3</a>].</p> Full article ">Figure 2
<p>The zonation common to East African mangrove forests [<a href="#B27-remotesensing-08-00140" class="html-bibr">27</a>].</p> Full article ">Figure 3
<p>Entropy (<b>A</b>), anisotropy (<b>B</b>), and alpha angle (<b>C</b>) calculated from the 2009 PALSAR dataset over the Rufiji Delta, Tanzania. The alpha angle units are radians. Anisotropy is a measure of the relative importance of the second and third eigenvectors and as such is complementary to entropy. Low entropy values lead to high noise in the eigenvectors used to derive anisotropy. Hence, anisotropy is only used in the presence of high entropy.</p> Full article ">Figure 4
<p>Volume scattering from the Arii and Yamaguchi models. (<b>A</b>) Arii volume scattering from the 2007 dataset; (<b>B</b>) Arii volume scattering from 2009; (<b>C</b>) Arii volume scattering from 2010; (<b>D</b>) Yamaguchi volume scattering from 2007 shows almost identical patterns to the Arii volume scattering; (<b>E</b>) The difference between the Arii and Yamaguchi models from 2007; (<b>F</b>) The difference between Arii volume scattering models from 2007 and 2010. Most of the strong negative differences are found over non-mangrove and non-agricultural locations.</p> Full article ">Figure 5
<p>Relative contributions of Arii model decomposition terms by species using data from 2010. Some overlap between <span class="html-italic">Rhizophora</span> and <span class="html-italic">Ceriops</span> is evident and may represent impure stands, misclassification or the relatively high degree of spatial averaging in the data. Two populations of <span class="html-italic">Sonneratia</span> may be identified due to the degree of inundation promoting double-bounce scattering.</p> Full article ">
2828 KiB  
Article
An Overview of the Joint Polar Satellite System (JPSS) Science Data Product Calibration and Validation
by Lihang Zhou, Murty Divakarla and Xingpin Liu
Remote Sens. 2016, 8(2), 139; https://doi.org/10.3390/rs8020139 - 8 Feb 2016
Cited by 47 | Viewed by 9231
Abstract
The Joint Polar Satellite System (JPSS) will launch its first JPSS-1 satellite in early 2017. The JPSS-1 and follow-on satellites will carry aboard an array of instruments including the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology [...] Read more.
The Joint Polar Satellite System (JPSS) will launch its first JPSS-1 satellite in early 2017. The JPSS-1 and follow-on satellites will carry aboard an array of instruments including the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS). These instruments are similar to the instruments currently operating on the Suomi National Polar-orbiting Partnership (S-NPP) satellite. In preparation for the JPSS-1 launch, the JPSS program at the Center for Satellite Applications and Research (JSTAR) Calibration/Validation (Cal/Val) teams, have laid out the Cal/Val plans to oversee JPSS-1 science products’ algorithm development efforts, verification and characterization of these algorithms during the pre-launch period, calibration and validation of the products during post-launch, and long-term science maintenance (LTSM). In addition, the team has developed the necessary schedules, deliverables and infrastructure for routing JPSS-1 science product algorithms for operational implementation. This paper presents an overview of these efforts. In addition, this paper will provide insight into the processes of both adapting S-NPP science products for JPSS-1 and performing upgrades for enterprise solutions, and will discuss Cal/Val processes and quality assurance procedures. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Sensor and Environmental Data (xDR) products from the S-NPP/JPSS-1 instruments suite.</p> Full article ">Figure 2
<p>Components of Cal/Val Process from Pre-Launch to Post-Launch.</p> Full article ">Figure 3
<p>Satellite observations contribution to reducing forecast errors (plot courtesy of Carla Cardinali and Sean Healy, ECMWF).</p> Full article ">Figure 4
<p>The multi-spectral capabilities of VIIRS are useful for investigating the volcano: VIIRS RGB composite of channels M4, M7, and M11 (red is 2.15 um, green is 0.86 um, and the blue is 0.55 um reflectance values). This combination makes vegetation appear green, ice appears dark cyan, clouds appear a light cyan color and the hot spot from the volcano appears red. The image is showing the eruption of Bárðarbunga in Iceland, as it appeared at 13:42 UTC 17 November 2014 (Image courtesy of C. Seaman, Cooperative Institute for Research in the Atmosphere, CIRA, Colorado State University).</p> Full article ">Figure 5
<p>CrIS radiance standard deviation per scan as monitored by the ICVS system.</p> Full article ">Figure 6
<p>(<b>a</b>) S-NPP VIIRS and (<b>b</b>) Aqua MODIS global Land Surface Temperature (LST) maps as depicted by the EDR product monitoring system. The VIIRS instrument’s wider swath and the cloud flag criteria used by the LST EDR product results in much better spatial coverage.</p> Full article ">Figure 7
<p>JPSS-1 Algorithm Cal/Val Timelines.</p> Full article ">Figure 8
<p>(<b>a</b>–<b>c</b>) Projected JPSS-1 Timelines (green bars) and comparison with the S-NPP timelines (red bars) in achieving (<b>a</b>) beta (top); (<b>b</b>) provisional (middle); and (<b>c</b>) validated (bottom) maturity status. The Cal/Val activities for JPSS-1 are expected to be much more accelerated than those for S-NPP, and JPSS-1 data products will be provided to decision makers/users with a much improved latency.</p> Full article ">
9149 KiB  
Article
Soumi NPP VIIRS Day/Night Band Stray Light Characterization and Correction Using Calibration View Data
by Shihyan Lee and Changyong Cao
Remote Sens. 2016, 8(2), 138; https://doi.org/10.3390/rs8020138 - 8 Feb 2016
Cited by 34 | Viewed by 8063
Abstract
The Soumi NPP VIIRS Day/Night Band (DNB) nighttime imagery quality is affected by stray light contamination. In this study, we examined the relationship between the Earth scene stray light and the signals in VIIRS’s calibrators to better understand stray light characteristics and to [...] Read more.
The Soumi NPP VIIRS Day/Night Band (DNB) nighttime imagery quality is affected by stray light contamination. In this study, we examined the relationship between the Earth scene stray light and the signals in VIIRS’s calibrators to better understand stray light characteristics and to improve upon the current correction method. Our analyses showed the calibrator signal to be highly predictive of Earth scene stray light and can provide additional stray light characteristics that are difficult to obtain from Earth scene data alone. In the current stray light correction regions (mid-to-high latitude), the stray light onset angles can be tracked by calibration view data to reduce correction biases. In the southern hemisphere, it is possible to identify the angular extent of the additional stray light feature in the calibration view data and develop a revised correction method to remove the additional stray light occurring during the southern hemisphere springtime. Outside of current stray light correction region, the analysis of calibration view data indicated occasional stray light contamination at low latitude and possible background biases caused by Moon illumination. As stray light affects a significant portion of nighttime scenes, further refinement in characterization and correction is important to ensure VIIRS DNB imagery quality for Soumi NPP and future missions. Full article
(This article belongs to the Collection Visible Infrared Imaging Radiometers and Applications)
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<p>Schematic of VIIRS data collection windows and their approximated angles. The scan angle at the Earth’s limb is ~62.5 degrees.</p> Full article ">Figure 2
<p>DNB calibration view signals for S-NPP orbit 15,758 on 12 November 2014. DNB detector 1 signals for SV, BB, and SD are shown in (<b>a</b>–<b>c</b>), respectively. The y-axis are DNB signals in W·cm<sup>−2</sup>·sr<sup>−1</sup>. The x-axis is the VIIRS Sun declination angle, in degrees. NH: northern hemisphere, SH: southern hemisphere.</p> Full article ">Figure 3
<p>Mean ratios of BB/SD and SV/SD signals around solar calibration events on August 25, 2014. The dashed lines indicate the approximated solar calibration angle range. SZA = solar zenith angle.</p> Full article ">Figure 4
<p>DNB detector 8 EV and calibration view dark signals near northern hemisphere stray light problem regions on October 5, 2013. (<b>a</b>–<b>c</b>) show calibration views. EV dark signals for beginning of scan (BOS), nadir, and end of scan (EOS) are shown in (<b>d</b>), (<b>e</b>), and (<b>f</b>), respectively. Black curves: dark signals; red curves: estimated stray light; blue curves: signals after stray light correction. SZA = solar zenith angle.</p> Full article ">Figure 5
<p>Stray light corrected images for December 3, 2013, 7:43 GMT, using LUTs derived from (<b>a</b>) 2013 December; (<b>b</b>) 2013 November; and (<b>c</b>) 2012 December data.</p> Full article ">Figure 6
<p>Median penumbra angle estimated using DNB SV data. The angle is estimated as the maximum slope in SV signal profile.</p> Full article ">Figure 7
<p>DNB detector 8 EV and calibration views dark signals near southern hemisphere stray light problem regions on October 5, 2013. (<b>a</b>–<b>c</b>) show calibration views. EV dark signals for beginning of scan (BOS), nadir, and end of scan (EOS) are shown in (<b>d</b>–<b>f</b>), respectively. Black curves are computed dark signals, red curves are estimated stray light, and blue curves are the signals after stray light correction. SZA = solar zenith angle.</p> Full article ">Figure 8
<p>DNB image from October 5, 2013, 5:06 GMT, corrected by the current method. The residual artifact from additional stray light can be seen as a large swath across the image.</p> Full article ">Figure 9
<p>DNB detector of eight dark signals near the southern hemisphere stray light problem regions on September 5, 2013 for (<b>a</b>) SV; (<b>b</b>) BB; and (<b>c</b>) EV at BOS. Black curves are computed dark signals, red curves are estimated stray light, and blue curves are the signals after stray light correction. SZA = solar zenith angle.</p> Full article ">Figure 10
<p>DNB EV dark signals near the southern hemisphere stray light problem regions on October 5, 2013. (<b>a</b>–<b>c</b>) show EV dark signals for beginning of scan (BOS), nadir, and end of scan (EOS), respectively. Black curves are computed dark signals, red curves are estimated stray light based on the updated method, and blue curves are the signals after stray light correction. SZA = solar zenith angle.</p> Full article ">Figure 11
<p>DNB image from October 5, 2013, 5:06 GMT, corrected by the updated method. The residual artifact from additional stray light is significantly reduced.</p> Full article ">Figure 12
<p>SD onset angle estimated using DNB SV data.</p> Full article ">Figure 13
<p>Stray light corrected images for October 5, 2013, 1:41 GMT, using LUTs derived from (<b>a</b>) 2013 October; (<b>b</b>) 2013 September; and (<b>c</b>) 2012 October data.</p> Full article ">Figure 14
<p>SNPP DNB image from December 28, 2014, 18:06 GMT. The red box indicates the approximated areas with contaminations.</p> Full article ">Figure 15
<p>DNB HGA scan averaged SV signals plotted against spacecraft Sun declination angle (SunDec) for orbit 16,420 on December 28, 2014. The signal increase arounda Sun declination angle of 80 degrees corresponded to the timing when EV striping occurred at the beginning of the scan in <a href="#remotesensing-08-00138-f014" class="html-fig">Figure 14</a>.</p> Full article ">Figure 16
<p>DNB BB daily mean dark signal DN for HGA, aggregation mode 1. The measured values for each detector are represented by symbols. The lines are fitted values using new moon data. The periodic increase in DN corresponded to the increase in lunar illumination. At full moon, the dark response is up to 5 dn (~ 8 × 10<sup>−11</sup> W·cm<sup>−2</sup>·sr<sup>−1</sup>) higher than the new moon.</p> Full article ">
2630 KiB  
Article
Assessment of the Suomi NPP VIIRS Land Surface Albedo Data Using Station Measurements and High-Resolution Albedo Maps
by Yuan Zhou, Dongdong Wang, Shunlin Liang, Yunyue Yu and Tao He
Remote Sens. 2016, 8(2), 137; https://doi.org/10.3390/rs8020137 - 8 Feb 2016
Cited by 27 | Viewed by 6070
Abstract
Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the [...] Read more.
Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the improved bright pixel sub-algorithm (BPSA) is a unique algorithm employed by VIIRS to routinely generate LSA EDR from VIIRS top-of-atmosphere (TOA) observations. As a product validation procedure, LSA EDR reached validated (V1 stage) maturity in December 2014. This study summarizes recent progress in algorithm refinement, and presents comprehensive validation and evaluation results of VIIRS LSA by using extensive field measurements, Moderate Resolution Imaging Spectroradiometer (MODIS) albedo product, and Landsat-retrieved albedo maps. Results indicate that: (1) by testing the updated desert-specific look-up-table (LUT) that uses a stricter standard to select the training data specific for desert aerosol type in our local environment, it is found that the VIIRS LSA retrieval accuracy is improved over a desert surface and the absolute root mean square error (RMSE) is reduced from 0.036 to 0.023, suggesting the potential of the updated desert LUT to the improve the VIIRS LSA product accuracy; (2) LSA retrieval on snow-covered surfaces is more accurate if the newly developed snow-specific LUT (RMSE = 0.082) replaces the generic LUT (RMSE = 0.093) that is employed in the current operational LSA EDR production; (3) VIIRS LSA is also comparable to high-resolution Landsat albedo retrieval (RMSE < 0.04), although Landsat albedo has a slightly higher accuracy, probably owing to higher spatial resolution with less impacts of mixed pixel; (4) VIIRS LSA retrievals agree well with the MODIS albedo product over various land surface types, with overall RMSE of lower than 0.05 and the overall bias as low as 0.025, demonstrating the comparable data quality between VIIRS and the MODIS LSA product. Full article
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<p>Comparison between VIIRS-retrieved albedo and ground truth over four vegetated sites for the years 2012 and 2013: (<b>a</b>) Bondville (Cropland); (<b>b</b>) Goodwin Creek (Grassland); (<b>c</b>) Table Mountain (Forest); and (<b>d</b>) Tiksi (Tundra). Albedo value beyond 0.4 is due to the snow cover on the sites during the winter season. Cloudy observations have been excluded by applying cloud masks.</p> Full article ">Figure 2
<p>Validation results over a desert site (Rock Desert) for the year 2013. Time series of VIIRS-retrieved albedo using (<b>a</b>) current and (<b>b</b>) updated desert-specific LUT, respectively. Comparison results between ground truth and VIIRS-retrieved albedo using (<b>c</b>) current and (<b>d</b>) updated desert specific LUT, respectively.</p> Full article ">Figure 3
<p>Validation results over a snow-covered site (Saddle) for the year 2013. (<b>a</b>) Time series of VIIRS-retrieved albedo using Lambertian LUT, generic BRDF LUT, and snow-specific BRDF LUT; Comparison results between ground truth and VIIRS-retrieved albedo using (<b>b</b>) Lambertian LUT; (<b>c</b>) generic BRDF LUT; and (<b>d</b>) snow-specific BRDF LUT, respectively.</p> Full article ">Figure 4
<p>Comparison results over all snow-covered sites located in Greenland from the GC-Net network for the year 2013. VIIRS albedo is retrieved using (<b>a</b>) Lambertian LUT; (<b>b</b>) generic BRDF LUT; and (<b>c</b>) snow-specific BRDF LUT, respectively.</p> Full article ">Figure 5
<p>Relationship between the solar zenith angle and the absolute difference of VIIRS albedo retrieval (|estimation-ground truth|) for (<b>a</b>) Lambertian LUT; and (<b>b</b>) snow-specific BRDF LUT.</p> Full article ">Figure 6
<p>Intercomparison results (first column) between VIIRS LSA and Landsat-retrieved albedo for the years 2012 and 2013. Three individual sites (USWcr: first row; Desert Rock: second row; DYE-2: third row) covered by different land types are shown. In addition, comparisons with field measurements are also provided for the both products/retrievals in the second and third columns, respectively.</p> Full article ">Figure 6 Cont.
<p>Intercomparison results (first column) between VIIRS LSA and Landsat-retrieved albedo for the years 2012 and 2013. Three individual sites (USWcr: first row; Desert Rock: second row; DYE-2: third row) covered by different land types are shown. In addition, comparisons with field measurements are also provided for the both products/retrievals in the second and third columns, respectively.</p> Full article ">Figure 7
<p>Inter-comparison results between VIIRS LSA and Landsat retrieved albedo over all sites listed in <a href="#remotesensing-08-00137-t001" class="html-table">Table 1</a>. (<b>a</b>) VIIRS validated against Landsat; (<b>b</b>) VIIRS validated against ground truth; (<b>c</b>) Landsat validated against ground truth.</p> Full article ">Figure 8
<p>Modified inter-comparison results by excluding snow-covered observations over all non-GCNet sites on the basis of <a href="#remotesensing-08-00137-f007" class="html-fig">Figure 7</a>. (<b>a</b>) VIIRS validated against Landsat; (<b>b</b>) VIIRS validated against ground truth; (<b>c</b>) Landsat validated against ground truth.</p> Full article ">Figure 9
<p>Intercomparison results (first column) between VIIRS LSA EDR and MODIS albedo product for the year 2013. Three individual sites (Goodwin Creek: first row; Desert Rock: second row; Saddle: third row) covered by different land types are shown, as well as the results of combining all the sites listed in <a href="#remotesensing-08-00137-t001" class="html-table">Table 1</a> (fourth row). In addition, comparisons with field measurements are also provided for both the products in the second and third columns, respectively.</p> Full article ">
6782 KiB  
Article
Deformation and Source Parameters of the 2015 Mw 6.5 Earthquake in Pishan, Western China, from Sentinel-1A and ALOS-2 Data
by Yangmao Wen, Caijun Xu, Yang Liu and Guoyan Jiang
Remote Sens. 2016, 8(2), 134; https://doi.org/10.3390/rs8020134 - 8 Feb 2016
Cited by 63 | Viewed by 9867
Abstract
In this study, Interferometric Synthetic Aperture Radar (InSAR) was used to determine the seismogenic fault and slip distribution of the 3 July 2015 Pishan earthquake in the Tarim Basin, western China. We obtained a coseismic deformation map from the ascending and descending Sentinel-1A [...] Read more.
In this study, Interferometric Synthetic Aperture Radar (InSAR) was used to determine the seismogenic fault and slip distribution of the 3 July 2015 Pishan earthquake in the Tarim Basin, western China. We obtained a coseismic deformation map from the ascending and descending Sentinel-1A satellite Terrain Observation with Progressive Scans (TOPS) mode and the ascending Advanced Land Observation Satellite-2 (ALOS-2) satellite Fine mode InSAR data. The maximum ground uplift and subsidence were approximately 13.6 cm and 3.2 cm, respectively. Our InSAR observations associated with focal mechanics indicate that the source fault dips to southwest (SW). Further nonlinear inversions show that the dip angle of the seimogenic fault is approximate 24°, with a strike of 114°, which is similar with the strike of the southeastern Pishan fault. However, this fault segment responsible for the Pishan event has not been mapped before. Our finite fault model reveals that the peak slip of 0.89 m occurred at a depth of 11.6 km, with substantial slip at a depth of 9–14 km and a near-uniform slip of 0.2 m at a depth of 0–7 km. The estimated moment magnitude was approximately Mw 6.5, consistent with seismological results. Full article
(This article belongs to the Special Issue Remote Sensing in Tibet and Siberia)
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<p>Tectonic setting of the 2015 Pishan earthquake, western China. The focal mechanism of the 2015 Pishan earthquake is from the GCMT catalog. Red circles are the aftershocks with M ≥ 2.0 from the International Seismological Centre [<a href="#B8-remotesensing-08-00134" class="html-bibr">8</a>] between 3 July 2015 and 22 November 2015. Blue boxes outline the spatial coverage of the Sentinel-1A SAR images (ascending track T056A and descendisng track T136D) and the ALOS-2 Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images (ascending track P160A). Black lines are the published faults from the map of active tectonics in China [<a href="#B2-remotesensing-08-00134" class="html-bibr">2</a>]. Blue vectors are the interseismic GPS velocities relative to a Eurasian reference frame with 95% confidence [<a href="#B9-remotesensing-08-00134" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Coseismic interferograms obtained from the ascending Sentinel-1A track T056A (<b>a</b>), the descending Sentinel-1A track T136D (<b>b</b>) and the ascending ALOS-2 track P160A (<b>c</b>). The interferograms are rewrapped with an interval of 6 cm.</p> Full article ">Figure 3
<p>Surface displacement of quasi-eastward (<b>a</b>) and quasi-upward (<b>b</b>) components derived from a combination of the Sentinel-1A ascending and descending tracks and the ALOS-2 ascending track.</p> Full article ">Figure 4
<p>Plot of the log-function of the sum of root-mean-square error and model roughness for a range of models with different smoothing factors (<span class="html-italic">k</span><sup>2</sup>) and dip angles. Red cross is the global minimum, which is chosen for the inversion presented in <a href="#remotesensing-08-00134-f005" class="html-fig">Figure 5</a>.</p> Full article ">Figure 5
<p>The 1 km × 1 km finite fault model of the 2015 Pishan earthquake (<b>a</b>), standard deviations in slip from the Monte Carlo estimation with 100 perturbed datasets (<b>b</b>) and the sum of moment release along strike distance (<b>c</b>).</p> Full article ">Figure 6
<p>Modeled interferograms for the ascending Sentinel-1A track T056A (<b>a</b>), the descending Sentinel-1A track T136D (<b>b</b>) and the ascending ALOS-2 track P160A (<b>c</b>); and their residuals (<b>d</b>–<b>f</b>) with the distributed slip model. The white line is the top boundary of the distributed slip model, and the black dashed lines represent the surface projections of the modeled fault.</p> Full article ">Figure 7
<p>Calculated static Coulomb failure stress changes caused by the 2015 Pishan event with the preferred InSAR distributed slip model and a friction coefficient of 0.4. (<b>a</b>) The distribution of Coulomb stress change at a depth of 8 km; (<b>b</b>) Cross-section of Coulomb stress change through profile AB.</p> Full article ">
1821 KiB  
Article
Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands
by Thomas Möckel, Jonas Dalmayne, Barbara C. Schmid, Honor C. Prentice and Karin Hall
Remote Sens. 2016, 8(2), 133; https://doi.org/10.3390/rs8020133 - 8 Feb 2016
Cited by 48 | Viewed by 7501
Abstract
Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the [...] Read more.
Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414–2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson’s diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 m × 4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400–700 nm) and to vegetation structural properties, such as above-ground biomass (700–1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity. Full article
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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<p>(<b>a</b>) The location of the study area on the Baltic Island of Öland, Sweden; (<b>b</b>) the distribution of grassland sites included in the present study (<span class="html-italic">n</span> = 52); (<b>c</b>) an example of the distribution of field plots within some of the grassland sites.</p> Full article ">Figure 2
<p>Schematic overview of the workflow used in the present study.</p> Full article ">Figure 3
<p>Pearson’s correlation coefficients (<span class="html-italic">r</span>) between single wavebands and the species richness ln(SR) (red), and the inverse Simpson’s diversity index iSDI (black) for the whole data set (<span class="html-italic">n</span> = 102). Correlations below the dotted line are significant (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Correlations between field-observed and predicted (<b>left column</b>) species richness (ln(SR)) and (<b>right column</b>) inverse Simpson’s diversity (iSDI) for the validation subset (<span class="html-italic">n</span> = 51). (<b>a</b>,<b>b</b>) show the field-observed <span class="html-italic">versus</span> the predicted correlations for the PLSR model based on the full set of wavebands (Model 1) (<span class="html-italic">n</span> = 245); (<b>c</b>,<b>d</b>) show the field-observed <span class="html-italic">versus</span> the predicted correlations for the model based on a subset of wavebands (Model 2) (<span class="html-italic">n</span> = 25 (for ln(SR)) or 35 (for iSDI)). The normalized prediction error (nRMSE<sub>P</sub>, %) indicates the quality of the model in predicting the observed species diversity measure, and the squared correlation (<span class="html-italic">R</span><sup>2</sup><sub>P</sub>) indicates the fit between the predicted and observed diversity value. The age-class of the grassland plots is also displayed (key: ○ young, ∆ intermediate, and + old). Black lines indicate the relationship between the predicted and the measured values.</p> Full article ">Figure 5
<p>Important wavebands (grey bars) selected with the help of an iterative variable deletion procedure, for estimating (<b>a</b>) the species richness (ln(SR)) and (<b>b</b>) the inverse Simpson’s diversity index (iSDI) in grassland plots using the calibration subset (<span class="html-italic">n</span> = 51). The black line represents the mean spectral reflectance curve for grassland plots in the whole data set (<span class="html-italic">n</span> = 102).</p> Full article ">Figure 6
<p>(<b>a</b>,<b>b</b>) Pearson’s correlation coefficients of the residuals of the PLSR models’ (Model 1 = dark; Model 2 = light) predictions of (<b>a</b>) the species richness (ln(SR)) and (<b>b</b>) the inverse Simpson’s diversity index (iSDI) with different environmental variables (moisture availability, Ellenberg mM; nutrient availability, Ellenberg mN; field-layer height, FLH; cover of bare ground, Bare ground; and cover of litter, Litter); (<b>c</b>,<b>d</b>) Distribution of the residuals of (<b>c</b>) the species richness (ln(SR)) and (<b>d</b>) the inverse Simpson’s diversity index (iSDI), within the three grassland age-classes, predicted by Model 1 (dark) and Model 2 (light).</p> Full article ">
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