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Remote Sens., Volume 13, Issue 23 (December-1 2021) – 228 articles

Cover Story (view full-size image): Synthetic aperture radar (SAR) interferometry has rapidly become a widely used technique for measuring Earth’s ground deformations, with applications to a plethora of natural and anthropogenic processes. With the increased amount of data provided by modern satellite SAR platforms, large-scale processing has faced incredible challenges due to the associated computational burden. The application of high-performance computing (HPC) methodologies to interferometric SAR processing is, therefore, gaining increasing attention from the scientific community: several approaches have been recently developed by adopting different parallel strategies, computing architectures, and programming models. This study provides a critical perspective on the state of the art of HPC methodologies applied to interferometric SAR processing, also discussing some open issues and outlining future trends. View this paper.
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18 pages, 7971 KiB  
Article
The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China
by Linye Song, Shangfeng Chen, Yun Li, Duo Qi, Jiankun Wu, Mingxuan Chen and Weihua Cao
Remote Sens. 2021, 13(23), 4956; https://doi.org/10.3390/rs13234956 - 06 Dec 2021
Cited by 1 | Viewed by 2140
Abstract
Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. [...] Read more.
Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region. Full article
(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology)
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16 pages, 2421 KiB  
Article
Quantifying Crown Morphology of Mixed Pine-Oak Forests Using Terrestrial Laser Scanning
by Sara Uzquiano, Ignacio Barbeito, Roberto San Martín, Martin Ehbrecht, Dominik Seidel and Felipe Bravo
Remote Sens. 2021, 13(23), 4955; https://doi.org/10.3390/rs13234955 - 06 Dec 2021
Cited by 11 | Viewed by 3444
Abstract
Mixed forests make up the majority of natural forests, and they are conducive to improving the resilience and resistance of forest ecosystems. Moreover, it is in the crown of the trees where the effect of inter- and intra-specific interaction between them is evident. [...] Read more.
Mixed forests make up the majority of natural forests, and they are conducive to improving the resilience and resistance of forest ecosystems. Moreover, it is in the crown of the trees where the effect of inter- and intra-specific interaction between them is evident. However, our knowledge of changes in crown morphology caused by density, competition, and mixture of specific species is still limited. Here, we provide insight on stand structural complexity based on the study of four response crown variables (Maximum Crown Width Height, MCWH; Crown Base Height, CBH; Crown Volume, CV; and Crown Projection Area, CPA) derived from multiple terrestrial laser scans. Data were obtained from six permanent plots in Northern Spain comprising of two widespread species across Europe; Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea (Matt.) Liebl.). A total of 193 pines and 256 oaks were extracted from the point cloud. Correlation test were conducted (ρ ≥ 0.9) and finally eleven independent variables for each target tree were calculated and categorized into size, density, competition and mixture, which was included as a continuous variable. Linear and non-linear multiple regressions were used to fit models to the four crown variables and the best models were selected according to the lowest AIC Index and biological sense. Our results provide evidence for species plasticity to diverse neighborhoods and show complementarity between pines and oaks in mixtures, where pines have higher MCWH and CBH than oaks but lower CV and CPA, contrary to oaks. The species complementarity in crown variables confirm that mixtures can be used to increase above ground structural diversity. Full article
(This article belongs to the Special Issue Terrestrial Laser Scanning of Forest Structure)
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23 pages, 91752 KiB  
Article
An Inter-Subband Processing Algorithm for Complex Clutter Suppression in Passive Bistatic Radar
by Luo Zuo, Jun Wang, Jinxin Sui and Nan Li
Remote Sens. 2021, 13(23), 4954; https://doi.org/10.3390/rs13234954 - 06 Dec 2021
Cited by 4 | Viewed by 2573
Abstract
Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. [...] Read more.
Clutter suppression is a challenging problem for passive bistatic radar systems, given the complexity of actual clutter scenarios (stationary, time-varying and fractional-order clutter). Such complex clutter induces intense sidelobes in the entire range-Doppler plane and thus degrades target-detection performance, especially for low-observable targets. In this paper, a novel method, denominated as the batch version of the extensive cancellation algorithm (ECA) in the frequency domain (ECA-FB), is presented for the first time, to suppress stationary clutter and its sidelobes. Specifically, in this method, the received signal is first divided into short batches in the frequency domain to coarsen the range resolution, and then the clutter is removed over each batch via ECA. Further, to suppress the time-varying clutter, a Doppler-shifted version of ECA-FB (ECA-FBD) is proposed. Compared with the popular ECA and ECA-B methods, the proposed ECA-FB and ECA-FBD obtained superior complex clutter suppression and slow-moving target detection performance with lower computational complexity. A series of simulation and experimental results are provided to demonstrate the validity of the proposed methods. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)
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30 pages, 47593 KiB  
Article
Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of Ips typographus L. Spots in Healthy Trees
by Azadeh Abdollahnejad, Dimitrios Panagiotidis, Peter Surový and Roman Modlinger
Remote Sens. 2021, 13(23), 4953; https://doi.org/10.3390/rs13234953 - 06 Dec 2021
Cited by 8 | Viewed by 2475
Abstract
In the last decade, thousands of hectares of forests have been lost in the Czech Republic, primarily related to European spruce bark beetle (Ips typographus L.), while more than 50% of the remaining Czech forests are in great danger, thus posing severe [...] Read more.
In the last decade, thousands of hectares of forests have been lost in the Czech Republic, primarily related to European spruce bark beetle (Ips typographus L.), while more than 50% of the remaining Czech forests are in great danger, thus posing severe threats to the resilience, stability, and functionality of those forests. The role of remote sensing in monitoring dynamic structural changes caused by pests is essential to understand and sustainably manage these forests. This study hypothesized a possible correlation between tree health status and multisource time series remote sensing data using different processed layers to predict the potential spread of attack by European spruce bark beetle in healthy trees. For this purpose, we used WorldView-2, Pléiades 1B, and SPOT-6 images for the period of April to September from 2018 to 2020; unmanned aerial vehicle (UAV) imagery data were also collected for use as a reference data source. Our results revealed that spectral resolution is crucial for the early detection of infestation. We observed a significant difference in the reflectance of different health statuses, which can lead to the early detection of infestation as much as two years in advance. More specifically, several bands from two different satellites in 2018 perfectly predicted the health status classes from 2020. This method could be used to evaluate health status classes in the early stage of infestation over large forested areas, which would provide a better understanding of the current situation and information for decision making and planning for the future. Full article
(This article belongs to the Section Forest Remote Sensing)
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18 pages, 9846 KiB  
Article
Driving Forces of the Changes in Vegetation Phenology in the Qinghai–Tibet Plateau
by Xigang Liu, Yaning Chen, Zhi Li, Yupeng Li, Qifei Zhang and Mei Zan
Remote Sens. 2021, 13(23), 4952; https://doi.org/10.3390/rs13234952 - 06 Dec 2021
Cited by 25 | Viewed by 3067
Abstract
Phenological change is an emerging hot topic in ecology and climate change research. Existing phenological studies in the Qinghai–Tibet Plateau (QTP) have focused on overall changes, while ignoring the different characteristics of changes in different regions. Here, we use the Global Inventory Modeling [...] Read more.
Phenological change is an emerging hot topic in ecology and climate change research. Existing phenological studies in the Qinghai–Tibet Plateau (QTP) have focused on overall changes, while ignoring the different characteristics of changes in different regions. Here, we use the Global Inventory Modeling and Mapping Studies (GIMMS3g) normalized difference vegetation index (NDVI) dataset as a basis to discuss the temporal and spatial changes in vegetation phenology in the Qinghai–Tibet Plateau from 1982 to 2015. We also analyze the response mechanisms of pre-season climate factor and vegetation phenology and reveal the driving forces of the changes in vegetation phenology. The results show that: (1) the start of the growing season (SOS) and the length of the growing season (LOS) in the QTP fluctuate greatly year by year; (2) in the study area, the change in pre-season precipitation significantly affects the SOS in the northeast (p < 0.05), while, the delay in the end of the growing season (EOS) in the northeast is determined by pre-season air temperature and precipitation; (3) pre-season precipitation in April or May is the main driving force of the SOS of different vegetation, while air temperature and precipitation in the pre-season jointly affect the EOS of different vegetation. The differences in and the diversity of vegetation types together lead to complex changes in vegetation phenology across different regions within the QTP. Therefore, addressing the characteristics and impacts of changes in vegetation phenology across different regions plays an important role in ecological protection in this region. Full article
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15 pages, 3864 KiB  
Article
Modeling Phenols, Anthocyanins and Color Intensity of Wine Using Pre-Harvest Sentinel-2 Images
by Sandra N. Fredes, Luis Á. Ruiz and Jorge A. Recio
Remote Sens. 2021, 13(23), 4951; https://doi.org/10.3390/rs13234951 - 06 Dec 2021
Cited by 4 | Viewed by 1977
Abstract
The inclusion of technological innovation and the development of remote sensing tools in wine production are an efficient and productive factor that supports the production and improves the quality of the wine produced. In this study we explored models based on Sentinel-2 image [...] Read more.
The inclusion of technological innovation and the development of remote sensing tools in wine production are an efficient and productive factor that supports the production and improves the quality of the wine produced. In this study we explored models based on Sentinel-2 image bands and spectral indices to estimate key wine quality variables, such as phenols (TP), anthocyanins (TA) and color intensity (CI), providing different sensory characteristics of wine. Two Cabernet Sauvignon wine harvest seasons were studied, 2017 and 2018, and models with coefficients of determination (R2) higher than 60% were obtained for color intensity and total anthocyanins during the first season, both in a period very close to harvest during the first days of April, so the high periodicity of Sentinel 2 becomes strategic. In addition, homogeneous sectors can be identified in the plots for selective harvesting and thus the winery space can be programmed appropriately. These results suggest further work on the number of samples in order to transform it into a useful tool with the potential to define a differentiated harvest and estimate the accumulation of phenolic compounds and the intensity of wine color, key elements in the final quality of the wine. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Precision Agriculture and Viticulture)
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14 pages, 4197 KiB  
Article
Combination of Models to Generate the First PAR Maps for Spain
by Francisco Ferrera-Cobos, Jose M. Vindel, Ousmane Wane, Ana A. Navarro, Luis F. Zarzalejo and Rita X. Valenzuela
Remote Sens. 2021, 13(23), 4950; https://doi.org/10.3390/rs13234950 - 06 Dec 2021
Cited by 1 | Viewed by 1846
Abstract
This work addresses the development of a PAR model in the entire territory of mainland Spain. Thus, a specific model is developed for each location of the study field. The new PAR model consists of a combination of the estimates of two previous [...] Read more.
This work addresses the development of a PAR model in the entire territory of mainland Spain. Thus, a specific model is developed for each location of the study field. The new PAR model consists of a combination of the estimates of two previous models that had unequal performances in different climates. In fact, one of them showed better results with Mediterranean climate, whereas the other obtained better results under oceanic climate. Interestingly, the new PAR model showed similar performance when validated at seven stations in mainland Spain with Mediterranean or oceanic climate. Furthermore, all validation slopes ranged from 0.99 to 1.00; the intercepts were less than 3.70 μmol m−2 s−1; the R2 were greater than 0.988, while MBE was closer to zero percent than −0.39%; and RMSE were less than 6.21%. The estimates of the PAR model introduced in this work were then used to develop PAR maps over mainland Spain that represent daily PAR averages of each month and a full year at all locations in the study field. Full article
(This article belongs to the Special Issue AI Datasets, Tools, and Specifications for Earth Observation Applications)
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17 pages, 10285 KiB  
Article
Mapping Flood Extent and Frequency from Sentinel-1 Imagery during the Extremely Warm Winter of 2020 in Boreal Floodplains and Forests
by Liis Sipelgas, Age Aavaste and Rivo Uiboupin
Remote Sens. 2021, 13(23), 4949; https://doi.org/10.3390/rs13234949 - 06 Dec 2021
Cited by 4 | Viewed by 2459
Abstract
The current study presents a methodology for water mapping from Sentinel-1 (S1) data and a flood extent analysis of the three largest floodplains in Estonia. The automatic processing scheme of S1 data was set up for the mapping of open-water flooding (OWF) and [...] Read more.
The current study presents a methodology for water mapping from Sentinel-1 (S1) data and a flood extent analysis of the three largest floodplains in Estonia. The automatic processing scheme of S1 data was set up for the mapping of open-water flooding (OWF) and flooding under vegetation (FUV). The extremely mild winter of 2019/2020 resulted in several large floods at floodplains that were detected from S1 imagery with a maximal OWF extent up to 5000 ha and maximal FUV extent up to 4500 ha. A significant correlation (r2 > 0.6) between the OWF extent and the closest gauge data was obtained for inland riverbank floodplains. The outcome enabled us to define the water level at which the water exceeds the shoreline and flooding starts. However, for a coastal river delta floodplain, a lower correlation (r2 < 0.34) with gauge data was obtained, and the excess of river coastline could not be related to a certain water level. At inland riverbank floodplains, the extent of FUV was three times larger compared to that of OWF. The correlation between the water level and FUV was <0.51, indicating that the river water level at these test sites can be used as a proxy for forest floods. Relating conventional gauge data to S1 time series data contributes to flood risk mitigation. Full article
(This article belongs to the Topic Water Management in the Era of Climatic Change)
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14 pages, 6800 KiB  
Article
Detecting 2020 Coral Bleaching Event in the Northwest Hainan Island Using CoralTemp SST and Sentinel-2B MSI Imagery
by Bailu Liu, Lei Guan and Hong Chen
Remote Sens. 2021, 13(23), 4948; https://doi.org/10.3390/rs13234948 - 06 Dec 2021
Cited by 11 | Viewed by 2843
Abstract
In recent years, coral reef ecosystems have been affected by global climate change and human factors, resulting in frequent coral bleaching events. A severe coral bleaching event occurred in the northwest of Hainan Island, South China Sea, in 2020. In this study, we [...] Read more.
In recent years, coral reef ecosystems have been affected by global climate change and human factors, resulting in frequent coral bleaching events. A severe coral bleaching event occurred in the northwest of Hainan Island, South China Sea, in 2020. In this study, we used the CoralTemp sea surface temperature (SST) and Sentinel-2B imagery to detect the coral bleaching event. From 31 May to 3 October, the average SST of the study area was 31.01 °C, which is higher than the local bleaching warning threshold value of 30.33 °C. In the difference images of 26 July and 4 September, a wide range of coral bleaching was found. According to the temporal variation in single band reflectance, the development process of bleaching is consistent with the changes in coral bleaching thermal alerts. The results show that the thermal stress level is an effective parameter for early warning of large-scale coral bleaching. High-resolution difference images can be used to detect the extent of coral bleaching. The combination of the two methods can provide better support for coral protection and research. Full article
(This article belongs to the Special Issue Advances of Remote Sensing on North Pacific Ecosystems, the Equatorial Pacific Band, and Adjacent Seas)
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20 pages, 19058 KiB  
Article
A Supplementary Module to Improve Accuracy of the Quality Assessment Band in Landsat Cloud Images
by Ruyin Cao, Yan Feng, Jin Chen and Ji Zhou
Remote Sens. 2021, 13(23), 4947; https://doi.org/10.3390/rs13234947 - 06 Dec 2021
Cited by 3 | Viewed by 1696
Abstract
Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested [...] Read more.
Cloud contamination is a serious obstacle for the application of Landsat data. To popularize the applications of Landsat data, each Landsat image includes the corresponding Quality Assessment (QA) band, in which cloud and cloud shadow pixels have been flagged. However, previous studies suggested that Landsat QA band still needs to be modified to fulfill the requirement of Landsat data applications. In this study, we developed a Supplementary Module to improve the original QA band (called QA_SM). On one hand, QA_SM extracts spectral and geometrical features in the target Landsat cloud image from the original QA band. On the other, QA_SM incorporates the temporal change characteristics of clouds and cloud shadows between the target and reference images. We tested the new method at four local sites with different land covers and the Landsat-8 cloud cover validation dataset (“L8_Biome”). The experimental results show that QA_SM performs better than the original QA band and the multi-temporal method ATSA (Automatic Time-Series Analyses). QA_SM decreases omission errors of clouds and shadows in the original QA band effectively but meanwhile does not increase commission errors. Besides, the better performance of QA_SM is less affected by the selections of reference images because QA_SM considers the temporal change of land surface reflectance that is not caused by cloud contamination. By further designing a quantitative assessment experiment, we found that the QA band generated by QA_SM improves cloud-removal performance on Landsat cloud images, suggesting the benefits of the new method to advance the applications of Landsat data. Full article
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17 pages, 6603 KiB  
Communication
The Effects of Climate and Bioclimate on COVID-19 Cases in Poland
by Piotr A. Werner, Oleh Skrynyk, Mariusz Porczek, Urszula Szczepankowska-Bednarek, Robert Olszewski and Małgorzta Kęsik-Brodacka
Remote Sens. 2021, 13(23), 4946; https://doi.org/10.3390/rs13234946 - 05 Dec 2021
Cited by 7 | Viewed by 2381
Abstract
The correlations between air temperatures, relative and absolute humidity, wind, cloudiness, precipitation and number of influenza cases have been extensively studied in the past. Because, initially, COVID-19 cases were similar to influenza cases, researchers were prompted to look for similar relationships. The aim [...] Read more.
The correlations between air temperatures, relative and absolute humidity, wind, cloudiness, precipitation and number of influenza cases have been extensively studied in the past. Because, initially, COVID-19 cases were similar to influenza cases, researchers were prompted to look for similar relationships. The aim of the study is to identify the effects of changes in air temperature on the number of COVID-19 infections in Poland. The hypothesis under consideration concerns an increase in the number of COVID-19 cases as temperature decreases. The spatial heterogeneity of the relationship under study during the first year and a half of the COVID-19 pandemic in Polish counties is thus revealed. Full article
(This article belongs to the Special Issue Advances to GIS for Sensing of Earth and Human Interaction)
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26 pages, 8403 KiB  
Article
Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets
by Jun Liu, Jiyan Wang, Junnan Xiong, Weiming Cheng, Huaizhang Sun, Zhiwei Yong and Nan Wang
Remote Sens. 2021, 13(23), 4945; https://doi.org/10.3390/rs13234945 - 05 Dec 2021
Cited by 26 | Viewed by 2887
Abstract
Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These [...] Read more.
Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies. Full article
(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment)
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18 pages, 6872 KiB  
Article
Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks
by Tahisa Neitzel Kuck, Paulo Fernando Ferreira Silva Filho, Edson Eyji Sano, Polyanna da Conceição Bispo, Elcio Hideiti Shiguemori and Ricardo Dalagnol
Remote Sens. 2021, 13(23), 4944; https://doi.org/10.3390/rs13234944 - 05 Dec 2021
Cited by 4 | Viewed by 4534
Abstract
It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due [...] Read more.
It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques. Full article
(This article belongs to the Special Issue Remote Sensing in the Amazon Biome)
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22 pages, 7807 KiB  
Article
Analysis of Hypersonic Platform-Borne SAR Imaging: A Physical Perspective
by Lihao Song, Bowen Bai, Xiaoping Li, Gezhao Niu, Yanming Liu, Liang Zhao and Hui Zhou
Remote Sens. 2021, 13(23), 4943; https://doi.org/10.3390/rs13234943 - 05 Dec 2021
Cited by 4 | Viewed by 1854
Abstract
The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization [...] Read more.
The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future. Full article
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27 pages, 53026 KiB  
Article
A Novel 4D Track-before-Detect Approach for Weak Targets Detection in Clutter Regions
by Bo Yan, Hua Zhang, Luping Xu, Yu Chen and Hongmin Lu
Remote Sens. 2021, 13(23), 4942; https://doi.org/10.3390/rs13234942 - 05 Dec 2021
Cited by 3 | Viewed by 1777
Abstract
A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed [...] Read more.
A 4D TBD approach is developed here for closely weak extended target tracking and overcoming heterogeneous clutter background and various clutter regions. The 4D measurements in this work are the points containing three positional information in spatial space and corresponding timestamp. The proposed method is mainly designed to address two issues. The first one is the dilemma between the weak target detection and difficult computation originating from the high dimensions of measurement. The second issue is the suppression of inhomogeneous background clutter and various clutter regions. The extension experiment using synthetic data showcases that no false alarm track would be built in the clutter regions, and the detection rate of close targets exceeds 94%. The experiments using real 3D radar also prove that the method works well in tracking closely maneuvering extended targets even if a clutter region exists. Full article
(This article belongs to the Special Issue Radar Signal Processing for Target Tracking)
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18 pages, 1717 KiB  
Article
Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection
by Rukhshanda Hussain, Yash Karbhari, Muhammad Fazal Ijaz, Marcin Woźniak, Pawan Kumar Singh and Ram Sarkar
Remote Sens. 2021, 13(23), 4941; https://doi.org/10.3390/rs13234941 - 05 Dec 2021
Cited by 33 | Viewed by 3056
Abstract
Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge [...] Read more.
Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures. Full article
(This article belongs to the Special Issue Convolutional Neural Networks for Object Detection)
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16 pages, 33164 KiB  
Article
Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires
by Taehee Kim, Suyeon Hwang and Jinmu Choi
Remote Sens. 2021, 13(23), 4940; https://doi.org/10.3390/rs13234940 - 04 Dec 2021
Cited by 7 | Viewed by 2243
Abstract
The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire [...] Read more.
The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness. Full article
(This article belongs to the Special Issue Disaster Monitoring Using Remote Sensing)
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22 pages, 85039 KiB  
Article
A Vehicle-Borne Mobile Mapping System Based Framework for Semantic Segmentation and Modeling on Overhead Catenary System Using Deep Learning
by Lei Xu, Shunyi Zheng, Jiaming Na, Yuanwei Yang, Chunlin Mu and Debin Shi
Remote Sens. 2021, 13(23), 4939; https://doi.org/10.3390/rs13234939 - 04 Dec 2021
Cited by 4 | Viewed by 2611
Abstract
Overhead catenary system (OCS) automatic detection is of important significance for the safe operation and maintenance of electrified railways. The vehicle-borne mobile mapping system (VMMS) may significantly improve the data acquisition. This paper proposes a VMMS-based framework to realize the automatic detection and [...] Read more.
Overhead catenary system (OCS) automatic detection is of important significance for the safe operation and maintenance of electrified railways. The vehicle-borne mobile mapping system (VMMS) may significantly improve the data acquisition. This paper proposes a VMMS-based framework to realize the automatic detection and modelling of OCS. The proposed framework performed semantic segmentation, model reconstruction and geometric parameters detection based on LiDAR point cloud using VMMS. Firstly, an enhanced VMMS is designed for accurate data generation. Secondly, an automatic searching method based on a two-level stereo frame is designed to filter the irrelevant non-OCS point cloud. Then, a deep learning network based on multi-scale feature fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary facility. Finally, the 3D modelling is performed based on the OCS segmentation result, and geometric parameters are then extracted. The experimental case study was conducted on a 100 km high-speed railway in Guangxi, China. The experimental results show that the proposed framework has a better accuracy of 96.37%, outperforming other state-of-art methods for segmentation. Compared with traditional manual laser measurement, the proposed framework can achieve a trustable accuracy within 10 mm for OCS geometric parameter detection. Full article
(This article belongs to the Special Issue Advances in Deep Learning Based 3D Scene Understanding from LiDAR)
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18 pages, 5208 KiB  
Article
Contribution of Changes in Snow Cover Extent to Shortwave Radiation Perturbations at the Top of the Atmosphere over the Northern Hemisphere during 2000–2019
by Xiaona Chen, Yaping Yang and Cong Yin
Remote Sens. 2021, 13(23), 4938; https://doi.org/10.3390/rs13234938 - 04 Dec 2021
Cited by 2 | Viewed by 1575
Abstract
Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA), results from variations in the terrestrial snow cover extent (SCE), and is critical for the regulation of the Earth’s energy [...] Read more.
Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA), results from variations in the terrestrial snow cover extent (SCE), and is critical for the regulation of the Earth’s energy budget. However, with the growing seasonal divergence of SCE over the Northern Hemisphere (NH) in the past two decades, novel insights pertaining to SnRF are lacking. Consequently, the contribution of SnRF to TOA shortwave radiation anomalies still remains unclear. Utilizing the latest datasets of snow cover, surface albedo, and albedo radiative kernels, this study investigated the distribution of SnRF over the NH and explored its changes from 2000 to 2019. The 20-year averaged annual mean SnRF in the NH was −1.13 ± 0.05 W m−2, with a weakening trend of 0.0047 Wm−2 yr−1 (p < 0.01) during 2000–2019, indicating that an extra 0.094 W m−2 of shortwave radiation was absorbed by the Earth climate system. Moreover, changes in SnRF were highly correlated with satellite-observed TOA shortwave flux anomalies (r = 0.79, p < 0.05) during 2000–2019. Additionally, a detailed contribution analysis revealed that the SnRF in snow accumulation months, from March to May, accounted for 58.10% of the annual mean SnRF variability across the NH. These results can assist in providing a better understanding of the role of snow cover in Earth’s climate system in the context of climate change. Although the rapid SCE decline over the NH has a hiatus for the period during 2000–2019, SnRF continues to follow a weakening trend. Therefore, this should be taken into consideration in current climate change models and future climate projections. Full article
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20 pages, 9456 KiB  
Article
GNSS Imaging of Strain Rate Changes and Vertical Crustal Motions over the Tibetan Plateau
by Yunfei Xiang, Hao Wang, Yuanyuan Chen and Yin Xing
Remote Sens. 2021, 13(23), 4937; https://doi.org/10.3390/rs13234937 - 04 Dec 2021
Cited by 2 | Viewed by 2178
Abstract
In this paper, we perform a comprehensive analysis of contemporary three-dimensional crustal deformations over the Tibetan Plateau. Considering that the coverage of continuous GNSS sites in the Tibetan Plateau is sparse, a newly designed method that mainly contains Spatial Structure Function (SSF) construction [...] Read more.
In this paper, we perform a comprehensive analysis of contemporary three-dimensional crustal deformations over the Tibetan Plateau. Considering that the coverage of continuous GNSS sites in the Tibetan Plateau is sparse, a newly designed method that mainly contains Spatial Structure Function (SSF) construction and Median Spatial Filtering (MSF) is adopted to conduct GNSS imaging of point-wise velocities, which can well reveal the spatial pattern of vertical crustal motions. The result illustrates that the Himalayan belt bordering Nepal appears significant uplift at the rates of ~3.5 mm/yr, while the low-altitude regions of Nepal and Bhutan near the Tibetan Plateau are undergoing subsidence. The result suggests that the subduction of the Indian plate is the driving force of the uplift and subsidence in the Himalayan belt and its adjacent regions. Similarly, the thrusting of the Tarim Basin is the main factor of the slight uplift and subsidence in the Tianshan Mountains and Tarim Basin, respectively. In addition, we estimate the strain rate changes over the Tibetan Plateau using high-resolution GNSS horizontal velocities. The result indicates that the Himalayan belt and southeastern Tibetan Plateau have accumulated a large amount of strain rate due to the Indian-Eurasian plate collision and blockage of the South China block, respectively. Full article
(This article belongs to the Special Issue Modelling Geodetic Time Series and Applications for Earth Science and Environmental Monitoring)
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21 pages, 12185 KiB  
Article
An Improved Fmask Method for Cloud Detection in GF-6 WFV Based on Spectral-Contextual Information
by Xiaomeng Yang, Lin Sun, Xinming Tang, Bo Ai, Hanwen Xu and Zhen Wen
Remote Sens. 2021, 13(23), 4936; https://doi.org/10.3390/rs13234936 - 04 Dec 2021
Cited by 2 | Viewed by 2174
Abstract
GF-6 is the first optical remote sensing satellite for precision agriculture observations in China. Accurate identification of the cloud in GF-6 helps improve data availability. However, due to the narrow band range contained in GF-6, Fmask version 3.2 for Landsat is not suitable [...] Read more.
GF-6 is the first optical remote sensing satellite for precision agriculture observations in China. Accurate identification of the cloud in GF-6 helps improve data availability. However, due to the narrow band range contained in GF-6, Fmask version 3.2 for Landsat is not suitable for GF-6. Hence, this paper proposes an improved Fmask based on the spectral-contextual information to solve the inapplicability of Fmask version 3.2 in GF-6. The improvements are divided into the following six aspects. The shortwave infrared (SWIR) in the “Basic Test” is replaced by blue band. The threshold in the original “HOT Test” is modified based on the comprehensive consideration of fog and thin clouds. The bare soil and rock are detected by the relationship between green and near infrared (NIR) bands. The bright buildings are detected by the relationship between the upper and lower quartiles of blue and red bands. The stratus with high humidity and fog_W (fog over water) are distinguished by the ratio of blue and red edge position 1 bands. Temperature probability for land is replaced by the HOT-based cloud probability (LHOT), and SWIR in brightness probability is replaced by NIR. The average cloud pixels accuracy (TPR) of the improved Fmask is 95.51%. Full article
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26 pages, 4222 KiB  
Article
Recent Spatiotemporal Trends in Glacier Snowline Altitude at the End of the Melt Season in the Qilian Mountains, China
by Zhongming Guo, Ninglian Wang, Baoshou Shen, Zhujun Gu, Yuwei Wu and Anan Chen
Remote Sens. 2021, 13(23), 4935; https://doi.org/10.3390/rs13234935 - 04 Dec 2021
Cited by 9 | Viewed by 1982
Abstract
Glaciers in the Qilian Mountains, China, play an important role in supplying freshwater to downstream populations, maintaining ecological balance, and supporting economic development on the Tibetan Plateau. Glacier snowline altitude (SLA) at the end of the melt season is an indicator of the [...] Read more.
Glaciers in the Qilian Mountains, China, play an important role in supplying freshwater to downstream populations, maintaining ecological balance, and supporting economic development on the Tibetan Plateau. Glacier snowline altitude (SLA) at the end of the melt season is an indicator of the Equilibrium line altitude (ELA), and can be used to estimate the mass balance and climate reconstruction. Here, we employ the height zone-area method to determine the SLA at the end of the melt season during the 1989–2018 period using Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) SLA and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data. The accuracy of glacier SLA obtained in 1989–2018 after adding MODIS SLA data to the years without Landsat data increased by about 78 m. The difference between the remote-sensing-derived SLA and measured equilibrium line altitude (ELA) is mostly within 50 m, suggesting that the SLA can serve as a proxy for the ELA at the end of the melt season. The SLA of Qiyi Glacier in the Qilian Mountains rose from 4690 ± 25 m to 5030 ± 25 m, with an average of 4900 ± 103 m during the 30 year study period. The western, central, eastern sections and the whole range of the Qilian Mountains exhibited an upward trend in SLA during the 30 year study period. The mean glacier SLAs were 4923 ± 137 m, 4864 ± 135 m, 4550 ± 149 m and 4779 ± 149 m for the western, central, eastern sections and the whole range, respectively. From the perspective of spatial distribution, regardless of the different orientation, grid scale and basin scale, the glacier SLA of Qilian Mountains showed an upward trend from 1989 to 2018, and the glacier SLA is in general located at a comparably higher altitude in the southern and western parts of the Qilian Mountains while it is located at a comparably lower altitude in its northern and eastern parts. In an ideal condition, climate sensitivity studies of ELA in Qilian Mountains show that if the summer mean temperature increases (decreases) by 1 °C, then ELA will increase (decrease) by about 102 m. If the annual total solid precipitation increases (decreases) by 10%, then the glacier ELA will decrease (rise) by about 6 m. The summer mean temperature is the main factor affecting the temporal trend of SLA, whereas both summer mean temperature and annual total precipitation influence the spatial change of SLA. Full article
(This article belongs to the Special Issue Advances of Remote Sensing in the Analysis of the Spatial and Temporal Variability of Land Surface)
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21 pages, 9001 KiB  
Article
Managing Flood Hazard in a Complex Cross-Border Region Using Sentinel-1 SAR and Sentinel-2 Optical Data: A Case Study from Prut River Basin (NE Romania)
by Cătălin I. Cîmpianu, Alin Mihu-Pintilie, Cristian C. Stoleriu, Andrei Urzică and Elena Huţanu
Remote Sens. 2021, 13(23), 4934; https://doi.org/10.3390/rs13234934 - 04 Dec 2021
Cited by 3 | Viewed by 2261
Abstract
In this study, an alternative solution for flood risk management in complex cross-border regions is presented. In these cases, due to different flood risk management legislative approaches, there is a lack of joint cooperation between the involved countries. As a main consequence, LiDAR-derived [...] Read more.
In this study, an alternative solution for flood risk management in complex cross-border regions is presented. In these cases, due to different flood risk management legislative approaches, there is a lack of joint cooperation between the involved countries. As a main consequence, LiDAR-derived digital elevation models and accurate flood hazard maps obtained by means of hydrological and hydraulic modeling are missing or are incomplete. This is also the case for the Prut River, which acts as a natural boundary between European Union (EU) member Romania and non-EU countries Ukraine and Republic of Moldova. Here, flood hazard maps were developed under the European Floods Directive (2007/60/EC) only for the Romanian territory and only for the 1% exceeding probability (respectively floods that can occur once every 100 years). For this reason, in order to improve the flood hazard management in the area and consider all cross-border territories, a fully remote sensing approach was considered. Using open-source SAR Sentinel-1 and Sentinel-2 data characterized by an improved temporal resolution, we managed to capture the maximum spatial extent of a flood event that took place in the aforementioned river sector (middle Prut River course) during the 24 and 27 June 2020. Moreover, by means of flood frequency analysis, the development of a transboundary flood hazard map with an assigned probability, specific to the maximum flow rate recorded during the event, was realized. Full article
(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment)
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15 pages, 6424 KiB  
Article
A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018
by Caixia Liu, Huabing Huang and Fangdi Sun
Remote Sens. 2021, 13(23), 4933; https://doi.org/10.3390/rs13234933 - 04 Dec 2021
Cited by 14 | Viewed by 2784
Abstract
As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies [...] Read more.
As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p < 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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18 pages, 5817 KiB  
Article
De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition
by Rui Zhou, Jiangtao Han, Zhenyu Guo and Tonglin Li
Remote Sens. 2021, 13(23), 4932; https://doi.org/10.3390/rs13234932 - 04 Dec 2021
Cited by 9 | Viewed by 1765
Abstract
Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, [...] Read more.
Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 6147 KiB  
Article
A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau
by Jiaxin Cai, Xiaowen Wang, Guoxiang Liu and Bing Yu
Remote Sens. 2021, 13(23), 4931; https://doi.org/10.3390/rs13234931 - 04 Dec 2021
Cited by 5 | Viewed by 2999
Abstract
Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, [...] Read more.
Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates. Full article
(This article belongs to the Special Issue Advances in SAR Image Processing and Applications)
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30 pages, 23106 KiB  
Article
Automatic Extraction of Indoor Structural Information from Point Clouds
by Dongyang Cheng, Junchao Zhang, Dangjun Zhao, Jianlai Chen and Di Tian
Remote Sens. 2021, 13(23), 4930; https://doi.org/10.3390/rs13234930 - 04 Dec 2021
Cited by 6 | Viewed by 2062
Abstract
We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground [...] Read more.
We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments. Full article
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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22 pages, 14966 KiB  
Article
On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula
by Amin Rahimi Dalkhani, Xin Zhang and Cornelis Weemstra
Remote Sens. 2021, 13(23), 4929; https://doi.org/10.3390/rs13234929 - 04 Dec 2021
Cited by 1 | Viewed by 2010
Abstract
Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been [...] Read more.
Seismic travel time tomography using surface waves is an effective tool for three-dimensional crustal imaging. Historically, these surface waves are the result of active seismic sources or earthquakes. More recently, however, surface waves retrieved through the application of seismic interferometry have also been exploited. Conventionally, two-step inversion algorithms are employed to solve the tomographic inverse problem. That is, a first inversion results in frequency-dependent, two-dimensional maps of phase velocity, which then serve as input for a series of independent, one-dimensional frequency-to-depth inversions. As such, a set of localized depth-dependent velocity profiles are obtained at the surface points. Stitching these separate profiles together subsequently yields a three-dimensional velocity model. Relatively recently, a one-step three-dimensional non-linear tomographic algorithm has been proposed. The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography. Specifically, the three-dimensional velocity field is parameterized by means of a polyhedral Voronoi tessellation. In this study, we investigate the potential of this algorithm for the purpose of recovering the three-dimensional surface-wave-velocity structure from ambient noise recorded on and around the Reykjanes Peninsula, southwest Iceland. To that end, we design a number of synthetic tests that take into account the station configuration of the Reykjanes seismic network. We find that the algorithm is able to recover the 3D velocity structure at various scales in areas where station density is high. In addition, we find that the standard deviation of the recovered velocities is low in those regions. At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations. This implies that the algorithm successfully adapts model resolution to the density of rays. It also adapts model resolution to the amount of noise in the travel times. Because the algorithm is computationally demanding, we modify the algorithm such that computational costs are reduced while sufficiently preserving non-linearity. We conclude that the algorithm can now be applied adequately to travel times extracted from station–station cross correlations by the Reykjanes seismic network. Full article
(This article belongs to the Special Issue Advances in Seismic Interferometry)
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17 pages, 2685 KiB  
Article
Three-Dimensional Urban Land Cover Classification by Prior-Level Fusion of LiDAR Point Cloud and Optical Imagery
by Yanming Chen, Xiaoqiang Liu, Yijia Xiao, Qiqi Zhao and Sida Wan
Remote Sens. 2021, 13(23), 4928; https://doi.org/10.3390/rs13234928 - 04 Dec 2021
Cited by 5 | Viewed by 2233
Abstract
The heterogeneity of urban landscape in the vertical direction should not be neglected in urban ecology research, which requires urban land cover product transformation from two-dimensions to three-dimensions using light detection and ranging system (LiDAR) point clouds. Previous studies have demonstrated that the [...] Read more.
The heterogeneity of urban landscape in the vertical direction should not be neglected in urban ecology research, which requires urban land cover product transformation from two-dimensions to three-dimensions using light detection and ranging system (LiDAR) point clouds. Previous studies have demonstrated that the performance of two-dimensional land cover classification can be improved by fusing optical imagery and LiDAR data using several strategies. However, few studies have focused on the fusion of LiDAR point clouds and optical imagery for three-dimensional land cover classification, especially using a deep learning framework. In this study, we proposed a novel prior-level fusion strategy and compared it with the no-fusion strategy (baseline) and three other commonly used fusion strategies (point-level, feature-level, and decision-level). The proposed prior-level fusion strategy uses two-dimensional land cover derived from optical imagery as the prior knowledge for three-dimensional classification. Then, a LiDAR point cloud is linked to the prior information using the nearest neighbor method and classified by a deep neural network. Our proposed prior-fusion strategy has higher overall accuracy (82.47%) on data from the International Society for Photogrammetry and Remote Sensing, compared with the baseline (74.62%), point-level (79.86%), feature-level (76.22%), and decision-level (81.12%). The improved accuracy reflects two features: (1) fusing optical imagery to LiDAR point clouds improves the performance of three-dimensional urban land cover classification, and (2) the proposed prior-level strategy directly uses semantic information provided by the two-dimensional land cover classification rather than the original spectral information of optical imagery. Furthermore, the proposed prior-level fusion strategy provides a series that fills the gap between two- and three-dimensional land cover classification. Full article
(This article belongs to the Special Issue Advances in Deep Learning Based 3D Scene Understanding from LiDAR)
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20 pages, 1697 KiB  
Article
Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images
by Zhao Wang, Fenlong Jiang, Tongfei Liu, Fei Xie and Peng Li
Remote Sens. 2021, 13(23), 4927; https://doi.org/10.3390/rs13234927 - 04 Dec 2021
Cited by 11 | Viewed by 2859
Abstract
Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global [...] Read more.
Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Data Exploitation)
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