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Remote Sens., Volume 10, Issue 12 (December 2018)

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Cover Story (view full-size image) The compromise between spatial and temporal resolution remains a challenge in remote sensing. With [...] Read more.
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Open AccessArticle Geometric, Environmental and Hardware Error Sources of a Ground-Based Interferometric Real-Aperture FMCW Radar System
Remote Sens. 2018, 10(12), 2070; https://doi.org/10.3390/rs10122070
Received: 28 September 2018 / Revised: 14 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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Abstract
Ground-based interferometric radar systems have numerous environmental monitoring applications in geoscience. Development of a relatively simple ground-based interferometric real-aperture FMCW radar (GB-InRAR) system that can be readily deployed in field without an established set of corner reflectors will meet the present and future [...] Read more.
Ground-based interferometric radar systems have numerous environmental monitoring applications in geoscience. Development of a relatively simple ground-based interferometric real-aperture FMCW radar (GB-InRAR) system that can be readily deployed in field without an established set of corner reflectors will meet the present and future need for real-time monitoring of the expected increased number of geohazard events due to climate changes. Several effects affect electromagnetic waves and limit the measurement accuracy, and a careful analysis of the setup of the deployed radar system in field is essential to achieve adequate results. In this paper, we present radar measurement of a moving square trihedral corner reflector from experiments conducted in both the field and laboratory, and assess the error sources with focus on the geometry, hardware and environmental effects on interferometric and differential interferometric measurements. A theoretical model is implemented to assess deviations between theory and measurements. The main observed effects are variations in radio refractivity, multipath interference and inter-reflector interference. Measurement error due to radar hardware and the environment are analyzed, as well as how the geometry of the measurement setup affects the nominal range-cell extent. It is found that for this experiment the deviation between interferometry and differential interferometry is mainly due to variations in the radio refractivity, and temperature-induced changes in the electrical length of the microwave cables. The results show that with careful design and analysis of radar parameters and radar system geometry the measurement accuracy of a GB-InRAR system without the use of deployed corner reflectors is comparable to the accuracy of differential interferometric measurements. A GB-InRAR system can therefore be used during sudden geo-hazard events without established corner reflector infrastructure, and the results are also valid for other high-precision interferometric radar systems. Full article
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Open AccessArticle Numerical Mapping and Modeling Permafrost Thermal Dynamics across the Qinghai-Tibet Engineering Corridor, China Integrated with Remote Sensing
Remote Sens. 2018, 10(12), 2069; https://doi.org/10.3390/rs10122069
Received: 30 November 2018 / Revised: 16 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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Abstract
Permafrost thermal conditions across the Qinghai–Tibet Engineering Corridor (QTEC) is of growing interest due to infrastructure development. Most modeling of the permafrost thermal regime has been conducted at coarser spatial resolution, which is not suitable for engineering construction in a warming climate. Here [...] Read more.
Permafrost thermal conditions across the Qinghai–Tibet Engineering Corridor (QTEC) is of growing interest due to infrastructure development. Most modeling of the permafrost thermal regime has been conducted at coarser spatial resolution, which is not suitable for engineering construction in a warming climate. Here we model the spatial permafrost thermal dynamics across the QTEC from the 2010 to the 2060 using the ground thermal model. Soil properties are defined based on field measurements and ecosystem types. The climate forcing datasets are synthesized from MODIS-LST products and the reanalysis product of near-surface air temperature. The climate projections are based on long-term observations of air temperature across the QTEC. The comparison of model results to field measurements demonstrates a satisfactory agreement for the purpose of permafrost thermal modeling. The results indicate a discontinuous permafrost distribution in the QTEC. Mean annual ground temperatures (MAGT) are lowest (<−2.0 °C) for the high mountains. In most upland plains, MAGTs range from −2.0 °C to 0 °C. For high mountains, the average active-layer thickness (ALT) is less than 2.0 m, while the river valley features ALT of more than 4.0 m. For upland plains, the modeled ALTs generally range from 3.0 m to 4.0 m. The simulated results for the future 50 years suggest that 12.0%~20.2% of the permafrost region will be involved in degradation, with an MAGT increase of 0.4 °C~2.3 °C, and the ALT increasing by 0.4 m~7.3 m. The results of this study are useful for the infrastructure development, although there are still several improvements in detailed forcing datasets and a locally realistic model. Full article
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Open AccessArticle A Novel Tilt Correction Technique for Irradiance Sensors and Spectrometers On-Board Unmanned Aerial Vehicles
Remote Sens. 2018, 10(12), 2068; https://doi.org/10.3390/rs10122068
Received: 22 October 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 19 December 2018
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Abstract
In unstable atmospheric conditions, using on-board irradiance sensors is one of the only robust methods to convert unmanned aerial vehicle (UAV)-based optical remote sensing data to reflectance factors. Normally, such sensors experience significant errors due to tilting of the UAV, if not installed [...] Read more.
In unstable atmospheric conditions, using on-board irradiance sensors is one of the only robust methods to convert unmanned aerial vehicle (UAV)-based optical remote sensing data to reflectance factors. Normally, such sensors experience significant errors due to tilting of the UAV, if not installed on a stabilizing gimbal. Unfortunately, such gimbals of sufficient accuracy are heavy, cumbersome, and cannot be installed on all UAV platforms. In this paper, we present the FGI Aerial Image Reference System (FGI AIRS) developed at the Finnish Geospatial Research Institute (FGI) and a novel method for optical and mathematical tilt correction of the irradiance measurements. The FGI AIRS is a sensor unit for UAVs that provides the irradiance spectrum, Real Time Kinematic (RTK)/Post Processed Kinematic (PPK) GNSS position, and orientation for the attached cameras. The FGI AIRS processes the reference data in real time for each acquired image and can send it to an on-board or on-cloud processing unit. The novel correction method is based on three RGB photodiodes that are tilted 10° in opposite directions. These photodiodes sample the irradiance readings at different sensor tilts, from which reading of a virtual horizontal irradiance sensor is calculated. The FGI AIRS was tested, and the method was shown to allow on-board measurement of irradiance at an accuracy better than ±0.8% at UAV tilts up to 10° and ±1.2% at tilts up to 15°. In addition, the accuracy of FGI AIRS to produce reflectance-factor-calibrated aerial images was compared against the traditional methods. In the unstable weather conditions of the experiment, both the FGI AIRS and the on-ground spectrometer were able to produce radiometrically accurate and visually pleasing orthomosaics, while the reflectance reference panels and the on-board irradiance sensor without stabilization or tilt correction both failed to do so. The authors recommend the implementation of the proposed tilt correction method in all future UAV irradiance sensors if they are not to be installed on a gimbal. Full article
(This article belongs to the Special Issue Drone Remote Sensing)
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Open AccessArticle Automatic Mapping of Thermokarst Landforms from Remote Sensing Images Using Deep Learning: A Case Study in the Northeastern Tibetan Plateau
Remote Sens. 2018, 10(12), 2067; https://doi.org/10.3390/rs10122067
Received: 18 November 2018 / Revised: 6 December 2018 / Accepted: 15 December 2018 / Published: 19 December 2018
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Abstract
Thawing of ice-rich permafrost causes thermokarst landforms on the ground surface. Obtaining the distribution of thermokarst landforms is a prerequisite for understanding permafrost degradation and carbon exchange at local and regional scales. However, because of their diverse types and characteristics, it is challenging [...] Read more.
Thawing of ice-rich permafrost causes thermokarst landforms on the ground surface. Obtaining the distribution of thermokarst landforms is a prerequisite for understanding permafrost degradation and carbon exchange at local and regional scales. However, because of their diverse types and characteristics, it is challenging to map thermokarst landforms from remote sensing images. We conducted a case study towards automatically mapping a type of thermokarst landforms (i.e., thermo-erosion gullies) in a local area in the northeastern Tibetan Plateau from high-resolution images by the use of deep learning. In particular, we applied the DeepLab algorithm (based on Convolutional Neural Networks) to a 0.15-m-resolution Digital Orthophoto Map (created using aerial photographs taken by an Unmanned Aerial Vehicle). Here, we document the detailed processing flow with key steps including preparing training data, fine-tuning, inference, and post-processing. Validating against the field measurements and manual digitizing results, we obtained an F1 score of 0.74 (precision is 0.59 and recall is 1.0), showing that the proposed method can effectively map small and irregular thermokarst landforms. It is potentially viable to apply the designed method to mapping diverse thermokarst landforms in a larger area where high-resolution images and training data are available. Full article
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Open AccessArticle Extraction of Anisotropic Characteristics of Scattering Centers and Feature Enhancement in Wide-Angle SAR Imagery Based on the Iterative Re-Weighted Tikhonov Regularization
Remote Sens. 2018, 10(12), 2066; https://doi.org/10.3390/rs10122066
Received: 1 November 2018 / Revised: 6 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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The anisotropic characteristic reflects discriminating features of the geometry of a scattering center. In this study, we propose a novel method based on the iterative re-weighted Tikhonov regularization (IRWTR) to achieve the extraction of anisotropic characteristics of scattering centers in the wide-angle SAR [...] Read more.
The anisotropic characteristic reflects discriminating features of the geometry of a scattering center. In this study, we propose a novel method based on the iterative re-weighted Tikhonov regularization (IRWTR) to achieve the extraction of anisotropic characteristics of scattering centers in the wide-angle SAR synthetic aperture radar (SAR) imaging. Moreover, based on the extracted anisotropic scattering behaviors, the incomplete imaging results of the distributed scattering centers are restored. In this paper, we first discussed the scattering property in SAR imagery from the perspective of attributed scattering center model (ASCM). The reason for the incomplete imaging results of the distributed scattering centers was also discussed based on the ASCM. Subsequently, we modeled the aspect-dependent amplitude responses of a scattering center as a linear combination of a set of orthogonal basis. Consequently, the extraction of anisotropic characteristics can be transformed into an inverse problem, which was solved by the proposed IRWTR with high efficiency and accuracy. After the extraction, we attempted to restore the complete image of a distributed scattering center, which consisted of only two points. The enhanced SAR image can reveal the actual shape of a target. Processing results of electromagnetic computation data validated that the proposed method is effective and efficient. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle Super-Resolution Reconstruction and Its Application Based on Multilevel Main Structure and Detail Boosting
Remote Sens. 2018, 10(12), 2065; https://doi.org/10.3390/rs10122065
Received: 28 October 2018 / Revised: 30 November 2018 / Accepted: 5 December 2018 / Published: 19 December 2018
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Abstract
Vivid main structure and rich texture detail are important factors with which to determine the quality of high-resolution images after super-resolution (SR) reconstruction. Owing to the loss of high-frequency information in the process of SR reconstruction and the limitation of the accurate estimation [...] Read more.
Vivid main structure and rich texture detail are important factors with which to determine the quality of high-resolution images after super-resolution (SR) reconstruction. Owing to the loss of high-frequency information in the process of SR reconstruction and the limitation of the accurate estimation of the unknown information in the inversion process, a gap still exists between the high-resolution image and the real image. The main structure can better preserve the edge structure of the image, and detail boosting can compensate for the missing high-frequency information in the reconstruction process. Therefore, a novel single remote-sensing image SR reconstruction method based on multilevel main structure and detail boosting (MMSDB-SR) is put forward in this paper. First, the multilevel main structure was obtained based on the decomposition of the remote-sensing image through use of the relative total variation model. Subsequently, multilevel texture detail information was obtained by a difference process. Second, the multilevel main structure and texture detail were reconstructed separately. The detail-boosting function was used to compensate for the missing high-frequency details in the reconstruction process. Finally, the high-resolution remote-sensing image with clear edge and rich texture detail can be obtained by fusing the multilevel main structure and texture-detail information. The experimental results show that the reconstructed high-resolution image has high clarity, high fidelity, and multi-detail visual effects, and the objective evaluation index exhibits significant improvement. Actual results show an average gain in entropy of up to 0.34 dB for an up-scaling of 2. Real results show an average gain in enhancement measure evaluation of up to 2.42 for an up-scaling of 2. The robustness and universality of the proposed SR method are verified. Full article
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Open AccessArticle Assessing Effect of Targeting Reduction of PM2.5 Concentration on Human Exposure and Health Burden in Hong Kong Using Satellite Observation
Remote Sens. 2018, 10(12), 2064; https://doi.org/10.3390/rs10122064
Received: 14 October 2018 / Revised: 1 December 2018 / Accepted: 5 December 2018 / Published: 19 December 2018
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Abstract
Targeting reduction of PM2.5 concentration lessens population exposure level and health burden more effectively than uniform reduction does. Quantitative assessment of effect of the targeting reduction is limited because of the lack of spatially explicit PM2.5 data. This study aimed to [...] Read more.
Targeting reduction of PM2.5 concentration lessens population exposure level and health burden more effectively than uniform reduction does. Quantitative assessment of effect of the targeting reduction is limited because of the lack of spatially explicit PM2.5 data. This study aimed to investigate extent of exposure and health benefits resulting from the targeting reduction of PM2.5 concentration. We took advantage of satellite observations to characterize spatial distribution of PM2.5 concentration at a resolution of 1 km. Using Hong Kong of China as the study region (804 satellite’s pixels covering its residential areas), human exposure level (cρ) and premature mortality attributable to PM2.5 (Mort) for 2015 were estimated to be 25.9 μg/m3 and 4112 people per year, respectively. We then performed 804 diagnostic tests that reduced PM2.5 concentrations by −1 μg/m3 in different areas and a reference test that uniformly spread the −1 μg/m3. We used a benefit rate from targeting reduction (BRT), which represented a ratio of declines in cρ (or Mort) with and without the targeting reduction, to quantify the extent of benefits. The diagnostic tests estimated the BRT levels for both human exposure and premature mortality to be 4.3 over Hong Kong. It indicates that the declines in human exposure and premature mortality quadrupled with a targeting reduction of PM2.5 concentration over Hong Kong. The BRT values for districts of Hong Kong could be as high as 5.6 and they were positively correlated to their spatial variabilities in population density. Our results underscore the substantial exposure and health benefits from the targeting reduction of PM2.5 concentration. To better protect public health in Hong Kong, super-regional and regional cooperation are essential. Meanwhile, local environmental policy is suggested to aim at reducing anthropogenic emissions from mobile and area (e.g., residential) sources in central and northwestern areas. Full article
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Open AccessLetter Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model
Remote Sens. 2018, 10(12), 2063; https://doi.org/10.3390/rs10122063
Received: 19 September 2018 / Revised: 13 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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Abstract
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of [...] Read more.
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees?
Remote Sens. 2018, 10(12), 2062; https://doi.org/10.3390/rs10122062
Received: 27 October 2018 / Revised: 11 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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Abstract
Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that [...] Read more.
Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts. Full article
(This article belongs to the Special Issue High-Resolution Thermal Imaging for Vegetation Monitoring)
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Open AccessArticle Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem
Remote Sens. 2018, 10(12), 2061; https://doi.org/10.3390/rs10122061
Received: 23 October 2018 / Revised: 5 December 2018 / Accepted: 15 December 2018 / Published: 18 December 2018
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Abstract
The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics [...] Read more.
The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations of proximal and remote sensing sensors: the ASD Fieldspec® 3 spectroradiometer, the Airborne Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined concomitant observations of biophysical variables and RF to test the capability of the models to reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between 6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at remote sensing scales. RRMSE increased in periods when large fractions of standing dead material mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially in summer and at mid-high view angles. Full article
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Open AccessArticle Comparative Analysis of Polarimetric SAR Calibration Methods
Remote Sens. 2018, 10(12), 2060; https://doi.org/10.3390/rs10122060
Received: 5 November 2018 / Revised: 7 December 2018 / Accepted: 15 December 2018 / Published: 18 December 2018
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Abstract
In the diverse applications of polarimetric Synthetic Aperture Radar (SAR) systems, it is a crucial to conduct polarimetric calibration, which aims to remove the radar system distortion effects prior to utilizing polarimetric SAR observations. The objective of this study is to evaluate the [...] Read more.
In the diverse applications of polarimetric Synthetic Aperture Radar (SAR) systems, it is a crucial to conduct polarimetric calibration, which aims to remove the radar system distortion effects prior to utilizing polarimetric SAR observations. The objective of this study is to evaluate the performance of different polarimetric calibration methods. Two widely used methods, the Van Zyl and Quegan methods, and one recently proposed method, such as the Villa method, have been selected among various calibration methods in literature. The selected methods have basic differences in their assumptions that are applied to the polarimetric system model. In order to evaluate the calibration performances under different system parameters and ground characteristics, comparative analysis of the calibration results were conducted on synthetic polarimetric SAR data and ALOS PALSAR quad-pol mode data. Based on the experimental results, the advantages and limitations of different methods were clarified, and a simple hybrid calibration method is presented to further improve the polarimetric calibration performance. Full article
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Open AccessArticle New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images
Remote Sens. 2018, 10(12), 2059; https://doi.org/10.3390/rs10122059
Received: 23 October 2018 / Revised: 7 December 2018 / Accepted: 12 December 2018 / Published: 18 December 2018
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The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies [...] Read more.
The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme. Full article
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Open AccessArticle Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin
Remote Sens. 2018, 10(12), 2058; https://doi.org/10.3390/rs10122058
Received: 2 November 2018 / Revised: 14 December 2018 / Accepted: 14 December 2018 / Published: 18 December 2018
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Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion) from deficit irrigation of pastures in the [...] Read more.
Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion) from deficit irrigation of pastures in the Upper Colorado River Basin. The study area has diverse topography with scattered fields and few precipitation gauges that are not representative of the basin. Gridded precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014–2016 irrigation seasons. First, the resolution at the farm level is discussed. Next, bias occurrence at different time scales (daily to monthly) is evaluated and addressed. Then, the coverage area of the gauge station, along with the impact of the dominant wind direction on the shape of the coverage area, is evaluated. Ultimately, available actual ET maps derived from the METRIC model are used to estimate spatial effective rainfall. The results show that the spatial resolutions of TRMM and PRISM are not adequate at the farm level, while Daymet is a better fit but lacks the adequate latency versus TRMM and PRISM. When compared against local weather station records, all three spatial datasets were found to have a bias that decreases at coarser temporal intervals. However, the performance of Daymet and PRISM at the monthly time step is acceptable, and they can be used for water resource management at the farm level. The adequacy of an existing gauge station for a given farm location depends on the willingness to accept the risk of the bias associated with a non-persistent, non-symmetric gauge coverage area that is highly correlated with the dominant wind direction. Among all goodness of fit statistics considered in the study, the interpretation of the summation of error makes more sense for quantifying the rainfall bias and risk for the user. Finally, based on the USDA-SCS model and actual spatial ET, overall, seasonal effective rainfall tends to be less than 60% of total rainfall for agricultural lands. Full article
(This article belongs to the Special Issue Remote Sensing for Crop Water Management)
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Open AccessArticle Annual Cropland Mapping Using Reference Landsat Time Series—A Case Study in Central Asia
Remote Sens. 2018, 10(12), 2057; https://doi.org/10.3390/rs10122057
Received: 19 November 2018 / Revised: 5 December 2018 / Accepted: 11 December 2018 / Published: 18 December 2018
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Abstract
Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to [...] Read more.
Mapping the spatial and temporal dynamics of cropland is an important prerequisite for regular crop condition monitoring, management of land and water resources, or tracing and understanding the environmental impacts of agriculture. Analyzing archives of satellite earth observations is a proven means to accurately identify and map croplands. However, existing maps of the annual cropland extent either have a low spatial resolution (e.g., 250–1000 m from Advanced Very High Resolution Radiometer (AVHRR) to Moderate-resolution Imaging Spectroradiometer (MODIS); and existing high-resolution maps (such as 30 m from Landsat) are not provided frequently (for example, on a regular, annual basis) because of the lack of in situ reference data, irregular timing of the Landsat and Sentinel-2 image time series, the huge amount of data for processing, and the need to have a regionally or globally consistent methodology. Against this backdrop, we propose a reference time-series-based mapping method (RBM), and create binary cropland vs. non-cropland maps using irregular Landsat time series and RBM. As a test case, we created and evaluated annual cropland maps at 30 m in seven distinct agricultural landscapes in Xinjiang, China, and the Aral Sea Basin. The results revealed that RBM could accurately identify cropland annually, with producer’s accuracies (PA) and user’s accuracies (UA) higher than 85% between 2006 and 2016. In addition, cropland maps by RBM were significantly more accurate than the two existing products, namely GlobaLand30 and Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC). Full article
(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity)
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Open AccessArticle Calibrations and Wind Observations of an Airborne Direct-Detection Wind LiDAR Supporting ESA’s Aeolus Mission
Remote Sens. 2018, 10(12), 2056; https://doi.org/10.3390/rs10122056
Received: 13 September 2018 / Revised: 7 December 2018 / Accepted: 13 December 2018 / Published: 18 December 2018
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Abstract
The Aeolus satellite mission of the European Space Agency (ESA) has brought the first wind LiDAR to space to satisfy the long-existing need for global wind profile observations. Until the successful launch on 22 August 2018, pre-launch campaign activities supported the validation of [...] Read more.
The Aeolus satellite mission of the European Space Agency (ESA) has brought the first wind LiDAR to space to satisfy the long-existing need for global wind profile observations. Until the successful launch on 22 August 2018, pre-launch campaign activities supported the validation of the measurement principle, the instrument calibration, and the optimization of retrieval algorithms. Therefore, an airborne prototype instrument has been developed, the ALADIN Airborne Demonstrator (A2D), with ALADIN being the Atmospheric Laser Doppler Instrument of Aeolus. Two airborne campaigns were conducted over Greenland, Iceland and the Atlantic Ocean in September 2009 and May 2015, employing the A2D as the first worldwide airborne direct-detection Doppler Wind LiDAR (DWL) and a well-established coherent 2-µm wind LiDAR. Both wind LiDAR instruments were operated on the same aircraft measuring Mie backscatter from aerosols and clouds as well as Rayleigh backscatter from molecules in parallel. This paper particularly focuses on the instrument response calibration method of the A2D and its importance for accurate wind retrieval results. We provide a detailed description of the analysis of wind measurement data gathered during the two campaigns, introducing a dedicated aerial interpolation algorithm that takes into account the different resolution grids of the two LiDAR systems. A statistical comparison of line-of-sight (LOS) winds for the campaign in 2015 yielded estimations of the systematic and random (mean absolute deviation) errors of A2D observations of about 0.7 m/s and 2.1 m/s, respectively, for the Rayleigh, and 0.05 m/s and 2.3 m/s, respectively, for the Mie channel. In view of the launch of Aeolus, differences between the A2D and the satellite mission are highlighted along the way, identifying the particular assets and drawbacks. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of the Atmosphere)
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Open AccessArticle Differing Responses to Rainfall Suggest More Than One Functional Type of Grassland in South Africa
Remote Sens. 2018, 10(12), 2055; https://doi.org/10.3390/rs10122055
Received: 28 September 2018 / Revised: 6 November 2018 / Accepted: 13 November 2018 / Published: 18 December 2018
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Abstract
Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. [...] Read more.
Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. Grasslands are indeed very responsive to variations in precipitation. This study evaluates the sensitivity of the grassland ecosystem to precipitation variability in space and time, by identifying the factors controlling this response, based on monthly precipitation data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) data from the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) datasets, used as proxy for productivity, at 60 grassland sites in South Africa. Our results show that MISR-HR products adequately capture the spatial and temporal variability in productivity at scales that are relevant to this study, and they are therefore a good tool to study climate change impacts on ecosystem at small spatial scales over large spatial and temporal domains. We show that combining several determinants and accounting for legacies improves our ability to understand patterns, identify areas of vulnerability, and predict the future of grassland productivity. Mean annual precipitation is a good predictor of mean grassland productivity. The grasslands with a mean annual rainfall above about 530 mm have a different functional response to those receiving less than that amount of rain, on average. On the more arid and less fertile soils, large inter-annual variability reduces productivity. Our study suggests that grasslands on the more marginal soils are the most vulnerable to climate change. Full article
(This article belongs to the Special Issue MISR)
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Open AccessArticle 2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events
Remote Sens. 2018, 10(12), 2054; https://doi.org/10.3390/rs10122054
Received: 16 October 2018 / Revised: 9 December 2018 / Accepted: 11 December 2018 / Published: 17 December 2018
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Abstract
Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in [...] Read more.
Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in order to prevent a disaster, or initiate emergency aid measures as quickly as possible in the event of an emergency. Especially, three-dimensional (3D) information that is derived using photogrammetry can be used to analyze the terrain and map existing structures that are set up at short notice. Using aerial imagery acquired during a German music festival in 2016 and the celebration of the German Protestant Church Assembly of 2017, the authors compare two-dimensional (2D) and novel fusion-based 3D change detection methods, and discuss their suitability for supporting large-scale events during the relevant phases of crisis management. This study serves to find out what added value the use of 3D change information can provide for on-site crisis management. Based on the results, an operational, fully automatic processor for crisis management operations and corresponding products for end users can be developed. Full article
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Open AccessArticle A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China
Remote Sens. 2018, 10(12), 2053; https://doi.org/10.3390/rs10122053
Received: 11 November 2018 / Revised: 7 December 2018 / Accepted: 12 December 2018 / Published: 17 December 2018
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Abstract
Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several [...] Read more.
Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively. Full article
(This article belongs to the Section Urban Remote Sensing)
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Open AccessArticle Convolutional Neural Network Based Multipath Detection Method for Static and Kinematic GPS High Precision Positioning
Remote Sens. 2018, 10(12), 2052; https://doi.org/10.3390/rs10122052
Received: 24 September 2018 / Revised: 22 November 2018 / Accepted: 14 December 2018 / Published: 17 December 2018
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Abstract
Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the [...] Read more.
Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm. Full article
(This article belongs to the Special Issue GPS/GNSS Contemporary Applications)
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Open AccessArticle A Novel Method for High-Voltage Bundle Conductor Reconstruction from Airborne LiDAR Data
Remote Sens. 2018, 10(12), 2051; https://doi.org/10.3390/rs10122051
Received: 26 October 2018 / Revised: 4 December 2018 / Accepted: 15 December 2018 / Published: 17 December 2018
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Abstract
The security of high-voltage power transmission corridors is significantly vital to the national economy and daily life. With its rapid development, LiDAR (Light Detection and Ranging) technology has been widely applied in the inspection of transmission lines. As the basis of potential hazard [...] Read more.
The security of high-voltage power transmission corridors is significantly vital to the national economy and daily life. With its rapid development, LiDAR (Light Detection and Ranging) technology has been widely applied in the inspection of transmission lines. As the basis of potential hazard detection, a robust and precise power line model is a necessary requirement for rapid and correct clearance. Thus, this paper proposes a novel method for high-voltage bundle conductor reconstruction, which can precisely reconstruct each sub-conductor. First, points in high-voltage power transmission corridors are detected and classified into four categories; second, for classified power lines, single power line spans are extracted, and bundle conductors are identified by analyzing the single spans’ fitting residuals; and then, each sub-conductor of bundle conductors is extracted by a projected dichotomy method on the XOY and XOZ planes, respectively; finally, a double-RANSAC (random sample consensus)-based algorithm was introduced to reconstruct each power line. The proposed method makes use of the distribution of bundle conductors in high-voltage transmission lines, and our experiments showed that it could preferably reconstruct the real structure of bundle conductors robustly with a high precision better than 0.2 m. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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Open AccessArticle The Use of Photogrammetry to Construct Time Series of Vegetation Permeability to Water and Seed Transport in Agricultural Waterways
Remote Sens. 2018, 10(12), 2050; https://doi.org/10.3390/rs10122050
Received: 19 October 2018 / Revised: 30 November 2018 / Accepted: 12 December 2018 / Published: 17 December 2018
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Abstract
Terrestrial vegetation has numerous positive effects on the main regulating services of agricultural channels, such as seed retention, pollutant mitigation, bank stabilization, and sedimentation, and this vegetation acts as a porous medium for the flow of matter through the channels. This vegetation also [...] Read more.
Terrestrial vegetation has numerous positive effects on the main regulating services of agricultural channels, such as seed retention, pollutant mitigation, bank stabilization, and sedimentation, and this vegetation acts as a porous medium for the flow of matter through the channels. This vegetation also limits the water conveyance in channels, and consequently is frequently removed by farmers to increase its porosity. However, the temporal effects of these management practices remain poorly understood. Indeed, the vegetation porosity exhibits important temporal variations according to the maintenance schedule, and the water level also varies with time inside a given channel section according to rainfall events or irrigation practices. To maximise the impacts of vegetation on agricultural channels, it is now of primary importance to measure vegetation porosity according to water level over a long time period rather than at a particular time. Time series of such complex vegetation characteristics have never been studied using remote sensing methods. Here, we present a new approach using the Structure-from-Motion approach using a Multi-View Stereo algorithm (SfM-MVS) technique to construct time series of herbaceous vegetation porosity in a real agricultural channel managed by five different practices: control, dredging, mowing, burning, and chemical weeding. We post-processed the time series of point clouds to create an indicator of vegetation porosity for the whole section and of the surface of the channel. Mowing and chemical weeding are the practices presenting the most favorable temporal evolutions of the porosity indicators regarding flow events. Burning did not succeed in restoring the porosity of the channel due to quick recovery of the vegetation and dephasing of the maintenance calendar with the flow events. The high robustness of the technique and the automatization of the SfM-MVS calculation together with the post-processing of the point clouds should help in handling time series of SfM-MVS data for applications in ecohydrology or agroecology. Full article
(This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications)
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Open AccessLetter Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology
Remote Sens. 2018, 10(12), 2049; https://doi.org/10.3390/rs10122049
Received: 29 September 2018 / Revised: 9 December 2018 / Accepted: 10 December 2018 / Published: 17 December 2018
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Abstract
In this study, the potential of Sentinel-1 data to seasonally monitor temperate forests was investigated by analyzing radar signatures observed from plots in the Fontainebleau Forest of the Ile de France region, France, for the period extending from March 2015 to January 2016. [...] Read more.
In this study, the potential of Sentinel-1 data to seasonally monitor temperate forests was investigated by analyzing radar signatures observed from plots in the Fontainebleau Forest of the Ile de France region, France, for the period extending from March 2015 to January 2016. Radar backscattering coefficients, σ0 and the amplitude of temporal interferometric coherence profiles in relation to environmental variables are shown, such as in situ precipitation and air temperature. The high temporal frequency of Sentinel-1 acquisitions (i.e., twelve days, or six, if both Sentinel-1A and B are combined over Europe) and the dual polarization configuration (VV and VH over most land surfaces) made a significant contribution. In particular, the radar backscattering coefficient ratio of VV to VH polarization, σ V V 0 / σ V H 0 , showed a well-pronounced seasonality that was correlated with vegetation phenology, as confirmed in comparison to NDVI profiles derived from Landsat-8 (r = 0.77) over stands of deciduous trees. These results illustrate the high potential of Sentinel-1 data for monitoring vegetation, and as these data are not sensitive to the atmosphere, the phenology could be estimated with more accuracy than optical data. These observations will be quantitatively analyzed with the use of electromagnetic models in the near future. Full article
(This article belongs to the Special Issue Multitemporal Remote Sensing for Forestry)
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Open AccessArticle Locking Status and Earthquake Potential Hazard along the Middle-South Xianshuihe Fault
Remote Sens. 2018, 10(12), 2048; https://doi.org/10.3390/rs10122048
Received: 16 October 2018 / Revised: 6 December 2018 / Accepted: 15 December 2018 / Published: 17 December 2018
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Abstract
By combining the seismogenic environment, seismic recurrence periods of strong historical earthquakes, precise locations of small–moderate earthquakes, and Coulomb stress changes of moderate–strong earthquakes, we analyze the potential locking status of a seismically quiet segment of Xianshuihe fault between Daofu County and Kangding [...] Read more.
By combining the seismogenic environment, seismic recurrence periods of strong historical earthquakes, precise locations of small–moderate earthquakes, and Coulomb stress changes of moderate–strong earthquakes, we analyze the potential locking status of a seismically quiet segment of Xianshuihe fault between Daofu County and Kangding City (SDK). The interseismic surface velocities between 1999 and 2017 are obtained from updated global positioning system (GPS) observations in this region. After removing the post-seismic relaxation effect caused by the 2008 Mw 7.9 Wenchuan earthquake that occurred around the fault segment, the observed velocities reveal a pronounced symmetric slip pattern along the SDK trace. The far field slip rate is 7.8 ± 0.4 mm/a, and the fault SDK is confirmed to be in an interseismic silent phase. The optimal locking depth is estimated at 7 km, which is perfectly distributed on the upper edge of the relocated hypocenters. A moment deficit analysis shows cumulative seismic moment between 1955 and 2018, corresponding to an Mw 6.6 event. Finally, based on a viscoelastic deformation model, we find that moderate–strong earthquakes in the surrounding area increase the Coulomb stress level by up to 2 bars on the SDK, significantly enhancing the future seismic potential. Full article
(This article belongs to the Special Issue Environmental and Geodetic Monitoring of the Tibetan Plateau)
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Open AccessArticle Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
Remote Sens. 2018, 10(12), 2047; https://doi.org/10.3390/rs10122047
Received: 25 October 2018 / Revised: 11 December 2018 / Accepted: 14 December 2018 / Published: 16 December 2018
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Abstract
Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data [...] Read more.
Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species. Full article
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Open AccessArticle The Spatio-Temporal Evolution of River Island Based on Landsat Satellite Imagery, Hydrodynamic Numerical Simulation and Observed Data
Remote Sens. 2018, 10(12), 2046; https://doi.org/10.3390/rs10122046
Received: 22 November 2018 / Revised: 11 December 2018 / Accepted: 12 December 2018 / Published: 16 December 2018
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Abstract
A river island is a shaped sediment accumulation body with its top above the water’s surface in crooked or branching streams. In this paper, four river islands in Yangzhong City in the lower reaches of the Yangtze River were studied. The spatio-temporal evolution [...] Read more.
A river island is a shaped sediment accumulation body with its top above the water’s surface in crooked or branching streams. In this paper, four river islands in Yangzhong City in the lower reaches of the Yangtze River were studied. The spatio-temporal evolution information of the islands was quantitatively extracted using the threshold value method, binarization model, and cluster analysis, based on Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) images of the Landsat satellite series from 1985 to 2015. The variation mechanism and influencing factors were analyzed using an unstructured-grid, Finite-Volume Coastal Ocean Model (FVCOM) hydrodynamic numerical simulation, as well as the water-sediment data measured by hydrological stations. The annual average total area of these islands was 251,224.46 m2 during 1985–2015, and the total area first increased during 1985–2000 and decreased later during 2000–2015. Generally, the total area increased during these 30 years. Taipingzhou island had the largest area and the biggest changing rate, Xishadao island had the smallest area, and Zhongxinsha island had the smallest changing rate. The river islands’ area change was influenced by river runoff, sediment discharge, and precipitation, and sediment discharge proved to be the most significant natural factor in island evolution. River island evolution was also found to be affected by both runoff and oceanic tide. The difference in flow-field caused silting up in the Leigongdao Island and the head of Taipingzhou Island, and a serious reduction in the middle and tail of Taipingzhou Island. The method used in this paper has good applicability to river islands in other rivers around the world. Full article
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Open AccessArticle Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China
Remote Sens. 2018, 10(12), 2045; https://doi.org/10.3390/rs10122045
Received: 14 September 2018 / Revised: 9 December 2018 / Accepted: 14 December 2018 / Published: 16 December 2018
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Abstract
This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies [...] Read more.
This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies with identical root mean square errors (RMSE). Nevertheless, MuSyQ albedo is better correlated with ground measurements when sufficient valid observations are available or snow-free. The opposite happens when less than seven valid observations are available. GLASS albedo presents a larger RMSE than MCD43A3 and MuSyQ albedos in comparison with ground measurements. Over surfaces with smaller seasonal variations, MCD43A3 and MuSyQ albedos show smaller RMSEs than GLASS albedo in comparison with HJ albedo. However, for surfaces with larger temporal variations, both RMSEs and R2 of GLASS albedo are comparable with MCD43A3 and MuSyQ. Generally, MCD43A3 and MuSyQ albedos featured the same RMSEs of 0.034 and similar R2 (0.920 and 0.903, respectively), which are better than GLASS albedo (RMSE = 0.043, R2 = 0.787). However, when it comes to comparison with aggregated HJ albedo, MuSyQ and GLASS albedos are with lower RMSEs of 0.027 and 0.032 and higher R2 of 0.900 and 0.898 respectively than MCD43A3 (RMSE = 0.038, R2 = 0.836). Despite the limited geographic region of the study area, they still provide an important insight into the accuracies of three albedo products. Full article
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
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Open AccessArticle On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
Remote Sens. 2018, 10(12), 2044; https://doi.org/10.3390/rs10122044
Received: 30 October 2018 / Revised: 10 December 2018 / Accepted: 12 December 2018 / Published: 16 December 2018
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Abstract
Dual-frequency GNSS data processing is currently one of the most useful techniques for sounding the ionosphere. Hence, this work was aimed at the evaluation of ground-based GNSS data for the continuous monitoring of polar patches in both hemispheres. In this contribution, we proposed [...] Read more.
Dual-frequency GNSS data processing is currently one of the most useful techniques for sounding the ionosphere. Hence, this work was aimed at the evaluation of ground-based GNSS data for the continuous monitoring of polar patches in both hemispheres. In this contribution, we proposed to use epoch-wise relative STEC values in order to detect these structures. The applied indicator is defined as a difference between an undifferenced geometry-free linear combination of GNSS signals and the background ionospheric variations, which were assessed with an iterative algorithm of four-degree polynomial fitting. The occurrence of patches during the St. Patrick geomagnetic storm was performed for validation purposes. The first part of the work confirmed the applicability of the relative STEC values for such investigations. On the other hand, it also revealed the limitations related to the inhomogeneous distribution of stations, which may affect the results in both hemispheres. This was confirmed with a preliminary cross-evaluation of GNSS and in situ SWARM datasets. Apart from the periods with a well-established coincidence, the opposite situation, when both methods indicated different parts of the polar ionosphere, was also observed. The second part of this contribution depicted the feasibility of continuous patch detection for both regions, and thus the interhemispheric comparison of the analyzed structures. It has demonstrated the strong disproportion between patches in the northern and southern hemispheres. This discrepancy seems to be related to the different amount of plasma propagating from the dusk sector, which is justified by the values of relative STEC at mid-latitudes. The observed structures are also strongly dependent on the orientation of the interplanetary magnetic field. Full article
(This article belongs to the Special Issue Environmental Research with Global Navigation Satellite System (GNSS))
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Open AccessArticle Ship Classification and Detection Based on CNN Using GF-3 SAR Images
Remote Sens. 2018, 10(12), 2043; https://doi.org/10.3390/rs10122043
Received: 22 October 2018 / Revised: 8 December 2018 / Accepted: 12 December 2018 / Published: 14 December 2018
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Abstract
Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible [...] Read more.
Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection. Full article
(This article belongs to the Special Issue Radar Imaging Theory, Techniques, and Applications)
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Open AccessArticle A Remote Sensing Approach for Mapping the Development of Ancient Water Management in the Near East
Remote Sens. 2018, 10(12), 2042; https://doi.org/10.3390/rs10122042
Received: 14 November 2018 / Revised: 7 December 2018 / Accepted: 10 December 2018 / Published: 14 December 2018
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Abstract
We present a novel approach that uses remote sensing to record and reconstruct traces of ancient water management throughout the whole region of Northern Mesopotamia, an area where modern agriculture and warfare has had a severe impact on the survival of archaeological remains [...] Read more.
We present a novel approach that uses remote sensing to record and reconstruct traces of ancient water management throughout the whole region of Northern Mesopotamia, an area where modern agriculture and warfare has had a severe impact on the survival of archaeological remains and their visibility in modern satellite imagery. However, analysis and interpretation of declassified stereoscopic spy satellite data from the 1960s and early 1970s revealed traces of ancient water management systems. We processed satellite imagery to facilitate image interpretation and used photogrammetry to reconstruct hydraulic pathways. Our results represent the first comprehensive map of water management features across the entirety of Northern Mesopotamia for the period ca. 1200 BC to AD 1500. In particular, this shows that irrigation was widespread throughout the region in the Early Islamic period, including within the zone traditionally regarded as “rain-fed”. However, we found that a high proportion of the ancient canal systems had been damaged or destroyed by 20th century changes to agricultural practices and land use. Given this, there is an urgent need to record these rapidly vanishing water management systems that were an integral part of the ancient agricultural landscape and that underpinned powerful states. Full article
(This article belongs to the Special Issue 2nd Edition Advances in Remote Sensing for Archaeological Heritage)
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Open AccessArticle The Influence of Instrumental Line Shape Degradation on the Partial Columns of O3, CO, CH4 and N2O Derived from High-Resolution FTIR Spectrometry
Remote Sens. 2018, 10(12), 2041; https://doi.org/10.3390/rs10122041
Received: 1 November 2018 / Revised: 12 December 2018 / Accepted: 13 December 2018 / Published: 14 December 2018
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Abstract
High resolution Fourier transform infrared (FTIR) measurement of direct sunlight does not only provide information of trace gas total columns, but also vertical distribution. Measured O3, CO, CH4, and N2O can be separated into multiple partial columns [...] Read more.
High resolution Fourier transform infrared (FTIR) measurement of direct sunlight does not only provide information of trace gas total columns, but also vertical distribution. Measured O3, CO, CH4, and N2O can be separated into multiple partial columns using the optimal estimation method (OEM). The retrieval of trace gas profiles is sensitive to the instrument line shape (ILS) of the FTIR spectrometer. In this paper, we present an investigation of the influence of ILS degradation on the partial column retrieval of O3, CO, CH4, and N2O. Sensitivities of the partial column, error, and degrees of freedom (DOFs) of each layer to different levels of ILS degradation for O3, CO, CH4, and N2O are estimated. We then evaluate the impact of ILS degradation on the long-term measurements. In addition, we derive the range of ILS degradation corresponding to the acceptable uncertainties of O3, CO, CH4, and N2O results. The results show that the uncertainties induced by the ILS degradation on the absolute value, error, and the DOFs of the partial column are altitude and gas species dependent. The uncertainties of the partial columns of O3 and CO are larger than those on CH4 and N2O. The stratospheric partial columns are more sensitive to the ILS degradation compared to the tropospheric part. Our result improves the understanding of the ILS degradation on the FTIR measurements, which is important for the quantification of the measurement uncertainties and minimizes the bias of the inter-comparison between different measurement platforms. This is especially useful for the validation of satellite observations, the data assimilation of chemical model simulations, and the quantification of the source/sink/trend from the FTIR measurements. Full article
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Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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