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Remote Sens., Volume 8, Issue 10 (October 2016)

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Open AccessArticle Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping
Remote Sens. 2016, 8(10), 787; doi:10.3390/rs8100787
Received: 7 June 2016 / Revised: 22 August 2016 / Accepted: 8 September 2016 / Published: 22 September 2016
Cited by 9 | PDF Full-text (11889 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Land cover mapping of the urban environment by means of remote sensing remains a distinct challenge due to the strong spectral heterogeneity and geometric complexity of urban scenes. Airborne imaging spectroscopy and laser altimetry have each made remarkable contributions to urban mapping but
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Land cover mapping of the urban environment by means of remote sensing remains a distinct challenge due to the strong spectral heterogeneity and geometric complexity of urban scenes. Airborne imaging spectroscopy and laser altimetry have each made remarkable contributions to urban mapping but synergistic use of these relatively recent data sources in an urban context is still largely underexplored. In this study a synergistic workflow is presented to cope with the strong diversity of materials in urban areas, as well as with the presence of shadow. A high-resolution APEX hyperspectral image and a discrete waveform LiDAR dataset covering the Eastern part of Brussels were made available for this research. Firstly, a novel shadow detection method based on LiDAR intensity-APEX brightness thresholding is proposed and compared to commonly used approaches for shadow detection. A combination of intensity-brightness thresholding with DSM model-based shadow detection is shown to be an efficient approach for shadow mask delineation. To deal with spectral similarity of different types of urban materials and spectral distortion induced by shadow cover, supervised classification of shaded and sunlit areas is combined with iterative LiDAR post-classification correction. Results indicate that height, slope and roughness features contribute to improved classification accuracies in descending order of importance. Results of this study illustrate the potential of synergistic application of hyperspectral imagery and LiDAR for urban land cover mapping. Full article
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Open AccessArticle Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities
Remote Sens. 2016, 8(10), 788; doi:10.3390/rs8100788
Received: 20 June 2016 / Revised: 6 September 2016 / Accepted: 19 September 2016 / Published: 22 September 2016
Cited by 3 | PDF Full-text (8994 KB) | HTML Full-text | XML Full-text
Abstract
The main factors influencing the spatiotemporal variability of urban climate are quite widely recognized, including, for example, the thermal properties of materials used for surfaces and buildings, the mass, height and layout of the buildings themselves and patterns of land use. However, the
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The main factors influencing the spatiotemporal variability of urban climate are quite widely recognized, including, for example, the thermal properties of materials used for surfaces and buildings, the mass, height and layout of the buildings themselves and patterns of land use. However, the roles played by particular factors vary from city to city with respect to differences in geographical location, overall size, number of inhabitants and more. In urban climatology, the concept of “local climate zones” (LCZs) has emerged over the past decade to address this heterogeneity. In this contribution, a new GIS-based method is used for LCZ delimitation in Prague and Brno, the two largest cities in the Czech Republic, while land surface temperatures (LSTs) derived from LANDSAT and ASTER satellite data are employed for exploring the extent to which LCZ classes discriminate with respect to LSTs. It has been suggested that correctly-delineated LCZs should demonstrate the features typical of LST variability, and thus, typical surface temperatures should differ significantly among most LCZs. Zones representing heavy industry (LCZ 10), dense low-rise buildings (LCZ 3) and compact mid-rise buildings (LCZ 2) were identified as the warmest in both cities, while bodies of water (LCZ G) and densely-forested areas (LCZ A) made up the coolest zones. ANOVA and subsequent multiple comparison tests demonstrated that significant temperature differences between the various LCZs prevail. The results of testing were similar for both study areas (89.3% and 91.7% significant LST differences for Brno and Prague, respectively). LSTs computed from LANDSAT differentiated better between LCZs, compared with ASTER. LCZ 8 (large low-rise buildings), LCZ 10 (heavy industry) and LCZ D (low plants) are well-differentiated zones in terms of their surface temperatures. In contrast, LCZ 2 (compact mid-rise), LCZ 4 (open high-rise) and LCZ 9 (sparsely built-up) are less distinguishable in both areas analyzed. Factors such as seasonality and thermal anisotropy remain a challenge for future research into LST differences. Full article
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Open AccessArticle Estimating the Total Nitrogen Concentration of Reed Canopy with Hyperspectral Measurements Considering a Non-Uniform Vertical Nitrogen Distribution
Remote Sens. 2016, 8(10), 789; doi:10.3390/rs8100789
Received: 12 May 2016 / Revised: 25 August 2016 / Accepted: 19 September 2016 / Published: 23 September 2016
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Abstract
The total nitrogen concentration (NC, g/100 g) of wetland plants is an important parameter to estimate the wetland health status and to calculate the nitrogen storage of wetland plants. Remote sensing has been widely used to estimate biophysical, physiological, and biochemical parameters of
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The total nitrogen concentration (NC, g/100 g) of wetland plants is an important parameter to estimate the wetland health status and to calculate the nitrogen storage of wetland plants. Remote sensing has been widely used to estimate biophysical, physiological, and biochemical parameters of plants. However, current studies place little emphasis on NC estimations by only taking nitrogen’s vertical distribution into consideration, resulting in limited accuracy and decreased practical value of the results. The main goal of this study is to develop a model, considering a non-uniform vertical nitrogen distribution to estimate the total NC of the reed canopy, which is one of the wetland’s dominant species, using hyperspectral data. Sixty quadrats were selected and measured based on an experimental design that considered vertical layer divisions within the reed canopy. Using the measured NCs of different leaf layers and corresponding spectra from the quadrats, the results indicated that the vertical distribution law of the NC was distinct, presenting an initial increase and subsequent decrease from the top layer to the bottom layer. The spectral indices MCARI/MTVI2, TCARI/OSAVI, MMTCI, DCNI, and PPR/NDVI had high R2 values when related to NC (R2 > 0.5) and low R2 when related to LAI (R2 < 0.2) and could minimize the influence of LAI and increase the sensitivity to changes in NC of the reed canopy. The relative variation rates (Rv, %) of these spectral indices, calculated from each quadrat, also indicated that the top three layers of the reed canopy were an effective depth to estimate NCs using hyperspectral data. A model was developed to estimate the total NC of the whole reed canopy based on PPR/DNVI with R2 = 0.88 and RMSE = 0.37%. The model, which considered the vertical distribution patterns of the NC and the effective canopy layers, has demonstrated great potential to estimate the total NC of the whole reed canopy. Full article
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
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Open AccessArticle A Case Study of Land-Surface-Temperature Impact from Large-Scale Deployment of Wind Farms in China from Guazhou
Remote Sens. 2016, 8(10), 790; doi:10.3390/rs8100790
Received: 13 April 2016 / Revised: 9 September 2016 / Accepted: 19 September 2016 / Published: 23 September 2016
Cited by 3 | PDF Full-text (4599 KB) | HTML Full-text | XML Full-text
Abstract
The wind industry in China has experienced a rapid expansion of capacity after 2009, especially in northwestern China, where the China’s first 10 GW-level wind power project is located. Based on the analysis from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST)
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The wind industry in China has experienced a rapid expansion of capacity after 2009, especially in northwestern China, where the China’s first 10 GW-level wind power project is located. Based on the analysis from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data for period of 2005–2012, the potential LST impacts from the large-scale wind farms in northwestern China’s Guazhou are investigated in this paper. It shows the noticeable nighttime warming trends on LST over the wind farm areas relative to the nearby non-wind-farm regions in Guazhou and that the nighttime LST warming is strongest in summer (0.51 °C/8 years), followed by autumn (0.48 °C/8 years) and weakest in winter (0.38 °C/8 years) with no warming trend observed in spring. Meanwhile, the quantitative comparison results firstly indicate that the nighttime LST warming from wind farm areas are less than those from the urban areas in this work. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Open AccessArticle Mapping Indigenous Settlement Topography in the Caribbean Using Drones
Remote Sens. 2016, 8(10), 791; doi:10.3390/rs8100791
Received: 15 July 2016 / Revised: 13 September 2016 / Accepted: 19 September 2016 / Published: 23 September 2016
Cited by 1 | PDF Full-text (17178 KB) | HTML Full-text | XML Full-text
Abstract
The archaeology of Amerindian settlements in the Caribbean has mostly been identified through scatters of artefacts; predominantly conglomerations of shells, ceramics and lithics. While archaeological material may not always be visible on the surface, particular settlement patterns may be identifiable by a topography
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The archaeology of Amerindian settlements in the Caribbean has mostly been identified through scatters of artefacts; predominantly conglomerations of shells, ceramics and lithics. While archaeological material may not always be visible on the surface, particular settlement patterns may be identifiable by a topography created through cultural action: earthen mounds interchanging with mostly circular flattened areas. In northern Hispaniola, recent foot surveys have identified more than 200 pre-colonial sites of which several have been mapped in high resolution. In addition, three settlements with topographical characteristics have been extensively excavated, confirming that the mounds and flattened areas may have had a cultural connotation in this region. Without the availability of high resolution LiDAR (Light Detection and Ranging) data, a photogrammetric approach using UAS (unmanned aircraft system, commonly known as drones) can fill the knowledge gap on a local scale, providing fast and reliable data collection and precise results. After photogrammetric processing, digital clearance of vegetation, and extraction of the georeferenced DEM (digital elevation model) and orthophoto, filters and enhancements provide an opportunity to visualize the results in GIS. The outcome provides an overview of site size, and distribution of mounds and flattened areas. Measurement of the topographic changes in a variety of past settlements defines likely zones of habitat, and provides clues on the actual dimensions and density of living space. Understanding the relation of the mounds and adjacent flat areas within their environment allows a discussion on how, and for what purpose, the settlement was founded at a particular location, and provides clues about its spatial organization. Full article
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
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Open AccessArticle Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes
Remote Sens. 2016, 8(10), 792; doi:10.3390/rs8100792
Received: 6 June 2016 / Revised: 16 September 2016 / Accepted: 20 September 2016 / Published: 23 September 2016
Cited by 2 | PDF Full-text (9199 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluates the performance of a Support Vector Machine (SVM) classifier to learn and detect changes in single- and multi-temporal X- and L-band Synthetic Aperture Radar (SAR) images under varying conditions. The purpose is to provide guidance on how to train a
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This study evaluates the performance of a Support Vector Machine (SVM) classifier to learn and detect changes in single- and multi-temporal X- and L-band Synthetic Aperture Radar (SAR) images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however, indicate that given dynamic parameter tuning, feature selection and an appropriate sampling approach, already small training samples (100 samples per class) are sufficient to produce high change detection rates. Moreover, the experiments show a good generalization ability of SVM which allows transfer and reuse of trained learning machines. Full article
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Open AccessArticle Caveats Concerning the Use of SRTM DEM Version 4.1 (CGIAR-CSI)
Remote Sens. 2016, 8(10), 793; doi:10.3390/rs8100793
Received: 12 August 2016 / Revised: 13 September 2016 / Accepted: 20 September 2016 / Published: 24 September 2016
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Abstract
This paper provides some recommendations concerning the use of version 4.1 of the near-global 3 arcsec Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) distributed by the Consortium for Spatial Information (CGIAR-CSI). This product is considered by most users to be a
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This paper provides some recommendations concerning the use of version 4.1 of the near-global 3 arcsec Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) distributed by the Consortium for Spatial Information (CGIAR-CSI). This product is considered by most users to be a void-filled version of the finished grade NASA SRTM DEM. However, in non-void areas, these DEMs can exhibit relative geolocation shifts and spatially correlated elevation differences up to tens of meters, the location and extent of which depends on the geographical location and on the download mirror of the version 4.1 product. Such differences are found to be partly due to changes introduced by NASA SRTM version 2.1, with respect to NASA SRTM version 2.0, on which CGIAR-CSI version 4.1 is based, and partly to processing and/or annotation errors affecting the CGIAR-CSI version 4.1 DEMs. Full article
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Open AccessArticle Testing a Modified PCA-Based Sharpening Approach for Image Fusion
Remote Sens. 2016, 8(10), 794; doi:10.3390/rs8100794
Received: 27 July 2016 / Revised: 6 September 2016 / Accepted: 19 September 2016 / Published: 24 September 2016
Cited by 3 | PDF Full-text (13897 KB) | HTML Full-text | XML Full-text
Abstract
Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted
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Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER) and high spatial-resolution panchromatic data (WorldView-2) for image fusion. A modified Principal Component Analysis (PCA)-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1–PC4) can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used—PCA sharpening and Gram–Schmidt sharpening (GS), both available in ENVI software (Version 5.2 and lower) as well as to the standard approach—sharpening Landsat 8 multispectral bands (MUL) using its own panchromatic (PAN) band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR) part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR) part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2) while keeping the proper albedo scaling when substituting the second PC. Full article
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Open AccessArticle Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series
Remote Sens. 2016, 8(10), 795; doi:10.3390/rs8100795
Received: 30 May 2016 / Revised: 31 August 2016 / Accepted: 19 September 2016 / Published: 24 September 2016
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Abstract
Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach
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Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentinel-1 imagery of two wetlands with different ecological characteristics; a seasonal isolated wetland in southern Spain and a coastal wetland in the south of France. We test the S1-omnibus method against a commonly-used pairwise comparison of consecutive images to demonstrate its advantages. Additionally, we compare it with a pairwise change detection method using a subset of consecutive Landsat images for the same period of time. The results show how S1-omnibus is capable of capturing in space and time changes produced by water surface dynamics, as well as by agricultural practices, whether they are sudden changes, as well as gradual. S1-omnibus is capable of detecting a wider array of short-term changes than when using consecutive pairs of Sentinel-1 images. When compared to the Landsat-based change detection method, both show an overall good agreement, although certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery
Remote Sens. 2016, 8(10), 796; doi:10.3390/rs8100796
Received: 24 June 2016 / Revised: 8 September 2016 / Accepted: 19 September 2016 / Published: 24 September 2016
Cited by 3 | PDF Full-text (17654 KB) | HTML Full-text | XML Full-text
Abstract
Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from
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Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle Changes in Mountain Glaciers, Lake Levels, and Snow Coverage in the Tianshan Monitored by GRACE, ICESat, Altimetry, and MODIS
Remote Sens. 2016, 8(10), 798; doi:10.3390/rs8100798
Received: 7 February 2016 / Revised: 18 September 2016 / Accepted: 20 September 2016 / Published: 26 September 2016
Cited by 1 | PDF Full-text (10409 KB) | HTML Full-text | XML Full-text
Abstract
The Tianshan mountain range is experiencing a notable environmental change as a result of global warming. In this paper; we adopt multiple remote sensing techniques to examine the diversified geophysical changes in the Tianshan; including glacier changes measured by Gravity Recovery and Climate
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The Tianshan mountain range is experiencing a notable environmental change as a result of global warming. In this paper; we adopt multiple remote sensing techniques to examine the diversified geophysical changes in the Tianshan; including glacier changes measured by Gravity Recovery and Climate Experiment (GRACE) and Ice, Cloud, and land Elevation Satellite (ICESat); lake level changes measured by radar altimetry; and snow coverage measured by moderate-resolution imaging spectroradiometer (MODIS). We find a rapid transition from dry years to wet years in 2010 in the western and northern Tianshan for all the geophysical measurements. The transition is likely caused by increasing westerlies and greatly pollutes the gravity signals in the edge of Tianshan. However, glaciers in the central Tianshan are unaffected and have been steadily losing mass at a rate of –4.0 ± 0.7 Gt/year during 2003–2014 according to space gravimetry and –3.4 ± 0.8 Gt/year during 2003–2009 according to laser altimetry. Our results show a weaker declining trend and greater linearity compared with earlier estimates; because we investigate the signal pattern in more detail. Finally; water level records of 60 years in Bosten Lake; China; are presented to show that for areas strongly dependent on meltwater; rising temperature can benefit the water supply in the short run; but cause it to deteriorate in the long run. Full article
(This article belongs to the Special Issue Remote Sensing in Tibet and Siberia)
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Open AccessArticle A Multi-View Dense Image Matching Method for High-Resolution Aerial Imagery Based on a Graph Network
Remote Sens. 2016, 8(10), 799; doi:10.3390/rs8100799
Received: 28 June 2016 / Revised: 18 September 2016 / Accepted: 22 September 2016 / Published: 26 September 2016
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Abstract
Multi-view dense matching is a crucial process in automatic 3D reconstruction and mapping applications. In this paper, we present a robust and effective multi-view dense matching algorithm for high-resolution aerial images based on a graph network. The overlap ratio and intersection angle between
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Multi-view dense matching is a crucial process in automatic 3D reconstruction and mapping applications. In this paper, we present a robust and effective multi-view dense matching algorithm for high-resolution aerial images based on a graph network. The overlap ratio and intersection angle between image pairs are used to find candidate stereo pairs and build the graph network. A Coarse-to-Fine strategy based on an improved Semi-Global Matching algorithm is applied for disparity computation across stereo pairs. Based on the constructed graph, point clouds of base views are generated by triangulating all connected image nodes, followed by a fusion process with the average reprojection error as a priority measure. The proposed method was successfully applied in experiments on aerial image test dataset provided by the ISPRS of Vaihingen, Germany and an oblique nadir image block of Zürich, Switzerland, using three kinds of matching configurations. The proposed method was compared to other state-of-art methods, SURE and PhotoScan. The results demonstrate that the proposed method delivers matches at higher completeness, efficiency, and accuracy than the other methods tested; the RMS for average reprojection error reached the sub pixel level and the actual positioning deviation was better than 1.5 GSD. Full article
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Open AccessArticle Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data
Remote Sens. 2016, 8(10), 800; doi:10.3390/rs8100800
Received: 18 April 2016 / Revised: 7 September 2016 / Accepted: 20 September 2016 / Published: 27 September 2016
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Abstract
Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (fpv)
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Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (fpv) and NPV (fnpv) using multispectral information. The purpose of this study is to evaluate several spectral unmixing approaches for retrieval of fpv and fnpv in the Otindag Sandy Land using GF-1 wide-field view (WFV) data. To deal with endmember variability, pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, and Automated Monte Carlo Unmixing Analysis, AutoMCU) endmember selection approaches were applied. Observed fractional cover data from 104 field sites were used for comparison. For fpv, all methods show statistically significant correlations with observed data, among which AutoMCU had the highest performance (R2 = 0.49, RMSE = 0.17), followed by MESMA (R2 = 0.48, RMSE = 0.21), and SMA (R2 = 0.47, RMSE = 0.27). For fnpv, MESMA had the lowest performance (R2 = 0.11, RMSE = 0.24) because of coupling effects of the NPV and bare soil endmembers, SMA overestimates fnpv (R2 = 0.41, RMSE = 0.20), but is significantly correlated with observed data, and AutoMCU provides the most accurate predictions of fnpv (R2 = 0.49, RMSE = 0.09). Thus, the AutoMCU approach is proven to be more effective than SMA and MESMA, and GF-1 WFV data are capable of distinguishing NPV from bare soil in the Otindag Sandy Land. Full article
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Open AccessArticle Catenary System Detection, Localization and Classification Using Mobile Scanning Data
Remote Sens. 2016, 8(10), 801; doi:10.3390/rs8100801
Received: 18 July 2016 / Revised: 19 September 2016 / Accepted: 22 September 2016 / Published: 27 September 2016
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Abstract
This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure
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This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures’ presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified. Full article
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Open AccessArticle Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands
Remote Sens. 2016, 8(10), 802; doi:10.3390/rs8100802
Received: 1 July 2016 / Revised: 25 August 2016 / Accepted: 15 September 2016 / Published: 27 September 2016
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Abstract
In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence
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In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the mowing event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a mowing event. Precipitation caused the coherence to decrease, impeding the detection of a mowing event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for mowed and unmowed grasslands. Additionally, an increase of coherence after a mowing event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered. Full article
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Open AccessArticle Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
Remote Sens. 2016, 8(10), 803; doi:10.3390/rs8100803
Received: 12 July 2016 / Revised: 13 September 2016 / Accepted: 14 September 2016 / Published: 28 September 2016
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Abstract
In Arctic regions, a major concern is the release of carbon from melting permafrost that could greatly exceed current human carbon emissions. Arctic rivers drain these organic-rich watersheds (Ob, Lena, Yenisei, Mackenzie, Yukon) but field measurements at the outlets of these great Arctic
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In Arctic regions, a major concern is the release of carbon from melting permafrost that could greatly exceed current human carbon emissions. Arctic rivers drain these organic-rich watersheds (Ob, Lena, Yenisei, Mackenzie, Yukon) but field measurements at the outlets of these great Arctic rivers are constrained by limited accessibility of sampling sites. In particular, the highest dissolved organic carbon (DOC) fluxes are observed throughout the ice breakup period that occurs over a short two to three-week period in late May or early June during the snowmelt-generated peak flow. The colored fraction of dissolved organic carbon (DOC) which absorbs UV and visible light is designed as chromophoric dissolved organic matter (CDOM). It is highly correlated to DOC in large arctic rivers and streams, allowing for remote sensing to monitor DOC concentrations from satellite imagery. High temporal and spatial resolutions remote sensing tools are highly relevant for the study of DOC fluxes in a large Arctic river. The high temporal resolution allows for correctly assessing this highly dynamic process, especially the spring freshet event (a few weeks in May). The high spatial resolution allows for assessing the spatial variability within the stream and quantifying DOC transfer during the ice break period when the access to the river is almost impossible. In this study, we develop a CDOM retrieval algorithm at a high spatial and a high temporal resolution in the Yenisei River. We used extensive DOC and DOM spectral absorbance datasets from 2014 and 2015. Twelve SPOT5 (Take5) and Landsat 8 (OLI) images from 2014 and 2015 were examined for this investigation. Relationships between CDOM and spectral variables were explored using linear models (LM). Results demonstrated the capacity of a CDOM algorithm retrieval to monitor DOC fluxes in the Yenisei River during a whole open water season with a special focus on the peak flow period. Overall, future Sentinel2/Landsat8 synergies are promising to monitor DOC fluxes in Arctic rivers and advance our understanding of the Earth’s carbon cycle. Full article
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Open AccessArticle Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images
Remote Sens. 2016, 8(10), 804; doi:10.3390/rs8100804
Received: 15 July 2016 / Revised: 8 September 2016 / Accepted: 22 September 2016 / Published: 29 September 2016
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Abstract
Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically,
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Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically, thereby improving the classification accuracy under the condition of small samples. This paper presents an improved synergetic classification scheme based on the concept of self-learning for HS and PAN images. The investigated scheme considers three basic rules, namely the identity rule, the uncertainty rule, and the diversity rule. By integrating the diversity of samples into the SL scheme, a more stable classifier is trained by using fewer samples. Experiments on three synthetic and real HS and PAN images reveal that the diversity criterion can avoid the problem of bias sampling, and has a certain advantage over the primary self-learning approach. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle An Iterative Approach to Ground Penetrating Radar at the Maya Site of Pacbitun, Belize
Remote Sens. 2016, 8(10), 805; doi:10.3390/rs8100805
Received: 17 May 2016 / Revised: 15 September 2016 / Accepted: 20 September 2016 / Published: 29 September 2016
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Abstract
Ground penetrating radar (GPR) surveys provide distinct advantages for archaeological prospection in ancient, complex, urban Maya sites, particularly where dense foliage or modern debris may preclude other remote sensing or geophysical techniques. Unidirectional GPR surveys using a 500 MHz shielded antenna were performed
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Ground penetrating radar (GPR) surveys provide distinct advantages for archaeological prospection in ancient, complex, urban Maya sites, particularly where dense foliage or modern debris may preclude other remote sensing or geophysical techniques. Unidirectional GPR surveys using a 500 MHz shielded antenna were performed at the Middle Preclassic Maya site of Pacbitun, Belize. The survey in 2012 identified numerous linear and circular anomalies between 1 m and 2 m deep. Based on these anomalies, one 1 m × 4 m unit and three smaller units were excavated in 2013. These test units revealed a curved plaster surface not previously found at Pacbitun. Post-excavation, GPR data were reprocessed to best match the true nature of excavated features. Additional GPR surveys oriented perpendicular to the original survey confirmed previously detected anomalies and identified new anomalies. The excavations provided information on the sediment layers in the survey area, which allowed better identification of weak radar reflections of the surfaces of a burnt, Middle Preclassic temple in the northern end of the survey area. Additional excavations of the area in 2014 and 2015 revealed it to be a large square structure, which was named El Quemado. Full article
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
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Open AccessArticle Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation
Remote Sens. 2016, 8(10), 807; doi:10.3390/rs8100807
Received: 2 June 2016 / Revised: 11 September 2016 / Accepted: 22 September 2016 / Published: 28 September 2016
Cited by 5 | PDF Full-text (4996 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite
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Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms
Remote Sens. 2016, 8(10), 808; doi:10.3390/rs8100808
Received: 5 August 2016 / Revised: 9 September 2016 / Accepted: 15 September 2016 / Published: 28 September 2016
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Abstract
Land surface temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative
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Land surface temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This study analyzes calibration strategies while considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere, indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis—Satellite Application Facility to calibrate its LST algorithms, applied both for current and forthcoming sensors. Full article
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Open AccessArticle A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements
Remote Sens. 2016, 8(10), 809; doi:10.3390/rs8100809
Received: 11 May 2016 / Revised: 14 September 2016 / Accepted: 22 September 2016 / Published: 29 September 2016
Cited by 2 | PDF Full-text (2527 KB) | HTML Full-text | XML Full-text
Abstract
Dual-Doppler lidar is a powerful remote sensing technique that can accurately measure horizontal wind speeds and enable the reconstruction of two-dimensional wind fields based on measurements from two separate lidars. Previous research has provided a framework of dual-Doppler algorithms for processing both radar
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Dual-Doppler lidar is a powerful remote sensing technique that can accurately measure horizontal wind speeds and enable the reconstruction of two-dimensional wind fields based on measurements from two separate lidars. Previous research has provided a framework of dual-Doppler algorithms for processing both radar and lidar measurements, but their application to wake measurements has not been addressed in detail yet. The objective of this paper is to reconstruct two-dimensional wind fields of wind turbine wakes and assess the performance of dual-Doppler lidar scanning strategies, using the newly developed Multiple-Lidar Wind Field Evaluation Algorithm (MuLiWEA). This processes non-synchronous dual-Doppler lidar measurements and solves the horizontal wind field with a set of linear equations, also considering the mass continuity equation. MuLiWEA was applied on simulated measurements of a simulated wind turbine wake, with two typical dual-Doppler lidar measurement scenarios. The results showed inaccuracies caused by the inhomogeneous spatial distribution of the measurements in all directions, related to the ground-based scanning of a wind field at wind turbine hub height. Additionally, MuLiWEA was applied on a real dual-Doppler lidar measurement scenario in the German offshore wind farm “alpha ventus”. It was concluded that the performance of both simulated and real lidar measurement scenarios in combination with MuLiWEA is promising. Although the accuracy of the reconstructed wind fields is compromised by the practical limitations of an offshore dual-Doppler lidar measurement setup, the performance shows sufficient accuracy to serve as a basis for 10 min average steady wake model validation. Full article
(This article belongs to the Special Issue Remote Sensing of Wind Energy)
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Open AccessArticle A Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors
Remote Sens. 2016, 8(10), 810; doi:10.3390/rs8100810
Received: 2 June 2016 / Revised: 14 September 2016 / Accepted: 24 September 2016 / Published: 30 September 2016
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Abstract
A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests
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A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests and HydroLight simulated data for waters ranging from clear open ocean case-1 to turbid coastal case-2 waters. The quantitative comparison shows that there are clearly some high performing TSS models that can potentially be applied in mapping TSS concentration for regions of uncertain water type. The highest performing TSS models tested were robust enough to retrieve TSS from different water types with Mean Absolute Relative Errors (MARE) of 69.96%–481.82% for HydroLight simulated data. The models were also compared in regional waters of northern Western Australia where the highest performing TSS models yielded a MARE in the range of 43.11%–102.59%. The range of Smallest Relative Error (SRE) and Largest Relative Error (LRE) between the highest and the lowest performing TSS models spanned three orders of magnitude, suggesting users must be cautious in selecting appropriate models for unknown water types. Full article
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Open AccessArticle Evaluation of the Initial Thematic Output from a Continuous Change-Detection Algorithm for Use in Automated Operational Land-Change Mapping by the U.S. Geological Survey
Remote Sens. 2016, 8(10), 811; doi:10.3390/rs8100811
Received: 3 May 2016 / Revised: 13 September 2016 / Accepted: 19 September 2016 / Published: 1 October 2016
Cited by 1 | PDF Full-text (4500 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The Continuous Change Detection and Classification (CCDC) algorithm is being evaluated as the
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The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The Continuous Change Detection and Classification (CCDC) algorithm is being evaluated as the likely methodology following early trials. Data for training and testing of CCDC thematic maps have been provided by the USGS Land Cover Trends (LC Trends) project, which offers sample-based, manually classified thematic land cover data at 2755 probabilistically located sample blocks across the conterminous United States. These samples represent a high quality, well distributed source of data to train the Random Forest classifier invoked by CCDC. We evaluated the suitability of LC Trends data to train the classifier by assessing the agreement of annual land cover maps output from CCDC with output from the LC Trends project within 14 Landsat path/row locations across the conterminous United States. We used a small subset of circa 2000 data from the LC Trends project to train the classifier, reserving the remaining Trends data from 2000, and incorporating LC Trends data from 1992, to evaluate measures of agreement across time, space, and thematic classes, and to characterize disagreement. Overall agreement ranged from 75% to 98% across the path/rows, and results were largely consistent across time. Land cover types that were well represented in the training data tended to have higher rates of agreement between LC Trends and CCDC outputs. Characteristics of disagreement are being used to improve the use of LC Trends data as a continued source of training information for operational production of annual land cover maps. Full article
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Open AccessArticle Panoramic Mosaics from Chang’E-3 PCAM Images at Point A
Remote Sens. 2016, 8(10), 812; doi:10.3390/rs8100812
Received: 11 March 2016 / Revised: 9 September 2016 / Accepted: 26 September 2016 / Published: 30 September 2016
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Abstract
This paper presents a unique approach for panoramic mosaics based on Moon surface images from the Chang’E-3 (CE-3) mission, with consideration of the exposure time and external illumination changes in CE-3 Panoramic Camera (PCAM) imaging. The engineering implementation involves algorithms of image feature
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This paper presents a unique approach for panoramic mosaics based on Moon surface images from the Chang’E-3 (CE-3) mission, with consideration of the exposure time and external illumination changes in CE-3 Panoramic Camera (PCAM) imaging. The engineering implementation involves algorithms of image feature points extraction by using Speed-Up Robust Features (SURF), and a newly defined measure is used to obtain the corresponding points in feature matching. Then, the transformation matrix is calculated and optimized between adjacent images by the Levenberg–Marquardt algorithm. Finally, an image is reconstructed by using a fade-in-fade-out method based on linear interpolation to achieve a seamless mosaic. The developed algorithm has been tested with CE-3 PCAM images at Point A (one of the rover sites where the rover is separated from the lander). This approach has produced accurate mosaics from CE-3 PCAM images, as is indicated by the value of the Peak Signal to Noise Ratio (PSNR), which is greater than 31 dB between the overlapped region of the images before and after fusion. Full article
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Open AccessCommunication A General-Purpose Spatial Survey Design for Collaborative Science and Monitoring of Global Environmental Change: The Global Grid
Remote Sens. 2016, 8(10), 813; doi:10.3390/rs8100813
Received: 4 June 2016 / Revised: 18 September 2016 / Accepted: 26 September 2016 / Published: 30 September 2016
PDF Full-text (1408 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe
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Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation. Full article
(This article belongs to the Special Issue Monitoring of Land Changes)
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Open AccessArticle A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery
Remote Sens. 2016, 8(10), 814; doi:10.3390/rs8100814
Received: 25 July 2016 / Revised: 30 August 2016 / Accepted: 26 September 2016 / Published: 30 September 2016
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Abstract
Very high resolution (VHR) remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene
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Very high resolution (VHR) remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene scale decomposition (OSSD)-based system is proposed for the classification of VHR images. Different from the traditional methods, the OSSD-based system aims to improve the classification performance by decomposing the complexity of an image’s content. First, an image scene is divided into sub-image blocks through segmentation to decompose the image content. Subsequently, each sub-image block is classified respectively, or each block is processed firstly through an image filter or spectral–spatial feature extraction method, and then each processed segment is taken as the feature input of a classifier. Finally, classified sub-maps are fused together for accuracy evaluation. The effectiveness of our proposed approach was investigated through experiments performed on different images with different supervised classifiers, namely, support vector machine, k-nearest neighbor, naive Bayes classifier, and maximum likelihood classifier. Compared with the accuracy achieved without OSSD processing, the accuracy of each classifier improved significantly, and our proposed approach shows outstanding performance in terms of classification accuracy. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States
Remote Sens. 2016, 8(10), 815; doi:10.3390/rs8100815
Received: 18 July 2016 / Revised: 19 September 2016 / Accepted: 26 September 2016 / Published: 1 October 2016
Cited by 6 | PDF Full-text (3863 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Satellite-retrieved aerosol optical depth (AOD) has become an important predictor of ground-level particulate matter (PM) and greatly empowered air pollution research worldwide. We evaluated the AOD parameters included in the Collection 6 aerosol product of the Moderate Resolution Imaging Spectroradiometer (MODIS) for two
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Satellite-retrieved aerosol optical depth (AOD) has become an important predictor of ground-level particulate matter (PM) and greatly empowered air pollution research worldwide. We evaluated the AOD parameters included in the Collection 6 aerosol product of the Moderate Resolution Imaging Spectroradiometer (MODIS) for two key factors affecting their applications in air quality research—coverage and accuracy—over the continental US. For the high confidence retrievals (QAC 3), the 10 km DB-DT combined AOD has the best coverage nationwide (29.7% of the days in a year in any given 12 km grid cell). While the Eastern US generally had more successful AOD retrievals, the highest spatial coverage of AOD parameters were found in California (>55%) and other vegetated parts of the Western US. If lower QAC retrievals were included, the coverage of the 10 km DB AOD was dramatically increased to 49.6%. In the Eastern US, the QAC 3 retrievals of all four AOD parameters are highly correlated with AERONET observations (correlation coefficients between 0.80 and 0.92). In the Western US, positive retrieval errors existed in all MODIS AOD parameters, resulting in lower correlations with AERONET. AOD retrieval errors showed significant dependence on flight geometry, land cover type, and weather conditions. To ensure appropriate use of these AOD values, air quality researchers should carefully balance the needs for coverage and accuracy, and develop additional data screening criteria based on their study design. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle Optimal Use of Space-Borne Advanced Infrared and Microwave Soundings for Regional Numerical Weather Prediction
Remote Sens. 2016, 8(10), 816; doi:10.3390/rs8100816
Received: 30 March 2016 / Revised: 9 August 2016 / Accepted: 19 September 2016 / Published: 30 September 2016
Cited by 1 | PDF Full-text (6387 KB) | HTML Full-text | XML Full-text
Abstract
Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space
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Satellite observations can either be assimilated as radiances or as retrieved physical parameters to reduce error in the initial conditions used by the Numerical Weather Prediction (NWP) model. Assimilation of radiances requires a radiative transfer model to convert atmospheric state in model space to that in radiance space, thus requiring a lot of computational resources especially for hyperspectral instruments with thousands of channels. On the other hand, assimilating the retrieved physical parameters is computationally more efficient as they are already in thermodynamic states, which can be compared with NWP model outputs through the objective analysis scheme. A microwave (MW) sounder and an infrared (IR) sounder have their respective observational limitation due to the characteristics of adopted spectra. The MW sounder observes at much larger field-of-view (FOV) compared to an IR sounder. On the other hand, MW has the capability to reveal the atmospheric sounding when the clouds are presented, but IR observations are highly sensitive to clouds, The advanced IR sounder is able to reduce uncertainties in the retrieved atmospheric temperature and moisture profiles due to its higher spectral-resolution than the MW sounder which has much broader spectra bands. This study tries to quantify the optimal use of soundings retrieved from the microwave sounder AMSU and infrared sounder AIRS onboard the AQUA satellite in the regional Weather and Research Forecasting (WRF) model through three-dimensional variational (3D-var) data assimilation scheme. Four experiments are conducted by assimilating soundings from: (1) clear AIRS single field-of-view (SFOV); (2) retrieved from using clear AMSU and AIRS observations at AMSU field-of-view (SUP); (3) all SFOV soundings within AMSU FOVs must be clear; and (4) SUP soundings which must have all clear SFOV soundings within the AMSU FOV. A baseline experiment assimilating only conventional data is generated for comparison. Various atmospheric state variables at different pressure levels are used to assess the impact from assimilating these different data by comparing them with European Centre for Medium Range Weather Forecast (ECMWF) reanalysis data. Results indicate assimilation of SUP soundings improve the mid and upper troposphere, whereas assimilation of SFOV soundings has positive impact on the lower troposphere. Two additional assimilation experiments are carried out to determine the combination of SUP and SFOV soundings that will provide the best performance throughout the troposphere. The results indicate that optimal combination is to assimilate clear-sky matched IR retrievals with non-matched MW soundings. Full article
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
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Open AccessArticle An Approach to Extended Fresnel Scattering for Modeling of Depolarizing Soil-Trunk Double-Bounce Scattering
Remote Sens. 2016, 8(10), 818; doi:10.3390/rs8100818
Received: 15 June 2016 / Revised: 8 September 2016 / Accepted: 26 September 2016 / Published: 1 October 2016
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Abstract
Focusing on scattering from natural media, dihedral (double bounce) scattering is often characterized as a soil-trunk double Fresnel reflection, like for instance, in most model-based decompositions. As soils are predominantly rough in agriculture, the classical Rank 1 dihedral scattering component has to be
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Focusing on scattering from natural media, dihedral (double bounce) scattering is often characterized as a soil-trunk double Fresnel reflection, like for instance, in most model-based decompositions. As soils are predominantly rough in agriculture, the classical Rank 1 dihedral scattering component has to be extended to account for soil roughness-induced depolarization. Therefore, an azimuthal Line of Sight (LoS) rotation is applied solely on the soil plane of the double-bounce reflection to generate a depolarized dihedral scattering signal in agriculture. The results of the sensitivity analysis are shown for a distributed target in coherency matrix representation. It reveals that the combination of coherency matrix elements T22XD + T33XD is quasi-independent of the roughness-induced depolarization, while (T22XD − T33XD)/(T22XD + T33XD) is quasi-independent of the dielectric properties of the reflecting media. Therefore, a depolarization-independent retrieval of soil moisture or a direct roughness retrieval from the extended dihedral scattering component might be possible in stalk-dominated agriculture under certain conditions (e.g., the influence of a differential phase stays at a low level: ϕ < 15°). The first analyses with L-band airborne-SAR data of DLR’s E-SAR and F-SAR systems in agricultural regions during the AgriSAR, OPAQUE, SARTEO and TERENO project campaigns state the existence and potential of the extended Fresnel scattering mechanism to represent dihedral scattering between a rough (tilled) soil and the stalks of the agricultural plants. Full article
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Open AccessArticle Urban Built-up Areas in Transitional Economies of Southeast Asia: Spatial Extent and Dynamics
Remote Sens. 2016, 8(10), 819; doi:10.3390/rs8100819
Received: 3 August 2016 / Revised: 15 September 2016 / Accepted: 27 September 2016 / Published: 2 October 2016
Cited by 1 | PDF Full-text (7025 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Urban built-up area, one of the most important measures of an urban landscape, is an essential variable for understanding ecological and socioeconomic processes in urban systems. With an interest in urban development in transitional economies in Southeast Asia, we recognized a lack of
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Urban built-up area, one of the most important measures of an urban landscape, is an essential variable for understanding ecological and socioeconomic processes in urban systems. With an interest in urban development in transitional economies in Southeast Asia, we recognized a lack of high-to-medium resolution (<60 m) built-up information for countries in the region, including Vietnam, Laos, Cambodia and Myanmar. In this study, we combined multiple remote sensing data, including Landsat, DMSP/OLS night time light, MODIS NDVI data and other ancillary spatial data, to develop a 30-m resolution urban built-up map of 2010 for the above four countries. Following the trend analysis of the DMSP/OLS time series and the 2010 urban built-up extent, we also quantified the spatiotemporal dynamics of urban built-up areas from 1992 to 2010. Among the four countries, Vietnam had the highest proportion of urban built-up area (0.91%), followed by Myanmar (0.15%), Cambodia (0.12%) and Laos (0.09%). Vietnam was also the fastest in new built-up development (increased ~8.8-times during the 18-year study period), followed by Laos, Cambodia and Myanmar, which increased at 6.0-, 3.6- and 0.24-times, respectively. Full article
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Open AccessArticle Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data
Remote Sens. 2016, 8(10), 820; doi:10.3390/rs8100820
Received: 2 July 2016 / Accepted: 28 September 2016 / Published: 5 October 2016
Cited by 1 | PDF Full-text (20875 KB) | HTML Full-text | XML Full-text
Abstract
On the basis of the Gaussian vertical backscatter (GVB) model, this paper proposes a new method for extracting pine forest height and forest underlying digital elevation model (FUDEM) from multi-baseline (MB) P-band polarimetric-interferometric radar (PolInSAR) data. Considering the linear ground-to-volume relationship, the GVB
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On the basis of the Gaussian vertical backscatter (GVB) model, this paper proposes a new method for extracting pine forest height and forest underlying digital elevation model (FUDEM) from multi-baseline (MB) P-band polarimetric-interferometric radar (PolInSAR) data. Considering the linear ground-to-volume relationship, the GVB is linked to the interferometric coherences of different polarizations. Subsequently, an inversion algorithm, weighted complex least squares adjustment (WCLSA), is formulated, including the mathematical model, the stochastic model and the parameter estimation method. The WCLSA method can take full advantage of the redundant observations, adjust the contributions of different observations and avoid null ground-to-volume ratio (GVR) assumption. The simulated experiment demonstrates that the WCLSA method is feasible to estimate the pure ground and volume scattering contributions. Finally, the WCLSA method is applied to E-SAR P-band data acquired over Krycklan Catchment covered with mixed pine forest. It is shown that the FUDEM highly agrees with those derived by LiDAR, with a root mean square error (RMSE) of 3.45 m, improved by 23.0% in comparison to the three-stage method. The difference between the extracted forest height and LiDAR forest height is assessed with a RMSE of 1.45 m, improved by 37.5% and 26.0%, respectively, for model and inversion aspects in comparison to three-stage inversion based on random volume over ground (RVoG) model. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Fine-Scale Sea Ice Structure Characterized Using Underwater Acoustic Methods
Remote Sens. 2016, 8(10), 821; doi:10.3390/rs8100821
Received: 30 June 2016 / Accepted: 24 September 2016 / Published: 5 October 2016
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Abstract
Antarctic sea ice is known to provide unique ecosystem habitat at the ice–ocean interface. Mapping sea ice characteristics—such as thickness and roughness—at high resolution from beneath the ice is difficult due to access. A Geoswath Plus phase-measuring bathymetric sonar mounted on an autonomous
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Antarctic sea ice is known to provide unique ecosystem habitat at the ice–ocean interface. Mapping sea ice characteristics—such as thickness and roughness—at high resolution from beneath the ice is difficult due to access. A Geoswath Plus phase-measuring bathymetric sonar mounted on an autonomous underwater vehicle (AUV) was employed in this study to collect data underneath the sea ice at Cape Evans in Antarctica in November 2014. This study demonstrates how acoustic data can be collected and processed to resolutions of 1 m for acoustic bathymetry and 5 cm for acoustic backscatter in this challenging environment. Different ice textures such as platelet ice, smooth ice, and sea ice morphologies, ranging in size from 1 to 50 m were characterized. The acoustic techniques developed in this work could provide a key to understanding the distribution of sea ice communities, as they are nondisruptive to the fragile ice environments and provide geolocated data over large spatial extents. These results improve our understanding of sea ice properties and the complex, highly variable ecosystem that exists at this boundary. Full article
(This article belongs to the Special Issue Underwater Acoustic Remote Sensing)
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Open AccessArticle Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging
Remote Sens. 2016, 8(10), 822; doi:10.3390/rs8100822
Received: 1 July 2016 / Revised: 15 September 2016 / Accepted: 28 September 2016 / Published: 7 October 2016
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Abstract
Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower
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Aerial and terrestrial thermography has become a practical tool to determine water stress conditions in vineyards. However, for proper use of this technique it is necessary to consider vine architecture (canopy zone analysis) and image thresholding approaches (determination of the upper and lower baseline temperature values). During the 2014–2015 growing season, an experimental study under different water conditions (slight, mild, moderate, and severe water stress) was carried out in a commercial vineyard (Vitis vinifera L., cv. Carménè). In this study thermal images were obtained from different canopy zones by using both aerial (>60 m height) and ground-based (sunlit, shadow and nadir views) thermography. Using customized code that was written specifically for this research, three different thresholding approaches were applied to each image: (i) the standard deviation technique (SDT); (ii) the energy balance technique (EBT); and (iii) the field reference temperature technique (FRT). Results obtained from three different approaches showed that the EBT had the best performance. The EBT was able to discriminate over 95% of the leaf material, while SDT and FRT were able to detect around 70% and 40% of the leaf material, respectively. In the case of canopy zone analysis, ground-based nadir images presented the best correlations with stomatal conductance (gs) and stem water potential (Ψstem), reaching determination coefficients (r2) of 0.73 and 0.82, respectively. The best relationships between thermal indices and plant-based variables were registered during the period of maximum atmospheric demand (near veraison) with significant correlations for all methods. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products
Remote Sens. 2016, 8(10), 824; doi:10.3390/rs8100824
Received: 20 July 2016 / Revised: 26 September 2016 / Accepted: 28 September 2016 / Published: 7 October 2016
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Abstract
Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP).
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Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R2 ≤ 0.94 for the red band, 0.50 ≤ R2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%–20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R2 and low root mean square errors (RMSE) (0.864 ≤ R2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m2 for biomass; and 0.631 ≤ R2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set. Full article
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Open AccessArticle Feature Extraction in the North Sinai Desert Using Spaceborne Synthetic Aperture Radar: Potential Archaeological Applications
Remote Sens. 2016, 8(10), 825; doi:10.3390/rs8100825
Received: 14 July 2016 / Revised: 19 September 2016 / Accepted: 27 September 2016 / Published: 7 October 2016
Cited by 1 | PDF Full-text (15330 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Techniques were implemented to extract anthropogenic features in the desert region of North Sinai using data from the first- and second-generation Phased Array type L-band Synthetic Aperture Radar (PALSAR-1 and 2). To obtain a synoptic view over the study area, a mosaic of
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Techniques were implemented to extract anthropogenic features in the desert region of North Sinai using data from the first- and second-generation Phased Array type L-band Synthetic Aperture Radar (PALSAR-1 and 2). To obtain a synoptic view over the study area, a mosaic of average, multitemporal (De Grandi) filtered PALSAR-1 σ° backscatter of North Sinai was produced. Two subset regions were selected for further analysis. The first included an area of abundant linear features of high relative backscatter in a strategic, but sparsely developed area between the Wadi Tumilat and Gebel Maghara. The second included an area of low backscatter anomaly features in a coastal sabkha around the archaeological sites of Tell el-Farama, Tell el-Mahzan, and Tell el-Kanais. Over the subset region between the Wadi Tumilat and Gebel Maghara, algorithms were developed to extract linear features and convert them to vector format to facilitate interpretation. The algorithms were based on mathematical morphology, but to distinguish apparent man-made features from sand dune ridges, several techniques were applied. The first technique took as input the average σ° backscatter and used a Digital Elevation Model (DEM) derived Local Incidence Angle (LAI) mask to exclude sand dune ridges. The second technique, which proved more effective, used the average interferometric coherence as input. Extracted features were compared with other available information layers and in some cases revealed partially buried roads. Over the coastal subset region a time series of PALSAR-2 spotlight data were processed. The coefficient of variation (CoV) of De Grandi filtered imagery clearly revealed anomaly features of low CoV. These were compared with the results of an archaeological field walking survey carried out previously. The features generally correspond with isolated areas identified in the field survey as having a higher density of archaeological finds, and interpreted as possible islands of dry land, which may have been surrounded by lagoons, rivers, and swamplands in antiquity. It is suggested that these surrounding areas may still have a higher water content, sufficient to be detected in processed Synthetic Aperture Radar (SAR) imagery. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map
Remote Sens. 2016, 8(10), 826; doi:10.3390/rs8100826
Received: 16 June 2016 / Revised: 22 September 2016 / Accepted: 27 September 2016 / Published: 8 October 2016
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Abstract
There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating
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There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400–2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since colors went from dark blue to light purple, and were related with soil texture and mineralogy of the region. The comparison between the traditional and digital soil maps showed a global accuracy of 69% for the TFS-1 map and 62% in the TFS-2, with kappa indices of 0.52 and 0.45, respectively. We randomly validated both digital and traditional maps with individual plots at field. We achieve a 75% and 80% agreement for digital and traditional maps, respectively, which allows us to conclude that traditional map is not necessarily the truth and digital is very close. The key of the strategy was to use bare soil image as a first step on the indication of soil variation in the area, indicating in-situ location for sample collection in all depths. The current strategy is innovative since we linked sensors from ground to space in addition with relief parameters and spectral libraries. The strategy indicates a more accurate map with less soil samples and lower cost. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle A New Image Processing Procedure Integrating PCI-RPC and ArcGIS-Spline Tools to Improve the Orthorectification Accuracy of High-Resolution Satellite Imagery
Remote Sens. 2016, 8(10), 827; doi:10.3390/rs8100827
Received: 21 June 2016 / Revised: 5 September 2016 / Accepted: 27 September 2016 / Published: 9 October 2016
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Abstract
Given the low accuracy of the traditional remote sensing image processing software when orthorectifying satellite images that cover mountainous areas, and in order to make a full use of mutually compatible and complementary characteristics of the remote sensing image processing software PCI-RPC (Rational
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Given the low accuracy of the traditional remote sensing image processing software when orthorectifying satellite images that cover mountainous areas, and in order to make a full use of mutually compatible and complementary characteristics of the remote sensing image processing software PCI-RPC (Rational Polynomial Coefficients) and ArcGIS-Spline, this study puts forward a new operational and effective image processing procedure to improve the accuracy of image orthorectification. The new procedure first processes raw image data into an orthorectified image using PCI with RPC model (PCI-RPC), and then the orthorectified image is further processed using ArcGIS with the Spline tool (ArcGIS-Spline). We used the high-resolution CBERS-02C satellite images (HR1 and HR2 scenes with a pixel size of 2 m) acquired from Yangyuan County in Hebei Province of China to test the procedure. In this study, when separately using PCI-RPC and ArcGIS-Spline tools directly to process the HR1/HR2 raw images, the orthorectification accuracies (root mean square errors, RMSEs) for HR1/HR2 images were 2.94 m/2.81 m and 4.65 m/4.41 m, respectively. However, when using our newly proposed procedure, the corresponding RMSEs could be reduced to 1.10 m/1.07 m. The experimental results demonstrated that the new image processing procedure which integrates PCI-RPC and ArcGIS-Spline tools could significantly improve image orthorectification accuracy. Therefore, in terms of practice, the new procedure has the potential to use existing software products to easily improve image orthorectification accuracy. Full article
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Open AccessArticle A Virtual Restoration Approach for Ancient Plank Road Using Mechanical Analysis with Precision 3D Data of Heritage Site
Remote Sens. 2016, 8(10), 828; doi:10.3390/rs8100828
Received: 6 August 2016 / Revised: 19 September 2016 / Accepted: 27 September 2016 / Published: 9 October 2016
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Abstract
The ancient plank road is a creative building in the history of Chinese ancient traffic through cliffs. In this paper, a virtual restoration approach for ancient plank road using mechanical analysis with precision 3D data of current heritage site is proposed. Firstly, an
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The ancient plank road is a creative building in the history of Chinese ancient traffic through cliffs. In this paper, a virtual restoration approach for ancient plank road using mechanical analysis with precision 3D data of current heritage site is proposed. Firstly, an aero photogrammetry with multiple view images from Unmanned Aerial Vehicle (UAV) imaging system is presented to obtain the 3D point cloud of ancient plank roads, which adopts a density image matching and aerial triangulation processing. In addition, a terrestrial laser scanner is integrated to obtain detail 3D data of the plank road. Secondly, a mechanical analysis method based on the precision 3D data of the current plank roads is proposed to determine their forms and restore each of their components with detail sizes. Finally, all components and background scene were added to the existing model to obtain a virtual restoration model, which indicates that it is effective and feasible to achieve a three-dimensional digital and virtual restoration of ancient sites. The Chiya Plank Road is taken as a virtual restoration example with the proposed approach. The restored 3D model of the ancient plank can be widely used for digital management, research, and visualization of ancient plank roads. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Effects of Urbanization and Seasonal Cycle on the Surface Urban Heat Island Patterns in the Coastal Growing Cities: A Case Study of Casablanca, Morocco
Remote Sens. 2016, 8(10), 829; doi:10.3390/rs8100829
Received: 13 June 2016 / Revised: 18 September 2016 / Accepted: 24 September 2016 / Published: 10 October 2016
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Abstract
The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data.
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The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data. To achieve this goal, a time series of Landsat TM/ETM+/OLI-TIRS images was acquired from 1984 to 2016 and analyzed. In addition, nocturnal MODIS images acquired from 2005 to 2015 were used to evaluate the nighttime SUHI. In order to better analyze intense heat produced by urban core, SUHI intensity (SUHII) was computed by quantifying the difference of land surface temperature (LST) between urban and rural areas. The urban core SUHII appears more significant in winter seasons than during summer, while the pattern of SUHII becomes moderate during intermediate seasons. During winter, the average daytime SUHII gradually increased in the residential area of Casablanca and in some small peri-urban cities by more than 1 °C from 1984 to 2015. The industrial areas of the Casablanca region were affected by a significant rise in SUHII exceeding 15 °C in certain industrial localities. In contrast, daytime SUHII shows a reciprocal effect during summer with emergence of a heat island in rural areas and development of cool islands in urban and peri-urban areas. During nighttime, the SUHII remains positive in urban areas year-round with higher values in winter as compared to summer. The results point out that the seasonal cycle of daytime SUHII as observed in the Casablanca region is different from other mid-latitude cities, where the highest values are often observed in summer during the day. Full article
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Open AccessArticle A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage
Remote Sens. 2016, 8(10), 830; doi:10.3390/rs8100830
Received: 25 August 2016 / Revised: 28 September 2016 / Accepted: 30 September 2016 / Published: 10 October 2016
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Abstract
An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l1 norm regularization model that enforces the local and nonlocal sparsity constraints
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An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l 1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes. Full article
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Open AccessArticle Urban Heat Islands as Viewed by Microwave Radiometers and Thermal Time Indices
Remote Sens. 2016, 8(10), 831; doi:10.3390/rs8100831
Received: 1 July 2016 / Revised: 16 September 2016 / Accepted: 27 September 2016 / Published: 10 October 2016
Cited by 1 | PDF Full-text (5211 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower
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Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower temporal coverage due to revisit frequency, limited swath width, and cloud cover. Algorithms have recently been developed to retrieve near-surface air temperatures using microwave radiometer data, which enables characterization of UHIs in metropolitan areas, major conurbations, and global megacities at regional to continental scales using temporally denser time series than those that have been available from TIR sensors. Here we examine how UHIs appear across the entire Western Hemisphere using surface air temperatures derived from the Advanced Microwave Scanning Radiometers (AMSRs), AMSR-E onboard the National Aeronautics and Space Administration’s (NASA’s) Aqua and AMSR2 onboard the Japan Aerospace eXploration Agency’s Global Change Observation Mission-Water1 (JAXA’s GCOM-W1) satellites. We compare these data with station observations from the Global Historical Climate Network (GHCN) for 27 major cities across North America (in 83 urban-rural groupings) to demonstrate the capability of microwave data in a UHI study. Two measures of thermal time, accumulated diurnal and nocturnal degree-days, are calculated from the remotely sensed surface air temperature time series to characterize the urban-rural thermal differences over multiple growing seasons. Daytime urban thermal accumulations from the microwave data were sometimes lower than in adjacent rural areas. In contrast, station observations showed consistently higher day and night thermal accumulations in cities. UHIs are more pronounced at night, with 55% (AMSRs) and 93% (GHCN) of urban-rural groupings showing higher accumulated nocturnal degree-days in cities. While urban-rural thermal gradients may vary according to different datasets or locations, day-night differences in thermal time metrics were consistently lower (>90% of urban-rural groupings) in urban areas than in rural areas for both datasets. We propose that the normalized difference accumulated thermal time index (NDATTI) is a more robust metric for comparative UHI studies than simple temperature differences because it can be calculated from either station or remotely sensed data and it attenuates latitudinal effects. Full article
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Open AccessArticle Comparing Road-Kill Datasets from Hunters and Citizen Scientists in a Landscape Context
Remote Sens. 2016, 8(10), 832; doi:10.3390/rs8100832
Received: 30 July 2016 / Revised: 23 September 2016 / Accepted: 28 September 2016 / Published: 10 October 2016
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Abstract
Road traffic has severe effects on animals, especially when road-kills are involved. In many countries, official road-kill data are provided by hunters or police; there are also road-kill observations reported by citizen scientists. The aim of the current study was to test whether
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Road traffic has severe effects on animals, especially when road-kills are involved. In many countries, official road-kill data are provided by hunters or police; there are also road-kill observations reported by citizen scientists. The aim of the current study was to test whether road-kill reports by hunters stem from similar landscapes than those reported by citizen scientists. We analysed the surrounding landscapes of 712 road-kill reportings of European hares in the province of Lower Austria. Our data showed that road-killed hares reported both by hunters and citizens are predominantly surrounded by arable land. No difference of hedges and solitary trees could be found between the two datasets. However, significant differences in landcover classes and surrounding road networks indicate that hunters’ and citizen scientists’ data are different. Hunters reported hares from landscapes with significantly higher percentages of arable land, and greater lengths of secondary roads. In contrast, citizens reported hares from landscapes with significantly higher percentages of urban or industrial areas and greater lengths of motorways, primary roads, and residential roads. From this we argue that hunters tend to report data mainly from their hunting areas, whereas citizens report data during their daily routine on the way to/from work. We conclude that a citizen science approach is an important source for road-kill data when used in addition to official data with the aim of obtaining an overview of road-kill events on a landscape scale. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Seasonal to Interannual Variability of Satellite-Based Precipitation Estimates in the Pacific Ocean Associated with ENSO from 1998 to 2014
Remote Sens. 2016, 8(10), 833; doi:10.3390/rs8100833
Received: 27 July 2016 / Revised: 7 September 2016 / Accepted: 28 September 2016 / Published: 11 October 2016
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Abstract
Based on a widely used satellite precipitation product (TRMM Multi-satellite Precipitation Analysis 3B43), we analyzed the spatiotemporal variability of precipitation over the Pacific Ocean for 1998–2014 at seasonal and interannual timescales, separately, using the conventional empirical orthogonal function (EOF) and investigated the seasonal
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Based on a widely used satellite precipitation product (TRMM Multi-satellite Precipitation Analysis 3B43), we analyzed the spatiotemporal variability of precipitation over the Pacific Ocean for 1998–2014 at seasonal and interannual timescales, separately, using the conventional empirical orthogonal function (EOF) and investigated the seasonal patterns associated with El Niño–Southern Oscillation (ENSO) cycles using season-reliant empirical orthogonal function (SEOF) analysis. Lagged correlation analysis was also applied to derive the lead/lag correlations of the first two SEOF modes for precipitation with Pacific Decadal Oscillation (PDO) and two types of El Niño, i.e., central Pacific (CP) El Niño and eastern Pacific (EP) El Niño. We found that: (1) The first two seasonal EOF modes for precipitation represent the annual cycle of precipitation variations for the Pacific Ocean and the first interannual EOF mode shows the spatiotemporal variability associated with ENSO; (2) The first SEOF mode for precipitation is simultaneously associated with the development of El Niño and most likely coincides with CP El Niño. The second SEOF mode lagged behind ENSO by one year and is associated with post-El Niño years. PDO modulates precipitation variability significantly only when ENSO occurs by strengthening and prolonging the impacts of ENSO; (3) Seasonally evolving patterns of the first two SEOF modes represent the consecutive precipitation patterns associated with the entire development of EP El Niño and the following recovery year. The most significant variation occurs over the tropical Pacific, especially in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ); (4) Dry conditions in the western basin of the warm pool and wet conditions along the ITCZ and SPCZ bands during the mature phase of El Niño are associated with warm sea surface temperatures in the central tropical Pacific, and a subtropical anticyclone dominating over the northwestern Pacific. These findings may be useful for prediction of seasonal precipitation anomalies over the Pacific Ocean during El Niño years and recovery years. Full article
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Open AccessArticle A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China
Remote Sens. 2016, 8(10), 835; doi:10.3390/rs8100835
Received: 7 June 2016 / Revised: 25 September 2016 / Accepted: 8 October 2016 / Published: 12 October 2016
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Abstract
Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a
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Environmental monitoring of Earth from space has provided invaluable information for understanding land–atmosphere water and energy exchanges. However, the use of satellite-based precipitation observations in hydrologic and environmental applications is often limited by their coarse spatial resolutions. In this study, we propose a downscaling approach based on precipitation–land surface characteristics. Daytime land surface temperature, nighttime land surface temperature, and day–night land surface temperature differences were introduced as variables in addition to the Normalized Difference Vegetation Index (NDVI), the Digital Elevation Model (DEM), and geolocation (longitude, latitude). Four machine learning regression algorithms, the classification and regression tree (CART), the k-nearest neighbors (k-NN), the support vector machine (SVM), and random forests (RF), were implemented to downscale monthly TRMM 3B43 V7 precipitation data from 25 km to 1 km over North China for the purpose of comparison of algorithm performance. The downscaled results were validated based on observations from meteorological stations and were also compared to a previous downscaling algorithm. According to the validation results, the RF-based model produced the results with the highest accuracy. It was followed by SVM, CART, and k-NN, but the accuracy of the downscaled results using SVM relied greatly on residual correction. The downscaled results were well correlated with the observations during the year, but the accuracies were relatively lower in July to September. Downscaling errors increase as monthly total precipitation increases, but the RF model was less affected by this proportional effect between errors and observation compared with the other algorithms. The variable importances of the land surface temperature (LST) feature variables were higher than those of NDVI, which indicates the significance of considering the precipitation–land surface temperature relationship when downscaling TRMM 3B43 V7 precipitation data. Full article
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Open AccessArticle Evaluation of the Performance of Three Satellite Precipitation Products over Africa
Remote Sens. 2016, 8(10), 836; doi:10.3390/rs8100836
Received: 19 May 2016 / Revised: 31 August 2016 / Accepted: 22 September 2016 / Published: 13 October 2016
Cited by 3 | PDF Full-text (9799 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products—Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) Morphing Technique (CMORPH)—over the
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We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products—Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) Morphing Technique (CMORPH)—over the African continent, using the Global Precipitation Climatology Project one Degree Day (GPCP-1dd) as a reference dataset for years 2001 to 2013. Different types of errors are characterized for each season as a function of spatial classifications (latitudinal bands, climatic zones and topography) and in relationship with the main rain-producing mechanisms in the continent: the Intertropical Convergence Zone (ITCZ) and the East African Monsoon. A bias correction of the satellite estimates is applied using a probability density function (pdf) matching approach, with a bias analysis as a function of rain intensity, season and latitude. The effects of bias correction on different error terms are analyzed, showing an almost elimination of the mean and variance terms in most of the cases. While raw estimates of TMPA show higher efficiency, all products have similar efficiencies after bias correction. PERSIANN consistently shows the smallest median errors when it correctly detects precipitation events. The areas with smallest relative errors and other performance measures follow the position of the ITCZ oscillating seasonally over the equator, illustrating the close relationship between satellite estimates and rainfall regime. Full article
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Open AccessArticle Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques
Remote Sens. 2016, 8(10), 837; doi:10.3390/rs8100837
Received: 30 June 2016 / Revised: 20 September 2016 / Accepted: 29 September 2016 / Published: 13 October 2016
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Abstract
This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many
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This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many environmental applications like landslide research. The methodology starts with the execution of UAV flights to acquire very high resolution images, which are oriented and georeferenced by means of aerial triangulation, bundle block adjustment and Structure from Motion (SfM) techniques, using ground control points (GCPs) as well as points transferred between flights. After Digital Surface Models (DSMs) and orthophotographs were obtained, both differential models and displacements at DSM check points between campaigns were calculated. Vertical and horizontal displacements in the range of a few decimeters to several meters were respectively measured. Finally, as the landslide occurred in an olive grove which presents a regular pattern, a semi-automatic approach to identifying and determining horizontal displacements between olive tree centroids was also developed. In conclusion, the study shows that landslide monitoring can be carried out with the required accuracy—in the order of 0.10 to 0.15 m—by means of the combination of non-invasive techniques such as UAV, photogrammetry and geographic information system (GIS). Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia
Remote Sens. 2016, 8(10), 838; doi:10.3390/rs8100838
Received: 22 August 2016 / Revised: 19 September 2016 / Accepted: 4 October 2016 / Published: 13 October 2016
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Abstract
This study analyses the expansion of urban growth and land cover changes in five Saudi Arabian cities (Riyadh, Jeddah, Makkah, Al-Taif and the Eastern Area) using Landsat images for the 1985, 1990, 2000, 2007 and 2014 time periods. The classification was carried out
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This study analyses the expansion of urban growth and land cover changes in five Saudi Arabian cities (Riyadh, Jeddah, Makkah, Al-Taif and the Eastern Area) using Landsat images for the 1985, 1990, 2000, 2007 and 2014 time periods. The classification was carried out using object-based image analysis (OBIA) to create land cover maps. The classified images were used to predict the land cover changes and urban growth for 2024 and 2034. The simulation model integrated the Markov chain (MC) and Cellular Automata (CA) modelling methods and the simulated maps were compared and validated to the reference maps. The simulation results indicated high accuracy of the MC–CA integrated models. The total agreement between the simulated and the reference maps was >92% for all the simulation years. The results indicated that all five cities showed a massive urban growth between 1985 and 2014 and the predicted results showed that urban expansion is likely to continue going for 2024 and 2034 periods. The transition probabilities of land cover, such as vegetation and water, are most likely to be urban areas, first through conversion to bare soil and then to urban land use. Integrating of time-series satellite images and the MC–CA models provides a better understanding of the past, current and future patterns of land cover changes and urban growth in this region. Simulation of urban growth will help planners to develop sustainable expansion policies that may reduce the future environmental impacts. Full article
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Open AccessArticle Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia
Remote Sens. 2016, 8(10), 839; doi:10.3390/rs8100839
Received: 6 July 2016 / Revised: 9 September 2016 / Accepted: 27 September 2016 / Published: 20 October 2016
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Abstract
Fire is one of the main factors directly impacting Amazonian forest biomass and dynamics. Because of Amazonia’s large geographical extent, remote sensing techniques are required for comprehensively assessing forest fire impacts at the landscape level. In this context, Light Detection and Ranging (LiDAR)
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Fire is one of the main factors directly impacting Amazonian forest biomass and dynamics. Because of Amazonia’s large geographical extent, remote sensing techniques are required for comprehensively assessing forest fire impacts at the landscape level. In this context, Light Detection and Ranging (LiDAR) stands out as a technology capable of retrieving direct measurements of vegetation vertical arrangement, which can be directly associated with aboveground biomass. This work aims, for the first time, to quantify post-fire changes in forest canopy height and biomass using airborne LiDAR in western Amazonia. For this, the present study evaluated four areas located in the state of Acre, called Rio Branco, Humaitá, Bonal and Talismã. Rio Branco and Humaitá burned in 2005 and Bonal and Talismã burned in 2010. In these areas, we inventoried a total of 25 plots (0.25 ha each) in 2014. Humaitá and Talismã are located in an open forest with bamboo and Bonal and Rio Branco are located in a dense forest. Our results showed that even ten years after the fire event, there was no complete recovery of the height and biomass of the burned areas (p < 0.05). The percentage difference in height between control and burned sites was 2.23% for Rio Branco, 9.26% for Humaitá, 10.03% for Talismã and 20.25% for Bonal. All burned sites had significantly lower biomass values than control sites. In Rio Branco (ten years after fire), Humaitá (nine years after fire), Bonal (four years after fire) and Talismã (five years after fire) biomass was 6.71%, 13.66%, 17.89% and 22.69% lower than control sites, respectively. The total amount of biomass lost for the studied sites was 16,706.3 Mg, with an average loss of 4176.6 Mg for sites burned in 2005 and 2890 Mg for sites burned in 2010, with an average loss of 3615 Mg. Fire impact associated with tree mortality was clearly detected using LiDAR data up to ten years after the fire event. This study indicates that fire disturbance in the Amazon region can cause persistent above-ground biomass loss and subsequent reduction of forest carbon stocks. Continuous monitoring of burned forests is required for depicting the long-term recovery trajectory of fire-affected Amazonian forests. Full article
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Open AccessArticle Focusing Translational Variant Bistatic Forward-Looking SAR Using Keystone Transform and Extended Nonlinear Chirp Scaling
Remote Sens. 2016, 8(10), 840; doi:10.3390/rs8100840
Received: 21 April 2016 / Revised: 1 September 2016 / Accepted: 4 October 2016 / Published: 13 October 2016
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Abstract
Bistatic Synthetic Aperture Radar (SAR) has attracted increasing attention in recent years due to its unique advantages, such as the ability of forward-looking imaging. In translational variant bistatic forward-looking SAR (TV-BFSAR), it is difficult to get a well focused image due to large
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Bistatic Synthetic Aperture Radar (SAR) has attracted increasing attention in recent years due to its unique advantages, such as the ability of forward-looking imaging. In translational variant bistatic forward-looking SAR (TV-BFSAR), it is difficult to get a well focused image due to large range cell migration (RCM) and 2-D variation of both Doppler characteristics and RCM. In this paper, an extended azimuth nonlinear chirp scaling (NLCS) algorithm is proposed to deal with these problems. Firstly, Keystone Transform (KT) is introduced to remove the spatial-variant linear RCM, which is of great significance in TV-BFSAR. Secondly, a correction factor is multiplied to the signal in range frequency domain to compensate for the residual RCM. At last, a fourth-order filtering together with azimuth NLCS is performed in every range gate to equalize both the azimuth-variant Doppler centroid and frequency modulation rate based on the azimuth numerical fitting. The proposed method is verified by simulation and real data processing. Multiple targets are generated and focused by the method, of which the peak sidelobe ratio (PSLR) is around −13 dB and integrated sidelobe ratio (ISLR) is around −10 dB. The method is accurate and can achieve high-resolution focusing for TV-BFSAR data. Full article
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Open AccessArticle Remote Sensing of Particle Cross-Sectional Area in the Bohai Sea and Yellow Sea: Algorithm Development and Application Implications
Remote Sens. 2016, 8(10), 841; doi:10.3390/rs8100841
Received: 26 July 2016 / Revised: 23 September 2016 / Accepted: 8 October 2016 / Published: 22 October 2016
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Abstract
Suspended particles in waters play an important role in determination of optical properties and ocean color remote sensing. To link suspended particles to their optical properties and thereby remote sensing reflectance (Rrs(λ)), cross-sectional area is a key factor.
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Suspended particles in waters play an important role in determination of optical properties and ocean color remote sensing. To link suspended particles to their optical properties and thereby remote sensing reflectance (Rrs(λ)), cross-sectional area is a key factor. Till now, there is still a lack of methodologies for derivation of the particle cross-sectional area concentration (AC) from satellite measurements, which consequently limits potential applications of AC. In this study, we investigated the relationship between AC and Rrs(λ) based on field measurements in the Bohai Sea (BS) and Yellow Sea (YS). Our analysis confirmed the strong dependence of Rrs(λ) on AC and that such dependence is stronger than on mass concentration. Subsequently, a remote sensing algorithm that uses the slope of Rrs(λ) between 490 and 555 nm was developed for retrieval of AC from satellite measurements of the Geostationary Ocean Color Imager (GOCI). In situ evaluations show that the algorithm displays good performance for deriving AC and is robust to uncertainties in Rrs(λ). When the algorithm was applied to satellite data, it performed well, with a coefficient of determination of 0.700, a root mean squared error of 2.126 m−1 and a mean absolute percentage error of 40.7%, and it yielded generally reasonable spatial and temporal distributions of AC in the BS and YS. The satellite-derived AC using our algorithm may offer useful information for modeling the inherent optical properties of suspended particles, deriving the water transparency, estimating the particle composition and possibly improving particle mass concentration estimations in future. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle A Multi-Resolution Blending Considering Changed Regions for Orthoimage Mosaicking
Remote Sens. 2016, 8(10), 842; doi:10.3390/rs8100842
Received: 18 July 2016 / Revised: 28 September 2016 / Accepted: 2 October 2016 / Published: 14 October 2016
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Abstract
Blending processing based on seamlines in image mosaicking is a procedure designed to obtain a smooth transition between images along seamlines and make seams invisible in the final mosaic. However, for high-resolution aerial orthoimages in urban areas, factors such as projection differences, moving
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Blending processing based on seamlines in image mosaicking is a procedure designed to obtain a smooth transition between images along seamlines and make seams invisible in the final mosaic. However, for high-resolution aerial orthoimages in urban areas, factors such as projection differences, moving objects, and radiometric differences in overlapping areas may result in ghosting and artifacts or visible shifts in the final mosaic. Such a mosaic is not a true reflection of the earth’s surface and may have a negative impact on image interpretation. Therefore, this paper presents a multi-resolution blending method considering changed regions to improve mosaic image quality. The method utilizes the region change rate (RCR) to distinguish changed regions from unchanged regions in overlapping areas. The RCR of each region is computed using image segmentation and change detection methods. Then, a mask image is generated considering changed regions, and Gaussian and Laplacian pyramids are constructed. Finally, a multi-resolution reconstruction is performed to obtain the final mosaic. Experimental results from digital aerial orthoimages in urban areas are provided to verify this method for blending processing based on seamlines in mosaicking. Comparisons with other methods further demonstrate the potential of the presented method, as shown in a detailed comparison in three typical cases of the seamline passing by buildings, the seamline passing through buildings, and the seamline passing through areas with large radiometric differences. Full article
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Open AccessArticle Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain
Remote Sens. 2016, 8(10), 843; doi:10.3390/rs8100843
Received: 19 July 2016 / Revised: 2 September 2016 / Accepted: 11 October 2016 / Published: 14 October 2016
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Abstract
Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations
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Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the “salt-and-pepper” artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA) approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects). Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the “salt-and-pepper” artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain. Full article
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Open AccessArticle A High-Fidelity Haze Removal Method Based on HOT for Visible Remote Sensing Images
Remote Sens. 2016, 8(10), 844; doi:10.3390/rs8100844
Received: 22 July 2016 / Revised: 22 September 2016 / Accepted: 11 October 2016 / Published: 14 October 2016
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Abstract
Spatially varying haze is a common feature of most satellite images currently used for land cover classification and mapping and can significantly affect image quality. In this paper, we present a high-fidelity haze removal method based on Haze Optimized Transformation (HOT), comprising of
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Spatially varying haze is a common feature of most satellite images currently used for land cover classification and mapping and can significantly affect image quality. In this paper, we present a high-fidelity haze removal method based on Haze Optimized Transformation (HOT), comprising of three steps: semi-automatic HOT transform, HOT perfection and percentile based dark object subtraction (DOS). Since digital numbers (DNs) of band red and blue are highly correlated in clear sky, the R-squared criterion is utilized to search the relative clearest regions of the whole scene automatically. After HOT transform, spurious HOT responses are first masked out and filled by means of four-direction scan and dynamic interpolation, and then homomorphic filter is performed to compensate for loss of HOT of masked-out regions with large areas. To avoid patches and halo artifacts, a procedure called percentile DOS is implemented to eliminate the influence of haze. Scenes including various land cover types are selected to validate the proposed method, and a comparison analysis with HOT and Background Suppressed Haze Thickness Index (BSHTI) is performed. Three quality assessment indicators are selected to evaluate the haze removed effect on image quality from different perspective and band profiles are utilized to analyze the spectral consistency. Experiment results verify the effectiveness of the proposed method for haze removal and the superiority of it in preserving the natural color of object itself, enhancing local contrast, and maintaining structural information of original image. Full article
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Open AccessArticle Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region
Remote Sens. 2016, 8(10), 845; doi:10.3390/rs8100845
Received: 27 July 2016 / Revised: 27 September 2016 / Accepted: 11 October 2016 / Published: 15 October 2016
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Abstract
Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they
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Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification. Full article
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Open AccessArticle Secondary Fault Activity of the North Anatolian Fault near Avcilar, Southwest of Istanbul: Evidence from SAR Interferometry Observations
Remote Sens. 2016, 8(10), 846; doi:10.3390/rs8100846
Received: 12 July 2016 / Revised: 1 October 2016 / Accepted: 11 October 2016 / Published: 18 October 2016
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Abstract
Strike-slip faults may be traced along thousands of kilometers, e.g., the San Andreas Fault (USA) or the North Anatolian Fault (Turkey). A closer look at such continental-scale strike faults reveals localized complexities in fault geometry, associated with fault segmentation, secondary faults and a
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Strike-slip faults may be traced along thousands of kilometers, e.g., the San Andreas Fault (USA) or the North Anatolian Fault (Turkey). A closer look at such continental-scale strike faults reveals localized complexities in fault geometry, associated with fault segmentation, secondary faults and a change of related hazards. The North Anatolian Fault displays such complexities nearby the mega city Istanbul, which is a place where earthquake risks are high, but secondary processes are not well understood. In this paper, long-term persistent scatterer interferometry (PSI) analysis of synthetic aperture radar (SAR) data time series was used to precisely identify the surface deformation pattern associated with the faulting complexity at the prominent bend of the North Anatolian Fault near Istanbul city. We elaborate the relevance of local faulting activity and estimate the fault status (slip rate and locking depth) for the first time using satellite SAR interferometry (InSAR) technology. The studied NW-SE-oriented fault on land is subject to strike-slip movement at a mean slip rate of ~5.0 mm/year and a shallow locking depth of <1.0 km and thought to be directly interacting with the main fault branch, with important implications for tectonic coupling. Our results provide the first geodetic evidence on the segmentation of a major crustal fault with a structural complexity and associated multi-hazards near the inhabited regions of Istanbul, with similarities also to other major strike-slip faults that display changes in fault traces and mechanisms. Full article
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Open AccessArticle Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation
Remote Sens. 2016, 8(10), 847; doi:10.3390/rs8100847
Received: 21 April 2016 / Revised: 5 October 2016 / Accepted: 8 October 2016 / Published: 17 October 2016
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Abstract
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but
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To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN − RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic. Full article
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Open AccessArticle Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
Remote Sens. 2016, 8(10), 848; doi:10.3390/rs8100848
Received: 24 June 2016 / Revised: 5 October 2016 / Accepted: 8 October 2016 / Published: 16 October 2016
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Abstract
A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery
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A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions. Full article
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Open AccessArticle Landsat and Local Land Surface Temperatures in a Heterogeneous Terrain Compared to MODIS Values
Remote Sens. 2016, 8(10), 849; doi:10.3390/rs8100849
Received: 9 August 2016 / Revised: 29 September 2016 / Accepted: 11 October 2016 / Published: 15 October 2016
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Abstract
Land Surface Temperature (LST) as provided by remote sensing onboard satellites is a key parameter for a number of applications in Earth System studies, such as numerical modelling or regional estimation of surface energy and water fluxes. In the case of Moderate Resolution
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Land Surface Temperature (LST) as provided by remote sensing onboard satellites is a key parameter for a number of applications in Earth System studies, such as numerical modelling or regional estimation of surface energy and water fluxes. In the case of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra or Aqua, pixels have resolutions near 1 km 2 , LST values being an average of the real subpixel variability of LST, which can be significant for heterogeneous terrain. Here, we use Landsat 7 LST decametre-scale fields to evaluate the temporal and spatial variability at the kilometre scale and compare the resulting average values to those provided by MODIS for the same observation time, for the very heterogeneous Campus of the University of the Balearic Islands (Mallorca, Western Mediterranean), with an area of about 1 km 2 , for a period between 2014 and 2016. Variations of LST between 10 and 20 K are often found at the sub-kilometre scale. In addition, MODIS values are compared to the ground truth for one point in the Campus, as obtained from a four-component net radiometer, and a bias of 3.2 K was found in addition to a Root Mean Square Error (RMSE) of 4.2 K. An indication of a more elaborated local measurement strategy in the Campus is given, using an array of radiometers distributed in the area. Full article
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Open AccessArticle Towards Operational Detection of Forest Ecosystem Changes in Protected Areas
Remote Sens. 2016, 8(10), 850; doi:10.3390/rs8100850
Received: 14 July 2016 / Revised: 21 September 2016 / Accepted: 11 October 2016 / Published: 16 October 2016
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Abstract
This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA
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This paper discusses the application of the Cross-Correlation Analysis (CCA) technique to multi-spatial resolution Earth Observation (EO) data for detecting and quantifying changes in forest ecosystems in two different protected areas, located in Southern Italy and Southern India. The input data for CCA investigation were elaborated from the forest layer extracted from an existing Land Cover/Land Use (LC/LU) map (time T1) and a more recent (T2, with T2 > T1) single date image. The latter consist of a High Resolution (HR) Landsat 8 OLI image and a Very High Resolution (VHR) Worldview-2 image, which were analysed separately. For the Italian site, the forest layer (1:5000) was first compared to the HR Landsat 8 OLI image and then to the VHR Worldview-2 image. For the Indian site, the forest layer (1:50,000) was compared to the Landsat 8 OLI image then the changes were interpreted using Worldview-2. The changes detected through CCA, at HR only, were compared against those detected by applying a traditional NDVI image differencing technique of two Landsat scenes at T1 and T2. The accuracy assessment, concerning the change maps of the multi-spatial resolution outputs, was based on stratified random sampling. The CCA technique allowed an increase in the value of the overall accuracy: from 52% to 68% for the Italian site and from 63% to 82% for the Indian site. In addition, a significant reduction of the error affecting the stratified changed area estimation for both sites was obtained. For the Italian site, the error reduction became significant at VHR (±2 ha) in respect to HR (±32 ha) even though both techniques had comparable overall accuracy (82%) and stratified changed area estimation. The findings obtained support the conclusions that CCA technique can be a useful tool to detect and quantify changes in forest areas due to both legal and illegal interventions, including relatively inaccessible sites (e.g., tropical forest) with costs remaining rather low. The data obtained through CCA intervention could not only support the commitments undertaken by the European Habitats Directive (92/43/EEC) and the Convention of Biological Diversity (CBD) but also satisfy UN Sustainable Development Goals (SDG). Full article
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Open AccessArticle GPD+ Wet Tropospheric Corrections for CryoSat-2 and GFO Altimetry Missions
Remote Sens. 2016, 8(10), 851; doi:10.3390/rs8100851
Received: 31 August 2016 / Revised: 28 September 2016 / Accepted: 11 October 2016 / Published: 16 October 2016
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Abstract
Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University
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Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University of Porto to retrieve improved WTC for radar altimeter missions. The GPD+ are WTC estimated by space-time objective analysis, by combining all available observations in the vicinity of the point: valid measurements from the on-board microwave radiometer (MWR), from GNSS coastal and island stations and from scanning imaging MWR on board various remote sensing missions. The GPD+ corrections are available both for missions which do not possess an on-board microwave radiometer such as CryoSat-2 (CS-2) and for all missions which carry this sensor, by addressing the various error sources inherent to the MWR-derived WTC. To ensure long-term stability of the corrections, the large set of radiometers used in this study have been calibrated with respect to the Special Sensor Microwave Imager (SSM/I) and the SSM/I Sounder (SSM/IS). The application of the algorithm to CS-2 and Geosat Follow-on (GFO), as representative altimetric missions without and with a MWR aboard the respective spacecraft, is described. Results show that, for both missions, the new WTC significantly reduces the sea level anomaly (SLA) variance with respect to the model-based corrections. For GFO, the new WTC also leads to a large reduction in SLA variance with respect to the MWR-derived WTC, recovering a large number of observations in the coastal and polar regions and full sets of tracks and several cycles when MWR measurements are missing or invalid. Overall, the algorithm allows the recovery of a significant number of measurements, ensuring the continuity and consistency of the correction in the open-ocean/coastal transition zone and at high latitudes. Full article
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Open AccessArticle Taking Advantage of the ESA G-POD Service to Study Ground Deformation Processes in High Mountain Areas: A Valle d’Aosta Case Study, Northern Italy
Remote Sens. 2016, 8(10), 852; doi:10.3390/rs8100852
Received: 15 July 2016 / Revised: 18 August 2016 / Accepted: 11 October 2016 / Published: 20 October 2016
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Abstract
This paper presents a methodology taking advantage of the GPOD-SBAS service to study the surface deformation information over high mountain regions. Indeed, the application of the advanced DInSAR over the arduous regions represents a demanding task. We implemented an iterative selection procedure of
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This paper presents a methodology taking advantage of the GPOD-SBAS service to study the surface deformation information over high mountain regions. Indeed, the application of the advanced DInSAR over the arduous regions represents a demanding task. We implemented an iterative selection procedure of the most suitable SAR images, aimed to preserve the largest number of SAR scenes, and the fine-tuning of several advanced configuration parameters. This method is aimed at minimizing the temporal decorrelation effects, principally due to snow cover, and maximizing the number of coherent targets and their spatial distribution. The methodology is applied to the Valle d’Aosta (VDA) region, Northern Italy, an alpine area characterized by high altitudes, complex morphology, and susceptibility to different mass wasting phenomena. The approach using GPOD-SBAS allows for the obtainment of mean deformation velocity maps and displacement time series relative to the time period from 1992 to 2000, relative to ESR-1/2, and from 2002 to 2010 for ASAR-Envisat. Our results demonstrate how the DInSAR application can obtain reliable information of ground displacement over time in these regions, and may represent a suitable instrument for natural hazards assessment. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards) Printed Edition available
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Open AccessArticle Spatiotemporal Variations of Lake Surface Temperature across the Tibetan Plateau Using MODIS LST Product
Remote Sens. 2016, 8(10), 854; doi:10.3390/rs8100854
Received: 13 June 2016 / Revised: 28 September 2016 / Accepted: 12 October 2016 / Published: 17 October 2016
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Abstract
Satellite remote sensing provides a powerful tool for assessing lake water surface temperature (LWST) variations, particularly for large water bodies that reside in remote areas. In this study, the MODIS land surface temperature (LST) product level 3 (MOD11A2) was used to investigate the
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Satellite remote sensing provides a powerful tool for assessing lake water surface temperature (LWST) variations, particularly for large water bodies that reside in remote areas. In this study, the MODIS land surface temperature (LST) product level 3 (MOD11A2) was used to investigate the spatiotemporal variation of LWST for 56 large lakes across the Tibetan Plateau and examine the factors affecting the LWST variations during 2000–2015. The results show that the annual cycles of LWST across the Tibetan Plateau ranged from −19.5 °C in early February to 25.1 °C in late July. Obvious diurnal temperature differences (DTDs) were observed for various lakes, ranging from 1.3 to 8.9 °C in summer, and large and deep lakes show less DTDs variations. Overall, a LWST trend cannot be detected for the 56 lakes in the plateau over the past 15 years. However, 38 (68%) lakes show a temperature decrease trend with a mean rate of −0.06 °C/year, and 18 (32%) lakes show a warming rate of (0.04 °C/year) based on daytime MODIS measurements. With respect to nighttime measurements, 27 (48%) lakes demonstrate a temperature increase with a mean rate of 0.051 °C/year, and 29 (52%) lakes exhibit a temperature decrease trend with a mean rate of −0.062 °C/year. The rate of LWST change was statistically significant for 19 (21) lakes, including three (eight) warming and 17 (13) cooling lakes for daytime (nighttime) measurements, respectively. This investigation indicates that lake depth and area (volume), attitude, geographical location and water supply sources affect the spatiotemporal variations of LWST across the Tibetan Plateau. Full article
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Open AccessArticle Manifold Learning Co-Location Decision Tree for Remotely Sensed Imagery Classification
Remote Sens. 2016, 8(10), 855; doi:10.3390/rs8100855
Received: 8 April 2016 / Revised: 1 October 2016 / Accepted: 11 October 2016 / Published: 19 October 2016
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Abstract
Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the
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Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT), which includes unfolding input data, unfolded distance calculations, MVU-based co-location rule generation, and MVU-based CL-DT generation. The proposed method has been validated by classifying remotely sensed imagery and is compared with four other types of methods, i.e., CL-DT, classification and regression tree (CART), random forests (RFs), and stacked auto-encoders (SAE), whose classification results are taken as “true values.” The experimental results demonstrate that: (1) the relative classification accuracies of the proposed method in three test areas are higher than CL-DT and CART, and are at the same level compared to RFs; and (2) the total number of nodes, the number of leaf nodes, and the number of levels are significantly decreased by the proposed method. The time taken for the data processing, decision tree generation, drawing of the tree, and generation of the rules are also shortened by the proposed method compared to CL-DT, CART, and RFs. Full article
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Open AccessArticle Spectral Discrimination of Vegetation Classes in Ice-Free Areas of Antarctica
Remote Sens. 2016, 8(10), 856; doi:10.3390/rs8100856
Received: 4 July 2016 / Revised: 21 September 2016 / Accepted: 11 October 2016 / Published: 18 October 2016
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Abstract
Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and
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Detailed monitoring of vegetation changes in ice-free areas of Antarctica is crucial to determine the effects of climate warming and increasing human presence in this vulnerable ecosystem. Remote sensing techniques are especially suitable in this distant and rough environment, with high spectral and spatial resolutions needed owing to the patchiness and similarity between vegetation elements. We analyze the reflectance spectra of the most representative vegetation elements in ice-free areas of Antarctica to assess the potential for discrimination. This research is aimed as a basis for future aircraft/satellite research for long-term vegetation monitoring. The study was conducted in the Barton Peninsula, King George Island. The reflectance of ground patches of different types of vegetation or bare ground (c. 0.25 m 2 , n = 30 patches per class) was recorded with a spectrophotometer measuring between 340 nm to 1025 nm at a resolution of 0.38 n m . We used Linear Discriminant Analysis (LDA) to classify the cover classes according to reflectance spectra, after reduction of the number of bands using Principal Component Analysis (PCA). The first five principal components explained an accumulated 99.4% of the total variance and were added to the discriminant function. The LDA classification resulted in c. 92% of cases correctly classified (a hit ratio 11.9 times greater than chance). The most important region for discrimination was the visible and near ultraviolet (UV), with the relative importance of spectral bands steeply decreasing in the Near Infra-Red (NIR) region. Our study shows the feasibility of discriminating among representative taxa of Antarctic vegetation using their spectral patterns in the near UV, visible and NIR. The results are encouraging for hyperspectral vegetation mapping in Antarctica, which could greatly facilitate monitoring vegetation changes in response to a changing environment, reducing the costs and environmental impacts of field surveys. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Oil Droplet Clouds Suspended in the Sea: Can They Be Remotely Detected?
Remote Sens. 2016, 8(10), 857; doi:10.3390/rs8100857
Received: 4 July 2016 / Revised: 14 September 2016 / Accepted: 10 October 2016 / Published: 18 October 2016
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Abstract
Oil floating on the sea surface can be detected by both passive and active methods using the ultraviolet-to-microwave spectrum, whereas oil immersed below the sea surface can signal its presence only in visible light. This paper presents an optical model representing a selected
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Oil floating on the sea surface can be detected by both passive and active methods using the ultraviolet-to-microwave spectrum, whereas oil immersed below the sea surface can signal its presence only in visible light. This paper presents an optical model representing a selected case of the sea polluted by an oil suspension for a selected concentration (10 ppm) located in a layer of exemplary thickness (5 m) separated from the sea surface by an unpolluted layer (thickness 1 m). The impact of wavelength and state of the sea surface on reflectance changes is presented based on the results of Monte Carlo ray tracing. A two-wavelength index of reflectance is proposed to detect oil suspended in the water column (645–469 nm). Full article
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Open AccessArticle A Shape-Adjusted Tridimensional Reconstruction of Cultural Heritage Artifacts Using a Miniature Quadrotor
Remote Sens. 2016, 8(10), 858; doi:10.3390/rs8100858
Received: 28 July 2016 / Revised: 28 September 2016 / Accepted: 12 October 2016 / Published: 20 October 2016
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Abstract
The innovative automated 3D modeling procedure presented here was used to reconstruct a Cultural Heritage (CH) object by means of an unmanned aerial vehicle. Using a motion capture system, a small low-cost quadrotor equipped with a miniature low-resolution Raspberry Pi camera module was
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The innovative automated 3D modeling procedure presented here was used to reconstruct a Cultural Heritage (CH) object by means of an unmanned aerial vehicle. Using a motion capture system, a small low-cost quadrotor equipped with a miniature low-resolution Raspberry Pi camera module was accurately controlled in the closed loop mode and made to follow a trajectory around the artifact. A two-stage process ensured the accuracy of the 3D reconstruction process. The images taken during the first circular trajectory were used to draw the artifact’s shape. The second trajectory was smartly and autonomously adjusted to match the artifact’s shape, then it provides new pictures taken close to the artifact and, thus, greatly improves the final 3D reconstruction in terms of the completeness, accuracy and quickness, in particular where the artifact’s shape is complex. The results obtained here using close-range photogrammetric methods show that the process of automated 3D model reconstruction based on a robotized quadrotor using a motion capture system is a realistic approach, which could provide a suitable new digital conservation tool in the cultural heritage field. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle The Tasks of the Crowd: A Typology of Tasks in Geographic Information Crowdsourcing and a Case Study in Humanitarian Mapping
Remote Sens. 2016, 8(10), 859; doi:10.3390/rs8100859
Received: 2 August 2016 / Revised: 8 September 2016 / Accepted: 11 October 2016 / Published: 18 October 2016
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Abstract
In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can
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In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, and how the quality of the produced information can be assessed for particular task types. To fill this gap, we analyse the existing literature and propose a typology of tasks in geographic information crowdsourcing, which distinguishes between classification, digitisation and conflation tasks. We then present a case study related to the “Missing Maps” project aimed at crowdsourced classification to support humanitarian aid. We use our typology to distinguish between the different types of crowdsourced tasks in the project and choose classification tasks related to identifying roads and settlements for an evaluation of the crowdsourced classification. This evaluation shows that the volunteers achieved a satisfactory overall performance (accuracy: 89%; sensitivity: 73%; and precision: 89%). We also analyse different factors that could influence the performance, concluding that volunteers were more likely to incorrectly classify tasks with small objects. Furthermore, agreement among volunteers was shown to be a very good predictor of the reliability of crowdsourced classification: tasks with the highest agreement level were 41 times more probable to be correctly classified by volunteers. The results thus show that the crowdsourced classification of remotely sensed imagery is able to generate geographic information about human settlements with a high level of quality. This study also makes clear the different sophistication levels of tasks that can be performed by volunteers and reveals some factors that may have an impact on their performance. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data
Remote Sens. 2016, 8(10), 860; doi:10.3390/rs8100860
Received: 31 July 2016 / Revised: 1 October 2016 / Accepted: 11 October 2016 / Published: 20 October 2016
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Abstract
Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia
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Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia (<2 ha) is smaller than the spatial resolution of most freely available satellite imagery, like Landsat and MODIS. In addition, existing methods to map yield require field-level data to develop and parameterize predictive algorithms that translate satellite vegetation indices to yield, yet these data are costly or difficult to obtain in many smallholder systems. To overcome these challenges, this study explores two issues. First, we employ new high spatial (2 m) and temporal (bi-weekly) resolution micro-satellite SkySat data to map sowing dates and yields of smallholder wheat fields in Bihar, India in the 2014–2015 and 2015–2016 growing seasons. Second, we compare how well we predict sowing date and yield when using ground data, like crop cuts and self-reports, versus using crop models, which require no on-the-ground data, to develop and parameterize prediction models. Overall, sow dates were predicted well (R2 = 0.41 in 2014–2015 and R2 = 0.62 in 2015–2016), particularly when using models that were parameterized using self-report sow dates collected close to the time of planting and when using imagery that spanned the entire growing season. We were also able to map yields fairly well (R2 = 0.27 in 2014–2015 and R2 = 0.33 in 2015–2016), with crop cut parameterized models resulting in the highest accuracies. While less accurate, we were able to capture the large range in sow dates and yields across farms when using models parameterized with crop model data and these estimates were able to detect known relationships between management factors (e.g., sow date, fertilizer, and irrigation) and yield. While these results are specific to our study site in India, it is likely that the methods employed and the lessons learned are applicable to smallholder systems more generally across the globe. This is of particular interest given that similar high spatio-temporal resolution micro-satellite data will become increasingly available in the coming years. Full article
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