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Remote Sens., Volume 13, Issue 16 (August-2 2021) – 287 articles

Cover Story (view full-size image): Management of fertilizers is an important agricultural practice and field of research to minimize environmental impacts and cost of production. Applying fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. In this paper, unmanned aerial vehicle multispectral imagery, vegetation indices, crop height, field topographic metrics, and soil properties were combined in machine learning models to predict canopy nitrogen (N). Compared to common modeling using only spectral variables, the inclusion of crop and environmental parameters improved N prediction allowing for more effective and efficient N fertilizer applications. The cover image was taken during ground data collection in the corn field, highlighting both the contrast and combination of nature and technology in agriculture today. View this paper.
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Article
DV-LOAM: Direct Visual LiDAR Odometry and Mapping
Remote Sens. 2021, 13(16), 3340; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163340 - 23 Aug 2021
Cited by 2 | Viewed by 919
Abstract
Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a [...] Read more.
Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods. Full article
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Article
Urban Growth Derived from Landsat Time Series Using Harmonic Analysis: A Case Study in South England with High Levels of Cloud Cover
Remote Sens. 2021, 13(16), 3339; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163339 - 23 Aug 2021
Cited by 2 | Viewed by 556
Abstract
Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can [...] Read more.
Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period. Full article
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Article
Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction
Remote Sens. 2021, 13(16), 3338; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163338 - 23 Aug 2021
Viewed by 616
Abstract
With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional [...] Read more.
With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Urban Applications)
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Article
Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework
Remote Sens. 2021, 13(16), 3337; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163337 - 23 Aug 2021
Cited by 1 | Viewed by 659
Abstract
Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides [...] Read more.
Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies. Full article
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Article
Dual-Task Semantic Change Detection for Remote Sensing Images Using the Generative Change Field Module
Remote Sens. 2021, 13(16), 3336; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163336 - 23 Aug 2021
Viewed by 565
Abstract
With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the [...] Read more.
With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances. Full article
(This article belongs to the Special Issue 3D City Modelling and Change Detection Using Remote Sensing Data)
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Article
Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks
Remote Sens. 2021, 13(16), 3335; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163335 - 23 Aug 2021
Cited by 1 | Viewed by 445
Abstract
This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm [...] Read more.
This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River. Full article
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Article
Diurnal Cycle Model of Lake Ice Surface Albedo: A Case Study of Wuliangsuhai Lake
Remote Sens. 2021, 13(16), 3334; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163334 - 23 Aug 2021
Viewed by 423
Abstract
Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid [...] Read more.
Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions—Laplace, Gauss, Gumbel, and Cauchy—were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5° indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions. Full article
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Article
Interseismic Slip and Coupling along the Haiyuan Fault Zone Constrained by InSAR and GPS Measurements
Remote Sens. 2021, 13(16), 3333; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163333 - 23 Aug 2021
Viewed by 541
Abstract
The Haiyuan fault zone is an important tectonic boundary and strong seismic activity belt in northeastern Tibet, but no major earthquake has occurred in the past ∼100 years, since the Haiyuan M8.5 event in 1920. The current state of strain accumulation and seismic [...] Read more.
The Haiyuan fault zone is an important tectonic boundary and strong seismic activity belt in northeastern Tibet, but no major earthquake has occurred in the past ∼100 years, since the Haiyuan M8.5 event in 1920. The current state of strain accumulation and seismic potential along the fault zone have attracted significant attention. In this study, we obtained the interseismic deformation field along the Haiyuan fault zone using Envisat/ASAR data in the period 2003–2010, and inverted fault kinematic parameters including the long-term slip rate, locking degree and slip deficit distribution based on InSAR and GPS individually and jointly. The results show that there is near-surface creep in the Laohushan segment of about 19 km. The locking degree changes significantly along the strike with the western part reaching 17 km and the eastern part of 3–7 km. The long-term slip rate gradually decreases from west 4.7 mm/yr to east 2.0 mm/yr. As such, there is large strain accumulation along the western part of the fault and shallow creep along the Laohushan segment; while in the eastern section, the degree of strain accumulation is low, which suggests the rupture segments of the 1920 earthquake may have been not completely relocked. Full article
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Article
Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning
Remote Sens. 2021, 13(16), 3332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163332 - 23 Aug 2021
Viewed by 548
Abstract
The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational [...] Read more.
The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades’ estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories’ distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60–0.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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Article
Understanding Spatio-Temporal Patterns of Land Use/Land Cover Change under Urbanization in Wuhan, China, 2000–2019
Remote Sens. 2021, 13(16), 3331; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163331 - 23 Aug 2021
Viewed by 616
Abstract
Exploring land use structure and dynamics is critical for urban planning and management. This study attempts to understand the Wuhan development mode since the beginning of the 21st century by profoundly investigating the spatio-temporal patterns of land use/land cover (LULC) change under urbanization [...] Read more.
Exploring land use structure and dynamics is critical for urban planning and management. This study attempts to understand the Wuhan development mode since the beginning of the 21st century by profoundly investigating the spatio-temporal patterns of land use/land cover (LULC) change under urbanization in Wuhan, China, from 2000 to 2019, based on continuous time series mapping using Landsat observations with a support vector machine. The results indicated rapid urbanization, with large LULC changes triggered. The built-up area increased by 982.66 km2 (228%) at the expense of a reduction of 717.14 km2 (12%) for cropland, which threatens food security to some degree. In addition, the natural habitat shrank to some extent, with reductions of 182.52 km2, 23.92 km2 and 64.95 km2 for water, forest and grassland, respectively. Generally, Wuhan experienced a typical urbanization course that first sped up, then slowed down and then accelerated again, with an obvious internal imbalance between the 13 administrative districts. Hanyang, Hongshan and Dongxihu specifically presented more significant land dynamicity, with Hanyang being the active center. Over the past 19 years, Wuhan mainly developed toward the east and south, with the urban gravity center transferred from the northwest to the southeast of Jiang’an district. Lastly, based on the predicted land allocation of Wuhan in 2029 by the patch-generating land use simulation (PLUS) model, the future landscape dynamic pattern was further explored, and the result shows a rise in the northern suburbs, which provides meaningful guidance for urban planners and managers to promote urban sustainability. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
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Article
Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations
Remote Sens. 2021, 13(16), 3330; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163330 - 23 Aug 2021
Viewed by 1037
Abstract
Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance [...] Read more.
Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16–13) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage. Full article
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Article
High Speed Maneuvering Platform Squint TOPS SAR Imaging Based on Local Polar Coordinate and Angular Division
Remote Sens. 2021, 13(16), 3329; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163329 - 23 Aug 2021
Viewed by 404
Abstract
This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is [...] Read more.
This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is calculated in local polar format. The Doppler ambiguity is resolved by nonlinear derotation and zero-padding. The recovered signal is divided into several blocks in Doppler according to the angular division. Keystone transform is used to remove the space-variant range cell migration (RCM) components. Thus, the residual RCM terms can be compensated by a unified phase function. Frequency domain perturbation terms are introduced to correct the space-variant Doppler chirp rate term. The focusing parameters are calculated according to the scene center of each angular block and the signal of each block can be processed in parallel. The image of each block is focused in range-Doppler domain. After the geometric correction, the final focused image can be obtained by directly combined the images of all angular blocks. Simulated SAR data has verified the effectiveness of the proposed algorithm. Full article
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Article
A New Multi-Scale Sliding Window LSTM Framework (MSSW-LSTM): A Case Study for GNSS Time-Series Prediction
Remote Sens. 2021, 13(16), 3328; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163328 - 23 Aug 2021
Cited by 1 | Viewed by 534
Abstract
GNSS time-series prediction plays an important role in the monitoring of crustal plate movement, and dam or bridge deformation, and the maintenance of global or regional coordinate frames. Deep learning is a state-of-the-art approach for extracting high-level abstract features from big data without [...] Read more.
GNSS time-series prediction plays an important role in the monitoring of crustal plate movement, and dam or bridge deformation, and the maintenance of global or regional coordinate frames. Deep learning is a state-of-the-art approach for extracting high-level abstract features from big data without any prior knowledge. Moreover, long short-term memory (LSTM) networks are a form of recurrent neural networks that have significant potential for processing time series. In this study, a novel prediction framework was proposed by combining a multi-scale sliding window (MSSW) with LSTM. Specifically, MSSW was applied for data preprocessing to effectively extract the feature relationship at different scales and simultaneously mine the deep characteristics of the dataset. Then, multiple LSTM neural networks were used to predict and obtain the final result by weighting. To verify the performance of MSSW-LSTM, 1000 daily solutions of the XJSS station in the Up component were selected for prediction experiments. Compared with the traditional LSTM method, our results of three groups of controlled experiments showed that the RMSE value was reduced by 2.1%, 23.7%, and 20.1%, and MAE was decreased by 1.6%, 21.1%, and 22.2%, respectively. Our results showed that the MSSW-LSTM algorithm can achieve higher prediction accuracy and smaller error, and can be applied to GNSS time-series prediction. Full article
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Rupture Kinematics and Coseismic Slip Model of the 2021 Mw 7.3 Maduo (China) Earthquake: Implications for the Seismic Hazard of the Kunlun Fault
Remote Sens. 2021, 13(16), 3327; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163327 - 23 Aug 2021
Cited by 1 | Viewed by 665
Abstract
The 21 May 2021 Maduo earthquake was the largest event to occur on a secondary fault in the interior of the active Bayanhar block on the north-central Tibetan plateau in the last twenty years. A detailed kinematic study of the Maduo earthquake helps [...] Read more.
The 21 May 2021 Maduo earthquake was the largest event to occur on a secondary fault in the interior of the active Bayanhar block on the north-central Tibetan plateau in the last twenty years. A detailed kinematic study of the Maduo earthquake helps us to better understand the seismogenic environments of the secondary faults within the block, and its relationship with the block-bounding faults. In this study, firstly, SAR images are used to obtain the coseismic deformation fields. Secondly, we use a strain model-based method and steepest descent method (SDM) to resolve the three-dimensional displacement components and to invert the coseismic slip distribution constrained by coseismic displacement fields, respectively. The three-dimensional displacement fields reveal a dominant left-lateral strike-slip motion, local horizontal displacement variations and widely distributed near-fault subsidence/uplift deformation. We prefer a five-segment fault slip model, with well constrained fault geometry featuring different dip angles and striking, constrained by InSAR observations. The peak coseismic slip is estimated to be ~5 m near longitude 98.9°E at a depth of ~4–7 km. Overall, the distribution of the coseismic slip on the fault is highly correlated to the measured surface displacement offsets along the entire rupture. We observe the moderate shallow slip deficit and limited afterslip deformation following the Maduo earthquake, it may indicate the effects of off-fault deformation during the earthquake and stable interseismic creep on the fault. The occurrence of the Maduo earthquake on a subsidiary fault updates the importance and the traditional estimate of the seismic hazards for the Kunlun fault. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring for Tectonic Deformation)
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Profiling of Dust and Urban Haze Mass Concentrations during the 2019 National Day Parade in Beijing by Polarization Raman Lidar
Remote Sens. 2021, 13(16), 3326; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163326 - 23 Aug 2021
Viewed by 486
Abstract
The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio [...] Read more.
The polarization–Raman Lidar combined sun photometer is a powerful method for separating dust and urban haze backscatter, extinction, and mass concentrations. The observation was performed in Beijing during the 2019 National Day parade, the particle depolarization ratio at 532 nm and Lidar ratio at 355 nm are 0.13 ± 0.05 and 52 ± 9 sr, respectively. It is the typical value of a mixture of dust and urban haze. Here we quantify the contributions of cross-regional transported natural dust and urban haze mass concentrations to Beijing’s air quality. There is a significant correlation between urban haze mass concentrations and surface PM2.5 (R = 0.74, p < 0.01). The contributions of local emissions to air pollution during the 2019 National Day parade were insignificant, mainly affected by regional transport, including urban haze in North China plain and Guanzhong Plain (Hebei, Tianjin, Shandong, and Shanxi), and dust aerosol in Mongolia regions and Xinjiang. Moreover, the trans-regional transmission of natural dust dominated the air pollution during the 2019 National Day parade, with a relative contribution to particulate matter mass concentrations exceeding 74% below 4 km. Our results highlight that controlling anthropogenic emissions over regional scales and focusing on the effects of natural dust is crucial and effective to improve Beijing’s air quality. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition)
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Article
Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests
Remote Sens. 2021, 13(16), 3325; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163325 - 23 Aug 2021
Viewed by 668
Abstract
The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of [...] Read more.
The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 > 0.8, RMSD < 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 > 0.72, 0.49; RMSE = 0.45 < 0.7, 0.94; nRMSE = 15.7% < 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP. Full article
(This article belongs to the Special Issue Virtual Forest)
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Article
High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning
Remote Sens. 2021, 13(16), 3324; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163324 - 23 Aug 2021
Cited by 1 | Viewed by 476
Abstract
In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at [...] Read more.
In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy. Full article
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Article
Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging
Remote Sens. 2021, 13(16), 3317; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163317 - 23 Aug 2021
Cited by 1 | Viewed by 552
Abstract
The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images [...] Read more.
The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves. Full article
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Article
Studying a Subsiding Urbanized Area from a Multidisciplinary Perspective: The Inner Sector of the Sarno Plain (Southern Apennines, Italy)
Remote Sens. 2021, 13(16), 3323; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163323 - 22 Aug 2021
Viewed by 935
Abstract
Defining the origin of ground deformation, which can be a very challenging task, may be approached through several investigative techniques. Ground deformation can originate in response to both natural (e.g., tectonics) and anthropic (e.g., groundwater pumping) contributions. These may either act simultaneously or [...] Read more.
Defining the origin of ground deformation, which can be a very challenging task, may be approached through several investigative techniques. Ground deformation can originate in response to both natural (e.g., tectonics) and anthropic (e.g., groundwater pumping) contributions. These may either act simultaneously or be somewhat correlated in space and time. For example, the location of structurally controlled basins may be the locus of enhanced human-induced subsidence. In this paper, we investigate the natural and anthropic contributions to ground deformation in the urbanized area of the inner Sarno plain, in the Southern Apennines. We used a multidisciplinary approach based on the collection and analysis of a combination of geomorphological, stratigraphical, structural, hydrogeological, GPS, and DInSAR datasets. Geomorphological, stratigraphical, and structural data suggested the occurrence of a graben-like depocenter, the Sarno basin, bounded by faults with evidence of activity in the last 39 ka. Geodetic data indicated that the Sarno basin also experienced ground deformation (mostly subsidence) in the last 30 years, with a possible anthropogenic contribution due to groundwater pumping. Hydrogeological data suggested that a significant portion of the subsidence detected by geodetic data can be ascribed to groundwater pumping from the alluvial plain aquifer, rather than to a re-activation of faults in the last 30 years. Our interpretation suggested that a positive feedback exists between fault activity and the location of area affected by human-induced subsidence. In fact, fault activity caused the accumulation of poorly consolidated deposits within the Sarno basin, which enhanced groundwater-induced subsidence. The multidisciplinary approach used here was proven to be successful within the study area and could therefore be an effective tool for investigating ground deformation in other urbanized areas worldwide. Full article
(This article belongs to the Special Issue GNSS for Geosciences)
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Article
Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning
Remote Sens. 2021, 13(16), 3322; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163322 - 22 Aug 2021
Viewed by 910
Abstract
Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. [...] Read more.
Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models. Full article
(This article belongs to the Special Issue Remote Sensing of Crop Lands and Crop Production)
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Article
Field Observations of Breaking of Dominant Surface Waves
Remote Sens. 2021, 13(16), 3321; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163321 - 22 Aug 2021
Viewed by 645
Abstract
The results of field observations of breaking of surface spectral peak waves, taken from an oceanographic research platform, are presented. Whitecaps generated by breaking surface waves were detected using video recordings of the sea surface, accompanied by co-located measurements of waves and wind [...] Read more.
The results of field observations of breaking of surface spectral peak waves, taken from an oceanographic research platform, are presented. Whitecaps generated by breaking surface waves were detected using video recordings of the sea surface, accompanied by co-located measurements of waves and wind velocity. Whitecaps were separated according to the speed of their movement, c, and then described in terms of spectral distributions of their areas and lengths over c. The contribution of dominant waves to the whitecap coverage varies with the wave age and attains more than 50% when seas are young. As found, the whitecap coverage and the total length of whitecaps generated by dominant waves exhibit strong dependence on the dominant wave steepness, ϵp, the former being proportional to ϵp6. This result supports a parameterization of the dissipation term, used in the WAM model. A semi-empirical model of the whitecap coverage, where contributions of breaking of dominant and equilibrium range waves are separated, is suggested. Full article
(This article belongs to the Special Issue Passive Remote Sensing of Oceanic Whitecaps)
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Article
Fog Measurements with IR Whole Sky Imager and Doppler Lidar, Combined with In Situ Instruments
Remote Sens. 2021, 13(16), 3320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163320 - 22 Aug 2021
Viewed by 562
Abstract
This study describes comprehensive measurements performed for four consecutive nights during a regional-scale radiation fog event in Israel’s central and southern areas in January 2021. Our data included both in situ measurements of droplets size distribution, visibility range, and meteorological parameters and remote [...] Read more.
This study describes comprehensive measurements performed for four consecutive nights during a regional-scale radiation fog event in Israel’s central and southern areas in January 2021. Our data included both in situ measurements of droplets size distribution, visibility range, and meteorological parameters and remote sensing with a thermal IR Whole Sky Imager and a Doppler Lidar. This work is the first extensive field campaign aimed to characterize fog properties in Israel and is a pioneer endeavor that encompasses simultaneous remote sensing measurements and analysis of a fog event with a thermal IR Whole Sky Imager. Radiation fog, as monitored by the sensor’s field of view, reveals three distinctive properties that make it possible to identify it. First, it exhibits an azimuthal symmetrical shape during the buildup phase. Second, the zenith brightness temperature is very close to the ground-level air temperature. Lastly, the rate of increase in cloud cover up to a completely overcast sky is very fast. Additionally, we validated the use of a Doppler Lidar as a tool for monitoring fog by proving that the measured backscatter-attenuation vertical profile agrees with the calculation of the Lidar equation fed with data measured by in situ instruments. It is shown that fog can be monitored by those two, off-the-shelf-stand-off-sensing technologies that were not originally designed for fog purposes. It enables the monitoring of fog properties such as type, evolution with time and vertical depth, and opens the path for future works of studying the different types of fog events. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Article
Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network
Remote Sens. 2021, 13(16), 3319; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163319 - 22 Aug 2021
Viewed by 672
Abstract
Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing [...] Read more.
Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow. Full article
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Article
SAR Imaging Distortions Induced by Topography: A Compact Analytical Formulation for Radiometric Calibration
Remote Sens. 2021, 13(16), 3318; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163318 - 22 Aug 2021
Viewed by 696
Abstract
Modeling of synthetic aperture radar (SAR) imaging distortions induced by topography is addressed and a novel radiometric calibration method is proposed in this paper. An analytical formulation of the problem is primarily provided in purely geometrical terms, by adopting the theoretical notions of [...] Read more.
Modeling of synthetic aperture radar (SAR) imaging distortions induced by topography is addressed and a novel radiometric calibration method is proposed in this paper. An analytical formulation of the problem is primarily provided in purely geometrical terms, by adopting the theoretical notions of the differential geometry of surfaces. The novel and conceptually simple formulation relies on a cylindrical coordinate system, whose longitudinal axis corresponds to the sensor flight direction. A 3D representation of the terrain shape is then incorporated into the SAR imaging model by resorting to a suitable parametrization of the observed ground surface. Within this analytical framework, the area-stretching function quantitatively expresses in geometrical terms the inherent local radiometric distortions. This paper establishes its analytical expression in terms of the magnitude of the gradient of the look-angle function uniquely defined in the image domain, thus resulting in being mathematically concise and amenable to a straightforward implementation. The practical relevance of the formulation is also illustrated from a computational perspective, by elucidating its effective discrete implementation. In particular, an inverse cylindrical mapping approach is adopted, thus avoiding the drawback of pixel area fragmentation and integration required in forward-mapping-based approaches. The effectiveness of the proposed SAR radiometric calibration method is experimentally demonstrated by using COSMO-SkyMed SAR data acquired over a mountainous area in Italy. Full article
(This article belongs to the Special Issue Electromagnetic Modeling in Microwave Remote Sensing)
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Article
Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for Hyperspectral Classification
Remote Sens. 2021, 13(16), 3316; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163316 - 21 Aug 2021
Cited by 1 | Viewed by 1043
Abstract
Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect the classification results. Recently, Conditional Variational Autoencoder Generative Adversarial Network (CVAEGAN) has been used to generate virtual samples to augment the training data, [...] Read more.
Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect the classification results. Recently, Conditional Variational Autoencoder Generative Adversarial Network (CVAEGAN) has been used to generate virtual samples to augment the training data, which could improve the classification performance. To further improve the classification performance, based on the CVAEGAN, we propose a Self-Attention-Based Conditional Variational Autoencoder Generative Adversarial Network (SACVAEGAN). Compared with CVAEGAN, we first use random latent vectors to obtain more enhanced virtual samples, which can improve the generalization performance. Then, we introduce the self-attention mechanism into our model to force the training process to pay more attention to global information, which can achieve better classification accuracy. Moreover, we explore model stability by incorporating the WGAN-GP loss function into our model to reduce the mode collapse probability. Experiments on three data sets and a comparison of the state-of-art methods show that SACVAEGAN has great advantages in accuracy compared with state-of-the-art HSI classification methods. Full article
(This article belongs to the Special Issue Deep Learning for Remote Sensing Data)
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Article
InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data
Remote Sens. 2021, 13(16), 3315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163315 - 21 Aug 2021
Viewed by 1100
Abstract
Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that [...] Read more.
Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided. Full article
(This article belongs to the Special Issue Radar Interferometry in Big Data Era)
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Article
Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains
Remote Sens. 2021, 13(16), 3314; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163314 - 21 Aug 2021
Cited by 1 | Viewed by 791
Abstract
Cambiophagous insects, fires and windthrow cause significant forest disturbances, generating ecological changes and economical losses. The bark beetle (Ips typographus L.), inhabiting coniferous forests and eliminating weakened trees, plays a key role in posing a threat to tree stands, which are dominated [...] Read more.
Cambiophagous insects, fires and windthrow cause significant forest disturbances, generating ecological changes and economical losses. The bark beetle (Ips typographus L.), inhabiting coniferous forests and eliminating weakened trees, plays a key role in posing a threat to tree stands, which are dominated by Norway spruce (Picea abies) and covers a large part of mountain areas, as well as the lowlands of Northern, Central and Eastern Europe. Due to the dynamics of the phenomena taking place, the EU recommends constant monitoring of forests in terms of large-area disturbances and factors affecting tree stands’ susceptibility to destruction. The right tools for this are multispectral satellite images, which regularly and free of charge provide up-to-date information on changes in the environment. The aim of this study was to develop a method of identifying disturbances of spruce stands, including the identification of bark beetle outbreaks. Sentinel 2 images from 2015–2018 were used for this purpose; the reference data were high-resolution aerial images, satellite WorldView 2, as well as field verification data. Support Vector Machines (SVM) distinguished six classes: deciduous forests, coniferous forests, grasslands, rocks, snags (dieback of standing trees) and cuts/windthrow. Remote sensing vegetation indices, Multivariate Alteration Detection (MAD), Multivariate Alteration Detection/Maximum Autocorrelation Factor (MAD/MAF), iteratively re-weighted Multivariate Alteration Detection (iMAD) and trained SVM signatures from another year, stacked band rasters allowed us to identify: (1) no changes; (2) dieback of standing trees; (3) logging or falling down of trees. The overall accuracy of the SVM classification oscillated between 97–99%; it was observed that in 2015–2018, as a result of the windthrow and bark beetle outbreaks and the consequences of those natural disturbances (e.g., sanitary cuts), approximately 62.5 km2 of coniferous stands (29%) died in the studied area of the Tatra Mountains. Full article
(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)
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Article
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks
Remote Sens. 2021, 13(16), 3313; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163313 - 21 Aug 2021
Viewed by 533
Abstract
Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in [...] Read more.
Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Dual-Satellite Alternate Switching Ranging/INS Integrated Navigation Algorithm for Broadband LEO Constellation Independent of Altimeter and Continuous Observation
Remote Sens. 2021, 13(16), 3312; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163312 - 21 Aug 2021
Cited by 1 | Viewed by 698
Abstract
In challenging environments such as forests, valleys and higher latitude areas, there are usually fewer than four visible satellites. For cases with only two visible satellites, we propose a dual-satellite alternate switching ranging integrated navigation algorithm based on the broadband low earth orbit [...] Read more.
In challenging environments such as forests, valleys and higher latitude areas, there are usually fewer than four visible satellites. For cases with only two visible satellites, we propose a dual-satellite alternate switching ranging integrated navigation algorithm based on the broadband low earth orbit (LEO) constellation, which integrates communication and navigation (ICN) technology. It is different from the traditional dual-satellite integrated navigation algorithm: the difference is that it can complete precise real-time navigation and positioning without an altimeter and continuous observation. First, we give the principle of our algorithm. Second, with the help of an unscented Kalman filter (UKF), we give the observation equation and state equation of our algorithm, and establish the mathematical model of multipath/non-line of sight (NLOS) and noise interference. Finally, based on the SpaceX constellation, for various scenarios, we analyze the performance of our algorithm through simulation. The results show that: our algorithm can effectively suppress the divergence of the inertial navigation system (INS), in the face of different multipath/NLOS interference and various noise environments it still keeps good robustness, and also has great advantages in various indicators compared with the traditional dual-satellite positioning algorithms and some existing 3-satellite advanced positioning algorithms. These results show that our algorithm can meet the real-time location service requirements in harsh and challenging environments, and provides a new navigation and positioning method when there are only two visible satellites. Full article
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
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Article
First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution
Remote Sens. 2021, 13(16), 3311; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163311 - 21 Aug 2021
Cited by 1 | Viewed by 2648
Abstract
The global spread of artificial light is eroding the natural night-time environment. The estimation of the pattern and rate of growth of light pollution on multi-decadal scales has nonetheless proven challenging. Here we show that the power of global satellite observable light emissions [...] Read more.
The global spread of artificial light is eroding the natural night-time environment. The estimation of the pattern and rate of growth of light pollution on multi-decadal scales has nonetheless proven challenging. Here we show that the power of global satellite observable light emissions increased from 1992 to 2017 by at least 49%. We estimate the hidden impact of the transition to solid-state light-emitting diode (LED) technology, which increases emissions at visible wavelengths undetectable to existing satellite sensors, suggesting that the true increase in radiance in the visible spectrum may be as high as globally 270% and 400% on specific regions. These dynamics vary by region, but there is limited evidence that advances in lighting technology have led to decreased emissions. Full article
(This article belongs to the Special Issue Light Pollution Monitoring Using Remote Sensing Data)
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