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Remote Sens., Volume 10, Issue 11 (November 2018)

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Open AccessArticle Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks
Remote Sens. 2018, 10(11), 1827; https://doi.org/10.3390/rs10111827 (registering DOI)
Received: 12 October 2018 / Revised: 12 November 2018 / Accepted: 15 November 2018 / Published: 17 November 2018
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Abstract
Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such
[...] Read more.
Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM. Full article
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Open AccessArticle The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation
Remote Sens. 2018, 10(11), 1826; https://doi.org/10.3390/rs10111826 (registering DOI)
Received: 14 September 2018 / Revised: 11 October 2018 / Accepted: 15 November 2018 / Published: 17 November 2018
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Abstract
Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer
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Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration’s (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data
Remote Sens. 2018, 10(11), 1825; https://doi.org/10.3390/rs10111825 (registering DOI)
Received: 24 September 2018 / Revised: 9 November 2018 / Accepted: 15 November 2018 / Published: 17 November 2018
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Abstract
The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for
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The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for estimating forest biomass from remote sensing data and forest inventory plots, there is no general agreement on which approach is most appropriate for biomass estimation across large areas. In this study, we compared several imputation approaches for estimating forest biomass using Landsat time-series and inventory plot data. We evaluated 18 kNN models to impute three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees). These models were developed using different distance techniques (Random Forest or RF, Gradient Nearest Neighbour or GNN, and Most Similar Neighbour or MSN) and different combinations of response variables (model scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees). We also assessed the ability of our imputation method to spatially predict biomass variables across large areas in relation to a forest disturbance history over a 30-year period (1987–2016). Our results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The lowest error rates were achieved by RF-based models with generalized root mean squared difference (gRMSD, RMSE divided by the standard deviation of the observed values) ranging from 0.74 to 1.24. Whereas gRMSD associated with MSN-based and GNN-based models ranged from 0.92 to 1.36 and from 1.04 to 1.42, respectively. The indirect imputation method generally achieved better biomass predictions than the direct imputation method. In particular, the kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass. This model reported a gRMSD of 0.89, 0.95 and 1.08 for total AGB, AGB of live trees and AGB of dead trees, respectively. In addition, spatial predictions of biomass showed relatively consistent trends with disturbance severity and time since disturbance across the time-series. As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this work helps those using these methods ensure their modelling and mapping practices are optimized. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Modified Superpixel Segmentation for Digital Surface Model Refinement and Building Extraction from Satellite Stereo Imagery
Remote Sens. 2018, 10(11), 1824; https://doi.org/10.3390/rs10111824 (registering DOI)
Received: 15 October 2018 / Revised: 6 November 2018 / Accepted: 15 November 2018 / Published: 17 November 2018
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Abstract
Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In
[...] Read more.
Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm. Full article
(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)
Open AccessArticle An Unsupervised Classification Algorithm for Multi- Temporal Irrigated Area Mapping in Central Asia
Remote Sens. 2018, 10(11), 1823; https://doi.org/10.3390/rs10111823 (registering DOI)
Received: 19 October 2018 / Revised: 12 November 2018 / Accepted: 14 November 2018 / Published: 17 November 2018
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Abstract
Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for
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Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77–96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Open AccessArticle TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach
Remote Sens. 2018, 10(11), 1822; https://doi.org/10.3390/rs10111822 (registering DOI)
Received: 25 September 2018 / Revised: 13 November 2018 / Accepted: 15 November 2018 / Published: 17 November 2018
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Abstract
Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric
[...] Read more.
Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods. Full article
(This article belongs to the Section Forest Remote Sensing)
Open AccessArticle Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm
Remote Sens. 2018, 10(11), 1821; https://doi.org/10.3390/rs10111821 (registering DOI)
Received: 22 October 2018 / Revised: 7 November 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
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Abstract
To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, the state-of-the-art infrared
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To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, the state-of-the-art infrared image-patch (IPI) model usually leaves background residuals in the target image. To fix this problem, a non-convex, tighter rank surrogate and weighted l1 norm are instead utilized, which can suppress the background better while preserving the target efficiently. Considering that many state-of-the-art methods are still unable to fully suppress sparse strong edges, the structured l2,1 norm was introduced to wipe out the strong residuals. Furthermore, with the help of exploiting the structured norm and tighter rank surrogate, the proposed model was more robust when facing various complex or blurry scenes. To solve this non-convex model, an efficient optimization algorithm based on alternating direction method of multipliers (ADMM) plus difference of convex (DC) programming was designed. Extensive experimental results illustrate that the proposed method not only shows superiority in background suppression and target enhancement, but also reduces the computational complexity compared with other baselines. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Object-Based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data
Remote Sens. 2018, 10(11), 1820; https://doi.org/10.3390/rs10111820 (registering DOI)
Received: 27 September 2018 / Revised: 12 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
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Abstract
Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing
[...] Read more.
Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML’s eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new Object-Based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. Based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the—Classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1–3%. Full article
(This article belongs to the Special Issue Selected Papers from Agro-Geoinformatics 2018)
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Open AccessArticle What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress
Remote Sens. 2018, 10(11), 1819; https://doi.org/10.3390/rs10111819 (registering DOI)
Received: 29 August 2018 / Revised: 22 October 2018 / Accepted: 13 November 2018 / Published: 16 November 2018
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Abstract
Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety
[...] Read more.
Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers. Full article
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Open AccessArticle Does Anthropogenic Land Use Change Play a Role in Changes of Precipitation Frequency and Intensity over the Loess Plateau of China?
Remote Sens. 2018, 10(11), 1818; https://doi.org/10.3390/rs10111818 (registering DOI)
Received: 13 September 2018 / Revised: 6 November 2018 / Accepted: 15 November 2018 / Published: 16 November 2018
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Abstract
Human transformation of landscapes is pervasive and accelerating across the Earth. However, existing studies have not provided a comprehensive picture of how precipitation frequency and intensity respond to vegetation cover change. Therefore, this study took the Loess Plateau as a typical example, and
[...] Read more.
Human transformation of landscapes is pervasive and accelerating across the Earth. However, existing studies have not provided a comprehensive picture of how precipitation frequency and intensity respond to vegetation cover change. Therefore, this study took the Loess Plateau as a typical example, and used satellite-based Normalized Difference Vegetation Index (NDVI) data and daily gridded climatic variables to assess the responses of precipitation dynamics to human-induced vegetation cover change. Results showed that the total precipitation amount exhibited little change at the regional scale, showing an upward but statistically insignificant (p > 0.05) trend of 7.6 mm/decade in the period 1982–2015. However, the frequency of precipitation with different intensities showed large variations over most of the Loess Plateau. The number of rainy days (light, moderate, heavy, very heavy and severe precipitation) increased in response to increased vegetation cover, especially in the central-eastern Loess Plateau. Anthropogenic land cover change is largely responsible for precipitation intensity changes. Additionally, this study also observed high spatially explicit heterogeneity in different precipitation intensities in response to vegetation cover change across the Loess Plateau. These findings provide some reference information for our understanding of precipitation frequency and intensity changes in response to regional vegetation cover change in the Loess Plateau. Full article
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Open AccessArticle Mass Balance of Novaya Zemlya Archipelago, Russian High Arctic, Using Time-Variable Gravity from GRACE and Altimetry Data from ICESat and CryoSat-2
Remote Sens. 2018, 10(11), 1817; https://doi.org/10.3390/rs10111817 (registering DOI)
Received: 3 October 2018 / Revised: 5 November 2018 / Accepted: 6 November 2018 / Published: 16 November 2018
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Abstract
We examine the mass balance of the glaciers in the Novaya Zemlya Archipelago, located in the Russian High Arctic using time series of time-variable gravity from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) mission, laser altimetry data from the NASA Ice Cloud
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We examine the mass balance of the glaciers in the Novaya Zemlya Archipelago, located in the Russian High Arctic using time series of time-variable gravity from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) mission, laser altimetry data from the NASA Ice Cloud and land Elevation Satellite (ICESat) mission, and radar altimetry data from the European Space Agency (ESA) CryoSat-2 mission. We present a new algorithm for detecting changes in glacier elevation from these satellite altimetry data and evaluate its performance in the case of Novaya Zemlya by comparing the results with GRACE. We find that the mass loss of Novaya Zemlya glaciers increased from 10 ± 5 Gt/year over 2003–2009 to 14 ± 4 Gt/year over 2010–2016, with a brief period of near-zero mass balance between 2009 and 2011. The results are consistent across the gravimetric and altimetric methods. Furthermore, the analysis of elevation change from CryoSat-2 indicates that the mass loss occurs at elevation below 700 m, where the highest thinning rates are found. We also find that marine-terminating glaciers in Novaya Zemlya are thinning significantly faster than land-terminating glaciers, which indicates an important role of ice dynamics of marine-terminating glaciers. We posit that the glacier changes have been caused by changes in atmospheric and ocean temperatures. We find that the increase in mass loss after 2010 is associated with a warming in air temperatures, which increased the surface melt rates. There is not enough information on the ocean temperature at the front of the glaciers to conclude on the role of the ocean, but we posit that the temperature of subsurface ocean waters must have increased during the observation period. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle Detection of Building and Infrastructure Instabilities by Automatic Spatiotemporal Analysis of Satellite SAR Interferometry Measurements
Remote Sens. 2018, 10(11), 1816; https://doi.org/10.3390/rs10111816 (registering DOI)
Received: 14 September 2018 / Revised: 1 November 2018 / Accepted: 6 November 2018 / Published: 16 November 2018
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Abstract
Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is
[...] Read more.
Satellite synthetic aperture radar (SAR) interferometry (InSAR) is a powerful technology to monitor slow ground surface movements. However, the extraction and interpretation of information from big sets of InSAR measurements is a complex and demanding task. In this paper, a new method is presented for automatically detecting potential instability risks affecting buildings and infrastructures, by searching for anomalies in the persistent scatterer (PS) deformations, either in the spatial or in the temporal dimensions. In the spatial dimension, in order to reduce the dataset size and improve data reliability, we utilize a hierarchical clustering method to obtain convergence points that are more trustworthy. Then, we detect deformations characterized by large values and spatial inhomogeneity. In the temporal dimension, we use a signal processing method to decompose the input into two main components: regular periodic deformations and piecewise linear deformations. After removing the periodic component, the velocity variation in each identified temporal partition is analyzed to detect anomalous velocity trends and accelerations. The method has been tested on different sites in China, based on InSAR measurements from COSMO-SkyMed data. The results, verified with in-field surveys, confirm the potential of the method for the automatic detection of deformation anomalies that could cause building or infrastructure stability problems. Full article
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Open AccessArticle Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory
Remote Sens. 2018, 10(11), 1815; https://doi.org/10.3390/rs10111815
Received: 20 September 2018 / Revised: 25 October 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is
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State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases. Full article
(This article belongs to the Special Issue Mobile Laser Scanning)
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Open AccessArticle Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models
Remote Sens. 2018, 10(11), 1814; https://doi.org/10.3390/rs10111814
Received: 14 September 2018 / Revised: 11 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses
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The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image
Remote Sens. 2018, 10(11), 1813; https://doi.org/10.3390/rs10111813
Received: 22 October 2018 / Revised: 12 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
The urban green cover in high-spatial resolution (HR) remote sensing images have obvious multiscale characteristics, it is thus not possible to properly segment all features using a single segmentation scale because over-segmentation or under-segmentation often occurs. In this study, an unsupervised cross-scale optimization
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The urban green cover in high-spatial resolution (HR) remote sensing images have obvious multiscale characteristics, it is thus not possible to properly segment all features using a single segmentation scale because over-segmentation or under-segmentation often occurs. In this study, an unsupervised cross-scale optimization method specifically for urban green cover segmentation is proposed. A global optimal segmentation is first selected from multiscale segmentation results by using an optimization indicator. The regions in the global optimal segmentation are then isolated into under- and fine-segmentation parts. The under-segmentation regions are further locally refined by using the same indicator as that in global optimization. Finally, the fine-segmentation part and the refined under-segmentation part are combined to obtain the final cross-scale optimized result. The green cover objects can be segmented at their specific optimal segmentation scales in the optimized segmentation result to reduce both under- and over-segmentation errors. Experimental results on two test HR datasets verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)
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Open AccessArticle Measuring Landscape Albedo Using Unmanned Aerial Vehicles
Remote Sens. 2018, 10(11), 1812; https://doi.org/10.3390/rs10111812
Received: 11 September 2018 / Revised: 11 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned
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Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation. Full article
(This article belongs to the Special Issue Remotely Sensed Albedo)
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Open AccessArticle Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia
Remote Sens. 2018, 10(11), 1811; https://doi.org/10.3390/rs10111811
Received: 31 August 2018 / Revised: 4 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is
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Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Drought Monitoring)
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Open AccessArticle ISAR Autofocus Imaging Algorithm for Maneuvering Targets Based on Phase Retrieval and Gabor Wavelet Transform
Remote Sens. 2018, 10(11), 1810; https://doi.org/10.3390/rs10111810
Received: 29 September 2018 / Revised: 6 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular
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The imaging issue of a rotating maneuvering target with a large angle and a high translational speed has been a challenging problem in the area of inverse synthetic aperture radar (ISAR) autofocus imaging, in particular when the target has both radial and angular accelerations. In this paper, on the basis of the phase retrieval algorithm and the Gabor wavelet transform (GWT), we propose a new method for phase error correction. The approach first performs the range compression on ISAR raw data to obtain range profiles, and then carries out the GWT transform as the time-frequency analysis tool for the rotational motion compensation (RMC) requirement. The time-varying terms, caused by rotational motion in the Doppler frequency shift, are able to be eliminated at the selected time frame. Furthermore, the processed backscattered signal is transformed to the one in the frequency domain while applying the phase retrieval to run the translational motion compensation (TMC). Phase retrieval plays an important role in range tracking, because the ISAR echo module is not affected by both radial velocity and the acceleration of the target. Finally, after the removal of both the rotational and translational motion errors, the time-invariant Doppler shift is generated, and radar returned signals from the same scatterer are always kept in the same range cell. Therefore, the unwanted motion effects can be removed by applying this approach to have an autofocused ISAR image of the maneuvering target. Furthermore, the method does not need to estimate any motion parameters of the maneuvering target, which has proven to be very effective for an ideal range–Doppler processing. Experimental and simulation results verify the feasibility of this approach. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
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Open AccessArticle Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images
Remote Sens. 2018, 10(11), 1809; https://doi.org/10.3390/rs10111809
Received: 20 September 2018 / Revised: 29 October 2018 / Accepted: 31 October 2018 / Published: 15 November 2018
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To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images.
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To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches. Full article
(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)
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Open AccessArticle Absolute Calibration of the European Sentinel-3A Surface Topography Mission over the Permanent Facility for Altimetry Calibration in west Crete, Greece
Remote Sens. 2018, 10(11), 1808; https://doi.org/10.3390/rs10111808
Received: 20 October 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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This work presents calibration results for the altimeter of Sentinel-3A Surface Topography Mission as determined at the Permanent Facility for Altimetry Calibration in west Crete, Greece. The facility has been providing calibration services for more than 15 years for all past (i.e., Envisat,
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This work presents calibration results for the altimeter of Sentinel-3A Surface Topography Mission as determined at the Permanent Facility for Altimetry Calibration in west Crete, Greece. The facility has been providing calibration services for more than 15 years for all past (i.e., Envisat, Jason-1, Jason-2, SARAL/AltiKa, HY-2A) and current (i.e., Sentinel-3A, Sentinel-3B, Jason-3) satellite altimeters. The groundtrack of the Pass No.14 of Sentinel-3A ascends west of the Gavdos island and continues north to the transponder site on the mountains of west Crete. This pass has been calibrated using three independent techniques activated at various sites in the region: (1) the transponder approach for its range bias, (2) the sea-surface method for the estimation of altimeter bias for its sea-surface heights, and (c) the cross-over analysis for inspecting height observations with respect to Jason-3. The other Pass No.335 of Sentinel-3A descends from southwest of Crete to south and intersects the Gavdos calibration site. Additionally, calibration values for this descending pass are presented, applying sea-surface calibration and crossover analysis. An uncertainty analysis for the altimeter biases derived by the transponder and by sea-surface calibrations is also introduced following the new standard of Fiducial Reference Measurements. Full article
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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Open AccessArticle Long-Term Ground-Based Measurements of Aerosol Optical Depth over Kuwait City
Remote Sens. 2018, 10(11), 1807; https://doi.org/10.3390/rs10111807
Received: 28 September 2018 / Revised: 6 November 2018 / Accepted: 10 November 2018 / Published: 15 November 2018
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Abstract
We analyze ten years (2008–2017) of ground-based observations of the Aerosol Optical Depth (AOD) in the atmosphere of Kuwait City, in Middle East. The measurements were conducted with a CIMEL sun-sky photometer, at various wavelengths. The daily average AOD at 500 nm (AOD
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We analyze ten years (2008–2017) of ground-based observations of the Aerosol Optical Depth (AOD) in the atmosphere of Kuwait City, in Middle East. The measurements were conducted with a CIMEL sun-sky photometer, at various wavelengths. The daily average AOD at 500 nm (AOD500) is 0.45, while the mean Ångström coefficient (AE), calculated from the pair of wavelengths 440 and 870 nm, is 0.61. The observed high AOD500 values (0.75–2.91), are due to regional sand and dust storm events, which are affecting Kuwait with a mean annual frequency of almost 20 days/year. The long-term record analysis of AOD500 and AE, shows a downward and upward tendency respectively, something which could be attributed to the continuous expansion and industrialization of the main city of Kuwait, in combination with the simultaneous increase of soil moisture over the area. By utilizing back trajectories of air masses for up to 4 days, we assessed the influence of various regions to the aerosol load over Kuwait. The high aerosol loads during spring, are attributed to the dominance of coarse particles from Saudi Arabia (AOD500 0.56–0.74), a source area that contributes the 56% to the mean annual AOD500. Other dust sources affecting significantly Kuwait originated from the regions of Iraq and Iran with contribution of 21%. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates
Remote Sens. 2018, 10(11), 1806; https://doi.org/10.3390/rs10111806
Received: 10 October 2018 / Revised: 2 November 2018 / Accepted: 13 November 2018 / Published: 15 November 2018
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Abstract
Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based
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Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based on the Two Sources Energy Balance (TSEB) model rationale which solves the surface energy balance equations for the soil and the canopy. SPARSE can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. The main innovative feature of SPARSE is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. The main objective of the paper is to assess the SPARSE model predictions of water stress and evapotranspiration components for its two proposed versions (the “patch” and “layer” resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. Over a large range of leaf area index values and for contrasting vegetation stress levels, SPARSE showed good retrieval performances of evapotranspiration and sensible heat fluxes. For cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. The bounded layer version of SPARSE provided the best estimates of latent heat flux over different sites and climates. Broad tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. The synchronous dynamics of observed and retrieved estimates underlined that the SPARSE retrieved water stress estimates from Thermal Infra-Red data were relevant tools for stress detection. Full article
(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))
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Open AccessReview An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes
Remote Sens. 2018, 10(11), 1805; https://doi.org/10.3390/rs10111805
Received: 1 October 2018 / Revised: 12 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
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Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead
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Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead to the developments of the stochastic radiative transfer equation (RTE) and the spectral invariants theory. The former simplifies the characteristics of canopy structures with a pair-correlation function so that the 3D information can be succinctly packed into a 1D equation. The latter indicates that the interactions between photons and canopy elements converge to certain invariant patterns quantifiable by a few wavelength independent parameters, which satisfy the law of energy conservation. By revealing the connections between plant structural characteristics and photon recollision probability, these developments significantly advance our understanding of the transportation of radiation within vegetation canopies. They enable a novel physically-based algorithm to simulate the “hot-spot” phenomenon of canopy bidirectional reflectance while conserving energy, a challenge known to the classic radiative transfer models. Therefore, these theoretical developments have a far-reaching influence in optical remote sensing of the biosphere. Full article
(This article belongs to the Special Issue Radiative Transfer Modelling and Applications in Remote Sensing)
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Open AccessArticle Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas
Remote Sens. 2018, 10(11), 1804; https://doi.org/10.3390/rs10111804
Received: 12 October 2018 / Revised: 8 November 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
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Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by
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Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761° and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data. Full article
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Open AccessArticle Radiometric Cross-Calibration of Tiangong-2 MWI Visible/NIR Channels over Aquatic Environments using MODIS
Remote Sens. 2018, 10(11), 1803; https://doi.org/10.3390/rs10111803
Received: 7 September 2018 / Revised: 21 October 2018 / Accepted: 12 November 2018 / Published: 14 November 2018
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The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites. The MWI imagery is not sufficiently radiometrically calibrated, and therefore, the cross-calibration is urgently needed to provide high quality
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The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites. The MWI imagery is not sufficiently radiometrically calibrated, and therefore, the cross-calibration is urgently needed to provide high quality ocean color products for MWI observations. We proposed a simple and effective cross-calibration scheme for MWI data using well calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) imagery over aquatic environments. The path radiance of the MWI was estimated using the quasi-synchronized MODIS images as well as the MODIS Rayleigh and aerosol look up tables (LUTs) from SeaWiFS Data Analysis System 7.4 (SeaDAS 7.4). The results showed that the coefficients of determination (R2) of the calibration coefficients were larger than 0.97, with sufficient matched areas to perform cross-calibration for MWI. Compared with the simulated Top of Atmosphere (TOA) radiance using synchronized MODIS images, all errors calculated with the calibration coefficients retrieved in this paper were less than 5.2%, and lower than the lab calibrated coefficients. The Rayleigh-corrected reflectance (ρrc), remote sensing reflectance (Rrs) and total suspended matter (TSM) products of MWI, MODIS and the Geostationary Ocean Color Imager (GOCI) images for Taihu Lake in China were compared. The distribution of ρrc of MWI, MODIS and GOCI agreed well, except for band 667 nm of MODIS, which might have been saturated in relatively turbid waters. Besides, the Rrs used to retrieve TSM among MWI, MODIS and GOCI was also consistent. The root mean square errors (RMSE), mean biases (MB) and mean ratios (MR) between MWI Rrs and MODIS Rrs (or GOCI Rrs) were less than 0.20 sr−1, 5.52% and within 1 ± 0.023, respectively. In addition, the derived TSM from MWI and GOCI also agreed with a R2 of 0.90, MB of 13.75%, MR of 0.97 and RMSE of 9.43 mg/L. Cross-calibration coefficients retrieved in this paper will contribute to quantitative applications of MWI. This method can be extended easily to other similar ocean color satellite missions. Full article
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Open AccessArticle Mapping Ecological Production and Benefits from Water Consumed in Agricultural and Natural Landscapes: A Case Study of the Pangani Basin
Remote Sens. 2018, 10(11), 1802; https://doi.org/10.3390/rs10111802
Received: 24 September 2018 / Revised: 28 October 2018 / Accepted: 8 November 2018 / Published: 14 November 2018
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Abstract
Scarcity of information on the water productivity of different water, land, and other ecosystems in Africa, hampers the optimal allocation of the limited water resources. This study presents an innovative method to quantify the spatial variability of biomass production, crop yield, and economic
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Scarcity of information on the water productivity of different water, land, and other ecosystems in Africa, hampers the optimal allocation of the limited water resources. This study presents an innovative method to quantify the spatial variability of biomass production, crop yield, and economic water productivity, in a data scarce landscape of the Pangani Basin. For the first time, gross return from carbon credits and other ecosystem services are considered, in the concept of Economic Water Productivity. The analysis relied on the MODIS satellite data of 250 m and eight-day resolutions, and the SEBAL model, utilizing Monteith’s framework for ecological production. In agriculture, irrigated sugarcane and rice achieved the highest water productivities in both biophysical and economic values. Rainfed and supplementary irrigated banana and maize productivities were significantly lower than the potential values, reflecting a wide spatial variability. In natural landscapes, forest and wetland showed the highest biomass production. However, the transition to economic productivity was low but showed the potential to increase significantly when non-market goods and services were considered. Spatially explicit information, from both biophysical and economic water productivity, provides a holistic outlook of the socio-environmental and the economic water values of a land-use activity, and can identify areas for improvement, and trade-offs in river basins. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Tikhonov Regularization Based Modeling and Sidereal Filtering Mitigation of GNSS Multipath Errors
Remote Sens. 2018, 10(11), 1801; https://doi.org/10.3390/rs10111801
Received: 9 October 2018 / Revised: 9 November 2018 / Accepted: 11 November 2018 / Published: 14 November 2018
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In Global Navigation Satellite System (GNSS) relative positioning applications, multipath errors are non-negligible. Mitigation of the multipath error is an important task for precise positioning and it is possible due to the repeatability, even without any rigorous mathematical model. Empirical modeling is required
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In Global Navigation Satellite System (GNSS) relative positioning applications, multipath errors are non-negligible. Mitigation of the multipath error is an important task for precise positioning and it is possible due to the repeatability, even without any rigorous mathematical model. Empirical modeling is required for this mitigation. In this work, the multipath error modeling using carrier phase measurement residuals is realized by solving a regularization problem. Two Tikhonov regularization schemes, namely with the first and the second order differences, are considered. For each scheme, efficient numerical algorithms are developed to find the solutions, namely the Thomas algorithm and Cholesky rank-one update algorithm for the first and the second differences, respectively. Regularization parameters or Lagrange multipliers are optimized using the bootstrap method. In experiment, data on the first day are processed to construct a multipath model for each satellite (except the reference one), and then the model is used to correct the measurement on the second day, namely following the sidereal filtering approach. The smoothness of the coordinates calculated using the corrected measurements is improved significantly compared to those using the raw measurement. The efficacy of the proposed method is illustrated by the actual calculation. Full article
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Open AccessFeature PaperArticle Accuracy Assessment of Global Food Security-Support Analysis Data (GFSAD) Cropland Extent Maps Produced at Three Different Spatial Resolutions
Remote Sens. 2018, 10(11), 1800; https://doi.org/10.3390/rs10111800
Received: 21 September 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 13 November 2018
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Monitoring global agriculture systems relies on accurate and timely cropland information acquired worldwide. Recently, the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program has produced Global Food Security-support Analysis Data (GFSAD) cropland extent maps at three different spatial
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Monitoring global agriculture systems relies on accurate and timely cropland information acquired worldwide. Recently, the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program has produced Global Food Security-support Analysis Data (GFSAD) cropland extent maps at three different spatial resolutions, i.e., GFSAD1km, GFSAD250m, and GFSAD30m. An accuracy assessment and comparison of these three GFSAD cropland extent maps was performed to establish their quality and reliability for monitoring croplands both at global and regional scales. Large area (i.e., global) assessment of GFSAD cropland extent maps was performed by dividing the entire world into regions using a stratification approach and collecting a reference dataset using a simple random sampling design. All three global cropland extent maps were assessed using a total reference dataset of 28,733 samples. The assessment results showed an overall accuracy of 72.3%, 80–98%, and 91.7% for GFSAD1km, 250 m (only for four continents), and 30 m maps, respectively. Additionally, a regional comparison of the three GFSAD cropland extent maps was analyzed for nine randomly selected study sites of different agriculture field sizes (i.e., small, medium, and large). The similarity among the three GFSAD cropland extent maps in these nine study sites was represented using a similarity matrix approach and two landscape metrics (i.e., Proportion of Landscape (PLAND) and Per Patch Unit (PPU)), which categorized the crop proportion and the crop pattern. A comparison of the results showed the similarities and differences in the cropland areas and their spatial extent when mapped at the three spatial resolutions and considering the different agriculture field sizes. Finally, specific recommendations were suggested for when to apply each of the three different GFSAD cropland extent maps for agriculture monitoring based on these agriculture field sizes. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images
Remote Sens. 2018, 10(11), 1799; https://doi.org/10.3390/rs10111799
Received: 19 September 2018 / Revised: 10 November 2018 / Accepted: 11 November 2018 / Published: 13 November 2018
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Abstract
In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input
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In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input neurons and (ii) selecting the correct training data. We focused on generating two optimal input data neurons that (i) strengthened ship targets and (ii) mitigated noise effects by image processing techniques, including median filtering, multi-looking, etc. The median filter and multi-look operations were used to reduce the background noise, and the median filter operation was also used to remove ships in an image in order to maximize the difference between the pixel values of ships and the sea. Through the root-mean-square difference calculation, most ship targets, even including small ships, were emphasized in the images. We tested the performance of the proposed method using X-band high-resolution SAR images including COSMO-SkyMed, KOMPSAT-5, and TerraSAR-X images. An intensity difference map and a texture difference map were extracted from the X-band SAR single-look complex (SLC) images, and then, the maps were used as input neurons for the ANN and SVM machine learning techniques. Finally, we created ship-probability maps through the machine learning techniques. To validate the ANN and SVM results, optimal threshold values were obtained by using the statistical approach and then used to identify ships from the ship-probability maps. Consequently, the level of recall achieved was greater than 90% in most cases. This means that the proposed method enables the detection of most ship targets from X-band SAR images with a reduced number of false detections from negative effects. Full article
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Open AccessArticle How Far Can Consumer-Grade UAV RGB Imagery Describe Crop Production? A 3D and Multitemporal Modeling Approach Applied to Zea mays
Remote Sens. 2018, 10(11), 1798; https://doi.org/10.3390/rs10111798
Received: 24 September 2018 / Revised: 7 November 2018 / Accepted: 7 November 2018 / Published: 13 November 2018
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In recent decades, remote sensing has increasingly been used to estimate the spatio-temporal evolution of crop biophysical parameters such as the above-ground biomass (AGB). On a local scale, the advent of unmanned aerial vehicles (UAVs) seems to be a promising trade-off between satellite/airborne
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In recent decades, remote sensing has increasingly been used to estimate the spatio-temporal evolution of crop biophysical parameters such as the above-ground biomass (AGB). On a local scale, the advent of unmanned aerial vehicles (UAVs) seems to be a promising trade-off between satellite/airborne and terrestrial remote sensing. This study aims to evaluate the potential of a low-cost UAV RGB solution to predict the final AGB of Zea mays. Besides evaluating the interest of 3D data and multitemporality, our study aims to answer operational questions such as when one should plan a combination of two UAV flights for AGB modeling. In this case, study, final AGB prediction model performance reached 0.55 (R-square) using only UAV information and 0.8 (R-square) when combining UAV information from a single flight with a single-field AGB measurement. The adding of UAV height information to the model improves the quality of the AGB prediction. Performing two flights provides almost systematically an improvement in AGB prediction ability in comparison to most single flights. Our study provides clear insight about how we can counter the low spectral resolution of consumer-grade RGB cameras using height information and multitemporality. Our results highlight the importance of the height information which can be derived from UAV data on one hand, and on the other hand, the lower relative importance of RGB spectral information. Full article
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