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Remote Sens., Volume 9, Issue 4 (April 2017)

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Cover Story Ice sheets hold the largest potential for sea level rise in the upcoming decades to centuries and [...] Read more.
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Editorial

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Open AccessEditorial The Role of Citizen Science in Earth Observation
Remote Sens. 2017, 9(4), 357; doi:10.3390/rs9040357
Received: 29 March 2017 / Revised: 29 March 2017 / Accepted: 29 March 2017 / Published: 11 April 2017
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Abstract
Citizen Science (CS) and crowdsourcing are two potentially valuable sources of data for Earth Observation (EO), which have yet to be fully exploited. Research in this area has increased rapidly during the last two decades, and there are now many examples of CS
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Citizen Science (CS) and crowdsourcing are two potentially valuable sources of data for Earth Observation (EO), which have yet to be fully exploited. Research in this area has increased rapidly during the last two decades, and there are now many examples of CS projects that could provide valuable calibration and validation data for EO, yet are not integrated into operational monitoring systems. A special issue on the role of CS in EO has revealed continued trends in applications, covering a diverse set of fields from disaster response to environmental monitoring (land cover, forests, biodiversity and phenology). These papers touch upon many key challenges of CS including data quality and citizen engagement as well as the added value of CS including lower costs, higher temporal frequency and use of the data for calibration and validation of remotely-sensed imagery. Although still in the early stages of development, CS for EO clearly has a promising role to play in the future. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Research

Jump to: Editorial, Review, Other

Open AccessArticle Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor
Remote Sens. 2017, 9(4), 303; doi:10.3390/rs9040303
Received: 16 January 2017 / Revised: 8 March 2017 / Accepted: 20 March 2017 / Published: 23 March 2017
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Abstract
The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known to be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead to distortions once the data are projected based on the provided orbit parameters. This can cause geolocation
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The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known to be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead to distortions once the data are projected based on the provided orbit parameters. This can cause geolocation errors of up to 10 km per pixel which is an obstacle for applications such as time series analysis, compositing/mosaicking of images, or the combination with other satellite data. Therefore, a fusion of two techniques to match the data in orbit projection has been developed to overcome this limitation, even without the precise knowledge of the orbit parameters. Both techniques attempt to find the best match between small image chips taken from a reference water mask in the first, and from a median Normalized Difference Vegetation Index (NDVI) mask in the second round. This match is determined shifting around the small image chips until the best correlation between reference and satellite data source is found for each respective image part. Only if both attempts result in the same shift in any direction, the position in the orbit is included in a third order polynomial warping process that will ultimately improve the geolocation accuracy of the AVHRR data. The warping updates the latitude and longitude layers and the contents of the data layers itself remain untouched. As such, original sensor measurements are preserved. An included automated quality assessment generates a quality layer that informs about the reliability of the matching. Full article
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Open AccessArticle Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh
Remote Sens. 2017, 9(4), 304; doi:10.3390/rs9040304
Received: 12 January 2017 / Revised: 9 March 2017 / Accepted: 15 March 2017 / Published: 23 March 2017
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Abstract
The communities living on the dangerous hillslopes in Chittagong City Corporation (CCC) in Bangladesh recurrently experience landslide hazards during the monsoon season. The frequency and intensity of landslides are increasing over time because of heavy rainfall occurring over a few days. Furthermore, rapid
[...] Read more.
The communities living on the dangerous hillslopes in Chittagong City Corporation (CCC) in Bangladesh recurrently experience landslide hazards during the monsoon season. The frequency and intensity of landslides are increasing over time because of heavy rainfall occurring over a few days. Furthermore, rapid urbanization through hill-cutting is another factor, which is believed to have a significant impact on the occurrence of landslides. This study aims to develop landslide susceptibility maps (LSMs) through the use of Dempster-Shafer weights of evidence (WoE) and the multiple regression (MR) method. Three different combinations with principal component analysis (PCA) and fuzzy membership techniques were used and tested. Twelve factor maps (i.e., slope, hill-cutting, geology, geomorphology, NDVI, soil moisture, precipitation and distance from existing buildings, stream, road and drainage network, and faults-lineaments) were prepared based on their association with historical landslide events. A landslide inventory map was prepared through field surveys for model simulation and validation purposes. The performance of the predicted LSMs was validated using the area under the relative operating characteristic (ROC) curve method. The overall success rates were 87.3%, 90.9%, 91.3%, and 93.9%, respectively for the WoE, MR with all the layers, MR with PCA layers, and MR with fuzzy probability layers. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessFeature PaperArticle No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning
Remote Sens. 2017, 9(4), 305; doi:10.3390/rs9040305
Received: 17 January 2017 / Revised: 13 March 2017 / Accepted: 20 March 2017 / Published: 23 March 2017
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Abstract
Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we
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Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
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Open AccessArticle A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images
Remote Sens. 2017, 9(4), 306; doi:10.3390/rs9040306
Received: 14 November 2016 / Revised: 20 March 2017 / Accepted: 21 March 2017 / Published: 24 March 2017
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Abstract
Surveys of windthrown trees, resulting from hurricanes and other types of natural disasters, are an important component of agricultural insurance, forestry statistics, and ecological monitoring. Aerial images are commonly used to determine the total area or number of downed trees, but conventional methods
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Surveys of windthrown trees, resulting from hurricanes and other types of natural disasters, are an important component of agricultural insurance, forestry statistics, and ecological monitoring. Aerial images are commonly used to determine the total area or number of downed trees, but conventional methods suffer from two primary issues: misclassification of windthrown trees due to the interference from other objects or artifacts, and poor extraction resolution when trunk diameters are small. The objective of this study is to develop a coarse-to-fine extraction technique for individual windthrown trees that reduces the effects of these common flaws. The developed method was tested using UAV imagery collected over rubber plantations on Hainan Island after the Nesat typhoon in China on 19 October 2011. First, a coarse extraction of the affected area was performed by analyzing the image spectrum and textural characteristics. A thinning algorithm was then used to simplify downed trees into skeletal structures. Finally, fine extraction of individual trees was achieved using a line detection algorithm. The completeness of windthrown trees in the study area was 75.7% and the correctness was 92.5%. While similar values have been reported in other studies, they often include constraints, such as tree height. This technique is proposed to be a more feasible extraction algorithm as it is capable of achieving low commission errors across a broad range of tree heights and sizes. As such, it is a viable option for extraction of windthrown trees with a small trunk diameter. Full article
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Open AccessArticle An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa
Remote Sens. 2017, 9(4), 307; doi:10.3390/rs9040307
Received: 8 January 2017 / Revised: 9 March 2017 / Accepted: 20 March 2017 / Published: 24 March 2017
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Abstract
Knowledge of evapotranspiration (ET) is essential for enhancing our understanding of the hydrological cycle, as well as for managing water resources, particularly in semi-arid regions. Remote sensing offers a comprehensive means of monitoring this phenomenon at different spatial and temporal intervals. Currently, several
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Knowledge of evapotranspiration (ET) is essential for enhancing our understanding of the hydrological cycle, as well as for managing water resources, particularly in semi-arid regions. Remote sensing offers a comprehensive means of monitoring this phenomenon at different spatial and temporal intervals. Currently, several satellite methods exist and are used to assess ET at various spatial and temporal resolutions with various degrees of accuracy and precision. This research investigated the performance of three satellite-based ET algorithms and two global products, namely land surface temperature/vegetation index (TsVI), Penman–Monteith (PM), and the Meteosat Second Generation ET (MET) and the Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM) global products, in two eco-regions of South Africa. Daily ET derived from the eddy covariance system from Skukuza, a sub-tropical, savanna biome, and large aperture boundary layer scintillometer system in Elandsberg, a Mediterranean, fynbos biome, during the dry and wet seasons, were used to evaluate the models. Low coefficients of determination (R2) of between 0 and 0.45 were recorded on both sites, during both seasons. Although PM performed best during periods of high ET at both sites, results show it was outperformed by other models during low ET times. TsVI and MET were similarly accurate in the dry season in Skukuza, as GLEAM was the most accurate in Elandsberg during the wet season. The conclusion is that none of the models performed well, as shown by low R2 and high errors in all the models. In essence, our results conclude that further investigation of the PM model is possible to improve its estimation of low ET measurements. Full article
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Open AccessArticle Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery
Remote Sens. 2017, 9(4), 308; doi:10.3390/rs9040308
Received: 30 December 2016 / Revised: 13 March 2017 / Accepted: 15 March 2017 / Published: 24 March 2017
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Abstract
Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in
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Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models
Remote Sens. 2017, 9(4), 309; doi:10.3390/rs9040309
Received: 28 December 2016 / Revised: 15 March 2017 / Accepted: 21 March 2017 / Published: 25 March 2017
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Abstract
Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI
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Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period). Full article
(This article belongs to the Special Issue Earth Observations for Precision Farming in China (EO4PFiC))
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Open AccessArticle A Hybrid Approach for Three-Dimensional Building Reconstruction in Indianapolis from LiDAR Data
Remote Sens. 2017, 9(4), 310; doi:10.3390/rs9040310
Received: 24 December 2016 / Revised: 14 March 2017 / Accepted: 20 March 2017 / Published: 26 March 2017
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Abstract
3D building models with prototypical roofs are more valuable in many applications than 2D building footprints. This research proposes a hybrid approach, combining the data- and model-driven approaches for generating LoD2-level building models by using medium resolution (0.91 m) LiDAR nDSM, the 2D
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3D building models with prototypical roofs are more valuable in many applications than 2D building footprints. This research proposes a hybrid approach, combining the data- and model-driven approaches for generating LoD2-level building models by using medium resolution (0.91 m) LiDAR nDSM, the 2D building footprint and the high resolution orthophoto for the City of Indianapolis, USA. The main objective is to develop a GIS-based procedure for automatic reconstruction of complex building roof structures in a large area with high accuracy, but without requiring high-density point data clouds and computationally-intensive algorithms. A multi-stage strategy, which combined step-edge detection, roof model selection and ridge detection techniques, was adopted to extract key features and to obtain prior knowledge for 3D building reconstruction. The entire roof can be reconstructed successfully by assembling basic models after their shapes were reconstructed. This research finally created a 3D city model at the Level of Detail 2 (LoD2) according to the CityGML standard for the downtown area of Indianapolis (included 519 buildings).The reconstruction achieved 90.6% completeness and 96% correctness for seven tested buildings whose roofs were mixed by different shapes of structures. Moreover, 86.3% of completeness and 90.9% of correctness were achieved for 38 commercial buildings with complex roof structures in the downtown area, which indicated that the proposed method had the ability for large-area building reconstruction. The major contribution of this paper lies in designing an efficient method to reconstruct complex buildings, such as those with irregular footprints and roof structures with flat, shed and tiled sub-structures mixed together. It overcomes the limitation that building reconstruction using coarse resolution LiDAR nDSM cannot be based on precise horizontal ridge locations, by adopting a novel ridge detection method. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery
Remote Sens. 2017, 9(4), 311; doi:10.3390/rs9040311
Received: 16 January 2017 / Revised: 9 March 2017 / Accepted: 23 March 2017 / Published: 26 March 2017
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Abstract
Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features
[...] Read more.
Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper, we adopt an innovative model named Fuzzy Autoencode Model (FAEM) to integrate the feature learning ability of stacked autoencode networks and the detection ability of fuzzy function for highly accurate cloud detection on remote sensing imagery. Our proposed method begins by selecting and fusing spectral, texture, and structure information. Thereafter, the proposed technique established a FAEM to learn the deep discriminative features from a great deal of selected information. Finally, the learned features are mapped to the corresponding cloud density map with a fuzzy function. To demonstrate the effectiveness of the proposed method, 172 Landsat ETM+ images and 25 GF-1 images with different spatial resolutions are used in this paper. For the convenience of accuracy assessment, ground truth data are manually outlined. Results show that the average RER (ratio of right rate and error rate) on Landsat images is greater than 29, while the average RER of Support Vector Machine (SVM) is 21.8 and Random Forest (RF) is 23. The results on GF-1 images exhibit similar performance as Landsat images with the average RER of 25.9, which is much higher than the results of SVM and RF. Compared to traditional methods, our technique has attained higher average cloud detection accuracy for either different spatial resolutions or various land surfaces. Full article
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Open AccessArticle Deep Learning Approach for Car Detection in UAV Imagery
Remote Sens. 2017, 9(4), 312; doi:10.3390/rs9040312
Received: 31 December 2016 / Revised: 12 March 2017 / Accepted: 24 March 2017 / Published: 27 March 2017
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Abstract
This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres) and the
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This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres) and the extremely high level of detail, which require suitable automatic analysis methods. Our proposed method begins by segmenting the input image into small homogeneous regions, which can be used as candidate locations for car detection. Next, a window is extracted around each region, and deep learning is used to mine highly descriptive features from these windows. We use a deep convolutional neural network (CNN) system that is already pre-trained on huge auxiliary data as a feature extraction tool, combined with a linear support vector machine (SVM) classifier to classify regions into “car” and “no-car” classes. The final step is devoted to a fine-tuning procedure which performs morphological dilation to smooth the detected regions and fill any holes. In addition, small isolated regions are analysed further using a few sliding rectangular windows to locate cars more accurately and remove false positives. To evaluate our method, experiments were conducted on a challenging set of real UAV images acquired over an urban area. The experimental results have proven that the proposed method outperforms the state-of-the-art methods, both in terms of accuracy and computational time. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Flood Monitoring Using Satellite-Based RGB Composite Imagery and Refractive Index Retrieval in Visible and Near-Infrared Bands
Remote Sens. 2017, 9(4), 313; doi:10.3390/rs9040313
Received: 24 November 2016 / Revised: 7 March 2017 / Accepted: 24 March 2017 / Published: 27 March 2017
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Abstract
Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery
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Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance (MOD09GA) data obtained from the Terra satellite, which is used to visualize and analyze these events. This study proposes using an RGB combination of MODIS band 6 (1.64 μm), band 5 (1.24 μm), and band 2 (0.86 μm) data from the visible and the near-infrared spectral ranges to map flood events. The flooding events that were investigated in this study occurred on 25 October 2015 along the Pampanga River in the Philippines, and on 28 July 2016 along the Poyang and Dongting Lakes in China. In the case of the Pampanga River, the inundated areas were estimated with surface reflectance (R) thresholds of 0.0 ≤ R6 ≤ 0.102, 0.0 ≤ R5 ≤ 0.138, and 0.03 ≤ R2 ≤ 0.148 for MODIS bands 6, 5, and 2, respectively, which were determined using Otsu’s method. The total inundated area was estimated to be 487.75 km2. This estimate was indirectly compared with the results obtained from SENTINEL-1A Synthetic Aperture Radar (SAR) data. The total inundated area on 26 October 2015 for the case of the Pampanga River was estimated to be 486.37 km2 using histogram analysis based on Otsu’s method. For the flooding case in China, the total estimated inundated area using MODIS RGB imagery on 28 July 2016 and SAR on 3 August 2016 was 1148.25 km2 and 1110.096 km2, respectively. In addition, RGB imagery results using MODIS 6-5-2 bands were supported by the refractive index retrieval along the inundation area. A threshold of 1.6 for the real part of the complex refractive index allows for the discrimination between the flooded and non-flooded areas using the Hong and ASH approximations. This study shows that the RGB composite techniques using advanced sensors with more bands and higher spatio-temporal resolutions, and supported by the refractive index retrieval method, are useful for estimating flood events. Full article
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Open AccessArticle Loess Landslide Inventory Map Based on GF-1 Satellite Imagery
Remote Sens. 2017, 9(4), 314; doi:10.3390/rs9040314
Received: 4 January 2017 / Revised: 20 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
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Abstract
Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented
[...] Read more.
Rainfall-induced landslides are a major threat in the hilly and gully regions of the Loess Plateau. Landslide mapping via field investigations is challenging and impractical in this complex region because of its numerous gullies. In this paper, an algorithm based on an object-oriented method (OOA) has been developed to recognize loess landslides by combining spectral, textural, and morphometric information with auxiliary topographic parameters based on high-resolution multispectral satellite data (GF-1, 2 m) and a high-precision DEM (5 m). The quality percentage (QP) values were all greater than 0.80, and the kappa indices were all higher than 0.85, indicating good landslide detection with the proposed approach. We quantitatively analyze the spectral, textural, morphometric, and topographic properties of loess landslides. The normalized difference vegetation index (NDVI) is useful for discriminating landslides from vegetation cover and water areas. Morphometric parameters, such as elongation and roundness, can potentially improve the recognition capacity and facilitate the identification of roads. The combination of spectral properties in near-infrared regions, the textural variance from a grey level co-occurrence matrix (GLCM), and topographic elevation data can be used to effectively discriminate terraces and buildings. Furthermore, loess flows are separated from landslides based on topographic position data. This approach shows great potential for quickly producing accurate results for loess landslides that are induced by extreme rainfall events in the hilly and gully regions of the Loess Plateau, which will help decision makers improve landslide risk assessment, reduce the risk from landslide hazards and facilitate the application of more reliable disaster management strategies. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Characterization of Snow Facies on the Greenland Ice Sheet Observed by TanDEM-X Interferometric SAR Data
Remote Sens. 2017, 9(4), 315; doi:10.3390/rs9040315
Received: 18 January 2017 / Revised: 21 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
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Abstract
This paper presents for the first time a detailed study on information content of X-band single-pass interferometric spaceborne SAR data with respect to snow facies characterization. An approach for classifying different snow facies of the Greenland Ice Sheet by exploiting X-band TanDEM-X interferometric
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This paper presents for the first time a detailed study on information content of X-band single-pass interferometric spaceborne SAR data with respect to snow facies characterization. An approach for classifying different snow facies of the Greenland Ice Sheet by exploiting X-band TanDEM-X interferometric synthetic aperture radar acquisitions is firstly detailed. Large-scale mosaics of radar backscatter and volume correlation factor, derived from quicklook images of the interferometric coherence, represent the starting point for applying an unsupervised classification method based on the c-means fuzzy clustering algorithm. The data was acquired during winter 2010/2011. A partition of four different snow facies was chosen and interpreted using reference melt data, snow density, and in situ measurements. The variations in the stratification and micro-structure of firn, such as the variations of density with depth and the presence of percolation features, are identified as relevant parameters for explaining the significant differences in the observed interferometric signatures among different snow facies. Moreover, a statistical analysis of backscatter and volume correlation factor provided useful parameters for characterizing the snow facies behavior and analyzing their dependency on the acquisition geometry. Finally, knowing the location and characterization of the different snow facies, the two-way X-band penetration depth over the whole Ice Sheet was estimated. The obtained mean values vary from 2.3 m for the outer snow facies up to 4.18 m for the inner one. The presented approach represents a starting point for a long-term monitoring of ice sheet dynamics, by acquiring time-series, and is of high relevance for the design of future SAR missions as well. Full article
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Open AccessArticle Texture-Guided Multisensor Superresolution for Remotely Sensed Images
Remote Sens. 2017, 9(4), 316; doi:10.3390/rs9040316
Received: 4 January 2017 / Revised: 14 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
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Abstract
This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the
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This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against misregistration and the resolution ratio and applicable to a wide variety of multisensor superresolution problems in remote sensing. The proposed methodology is applied to six different types of multisensor superresolution, which fuse the following image pairs: multispectral and panchromatic images, hyperspectral and panchromatic images, hyperspectral and multispectral images, optical and synthetic aperture radar images, thermal-hyperspectral and RGB images, and digital elevation model and multispectral images. The experimental results demonstrate the effectiveness and high general versatility of TGMS. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
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Open AccessArticle Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards
Remote Sens. 2017, 9(4), 317; doi:10.3390/rs9040317
Received: 28 November 2016 / Revised: 8 March 2017 / Accepted: 23 March 2017 / Published: 28 March 2017
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Abstract
Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming
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Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry. Full article
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Open AccessArticle Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations
Remote Sens. 2017, 9(4), 318; doi:10.3390/rs9040318
Received: 4 January 2017 / Revised: 16 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
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Abstract
Informative spectral bands for green leaf area index (LAI) estimation in two crops were identified and generic models for soybean and maize were developed and validated using spectral data taken at close range. The objective of this paper was to test developed models
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Informative spectral bands for green leaf area index (LAI) estimation in two crops were identified and generic models for soybean and maize were developed and validated using spectral data taken at close range. The objective of this paper was to test developed models using Aqua and Terra MODIS, Landsat TM and ETM+, ENVISAT MERIS surface reflectance products, and simulated data of the recently-launched Sentinel 2 MSI and Sentinel 3 OLCI. Special emphasis was placed on testing generic models which require no re-parameterization for these species. Four techniques were investigated: support vector machines (SVM), neural network (NN), multiple linear regression (MLR), and vegetation indices (VI). For each technique two types of models were tested based on (a) reflectance data, taken at close range and resampled to simulate spectral bands of satellite sensors; and (b) surface reflectance satellite products. Both types of models were validated using MODIS, TM/ETM+, and MERIS data. MERIS was used as a prototype of OLCI Sentinel-3 data which allowed for assessment of the anticipated accuracy of OLCI. All models tested provided a robust and consistent selection of spectral bands related to green LAI in crops representing a wide range of biochemical and structural traits. The MERIS observations had the lowest errors (around 11%) compared to the remaining satellites with observational data. Sentinel 2 MSI and OLCI Sentinel 3 estimates, based on simulated data, had errors below 8%. However the accuracy of these models with actual MSI and OLCI surface reflectance products remains to be determined. Full article
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Open AccessArticle Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices
Remote Sens. 2017, 9(4), 319; doi:10.3390/rs9040319
Received: 28 December 2016 / Revised: 15 March 2017 / Accepted: 27 March 2017 / Published: 28 March 2017
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Abstract
Crop biomass is a critical variable for characterizing crop growth development, understanding dry matter partitioning, and predicting grain yield. Previous studies on the spectroscopic estimation of crop biomass focused on the use of various spectral indices based on chlorophyll absorption features and found
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Crop biomass is a critical variable for characterizing crop growth development, understanding dry matter partitioning, and predicting grain yield. Previous studies on the spectroscopic estimation of crop biomass focused on the use of various spectral indices based on chlorophyll absorption features and found that they often became saturated at high biomass levels. Given that crop biomass is commonly expressed as the dry weight of canopy components per unit ground area, it may be better estimated using the spectral indices that directly characterize dry matter absorption. This study aims to evaluate a group of four dry matter indices (DMIs) by comparison with a group of four chlorophyll indices (CIs) for estimating the biomass of individual components (e.g., leaves, stems) and their combinations with the field data collected from a two-year rice cultivation experiment. The Red-edge Chlorophyll Index (CIRed-edge) of the CI group exhibited the best relationship with leaf biomass (R2 = 0.82) for the whole growing season and with total biomass (R2 = 0.81), but only for the growth stages before heading. However, the Normalized Difference Index for Leaf Mass per Area (NDLMA) of the DMI group showed the best relationships with both stem biomass (R2 = 0.81) and total biomass (R2 = 0.81) for the whole season. This research demonstrated the suitability of dry matter indices and provided physical explanations for the superior performance of dry matter indices over chlorophyll indices for the estimation of whole-season total biomass. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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Open AccessArticle Mapping the Expansion of Boom Crops in Mainland Southeast Asia Using Dense Time Stacks of Landsat Data
Remote Sens. 2017, 9(4), 320; doi:10.3390/rs9040320
Received: 23 December 2016 / Revised: 10 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
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Abstract
We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was
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We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was performed using Support Vector Machines (SVM), which are supervised non-parametric statistical learning techniques. To select the most suitable SMV classifier and the related parameter settings, we used the training data and performed a two-dimensional grid search with a three-fold internal cross-validation. We worked in seven Landsat footprints and found the linear kernel to be the most suitable for all footprints, but the most suitable regularization parameter C varied across the footprints. We distinguished a total of 41 LCLUCs (13 to 31 classes per footprint) in very dynamic and heterogeneous landscapes. The approach proved useful for distinguishing subtle changes over time and to map a variety of land covers, tree crops, and transformations as long as sufficient training points could be collected for each class. While to date, this approach has only been applied to mapping urban extent and expansion, this study shows that it is also useful for mapping change in rural settings, especially when images from phenologically relevant acquisition dates are included. Full article
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Open AccessArticle Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes
Remote Sens. 2017, 9(4), 322; doi:10.3390/rs9040322
Received: 16 January 2017 / Revised: 18 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
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Abstract
Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance (RW). In
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Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity
Remote Sens. 2017, 9(4), 323; doi:10.3390/rs9040323
Received: 24 January 2017 / Revised: 17 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
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Abstract
Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the
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Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS) measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA). Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Polarization Patterns of Transmitted Celestial Light under Wavy Water Surfaces
Remote Sens. 2017, 9(4), 324; doi:10.3390/rs9040324
Received: 3 January 2017 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 29 March 2017
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Abstract
This paper presents a model to describe the polarization patterns of celestial light, which includes sunlight and skylight, when refracted by wavy water surfaces. The polarization patterns and intensity distribution of refracted light through the wave water surface were calculated. The model was
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This paper presents a model to describe the polarization patterns of celestial light, which includes sunlight and skylight, when refracted by wavy water surfaces. The polarization patterns and intensity distribution of refracted light through the wave water surface were calculated. The model was validated by underwater experimental measurements. The experimental and theoretical values agree well qualitatively. This work provides a quantitative description of the repolarization and transmittance of celestial light transmitted through wave water surfaces. The effects of wind speed and incident sources on the underwater refraction polarization patterns are discussed. Scattering skylight dominates the polarization patterns while direct solar light is the dominant source of the intensity of the underwater light field. Wind speed has an influence on disturbing the patterns under water. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Slip Model for the 25 November 2016 Mw 6.6 Aketao Earthquake, Western China, Revealed by Sentinel-1 and ALOS-2 Observations
Remote Sens. 2017, 9(4), 325; doi:10.3390/rs9040325
Received: 13 February 2017 / Revised: 19 March 2017 / Accepted: 28 March 2017 / Published: 29 March 2017
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Abstract
On 25 November 2016 (UTC 14:24:30), an Mw 6.6 dextral strike-slip earthquake ruptured Aketao county in the northwestern portion of the Kongur Shan extensional system, western China. We extracted surface deformation maps and investigated the distribution of the coseismic slip of the 2016
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On 25 November 2016 (UTC 14:24:30), an Mw 6.6 dextral strike-slip earthquake ruptured Aketao county in the northwestern portion of the Kongur Shan extensional system, western China. We extracted surface deformation maps and investigated the distribution of the coseismic slip of the 2016 Aketao earthquake by exploiting the Interferometric Synthetic Aperture Radar data imaged by the Sentinel-1 satellites of the European Space Agency and the ALOS-2 satellite of the Japanese Aerospace Exploration Agency. Assuming the crust of the earth is an elastic half-space homogeneous medium, the best fitting slip model suggests a dip angle of 78° for the seismogenic fault. The rupture of the 2016 Aketao earthquake may have consisted of two sub-events that occurred in rapid succession within a few seconds, resulting in two large discrete asperities with maximum slip of ~0.85 m, which were separated by a ~6 km-wide small slip gap. The maximum slip for the sub-event near the epicenter was mainly concentrated at a depth of ~10 km and that of the other at a depth of ~5 km. The estimated total seismic moment from the optimal slip model is 1.58 × 1019 N•m, corresponding to an event with a moment magnitude of 6.74. More than 65% of the aftershocks occurred in the areas of increased Coulomb failure stress, in which the stress was estimated to have been increased by at least 0.1 bar. Matching the potential barrier on the fault with the depth distribution of aftershocks implies that friction on the causative fault was heterogeneous, which may play a primary role in controlling the active behavior of the Muji fault. Full article
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Open AccessArticle Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows
Remote Sens. 2017, 9(4), 326; doi:10.3390/rs9040326
Received: 30 December 2016 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
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Abstract
Accurate and reliable information about the situation in refugee or internally displaced person camps is very important for planning any kind of help like health care, infrastructure, or vaccination campaigns. The number and spatial distribution of single dwellings extracted semi-automatically from very high-resolution
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Accurate and reliable information about the situation in refugee or internally displaced person camps is very important for planning any kind of help like health care, infrastructure, or vaccination campaigns. The number and spatial distribution of single dwellings extracted semi-automatically from very high-resolution (VHR) satellite imagery as an indicator for population estimations can provide such important information. The accuracy of the extracted dwellings can vary quite a lot depending on various factors. To enhance established single dwelling extraction approaches, we have tested the integration of stratified template matching methods in object-based image analysis (OBIA) workflows. A template library for various dwelling types (template samples are taken from ten different sites using 16 satellite images), incorporating the shadow effect of dwellings, was established. Altogether, 18 template classes were created covering typically occurring dwellings and their cast shadows. The created template library aims to be generally applicable in similar conditions. Compared to pre-existing OBIA classifications, the approach could increase the producer’s accuracy by 11.7 percentage points on average and slightly increase the user’s accuracy. These results show that the stratified integration of template matching approaches in OBIA workflows is a possibility to further improve the results of semi-automated dwelling extraction, especially in complex situations. Full article
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Open AccessArticle Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data
Remote Sens. 2017, 9(4), 327; doi:10.3390/rs9040327
Received: 13 February 2017 / Revised: 18 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
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Abstract
The Soil Moisture Active Passive (SMAP) mission was designed to provide global mapping of soil moisture (SM) on nested 3, 9, and 36 km earth grids measured by L-band passive and active microwave sensors. The validation of SMAP SM products is crucial for
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The Soil Moisture Active Passive (SMAP) mission was designed to provide global mapping of soil moisture (SM) on nested 3, 9, and 36 km earth grids measured by L-band passive and active microwave sensors. The validation of SMAP SM products is crucial for the application of the products and improvement of the retrieval algorithm. Since the SMAP SM products were released, much effort has been invested in the evaluation of the SMAP radiometer SM product (SMAP_P). However, there has been little validation of SMAP radar (SMAP_A) and active/passive combined (SMAP_AP) SM products. This paper presents an evaluation of SMAP_P, SMAP_A and SMAP_AP SM products by using distributed ground observations networks in different landscapes in the Heihe River Basin of northwestern China. The standard error metrics of SMAP products and relative error are applied to measure the products’ performances. The results show that the SMAP SM products exhibit consistent spatial-temporal variation with the ground measurements and typical precipitation events. Three products show various types of performance capability (e.g., active, passive and combined), surface coverage (e.g., bare, vegetated) and climatic region (e.g., cold, arid). Relatively, the SMAP_P shows the best performance, while the SMAP_A performs the worst. The best performances are observed over bare soils but with overestimation and the largest relative error, and unsatisfactory accuracies are observed over cold regions and woody vegetated surfaces with underestimation. The vegetation effect and the freezing-thawing cycle may be major factors that led to an unsatisfactory performance. Efforts on resolving the influence of these factors are expected to improve the accuracy and to promote the application of SMAP SM products over these regions. Overall, this evaluation provides an understanding of SMAP SM products over cold and arid regions, and suggestions for the further refinement of the SMAP SM retrieval algorithms. Full article
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Open AccessArticle Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees
Remote Sens. 2017, 9(4), 328; doi:10.3390/rs9040328
Received: 18 October 2016 / Revised: 16 March 2017 / Accepted: 24 March 2017 / Published: 30 March 2017
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Abstract
Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the
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Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease’s geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs. Full article
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Open AccessArticle An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology
Remote Sens. 2017, 9(4), 329; doi:10.3390/rs9040329
Received: 30 December 2016 / Revised: 17 March 2017 / Accepted: 24 March 2017 / Published: 30 March 2017
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Abstract
Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA—similar to other emerging paradigms—lacks formal expressions and objective modelling structures and in particular semantic classification
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Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA—similar to other emerging paradigms—lacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontology—as compared to the decision tree classification without using the ontology—yielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations. Full article
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Open AccessArticle Building Damage Assessment Using Multisensor Dual-Polarized Synthetic Aperture Radar Data for the 2016 M 6.2 Amatrice Earthquake, Italy
Remote Sens. 2017, 9(4), 330; doi:10.3390/rs9040330
Received: 15 February 2017 / Revised: 26 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
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Abstract
On 24 August 2016, the M 6.2 Amatrice earthquake struck central Italy, well-known as a seismically active region, causing considerable damage to buildings in the town of Amatrice and the surrounding area. Damage from this earthquake was assessed quantitatively by means of multitemporal
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On 24 August 2016, the M 6.2 Amatrice earthquake struck central Italy, well-known as a seismically active region, causing considerable damage to buildings in the town of Amatrice and the surrounding area. Damage from this earthquake was assessed quantitatively by means of multitemporal synthetic aperture radar (SAR) coherence and SAR intensity methods using dual-polarized SAR data obtained from the Sentinel-1 (VV, VH) and ALOS-2 (HH, HV) satellites. We developed linear discriminant functions based on three items: (1) the differential coherence values; (2) the differential backscattering intensity values of pre- and post-event images; and (3) a binary damage map of the optical pre- and post-event imagery. The accuracy of the proposed model was 84% for the Sentinel-1 data and 76% for the ALOS-2 data. The damage proxy maps deduced from the linear discriminant functions can be useful in the parcel-by-parcel assessment of building damage and development of spatial models for the allocation of urban search and rescue operations. Full article
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Open AccessArticle A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation
Remote Sens. 2017, 9(4), 331; doi:10.3390/rs9040331
Received: 13 February 2017 / Revised: 23 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
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Abstract
The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage
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The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method. Full article
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Open AccessArticle The Observed Impacts of Wind Farms on Local Vegetation Growth in Northern China
Remote Sens. 2017, 9(4), 332; doi:10.3390/rs9040332
Received: 18 January 2017 / Revised: 28 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
Cited by 1 | PDF Full-text (7940 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Wind farms (WFs) can affect the local climate, and local climate change may influence underlying vegetation. Some studies have shown that WFs affect certain aspects of the regional climate, such as temperature and rainfall. However, there is still no evidence to demonstrate whether
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Wind farms (WFs) can affect the local climate, and local climate change may influence underlying vegetation. Some studies have shown that WFs affect certain aspects of the regional climate, such as temperature and rainfall. However, there is still no evidence to demonstrate whether WFs can affect local vegetation growth, a significant part of the overall assessment of WF effects. In this research, based on the moderate-resolution imaging spectroradiometer (MODIS) vegetation index, productivity and other remote-sensing data from 2003 to 2014, the effects of WFs in the Bashang area of Northern China on vegetation growth and productivity in the summer (June–August) were analyzed. The results showed that: (1) WFs had a significant inhibiting effect on vegetation growth, as demonstrated by decreases in the leaf area index (LAI), the enhanced vegetation index (EVI), and the normalized difference vegetation index (NDVI) of approximately 14.5%, 14.8%, and 8.9%, respectively, in the 2003–2014 summers. There was also an inhibiting effect of 8.9% on summer gross primary production (GPP) and 4.0% on annual net primary production (NPP) coupled with WFs; and (2) the major impact factors might be the changes in temperature and soil moisture: WFs suppressed soil moisture and enhanced water stress in the study area. This research provides significant observational evidence that WFs can inhibit the growth and productivity of the underlying vegetation. Full article
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Open AccessArticle Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China
Remote Sens. 2017, 9(4), 333; doi:10.3390/rs9040333
Received: 9 January 2017 / Revised: 27 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
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Abstract
Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has
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Landslide mapping (LM) has recently become an important research topic in remote sensing and geohazards. The area near the Three Gorges Reservoir (TGR) along the Yangtze River in China is one of the most landslide-prone regions in the world, and the area has suffered widespread and significant landslide events in recent years. In our study, an object-oriented landslide mapping (OOLM) framework was proposed for reliable and accurate LM from ‘ZY-3’ high spatial resolution (HSR) satellite images. The framework was based on random forests (RF) and mathematical morphology (MM). RF was first applied as an object feature information reduction tool to identify the significant features for describing landslides, and it was then combined with MM to map the landslides. Three object-feature domains were extracted from the ‘ZY-3’ HSR data: layer information, texture, and geometric features. A total group of 124 features and 24 landslides were used as inputs to determine the landslide boundaries and evaluate the landslide classification accuracy. The results showed that: (1) the feature selection (FS) method had a positive influence on effective landslide mapping; (2) by dividing the data into two sets, training sets which consisted of 20% of the landslide objects (OLS) and non-landslide objects (ONLS), and test sets which consisted of the remaining 80% of the OLS and ONLS, the selected feature subsets were combined for training to obtain an overall classification accuracy of 93.3% ± 0.12% of the test sets; (3) four MM operations based on closing and opening were used to improve the performance of the RF classification. Seven accuracy evaluation indices were used to compare the accuracies of these landslide mapping methods. Finally, the landslide inventory maps were obtained. Based on its efficiency and accuracy, the proposed approach can be employed for rapid response to natural hazards in the Three Gorges area. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Biomass Estimation of Xerophytic Forests Using Visible Aerial Imagery: Contrasting Single-Tree and Area-Based Approaches
Remote Sens. 2017, 9(4), 334; doi:10.3390/rs9040334
Received: 2 February 2017 / Revised: 24 March 2017 / Accepted: 28 March 2017 / Published: 31 March 2017
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Abstract
A large part of arid areas in tropical and sub-tropical regions are dominated by sparse xerophytic vegetation, which are essential for providing products and services for local populations. While a large number of researches already exist for the derivation of wall-to-wall estimations of
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A large part of arid areas in tropical and sub-tropical regions are dominated by sparse xerophytic vegetation, which are essential for providing products and services for local populations. While a large number of researches already exist for the derivation of wall-to-wall estimations of above ground biomass (AGB) with remotely sensed data, only a few of them are based on the direct use of non-photogrammetric aerial photography. In this contribution we present an experiment carried out in a study area located in the Santiago Island in the Cape Verde archipelago where a National Forest Inventory (NFI) was recently carried out together with a new acquisition of a visible high-resolution aerial orthophotography. We contrasted two approaches: single-tree, based on the automatic delineation of tree canopies; and area-based, on the basis of an automatic image classification. Using 184 field plots collected for the NFI we created parametric models to predict AGB on the basis of the crown projection area (CPA) estimated from the two approaches. Both the methods produced similar root mean square errors (RMSE) at pixel level 45% for the single-tree and 42% for the area-based. However, the latest was able to better predict the AGB along all the variable range, limiting the saturation problem which is evident when the CPA tends to reach the full coverage of the field plots. These findings demonstrate that in regions dominated by sparse vegetation, a simple aerial orthophoto can be used to successfully create AGB wall-to-wall predictions. The level of these estimations’ uncertainty permits the derivation of small area estimations useful for supporting a more correct implementation of sustainable management practices of wood resources. Full article
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Open AccessArticle Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation
Remote Sens. 2017, 9(4), 335; doi:10.3390/rs9040335
Received: 24 January 2017 / Revised: 23 March 2017 / Accepted: 28 March 2017 / Published: 1 April 2017
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Abstract
Hyperspectral image (HSI) clustering is generally a challenging task because of the complex spectral-spatial structure. Based on the assumption that all the pixels are sampled from the union of subspaces, recent works have introduced a robust technique—the sparse subspace clustering (SSC) algorithm and
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Hyperspectral image (HSI) clustering is generally a challenging task because of the complex spectral-spatial structure. Based on the assumption that all the pixels are sampled from the union of subspaces, recent works have introduced a robust technique—the sparse subspace clustering (SSC) algorithm and its enhanced versions (SSC models incorporating spatial information)—to cluster HSIs, achieving excellent performances. However, these methods are all based on the linear representation model, which conflicts with the well-known nonlinear structure of HSIs and limits their performance to a large degree. In this paper, to overcome these obstacles, we present a new kernel sparse subspace clustering algorithm with a spatial max pooling operation (KSSC-SMP) for hyperspectral remote sensing data interpretation. The proposed approach maps the feature points into a much higher dimensional kernel space to extend the linear sparse subspace clustering model to nonlinear manifolds, which can better fit the complex nonlinear structure of HSIs. With the help of the kernel sparse representation, a more accurate representation coefficient matrix can be obtained. A spatial max pooling operation is utilized for the representation coefficients to generate more discriminant features by integrating the spatial-contextual information, which is essential for the accurate modeling of HSIs. This paper is an extension of our previous conference paper, and a number of enhancements are put forward. The proposed algorithm was evaluated on two well-known hyperspectral data sets—the Salinas image and the University of Pavia image—and the experimental results clearly indicate that the newly developed KSSC-SMP algorithm can obtain very competitive clustering results for HSIs, outperforming the current state-of-the-art clustering methods. Full article
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Open AccessArticle Combining Unmanned Aerial Systems and Sensor Networks for Earth Observation
Remote Sens. 2017, 9(4), 336; doi:10.3390/rs9040336
Received: 30 December 2016 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 1 April 2017
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Abstract
The combination of remote sensing and sensor network technologies can provide unprecedented earth observation capabilities, and has attracted high R&D interest in recent years. However, the procedures and tools used for deployment, geo-referenciation and collection of logged measurements in the case of traditional
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The combination of remote sensing and sensor network technologies can provide unprecedented earth observation capabilities, and has attracted high R&D interest in recent years. However, the procedures and tools used for deployment, geo-referenciation and collection of logged measurements in the case of traditional environmental monitoring stations are not suitable when dealing with hundreds or thousands of sensor nodes deployed in an environment of tenths of hectares. This paper presents a scheme based on Unmanned Aerial Systems that intends to give a step forward in the use of sensor networks for environment observation. The presented scheme includes methods, tools and technologies to solve sensor node deployment, localization and collection of measurements. The presented scheme is scalable—it is suitable for medium–large environments with a high number of sensor nodes—and highly autonomous—it is operated with very low human intervention. This paper presents the scheme including its main components, techniques and technologies, and describes its implementation and evaluation in field experiments. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification
Remote Sens. 2017, 9(4), 337; doi:10.3390/rs9040337
Received: 7 January 2017 / Revised: 28 March 2017 / Accepted: 29 March 2017 / Published: 1 April 2017
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Abstract
In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from
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In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint representations for data association across domains. This scheme makes homogeneous domain adaption algorithms suitable for heterogeneous domain adaptation problems. Additionally, inspired by fusion methods such as Ensemble Learning (EL), this work proposes a weighted voting scheme based on canonical correlation coefficients to combine classification results in multiple correlation subspaces. Finally, the semi-supervised MVCCAE extends the original procedure by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA). To validate the performances of the proposed supervised procedure, a single-view canonical analysis (SVCCA) with the same base classifier (Random Forests) is used. Similarly, to evaluate the performance of the semi-supervised approach, a comparison is made with other techniques such as Logistic label propagation (LLP) and the Laplacian support vector machine (LapSVM). All of the approaches are tested on two real hyperspectral images, which are considered the target domain, with a classifier trained from synthetic low-dimensional multispectral images, which are considered the original source domain. The experimental results confirm that multi-view canonical correlation can overcome the limitations of SVCCA. Both of the proposed procedures outperform the ones used in the comparison with respect to not only the classification accuracy but also the computational efficiency. Moreover, this research shows that canonical correlation weighted voting (CCWV) is a valid option with respect to other ensemble schemes and that because of their ability to balance diversity and accuracy, canonical views extracted using partially joint random view generation are more effective than those obtained by exploiting disjoint random view generation. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Open AccessArticle Retrieving 3-D Large Displacements of Mining Areas from a Single Amplitude Pair of SAR Using Offset Tracking
Remote Sens. 2017, 9(4), 338; doi:10.3390/rs9040338
Received: 7 February 2017 / Revised: 29 March 2017 / Accepted: 31 March 2017 / Published: 2 April 2017
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Abstract
Due to the side-looking imaging geometry of the current synthetic aperture radar (SAR) sensors, only ground deformation along the radar’s line-of-sight (LOS) and azimuth directions can be potentially obtained from a single amplitude pair (SAP) of SAR using offset tracking (OT) procedures. This
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Due to the side-looking imaging geometry of the current synthetic aperture radar (SAR) sensors, only ground deformation along the radar’s line-of-sight (LOS) and azimuth directions can be potentially obtained from a single amplitude pair (SAP) of SAR using offset tracking (OT) procedures. This significantly hinders the accurate assessment of mining-related hazards and better understanding of the mining subsidence mechanism. In this paper, we propose a method for completely retrieving three-dimensional (3-D) mining-induced displacements with OT-derived observations of LOS deformation from a single amplitude pair of SAR (referred to as OT-SAP hereinafter). The OT-SAP method first constructs two extra constraints at each pixel of the mining area based on the proportional relationship between the horizontal motion of the mining area and the gradients of the vertical subsidence in the east and north directions. The full 3-D mining-induced displacements are then solved by coupling the two constructed extra constraints with the OT-derived observations of the LOS deformation. The Daliuta coal mining area in China was selected to test the proposed OT-SAP method. The results show that the maximum 3-D displacements of this mining area were about 4.3 m, 1.1 m, and 1.3 m in the vertical, east, and north directions, respectively, from 21 November 2012 to 6 February 2013. The accuracies of the retrieved displacements in the vertical and horizontal directions are about 0.201 m and 0.214 m, respectively, which are much smaller than the mining-induced displacements in this mining area and can satisfy the basic requirements of mining deformation monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle Object-Based Detection of Lakes Prone to Seasonal Ice Cover on the Tibetan Plateau
Remote Sens. 2017, 9(4), 339; doi:10.3390/rs9040339
Received: 30 November 2016 / Revised: 20 March 2017 / Accepted: 30 March 2017 / Published: 2 April 2017
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Abstract
The Tibetan Plateau, the world’s largest orogenic plateau, hosts thousands of lakes that play prominent roles as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies have reported that the size of lakes on the
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The Tibetan Plateau, the world’s largest orogenic plateau, hosts thousands of lakes that play prominent roles as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies have reported that the size of lakes on the Tibetan Plateau has changed rapidly in recent years, possibly because of atmospheric warming. Tracking these changes systematically with remote sensing data is challenging given the different spectral signatures of water, the potential for confusing lakes with glaciers, and difficulties in classifying frozen or partly frozen lakes. Object-based image analysis (OBIA) offers new opportunities for automated classification in this context, and we have explored this method for mapping lakes from LANDSAT images and Shuttle Radar Topography Mission (SRTM) elevation data. We tested our algorithm for most of the Tibetan Plateau, where lakes in tectonic depressions or blocked by glaciers and sediments have different surface colours and seasonal ice cover in images obtained in 1995 and 2015. We combined a modified normalised difference water index (MNDWI) with OBIA and local topographic slope data in order to classify lakes with an area >10 km2. Our method derived 323 water bodies, with a total area of 31,258 km2, or 2.6% of the study area (in 2015). The same number of lakes had covered only 24,892 km2 in 1995; lake area has increased by ~26% in the past two decades. The classification had estimated producer’s and user’s accuracies of 0.98, with a Cohen’s kappa and F-score of 0.98, and may thus be a useful approximation for quantifying regional hydrological budgets. We have shown that our method is flexible and transferable to detecting lakes in diverse physical settings on several continents with similar success rates. Full article
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Open AccessArticle Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis
Remote Sens. 2017, 9(4), 340; doi:10.3390/rs9040340
Received: 3 February 2017 / Revised: 23 March 2017 / Accepted: 1 April 2017 / Published: 2 April 2017
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Abstract
The Moderate Resolution Imaging Spectroradiometer (MODIS) provides widespread Aerosol Optical Depth (AOD) datasets for climatological and environmental health research. Since MODIS AOD clearly lacks coverage in orbit-scanning gaps and cloud obscuration, some applications will benefit from data recovery using multi-temporal AOD. Aimed at
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The Moderate Resolution Imaging Spectroradiometer (MODIS) provides widespread Aerosol Optical Depth (AOD) datasets for climatological and environmental health research. Since MODIS AOD clearly lacks coverage in orbit-scanning gaps and cloud obscuration, some applications will benefit from data recovery using multi-temporal AOD. Aimed at qualitatively describing the relationship between multi-temporal AOD, AOD loadings and Normalized Difference Vegetation Index (NDVI) have been considered based on the mechanism of satellite AOD retrieval. Accordingly, the NDVI-based Weighted Linear Regression (NWLR) has been proposed to recover AOD by synthetically weighing AOD similarity, spatial proximity, and NDVI similarity. To evaluate the performance of AOD recovery, simulated experiments applying gap and window masks were conducted in South Asia and Beijing, respectively. The evaluation results demonstrated that the linear regression R2 achieved 0.8 and the absolute relative errors remained steady. Further validation was conducted between the recovered and actual AODs using 56 Aerosol Robotic Network (AERONET) sites in East and South Asia from 2013 to 2015, which demonstrated that over 41% of recovered AODs fell within the expected error (EE) envelope. Additional validation conducted in South Asia and Beijing showed that recovery by NWLR did not expand satellite-derived AOD errors, and the accuracy of recovered AOD was consistent with the accuracy of the original Aqua MODIS Deep Blue (DB) AOD. The recovery results illustrated that AOD coverage was improved in most regions, especially in North China, Mongolia, and South Asia, which could provide better support in aerosol spatio-temporal analysis and aerosol data assimilation. Full article
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Open AccessFeature PaperArticle Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data
Remote Sens. 2017, 9(4), 341; doi:10.3390/rs9040341
Received: 21 December 2016 / Revised: 29 March 2017 / Accepted: 31 March 2017 / Published: 2 April 2017
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Abstract
Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind
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Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing’anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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Open AccessArticle Polarimetric Calibration for a Ground-Based Radar, and Comparison of the Polarimetric Parameters with Air-Borne SAR Obtained from a Forest
Remote Sens. 2017, 9(4), 342; doi:10.3390/rs9040342
Received: 27 December 2016 / Revised: 15 March 2017 / Accepted: 31 March 2017 / Published: 3 April 2017
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Abstract
A polarimetric ground-based radar (GB-radar) system operated in the L-band has been developed. The frequency range of GB-radar is 1.215 to 1.3 GHz, which is the same as that of Japanese satellite-borne SAR, PALSAR-2 (Phased Array type L-band Synthetic Aperture Radar), and Japanese
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A polarimetric ground-based radar (GB-radar) system operated in the L-band has been developed. The frequency range of GB-radar is 1.215 to 1.3 GHz, which is the same as that of Japanese satellite-borne SAR, PALSAR-2 (Phased Array type L-band Synthetic Aperture Radar), and Japanese L-band air-borne radar, i.e., Pi-SAR-L2 (Polarimetric and Interferometric Airborne Synthetic Aperture Radar L2). Polarimetric calibration was carried out twice in the field to calibrate and validate the GB-radar data. Cross-talk and channel imbalance are improved for the both experiments, and are from −13.3 dB to −30.7 dB, and from 1.06 to 1.00, respectively for the first experiment, after calibration. The calibrated cross-talk and channel imbalance values were comparable to −31.7 dB and 1.013, which were obtained using PALSAR. Radiometric calibration and antenna pattern correction were also carried out in the second experiment. Forest observations were also carried out simultaneously by GB-radar and Pi-SAR-L2 in the second experiment. The range profiles obtained by GB-radar and Pi-SAR-L2 were compared for several polarimetric parameters, namely, radar backscattering coefficient, polarimetric coherence, eigenvalue-decomposition parameters, and four-component-decomposition parameters. Both range profiles matched moderately well and showed good performance that could compensate for the limited possibility of satellite/air-borne SAR observation. Full article
(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)
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Open AccessArticle A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters
Remote Sens. 2017, 9(4), 343; doi:10.3390/rs9040343
Received: 7 February 2017 / Revised: 17 March 2017 / Accepted: 23 March 2017 / Published: 4 April 2017
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Abstract
Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques
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Eutrophication is an increasing problem in coastal waters of the Baltic Sea. Moreover, algal blooms, which occur every summer in the Gulf of Gdansk can deleteriously impact human health, the aquatic environment, and economically important fisheries, tourism, and recreation industries. Traditional laboratory-based techniques for water monitoring are expensive and time consuming, which usually results in limited numbers of observations and discontinuity in space and time. The use of hyperspectral radiometers for coastal water observation provides the potential for more detailed remote optical monitoring. A statistical approach to develop local models for the estimation of optically significant components from in situ measured hyperspectral remote sensing reflectance in case 2 waters is presented in this study. The models, which are based on empirical orthogonal function (EOF) analysis and stepwise multilinear regression, allow for the estimation of parameters strongly correlated with phytoplankton (pigment concentration, absorption coefficient) and coloured detrital matter abundance (absorption coefficient) directly from reflectance spectra measured in situ. Chlorophyll a concentration, which is commonly used as a proxy for phytoplankton biomass, was retrieved with low error (median percent difference, MPD = 17%, root mean square error RMSE = 0.14 in log10 space) and showed a high correlation with chlorophyll a measured in situ (R = 0.84). Furthermore, phycocyanin and phycoerythrin, both characteristic pigments for cyanobacteria species, were also retrieved reliably from reflectance with MPD = 23%, RMSE = 0.23, R2 = 0.77 and MPD = 24%, RMSE = 0.15, R2 = 0.74, respectively. The EOF technique proved to be accurate in the derivation of the absorption spectra of phytoplankton and coloured detrital matter (CDM), with R2 (λ) above 0.83 and RMSE around 0.10. The approach was also applied to satellite multispectral remote sensing reflectance data, thus allowing for improved temporal and spatial resolution compared with the in situ measurements. The EOF method tested on simulated Medium Resolution Imaging Spectrometer (MERIS) or Ocean and Land Colour Instrument (OLCI) data resulted in RMSE = 0.16 for chl-a and RMSE = 0.29 for phycocyanin. The presented methods, applied to both in situ and satellite data, provide a powerful tool for coastal monitoring and management. Full article
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Open AccessArticle Crosstalk Effect in SNPP VIIRS
Remote Sens. 2017, 9(4), 344; doi:10.3390/rs9040344
Received: 6 February 2017 / Revised: 21 March 2017 / Accepted: 1 April 2017 / Published: 4 April 2017
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Abstract
An investigation has been carried out to examine the crosstalk contamination in the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) spacecraft. Prior to this study, the cause of the pronounced striping in Earth View (EV) images and
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An investigation has been carried out to examine the crosstalk contamination in the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) spacecraft. Prior to this study, the cause of the pronounced striping in Earth View (EV) images and obvious discontinuity in the EV brightness temperature (BT) of the thermal emissive bands (TEB) during black body (BB) warm-up cool-down (WUCD) calibration observed since launch has not been identified. Meanwhile, it has been recently demonstrated in the MODerate-resolution Imaging Spectroradiometer (MODIS) long-wave infrared (LWIR) photovoltaic (PV) bands that the crosstalk effect induces the same erroneous features. In this investigation, it is shown that the established lunar imagery analysis indeed verifies the existence of crosstalk contamination in SNPP VIIRS TEB. The crosstalk effect is quantitatively characterized by deriving the crosstalk coefficients from the scheduled lunar observations. The magnitude of the effect is comparatively smaller than that in MODIS LWIR PV bands, but is of a large enough magnitude to induce the aforementioned artificial features. Among all SNPP VIIRS TEB, Band M14 has the largest crosstalk contamination from Band M15, while Bands M13, M15, M16, and I5 have pronounced crosstalk effects as well. One new detail of the crosstalk effect specific to SNPP VIIRS, differing from the MODIS result, is the distinctive two-group pattern of odd and even detectors for each affected band due to the arrangement of the detector on the focal plane assembly (FPA). This is fully consistent with the earlier finding that this odd-even detector arrangement contributes to striping in the sea surface temperature (SST) products. Our analyses additionally suggest an explanation of the large temperature anomalies appearing during the WUCD time periods. The parallel effort examining the potential crosstalk contamination in SNPP VIIRS reflective solar bands, however, reveals no observable effect. Full article
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Open AccessArticle A Knowledge-Based Search Strategy for Optimally Structuring the Terrain Dependent Rational Function Models
Remote Sens. 2017, 9(4), 345; doi:10.3390/rs9040345
Received: 19 January 2017 / Revised: 19 March 2017 / Accepted: 30 March 2017 / Published: 11 April 2017
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Abstract
Identifying the optimal structure of terrain dependent Rational Function Models (RFMs) not only decreases the number of Ground Control Points (GCPs) required, but also increases the accuracy of the model, by reducing the multi-collinearity of Rational Polynomials Coefficients (RPCs) and avoiding the ill-posed
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Identifying the optimal structure of terrain dependent Rational Function Models (RFMs) not only decreases the number of Ground Control Points (GCPs) required, but also increases the accuracy of the model, by reducing the multi-collinearity of Rational Polynomials Coefficients (RPCs) and avoiding the ill-posed problem. Global optimization algorithms such as Genetic Algorithm (GA), evaluate the different combinations of parameters effectively. Therefore, they have a high ability to detect the optimal structure of RFMs. However, one drawback of these algorithms is their high computation cost. This article proposes a knowledge-based search strategy to overcome this deficiency. The backbone of the proposed method relies on the technical knowledge about the geometric condition of image at the time of acquisition, as well as the effect of external factors such as terrain relief on the image. This method was evaluated on four different datasets, including a SPOT-1A, a SPOT-1B, an IKONOS-Geo image, and a GeoEye-Geo imagery, using various number of GCPs. Experimental results demonstrate the efficiency of the proposed method to achieve a sub-pixel accuracy using few GCPs (only 4–6 points) in all datasets. The results also indicate that the proposed method improves the computation speed by 140 times when comparing with GA. Full article
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Open AccessArticle Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta
Remote Sens. 2017, 9(4), 346; doi:10.3390/rs9040346
Received: 9 December 2016 / Revised: 26 February 2017 / Accepted: 1 April 2017 / Published: 5 April 2017
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Abstract
Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy
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Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR) model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1) comparison with other regular GWR models by varying the number of auxiliary variables; and (2) comparison with observed ground-level PM2.5 concentrations. The result shows that our GWR models of “AOD + 3” with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM2.5 concentrations with only slight overfitting. The determination coefficients R2 of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM2.5 relationships and provides a good alternative in retrieving daily PM2.5 concentrations from remote sensing images in the YRD. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
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Open AccessArticle An Analysis of the Discrepancies between MODIS and INSAT-3D LSTs in High Temperatures
Remote Sens. 2017, 9(4), 347; doi:10.3390/rs9040347
Received: 17 December 2016 / Revised: 30 March 2017 / Accepted: 1 April 2017 / Published: 5 April 2017
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Abstract
In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be
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In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be used as references for validating geostationary LST products. This study attempted to investigate the discrepancies between a Moderate Resolution Imaging Spectro-radiometer (MODIS) and Indian National Satellite (INSAT-3D) LSTs for high temperatures, focusing on six deserts with sand dune land cover in the Middle East from 3 March 2015 to 24 August 2016. Firstly, the variability of LSTs in the deserts of the study area was analyzed by comparing the mean, Standard Deviation (STD), skewness, minimum, and maximum criteria for each observation time. The mean value of the LST observations indicated that the MYD-D observation times are closer to those of diurnal maximum and minimum LSTs. At all times, the LST observations exhibited a negative skewness and the STD indicated higher variability during times of MOD-D. The observed maximum LSTs from MODIS collection 6 showed higher values in comparison with the last versions of LSTs for hot spot regions around the world. After the temporal, spatial, and geometrical matching of LST products, the mean of the MODIS—INSAT LST differences was calculated for the study area. The results demonstrated that discrepancies increased with temperature up to +15.5 K. The slopes of the mean differences were relatively similar for all deserts except for An Nafud, suggesting an effect of View Zenith Angle (VZA). For modeling the discrepancies between two sensors in continuous space, the Diurnal Temperature Cycles (DTC) of both sensors were constructed and compared. The sample DTC models approved the results from discrete LST subtractions and proposed the uncertainties within MODIS DTCs. The authors proposed that the observed LST discrepancies in high temperatures could be the result of inherent differences in LST retrieval algorithms. Full article
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Open AccessArticle A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images
Remote Sens. 2017, 9(4), 349; doi:10.3390/rs9040349
Received: 14 January 2017 / Revised: 27 March 2017 / Accepted: 1 April 2017 / Published: 20 April 2017
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Abstract
In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four
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In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Open AccessArticle Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner
Remote Sens. 2017, 9(4), 350; doi:10.3390/rs9040350
Received: 15 February 2017 / Revised: 26 March 2017 / Accepted: 1 April 2017 / Published: 6 April 2017
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Abstract
Airborne laser scanning is now widely used for forest inventories. An essential part of inventory is a collection of field reference data including measurements of tree stem diameter at breast height (DBH). Traditionally this is acquired through manual measurements. The recent development of
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Airborne laser scanning is now widely used for forest inventories. An essential part of inventory is a collection of field reference data including measurements of tree stem diameter at breast height (DBH). Traditionally this is acquired through manual measurements. The recent development of terrestrial laser scanning (TLS) systems in terms of capacity and weight have made these systems attractive tools for extracting DBH. Multiple TLS scans are often merged into a single point cloud before the information extraction. This technique requires good position and orientation accuracy for each scan location. In this study, we propose a novel method that can operate under a relatively coarse positioning and orientation solution. The method divides the laser measurements into limited time intervals determined by the laser scan rotation. Tree positions and DBH are then automatically extracted from each laser scan rotation. To improve tree identification, the estimated center points are subsequently processed by an iterative closest point algorithm. In a small reference data set from a single field plot consisting of 18 trees, it was found that 14 were automatically identified by this method. The estimated DBH had a mean differences of 0.9 cm and a root mean squared error of 1.5 cm. The proposed method enables fast and efficient data acquisition and a 250 m2 field plot was measured within 30 s. Full article
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Open AccessArticle High and Medium Resolution Satellite Imagery to Evaluate Late Holocene Human–Environment Interactions in Arid Lands: A Case Study from the Central Sahara
Remote Sens. 2017, 9(4), 351; doi:10.3390/rs9040351
Received: 23 November 2016 / Revised: 23 March 2017 / Accepted: 30 March 2017 / Published: 6 April 2017
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Abstract
We present preliminary results of an Earth observation approach for the study of past human occupation and landscape reconstruction in the Central Sahara. This region includes a variety of geomorphological features such as palaeo-oases, dried river beds, alluvial fans and upland plateaux whose
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We present preliminary results of an Earth observation approach for the study of past human occupation and landscape reconstruction in the Central Sahara. This region includes a variety of geomorphological features such as palaeo-oases, dried river beds, alluvial fans and upland plateaux whose geomorphological characteristics, in combination with climate changes, have influenced patterns of human dispersal and sociocultural activities during the late Holocene. In this paper, we discuss the use of medium- and high-resolution remotely sensed data for the mapping of anthropogenic features and paleo- and contemporary hydrology and vegetation. In the absence of field inspection in this inaccessible region, we use different remote sensing methods to first identify and classify archaeological features, and then explore the geomorphological factors that might have influenced their spatial distribution. Full article
(This article belongs to the Special Issue Remote Sensing for Cultural Heritage)
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Open AccessArticle Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem
Remote Sens. 2017, 9(4), 352; doi:10.3390/rs9040352
Received: 30 September 2016 / Revised: 20 February 2017 / Accepted: 1 April 2017 / Published: 7 April 2017
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Abstract
The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called ‘regime shifts’ or ‘critical transitions’, can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and
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The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called ‘regime shifts’ or ‘critical transitions’, can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation-water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone. Full article
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Open AccessArticle Recent Landslide Movement in Tsaoling, Taiwan Tracked by TerraSAR-X/TanDEM-X DEM Time Series
Remote Sens. 2017, 9(4), 353; doi:10.3390/rs9040353
Received: 14 February 2017 / Revised: 24 March 2017 / Accepted: 5 April 2017 / Published: 7 April 2017
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Abstract
The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely
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The Tsaoling Landslide in Taiwan has captured attentions of researchers worldwide due to its frequent catastrophic failure and distinctive features. Thanks to the launch of TerraSAR-X/TanDEM-X (TSX/TDX) constellation, retrieval of global DEM with high spatial resolution and accuracy becomes possible, which is extremely useful for the study of natural hazards (e.g., landslides) globally. We attempt here for the first time to track recent landslide movements in Tsaoling Taiwan by analyzing DEM time series reconstructed from TSX/TDX image pairs. Quality improvement of InSAR derived DEM through an iterated differential operation is addressed during the data processing. Five cliffs and the Chingshui River are selected to determine the spatial pattern of morphologic changes of the landslide. The results show that: (a) A large amount of collapses occurred on dip slopes in the period from 2011 to 2014 and on surrounding debris deposits during the rainy seasons; (b) The average recession rate of the Chunqui Cliff decreased from 24.4 m/yr to 19.6 m/yr compared with the result between 1999 and 2009; (c) The Tsaoling Landslide has lost 6.90 ×106 m³ of soil from November of 2011 to April of 2014, which shows a positive correlation of 0.853 with rainfall; (d) The Chingshui River is undergoing a gradual bed erosion with a volumes of 1.84 ×106 m³. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessArticle HRTT: A Hierarchical Roof Topology Structure for Robust Building Roof Reconstruction from Point Clouds
Remote Sens. 2017, 9(4), 354; doi:10.3390/rs9040354
Received: 23 January 2017 / Revised: 31 March 2017 / Accepted: 7 April 2017 / Published: 8 April 2017
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Abstract
The identification and representation of building roof topology are basic, but important, issues for 3D building model reconstruction from point clouds. Always, the roof topology is expressed by the roof topology graph (RTG), which stores the plane–plane adjacencies as graph edges. As the
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The identification and representation of building roof topology are basic, but important, issues for 3D building model reconstruction from point clouds. Always, the roof topology is expressed by the roof topology graph (RTG), which stores the plane–plane adjacencies as graph edges. As the decision of the graph edges is often based on local statistics between adjacent planes, topology errors can be easily produced because of noise, lack of data, and resulting errors in pre-processing steps. In this work, the hierarchical roof topology tree (HRTT) is proposed, instead of traditional RTG, to represent the topology relationships among different roof elements. Building primitives or child structures are taken as inside tree nodes; thus, the plane–model and model–model relations can be well described and further exploited. Integral constraints and extra verifying procedures can also be easily introduced to improve the topology quality. As for the basic plane-to-plane adjacencies, we no longer decide all connections at the same time, but rather we decide the robust ones preferentially. Those robust connections will separate the whole model into simpler components step-by-step and produce the basic semantic information for the identification of ambiguous ones. In this way, the effects from structures of minor importance or spurious ridges can be limited to the building locale, while the common features can be detected integrally. Experiments on various data show that the proposed method can obviously improve the topology quality and produce more precise results. Compared with the RTG-based method, two topology quality indices increase from 80.9% and 79.8% to 89.4% and 88.2% in the test area. The integral model quality indices at the pixel level and the plane level also achieve the precision of 90.3% and 84.7%, accordingly. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
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Open AccessArticle What is the Point? Evaluating the Structure, Color, and Semantic Traits of Computer Vision Point Clouds of Vegetation
Remote Sens. 2017, 9(4), 355; doi:10.3390/rs9040355
Received: 11 February 2017 / Revised: 31 March 2017 / Accepted: 7 April 2017 / Published: 9 April 2017
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Abstract
Remote sensing of the structural and spectral traits of vegetation is being transformed by structure from motion (SFM) algorithms that combine overlapping images to produce three-dimensional (3D) red-green-blue (RGB) point clouds. However, much remains unknown about how these point clouds are used to
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Remote sensing of the structural and spectral traits of vegetation is being transformed by structure from motion (SFM) algorithms that combine overlapping images to produce three-dimensional (3D) red-green-blue (RGB) point clouds. However, much remains unknown about how these point clouds are used to observe vegetation, limiting the understanding of the results and future applications. Here, we examine the content and quality of SFM point cloud 3D-RGB fusion observations. An SFM algorithm using the Scale Invariant Feature Transform (SIFT) feature detector was applied to create the 3D-RGB point clouds of a single tree and forest patches. The fusion quality was evaluated using targets placed within the tree and was compared to fusion measurements from terrestrial LIDAR (TLS). K-means clustering and manual classification were used to evaluate the semantic content of SIFT features. When targets were fully visible in the images, SFM assigned color in the correct place with a high accuracy (93%). The accuracy was lower when targets were shadowed or obscured (29%). Clustering and classification revealed that the SIFT features highlighted areas that were brighter or darker than their surroundings, showing little correspondence with canopy objects like leaves or branches, though the features showed some relationship to landscape context (e.g., canopy, pavement). Therefore, the results suggest that feature detectors play a critical role in determining how vegetation is sampled by SFM. Future research should consider developing feature detectors that are optimized for vegetation mapping, including extracting elements like leaves and flowers. Features should be considered the fundamental unit of SFM mapping, like the pixel in optical imaging and the laser pulse of LIDAR. Under optimal conditions, SFM fusion accuracy exceeded that of TLS, and the two systems produced similar representations of the overall tree shape. SFM is the lower-cost solution for obtaining accurate 3D-RGB fusion measurements of the outer surfaces of vegetation, the critical zone of interaction between vegetation, light, and the atmosphere from leaf to canopy scales. Full article
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Open AccessArticle Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data
Remote Sens. 2017, 9(4), 356; doi:10.3390/rs9040356
Received: 17 January 2017 / Revised: 24 March 2017 / Accepted: 4 April 2017 / Published: 9 April 2017
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Abstract
Correction of terrestrial Light Detection and Ranging (LiDAR) intensity data has been increasingly studied in recent years. The purpose is to obtain additional insight into the scanned environment that is not available from the geometric information alone. Radiometric correction, as the name implies,
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Correction of terrestrial Light Detection and Ranging (LiDAR) intensity data has been increasingly studied in recent years. The purpose is to obtain additional insight into the scanned environment that is not available from the geometric information alone. Radiometric correction, as the name implies, corrects the received intensity to standard reflectance values in the range of ( 0 , 1 ) . This correction typically compensates for the dependence of angle and distance. This paper presents an additional compensation for temperature that may be necessary for some LiDAR instruments such as the Faro Focus 3 D X 330 laser scanner. It is also shown that temperature compensation is not necessary for the Riegl VZ–400. Another important contribution of this work is the verification of a previously published radiometric correction in different environments. The correction was applied to two different Terrestrial Laser Scanner (TLS) instruments: a Faro Focus 3 D X 330 and Riegl VZ-400. Overall, the VZ-400, without temperature compensation, produced better results with a Root Mean Square (RMS) of the standard deviation of error being 0.053 and a RMS of the mean error of 0.036 compared to 0.069 and 0.046 for the Faro Focus 3 D X 330. It was found, for the case of the Faro device, that the temperature of the instrument played an important role in the accuracy of the results. The proposed temperature compensation method improved the RMS standard deviation of the error by 1.4 times and the RMS of the error by 2.6 times, compared to the uncompensated results. Full article
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Open AccessArticle An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification
Remote Sens. 2017, 9(4), 358; doi:10.3390/rs9040358
Received: 19 December 2016 / Revised: 5 April 2017 / Accepted: 6 April 2017 / Published: 11 April 2017
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Abstract
This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. The processing chain is implemented in Python and relies on existing open-source software GRASS GIS and R. The complete tool chain is available in open access
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This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. The processing chain is implemented in Python and relies on existing open-source software GRASS GIS and R. The complete tool chain is available in open access and is adaptable to specific user needs. For automation purposes, we developed two GRASS GIS add-ons enabling users (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual machine learning classifiers or their prediction combinations through voting-schemes. We tested the performance of the processing chain using sub-metric multispectral and height data on two very different urban environments: Ouagadougou, Burkina Faso in sub-Saharan Africa and Liège, Belgium in Western Europe. Using a hierarchical classification scheme, the overall accuracy reached 93% at the first level (5 classes) and about 80% at the second level (11 and 9 classes, respectively). Full article
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Open AccessArticle A SAR-Based Index for Landscape Changes in African Savannas
Remote Sens. 2017, 9(4), 359; doi:10.3390/rs9040359
Received: 24 March 2017 / Revised: 5 April 2017 / Accepted: 9 April 2017 / Published: 11 April 2017
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Abstract
Change detection is one of the main applications in earth observation but currently there are only a few approaches based on radar imagery. Available techniques strongly focus on optical data. These techniques are often limited to static analyses of image pairs and are
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Change detection is one of the main applications in earth observation but currently there are only a few approaches based on radar imagery. Available techniques strongly focus on optical data. These techniques are often limited to static analyses of image pairs and are frequently lacking results which address the requirements of the user. Some of these shortcomings include integration of user’s expertise, transparency of methods, and communication of results in a comprehensive understandable way. This study introduces an index describing changes in the savanna ecosystem around the refugee camp Djabal, Eastern Chad, based on a time-series of ALOS PALSAR data between 2007 and 2017. Texture based land-use/land cover classifications are transferred to values of natural resources which include comprehensive pertinent expert knowledge about the contributions of the classes to environmental integrity and human security. Changes between the images are analyzed, within grid cells of one kilometer diameter, according to changes of natural resources and the variability of these changes. Our results show the highest resource availability for the year of 2008 but no general decline in natural resources. Largest loss of resources occurred between 2010 and 2011 but regeneration could be observed in the following years. Neither the settlements nor the wadi areas of high ecologic importance underwent significant changes during the last decade. Full article
(This article belongs to the Special Issue Advances in SAR: Sensors, Methodologies, and Applications)
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Open AccessArticle A New Spatial Attraction Model for Improving Subpixel Land Cover Classification
Remote Sens. 2017, 9(4), 360; doi:10.3390/rs9040360
Received: 14 January 2017 / Revised: 1 April 2017 / Accepted: 7 April 2017 / Published: 11 April 2017
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Abstract
Subpixel mapping (SPM) is a technique that produces hard classification maps at a spatial resolution finer than that of the input images produced when handling mixed pixels. Existing spatial attraction model (SAM) techniques have been proven to be an effective SPM method. The
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Subpixel mapping (SPM) is a technique that produces hard classification maps at a spatial resolution finer than that of the input images produced when handling mixed pixels. Existing spatial attraction model (SAM) techniques have been proven to be an effective SPM method. The techniques mostly differ in the way in which they compute the spatial attraction, for example, from the surrounding pixels in the subpixel/pixel spatial attraction model (SPSAM), from the subpixels within the surrounding pixels in the modified SPSAM (MSPSAM), or from the subpixels within the surrounding pixels and the touching subpixels within the central pixel in the mixed spatial attraction model (MSAM). However, they have a number of common defects, such as a lack of consideration of the attraction from subpixels within the central pixel and the unequal treatment of attraction from surrounding subpixels of the same distance. In order to overcome these defects, this study proposed an improved SAM (ISAM) for SPM. ISAM estimates the attraction value of the current subpixel at the center of a moving window from all subpixels within the window, and moves the window one subpixel per step. Experimental results from both Landsat and MODIS imagery have proven that ISAM, when compared with other SAMs, can improve SPM accuracies and is a more efficient SPM technique than MSPSAM and MSAM. Full article
(This article belongs to the Special Issue Learning to Understand Remote Sensing Images)
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Open AccessArticle Directional Spreading Function of the Gravity-Capillary Wave Spectrum Derived from Radar Observations
Remote Sens. 2017, 9(4), 361; doi:10.3390/rs9040361
Received: 3 December 2016 / Revised: 19 March 2017 / Accepted: 1 April 2017 / Published: 12 April 2017
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Abstract
Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we
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Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we use radar observations to derive the directional spreading function of the gravity-capillary wave spectrum, which is expressed as the second-order Fourier series expansion. So far the standard form of the second-order harmonic coefficient has not been proposed to correctly unify the gravity and gravity-capillary wave. Our strategy is to introduce a correcting term to replace the inaccurate gravity-capillary spectral component in Elfouhaily’s directional spreading function. The second-order harmonic coefficient at L, C and Ku band calculated by the radar observation is used to fit the correcting term to obtain one at the full gravity-capillary wave region. According to our proposed the directional spreading function, there is a spectral region between the gravity and gravity-capillary range where it signifies the negative upwind–crosswind asymmetry at low and moderate speed range. And this is not reflected by the previous models, but has been confirmed by radar observations. The Root Mean Square Difference (RMSD) of the proposed second-order harmonic coefficient versus the radar-observed one at L, C band Ku band is 0.0438, 0.0263 and 0.0382, respectively. The overall bias and RMSD are −0.0029 and 0.0433 for the whole second-order harmonic coefficient range, respectively. The result verifies the accuracy of the proposed directional spreading function at L, C band Ku band. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Spaceborne GNSS-R from the SMAP Mission: First Assessment of Polarimetric Scatterometry over Land and Cryosphere
Remote Sens. 2017, 9(4), 362; doi:10.3390/rs9040362
Received: 21 December 2016 / Revised: 15 March 2017 / Accepted: 7 April 2017 / Published: 12 April 2017
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Abstract
This work describes the first global scale assessment of a Global Navigation Satellite Systems Reflectometry (GNSS-R) experiment performed on-board the Soil Moisture Active Passive (SMAP) mission for soil moisture and biomass determination. Scattered GPS L2 signals (1227.6 MHz) were collected by the SMAP’s
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This work describes the first global scale assessment of a Global Navigation Satellite Systems Reflectometry (GNSS-R) experiment performed on-board the Soil Moisture Active Passive (SMAP) mission for soil moisture and biomass determination. Scattered GPS L2 signals (1227.6 MHz) were collected by the SMAP’s dual-polarization (Horizontal H and Vertical V) radar receiver and then processed on-ground using a known replica of the GPS L2C code. The scattering properties over land are evaluated using the Signal-to-Noise Ratio (SNR), the Polarimetric Ratio (PR), and the width of the waveforms’ trailing and leading edges. These parameters show sensitivity to the effects of the Earth’s topography and Above Ground Biomass (ABG) even over Amazonian and Boreal forests. These effects are shown to be an important factor in precise soil moisture and biomass determination. Additionally, it is found that PR shows sensitivity to soil moisture content over different land cover types. In particular, the following values of the PR are found over: (a) tropical forests ~−1.2 dB; (b) boreal forests ~0.8 dB; (c) Greenland ~2.8 dB; and (d) the Sahara Desert ~3.2 dB. Full article
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Open AccessArticle Underlying Topography Estimation over Forest Areas Using High-Resolution P-Band Single-Baseline PolInSAR Data
Remote Sens. 2017, 9(4), 363; doi:10.3390/rs9040363
Received: 19 February 2017 / Revised: 26 March 2017 / Accepted: 9 April 2017 / Published: 12 April 2017
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Abstract
This paper discusses the potential and limitations of high-resolution P-band polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) in underlying topography estimation over forest areas. Time-frequency (TF) analysis in the azimuth direction is utilized to separate the ground scattering contribution from the total PolInSAR
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This paper discusses the potential and limitations of high-resolution P-band polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) in underlying topography estimation over forest areas. Time-frequency (TF) analysis in the azimuth direction is utilized to separate the ground scattering contribution from the total PolInSAR signal, without the use of any physical model, because the P-band PolInSAR data have a significant penetration depth and sufficient observation angle interval. To achieve this goal, a one-dimensional polynomial fitting (PF) method is proposed for correcting the residual motion error (RME). The Krycklan catchment test site, which is covered with pine forest, was selected to test the performance of the digital elevation model (DEM) inversion. The results show that the PF method can correct the RMEs for the sub-look interferograms well. When compared to the existing line-fit method, the TF+PF method can provide a more accurate DEM (the accuracy is improved by 26.9%). Moreover, the performance of the DEM inversion is free from the random-volume-over-ground assumption. Full article
(This article belongs to the Special Issue Calibration and Validation of Synthetic Aperture Radar)
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Open AccessArticle Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
Remote Sens. 2017, 9(4), 364; doi:10.3390/rs9040364
Received: 27 January 2017 / Revised: 18 March 2017 / Accepted: 1 April 2017 / Published: 12 April 2017
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Abstract
Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be processed
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Satellite remote sensing data including Landsat-8 (optical), Sentinel-1, and RADARSAT-2 (synthetic aperture radar (SAR) missions) have recently become routinely available for large scale ice velocity mapping of ice sheets in Greenland and Antarctica. These datasets are too large in size to be processed and calibrated manually as done in the past. Here, we describe a methodology to process the SAR and optical data in a synergistic fashion and automatically calibrate, mosaic, and integrate these data sets together into seamless, ice-sheet-wide, products. We employ this approach to produce annual mosaics of ice motion in Antarctica and Greenland with all available data acquired on a particular year. We find that the precision of a Landsat-8 pair is lower than that of its SAR counterpart, but due to the large number of Landsat-8 acquisitions, combined with the high persistency of optical surface features in the Landsat-8 data, we obtain accurate velocity products from Landsat that integrate well with the SAR-derived velocity products. The resulting pool of remote sensing products is a significant advance for observing changes in ice dynamics over the entire ice sheets and their contribution to sea level. In preparation for the next generation sensors, we discuss the implications of the results for the upcoming NASA-ISRO SAR mission (NISAR). Full article
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Open AccessArticle The Use of Landscape Metrics and Transfer Learning to Explore Urban Villages in China
Remote Sens. 2017, 9(4), 365; doi:10.3390/rs9040365
Received: 4 January 2017 / Revised: 18 March 2017 / Accepted: 9 April 2017 / Published: 13 April 2017
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Abstract
Urban villages (UVs), the main settlements of rural migrant workers and low-income groups in metropolitan areas of China, have become of major concern to city managers and researchers due to the rapid urbanization in recent years. A clear understanding of their evolution and
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Urban villages (UVs), the main settlements of rural migrant workers and low-income groups in metropolitan areas of China, have become of major concern to city managers and researchers due to the rapid urbanization in recent years. A clear understanding of their evolution and spatial relationships with the city is of great importance to policy formulation, implementation and assessment. In this paper, we propose a new framework based on landscape metrics and transfer learning for the long-term monitoring and analysis of UVs, and we apply it to Shenzhen and Wuhan, two metropolitan cities of China, with high-resolution satellite images acquired from 2003–2012 and 2009–2015, respectively. In the framework, landscape metrics are used for identifying the UVs and quantifying their evolution patterns on the basis of a city-UV-building hierarchical landscape model. Transfer learning is also introduced to use the samples and features across the spatial and temporal domains, which reduces the time and labor cost, as well as improves the mapping accuracies by 3–10%. The results show that the total area of UVs has decreased by less than 6 % in Shenzhen and more than 45 % in Wuhan. Moreover, we observe significant spatial correlations in the development of UVs in Shenzhen. By contrast, no strong spatial correlations are found in Wuhan’s UVs, indicating that their development is largely independent of the spatial location. The results reveal two typical strategies, i.e., demolition and renovation, towards the redevelopment of UVs in China. Full article
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
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Open AccessArticle Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data
Remote Sens. 2017, 9(4), 366; doi:10.3390/rs9040366
Received: 1 February 2017 / Revised: 4 April 2017 / Accepted: 9 April 2017 / Published: 13 April 2017
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Abstract
Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface
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Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 2000–2010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approachAn independent validation dataset was also developed using Google Earth™ imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 ± 7.84 km2. Full article
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Open AccessArticle Turbidity in Apalachicola Bay, Florida from Landsat 5 TM and Field Data: Seasonal Patterns and Response to Extreme Events
Remote Sens. 2017, 9(4), 367; doi:10.3390/rs9040367
Received: 8 February 2017 / Revised: 27 March 2017 / Accepted: 9 April 2017 / Published: 13 April 2017
PDF Full-text (10361 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Synoptic monitoring of estuaries, some of the most bio-diverse and productive environments on Earth, is essential to study small-scale water dynamics and its role on spatiotemporal variation in water quality important to indigenous marine species and surrounding human settlements. We present a detailed
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Synoptic monitoring of estuaries, some of the most bio-diverse and productive environments on Earth, is essential to study small-scale water dynamics and its role on spatiotemporal variation in water quality important to indigenous marine species and surrounding human settlements. We present a detailed study of turbidity, an optical index of water quality, in Apalachicola Bay, Florida (USA) using historical in situ measurements and Landsat 5 TM data archive acquired from 2004 to 2011. Data mining techniques such as time-series decomposition, principal component analysis, and classification tree-based models were utilized to decipher time-series for examining variations in physical forcings, and their effects on diurnal and seasonal variability in turbidity in Apalachicola Bay. Statistical analysis showed that the bay is highly dynamic in nature, both diurnally and seasonally, and its water quality (e.g., turbidity) is largely driven by interactions of different physical forcings such as river discharge, wind speed, tides, and precipitation. River discharge and wind speed are the most influential forcings on the eastern side of river mouth, whereas all physical forcings were relatively important to the western side close to the major inlet, the West Pass. A bootstrap-optimized and atmospheric-corrected single-band empirical relationship (Turbidity (NTU) = 6568.23 × (Reflectance (Band 3))1.95; R2 = 0.77 ± 0.06, range = 0.50–0.91, N = 50) is proposed with seasonal thresholds for its application in various seasons. The validation of this relationship yielded R2 = 0.70 ± 0.15 (range = −0.96–0.97; N = 38; RMSE = 7.78 ± 2.59 NTU; Bias (%) = −8.70 ± 11.48). Complex interactions of physical forcings and their effects on water dynamics have been discussed in detail using Landsat 5 TM-based turbidity maps during major events between 2004 and 2011. Promising results of the single-band turbidity algorithm with Landsat 8 OLI imagery suggest its potential for long-term monitoring of water turbidity in a shallow water estuary such as Apalachicola Bay. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images
Remote Sens. 2017, 9(4), 368; doi:10.3390/rs9040368
Received: 28 December 2016 / Revised: 5 April 2017 / Accepted: 7 April 2017 / Published: 13 April 2017
Cited by 2 | PDF Full-text (5318 KB) | HTML Full-text | XML Full-text
Abstract
Like computer vision before, remote sensing has been radically changed by the introduction of deep learning and, more notably, Convolution Neural Networks. Land cover classification, object detection and scene understanding in aerial images rely more and more on deep networks to achieve new
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Like computer vision before, remote sensing has been radically changed by the introduction of deep learning and, more notably, Convolution Neural Networks. Land cover classification, object detection and scene understanding in aerial images rely more and more on deep networks to achieve new state-of-the-art results. Recent architectures such as Fully Convolutional Networks can even produce pixel level annotations for semantic mapping. In this work, we present a deep-learning based segment-before-detect method for segmentation and subsequent detection and classification of several varieties of wheeled vehicles in high resolution remote sensing images. This allows us to investigate object detection and classification on a complex dataset made up of visually similar classes, and to demonstrate the relevance of such a subclass modeling approach. Especially, we want to show that deep learning is also suitable for object-oriented analysis of Earth Observation data as effective object detection can be obtained as a byproduct of accurate semantic segmentation. First, we train a deep fully convolutional network on the ISPRS Potsdam and the NZAM/ONERA Christchurch datasets and show how the learnt semantic maps can be used to extract precise segmentation of vehicles. Then, we show that those maps are accurate enough to perform vehicle detection by simple connected component extraction. This allows us to study the repartition of vehicles in the city. Finally, we train a Convolutional Neural Network to perform vehicle classification on the VEDAI dataset, and transfer its knowledge to classify the individual vehicle instances that we detected. Full article
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Open AccessArticle Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds
Remote Sens. 2017, 9(4), 369; doi:10.3390/rs9040369
Received: 7 February 2017 / Revised: 4 April 2017 / Accepted: 9 April 2017 / Published: 13 April 2017
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Abstract
The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1°, these products should provide more detailed information than their latest widely-adapted (relatively coarser
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The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1°, these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25°) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs. Full article
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Open AccessArticle Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data
Remote Sens. 2017, 9(4), 370; doi:10.3390/rs9040370
Received: 25 December 2016 / Revised: 3 April 2017 / Accepted: 13 April 2017 / Published: 15 April 2017
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Abstract
Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are key variables in many global models of climate, hydrology, biogeochemistry, and ecology. These parameters are being operationally produced from Terra and Aqua MODIS bidirectional reflectance factor (BRF) data.
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Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are key variables in many global models of climate, hydrology, biogeochemistry, and ecology. These parameters are being operationally produced from Terra and Aqua MODIS bidirectional reflectance factor (BRF) data. The MODIS science team has developed, and plans to release, a new version of the BRF product using the multi-angle implementation of atmospheric correction (MAIAC) algorithm from Terra and Aqua MODIS observations. This paper presents analyses of LAI and FPAR retrievals generated with the MODIS LAI/FPAR operational algorithm using Terra MAIAC BRF data. Direct application of the operational algorithm to MAIAC BRF resulted in an underestimation of the MODIS Collection 6 (C6) LAI standard product by up to 10%. The difference was attributed to the disagreement between MAIAC and MODIS BRFs over the vegetation by −2% to +8% in the red spectral band, suggesting different accuracies in the BRF products. The operational LAI/FPAR algorithm was adjusted for uncertainties in the MAIAC BRF data. Its performance evaluated on a limited set of MAIAC BRF data from North and South America suggests an increase in spatial coverage of the best quality, high-precision LAI retrievals of up to 10%. Overall MAIAC LAI and FPAR are consistent with the standard C6 MODIS LAI/FPAR. The increase in spatial coverage of the best quality LAI retrievals resulted in a better agreement of MAIAC LAI with field data compared to the C6 LAI product, with the RMSE decreasing from 0.80 LAI units (C6) down to 0.67 (MAIAC) and the R2 increasing from 0.69 to 0.80. The slope (intercept) of the satellite-derived vs. field-measured LAI regression line has changed from 0.89 (0.39) to 0.97 (0.25). Full article
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Open AccessArticle Surface Motion and Structural Instability Monitoring of Ming Dynasty City Walls by Two-Step Tomo-PSInSAR Approach in Nanjing City, China
Remote Sens. 2017, 9(4), 371; doi:10.3390/rs9040371
Received: 11 January 2017 / Revised: 21 March 2017 / Accepted: 13 April 2017 / Published: 15 April 2017
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Abstract
Spaceborne Multi-Temporal Synthetic Aperture Radar (SAR) Interferometry (MT-InSAR) has been a valuable tool in mapping motion phenomena in different scenarios. Recently, the capabilities of MT-InSAR for risk monitoring and preventive analysis of heritage sites have increasingly been exploited. Considering the limitations of conventional
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Spaceborne Multi-Temporal Synthetic Aperture Radar (SAR) Interferometry (MT-InSAR) has been a valuable tool in mapping motion phenomena in different scenarios. Recently, the capabilities of MT-InSAR for risk monitoring and preventive analysis of heritage sites have increasingly been exploited. Considering the limitations of conventional MT-InSAR techniques, in this study a two-step Tomography-based Persistent Scatterers (PS) Interferometry (Tomo-PSInSAR) approach is proposed for monitoring ground deformation and structural instabilities over the Ancient City Walls (Ming Dynasty) in Nanjing city, China. For the purpose of this study we utilized 26 Stripmap acquisitions from TerraSAR-X and TanDEM-X missions, spanning from May 2013 to February 2015. As a first step, regional-scale surface deformation rates on single PSs were derived (ranging from −40 to +5 mm/year) and used for identifying deformation hotspots as well as for the investigation of a potential correlation between urbanization and the occurrence of surface subsidence. As a second step, structural instability parameters of ancient walls (linear motion rates, non-linear motions and material thermodynamics) were estimated by an extended four-dimensional Tomo-PSInSAR model. The model applies a two-tier network strategy; that is, the detection of most reliable single PSs in the first-tier Delaunay triangulation network followed by the detection of remaining single PSs and double PSs on the second-tier local star network referring to single SPs extracted in the first-tier network. Consequently, a preliminary phase calibration relevant to the Atmospheric Phase Screen (APS) is not needed. Motion heterogeneities in the spatial domain, either caused by thermal kinetics or displacement trends, were also considered. This study underlines the potential of the proposed Tomo-PSInSAR solution for the monitoring and conservation of cultural heritage sites. The proposed approach offers a quantitative indicator to local authorities and planners for assessing potential damages as well as for the design of remediation activities. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data
Remote Sens. 2017, 9(4), 372; doi:10.3390/rs9040372
Received: 21 January 2017 / Revised: 4 April 2017 / Accepted: 13 April 2017 / Published: 16 April 2017
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Abstract
It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral
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It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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Open AccessArticle Multispectral LiDAR Point Cloud Classification: A Two-Step Approach
Remote Sens. 2017, 9(4), 373; doi:10.3390/rs9040373
Received: 20 September 2016 / Revised: 7 April 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
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Abstract
Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its
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Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50–11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%. Full article
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Open AccessArticle Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series
Remote Sens. 2017, 9(4), 374; doi:10.3390/rs9040374
Received: 3 February 2017 / Revised: 4 April 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
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Abstract
Buried archeological features show up as crop marks that are mostly visible using high-resolution image data. Such data are costly and restricted to small regions and time domains. However, a time series of freely available medium resolution imagery can be employed to detect
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Buried archeological features show up as crop marks that are mostly visible using high-resolution image data. Such data are costly and restricted to small regions and time domains. However, a time series of freely available medium resolution imagery can be employed to detect crop growth changes to reveal subtle surface marks in large areas. This paper aims to study the classical Chinese settlements of Taosi and Erlitou over large areas using Landsat NDVI time series crop phenology to determine the optimum periods for detection and monitoring of crop anomalies. Burial areas (such as the palace area and the sacrificial area) were selected as the research area while the surrounding empty fields with a low density of ancient features were used as reference regions. Landsat NDVI covering two years’ growth periods of wheat and maize were computed and analyzed using Pearson’s correlation coefficient and Euclidean distance. Similarities or disparities between the burial areas and their empty areas were computed using the Hausdorff distance. Based on the phenology of crop growth, the time series NDVI images of winter wheat and summer maize were generated to analyze crop anomalies in the archeological sites. Results show that the Hausdorff distance was high during the critical stages of water for both crops and that the images of high Hausdorff distance can provide more obvious subsurface archeological information. Full article
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Open AccessArticle Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data
Remote Sens. 2017, 9(4), 375; doi:10.3390/rs9040375
Received: 12 January 2017 / Revised: 29 March 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
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Abstract
Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense
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