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Remote Sens., Volume 16, Issue 15 (August-1 2024) – 183 articles

Cover Story (view full-size image): Space-based atmospheric lidars provide critical information about the vertical distribution of clouds and aerosols. This is useful for studying Earth's radiation budget and climate. Detection of hazardous volcanic or smoke plumes is another important application. However, the photon counting detectors which give these lidars excellent sensitivity at night are strongly affected by solar background during the day. Horizontally averaging the data to 33 or more times the original distance is required for daytime feature detection to even approach the level achievable at nighttime. We trained a state-of-the-art deep learning image denoising model using raw CATS signals. Tests on simulated and real data showed the resulting denoised signal allowed more accurate feature detection at much higher resolution compared with standard averaging. View this paper
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18 pages, 4287 KiB  
Article
Advanced GNSS Spoofing Detection: Aggregated Correlation Residue Likelihood Analysis
by Ning Ji, Yongnan Rao, Xue Wang and Decai Zou
Remote Sens. 2024, 16(15), 2868; https://doi.org/10.3390/rs16152868 - 5 Aug 2024
Viewed by 523
Abstract
Compared to conventional spoofing, emerging spoofing attacks pose a heightened threat to security applications within the global navigation satellite system (GNSS) due to their subtly designed signal structures. In response, a novel spoofing detection method entitled aggregated correlation residue likelihood analysis (A-CoRLiAn) is [...] Read more.
Compared to conventional spoofing, emerging spoofing attacks pose a heightened threat to security applications within the global navigation satellite system (GNSS) due to their subtly designed signal structures. In response, a novel spoofing detection method entitled aggregated correlation residue likelihood analysis (A-CoRLiAn) is proposed in this study. Requiring only the addition of a pair of supplementary correlators, A-CoRLiAn harnesses correlation residues to formulate a likelihood metric, subsequently aggregating weighted decisions from all tracked satellites to ascertain the presence of spoofing. Evaluated under six diverse spoofing scenarios (including emerging challenges) in the Texas Spoofing Test Battery (TEXBAT) via Monte Carlo simulations, A-CoRLiAn yields a detection rate of 99.71%, demonstrating sensitivity, robustness, autonomy, and a lightweight architecture conducive to real-time implementation against spoofing threats. Full article
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25 pages, 3741 KiB  
Article
Multi-Stage Frequency Attention Network for Progressive Optical Remote Sensing Cloud Removal
by Caifeng Wu, Feng Xu, Xin Li, Xinyuan Wang, Zhennan Xu, Yiwei Fang and Xin Lyu
Remote Sens. 2024, 16(15), 2867; https://doi.org/10.3390/rs16152867 - 5 Aug 2024
Viewed by 660
Abstract
Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, which may inadvertently alter the intrinsic qualities of cloud-free areas, leading to [...] Read more.
Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, which may inadvertently alter the intrinsic qualities of cloud-free areas, leading to image distortions. To address this issue, we propose a multi-stage frequency attention network (MFCRNet), a progressive paradigm for optical RSI cloud removal. MFCRNet hierarchically deploys frequency cloud removal modules (FCRMs) to refine the cloud edges while preserving the original characteristics of the non-cloud regions in the frequency domain. Specifically, the FCRM begins with a frequency attention block (FAB) that transforms the features into the frequency domain, enhancing the differentiation between cloud-covered and cloud-free regions. Moreover, a non-local attention block (NAB) is employed to augment and disseminate contextual information effectively. Furthermore, we introduce a collaborative loss function that amalgamates semantic, boundary, and frequency-domain information. The experimental results on the RICE1, RICE2, and T-Cloud datasets demonstrate that MFCRNet surpasses the contemporary models, achieving superior performance in terms of mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), validating its efficacy regarding the cloud removal from optical RSIs. Full article
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20 pages, 4368 KiB  
Article
Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response
by Xiangli He, Chong Xu, Shengquan Tang, Yuandong Huang, Wenwen Qi and Zikang Xiao
Remote Sens. 2024, 16(15), 2866; https://doi.org/10.3390/rs16152866 - 5 Aug 2024
Viewed by 584
Abstract
The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote [...] Read more.
The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response. Full article
(This article belongs to the Topic Geotechnics for Hazard Mitigation)
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20 pages, 10767 KiB  
Article
A Phase-Only Optimization Null Control Method for FDA-MIMO Based on ADMM
by Mengxuan Xiao, Taiyang Hu, Xiaolang Shao, Yifan Wu and Zelong Xiao
Remote Sens. 2024, 16(15), 2865; https://doi.org/10.3390/rs16152865 - 5 Aug 2024
Viewed by 510
Abstract
This paper investigates null control within the transmit–receive beampattern of Frequency Diverse Array-Multiple-Input and Multiple-Output (FDA-MIMO) systems, presenting a novel phase-only optimization approach for achieving null control in FDA-MIMO. We employ an alternating multiplier framework, which transforms the intricate and inherent constant modulus [...] Read more.
This paper investigates null control within the transmit–receive beampattern of Frequency Diverse Array-Multiple-Input and Multiple-Output (FDA-MIMO) systems, presenting a novel phase-only optimization approach for achieving null control in FDA-MIMO. We employ an alternating multiplier framework, which transforms the intricate and inherent constant modulus constraint and numerous amplitude constraints in optimization into more manageable projection problems. By employing a phase-only optimization strategy, the intricate hardware and computational burdens associated with null control in FDA-MIMO are effectively alleviated. The simulation results indicate that the algorithm proposed in this paper exhibits excellent null control ability while precisely maintaining constant modulus constraints, and it possesses an extremely high computational efficiency. Full article
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17 pages, 5546 KiB  
Technical Note
Application of Atmospheric Augmentation for PPP-RTK with Instantaneous Ambiguity Resolution in Kinematic Vehicle Positioning
by Zhu-Feng Shao, Dun-Wei Gong, Zi-Yang Qu, Sheng-Yi Xu, Xiao-Ting Lei and Zhen Li
Remote Sens. 2024, 16(15), 2864; https://doi.org/10.3390/rs16152864 - 5 Aug 2024
Viewed by 376
Abstract
The long convergence time and non-robust positioning accuracy are the main factors limiting the application of precision single-point positioning (PPP) in kinematic vehicle navigation. Therefore, a dual/triple-frequency multi-constellation PPP-RTK method with atmospheric augmentation is proposed to achieve cm-level reliable kinematic positioning. The performance [...] Read more.
The long convergence time and non-robust positioning accuracy are the main factors limiting the application of precision single-point positioning (PPP) in kinematic vehicle navigation. Therefore, a dual/triple-frequency multi-constellation PPP-RTK method with atmospheric augmentation is proposed to achieve cm-level reliable kinematic positioning. The performance was assessed using a set of static station and kinematic vehicle positioning experiments conducted in Wuhan. In the static experiments, instantaneous convergence within 1 s and centimeter-level positioning accuracy were achieved for PPP-RTK using dual-frequency observation. For the kinematic experiments, instantaneous convergence was also achieved for dual-frequency PPP-RTK in open areas, with RMS of 2.6 cm, 2.6 cm, and 7.5 cm in the north, east, and up directions, respectively, with accuracy similar to short-baseline real-time kinematic positioning (RTK). Horizontal positioning errors of less than 0.1 m and 3D positional errors of less than 0.2 m were 99.54% and 98.46%, respectively. Additionally, after the outage of GNSS and during satellite reduction in obstructed environments, faster reconvergence and greater accuracy stability were realized compared with PPP without atmospheric enhancement. Triple-frequency PPP-RTK was able to further enhance the robustness and accuracy of positioning, with RMS of 2.2 cm, 2.0 cm, and 7.3 cm, respectively. In summary, a performance similar to RTK was achieved based on dual-frequency PPP-RTK, demonstrating that PPP-RTK has the potential for lane-level navigation. Full article
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22 pages, 18750 KiB  
Article
Application of Digital Twin Technology in Synthetic Aperture Radar Ground Moving Target Intelligent Detection System
by Hui Liu, He Yan, Jialin Hao, Wenshuo Xu, Zhou Min and Daiyin Zhu
Remote Sens. 2024, 16(15), 2863; https://doi.org/10.3390/rs16152863 - 5 Aug 2024
Viewed by 564
Abstract
In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this [...] Read more.
In recent years, the detection performance of SAR-GMTI (synthetic aperture radar-ground moving target indication) algorithm based on deep learning has always been limited by insufficient measured data due to the heavy operation complexity and high cost of real SAR systems. To solve this problem, this paper proposes an overall DT-based implementation framework for SAR ground moving target intelligent detection tasks. In particular, by virtue of a SAR imaging algorithm, a high-fidelity twin replica of SAR moving targets is established in digital space through parameter traversal based on the prior target characteristics of the obtained measured datasets. Then, the constructed SAR twin datasets is fed into the neural network model to train an intelligent detector by fully learning features of the moving targets and preset the SAR scene in the twin space, which can realize the robust detection of ground moving targets in related practical scenarios with no need for multiple and complex field experiments. Moreover, the effectiveness of the proposed framework is verified on the MiniSAR measured system, and a comparison with traditional CFAR detection method is given simultaneously. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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13 pages, 2715 KiB  
Technical Note
Laser Observations of GALILEO Satellites at the CBK PAN Astrogeodynamic Observatory in Borowiec
by Paweł Lejba, Piotr Michałek, Tomasz Suchodolski, Adrian Smagło, Mateusz Matyszewski and Stanisław Zapaśnik
Remote Sens. 2024, 16(15), 2862; https://doi.org/10.3390/rs16152862 - 5 Aug 2024
Viewed by 495
Abstract
The laser station (BORL) owned by the Space Research Centre of the Polish Academy of Sciences and situated at the Astrogeodynamic Observatory in Borowiec near Poznań regularly observes more than 100 different objects in low Earth orbit (LEO) and medium Earth orbit (MEO). [...] Read more.
The laser station (BORL) owned by the Space Research Centre of the Polish Academy of Sciences and situated at the Astrogeodynamic Observatory in Borowiec near Poznań regularly observes more than 100 different objects in low Earth orbit (LEO) and medium Earth orbit (MEO). The BORL sensor’s laser observation range is from 400 km to 24,500 km. The laser measurements taken by the BORL sensor are utilized to create various products, including the geocentric positions and movements of ground stations, satellite orbits, the components of the Earth’s gravitational field and their changes over time, Earth’s orientation parameters (EOPs), and the validation of the precise Galileo orbits derived using microwave measurements, among others. These products are essential for supporting local and global geodetic and geophysics research related to time. They are crucial for the International Terrestrial Reference Frame (ITRF), which is managed by the International Earth Rotation and Reference Systems Service (IERS). In 2023, the BORL laser station expanded its list of tracked objects to include all satellites of the European satellite navigation system GALILEO, totaling 28 satellites. During that year, the BORL laser station recorded 77 successful passes of GALILEO satellites, covering a total of 21 objects. The measurements taken allowed for the registration of 7419 returns, resulting in 342 normal points. The average RMS for all successful GALILEO observations in 2023 was 13.5 mm. Full article
(This article belongs to the Special Issue Advanced Remote Sensing Technology in Modern Geodesy)
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19 pages, 47532 KiB  
Article
Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake
by Siyuan Ma, Xiaoyi Shao and Chong Xu
Remote Sens. 2024, 16(15), 2861; https://doi.org/10.3390/rs16152861 - 5 Aug 2024
Viewed by 422
Abstract
On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides [...] Read more.
On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher. Full article
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24 pages, 8278 KiB  
Article
Radar Target Classification Using Enhanced Doppler Spectrograms with ResNet34_CA in Ubiquitous Radar
by Qiang Song, Shilin Huang, Yue Zhang, Xiaolong Chen, Zebin Chen, Xinyun Zhou and Zhenmiao Deng
Remote Sens. 2024, 16(15), 2860; https://doi.org/10.3390/rs16152860 - 5 Aug 2024
Viewed by 399
Abstract
Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification [...] Read more.
Ubiquitous Radar has become an essential tool for preventing bird strikes at airports, where accurate target classification is of paramount importance. The working mode of Ubiquitous Radar, which operates in track-then-identify (TTI) mode, provides both tracking information and Doppler information for the classification and recognition module. Moreover, the main features of the target’s Doppler information are concentrated around the Doppler main spectrum. This study innovatively used tracking information to generate a feature enhancement layer that can indicate the area where the main spectrum is located and combines it with the RGB three-channel Doppler spectrogram to form an RGBA four-channel Doppler spectrogram. Compared with the RGB three-channel Doppler spectrogram, this method increases the classification accuracy for four types of targets (ships, birds, flapping birds, and bird flocks) from 93.13% to 97.13%, an improvement of 4%. On this basis, this study integrated the coordinate attention (CA) module into the building block of the 34-layer residual network (ResNet34), forming ResNet34_CA. This integration enables the network to focus more on the main spectrum information of the target, thereby further improving the classification accuracy from 97.13% to 97.22%. Full article
(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application)
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17 pages, 14390 KiB  
Article
Scan-to-HBIM-to-VR: An Integrated Approach for the Documentation of an Industrial Archaeology Building
by Maria Alessandra Tini, Anna Forte, Valentina Alena Girelli, Alessandro Lambertini, Domenico Simone Roggio, Gabriele Bitelli and Luca Vittuari
Remote Sens. 2024, 16(15), 2859; https://doi.org/10.3390/rs16152859 - 5 Aug 2024
Viewed by 556
Abstract
In this paper, we propose a comprehensive and optimised workflow for the documentation and the future maintenance and management of a historical building, integrating the state of the art of different techniques, in the challenging context of industrial archaeology. This approach has been [...] Read more.
In this paper, we propose a comprehensive and optimised workflow for the documentation and the future maintenance and management of a historical building, integrating the state of the art of different techniques, in the challenging context of industrial archaeology. This approach has been applied to the hydraulic work of the “Sostegno del Battiferro” in Bologna, Italy, an example of built industrial heritage whose construction began in 1439 and remains in active use nowadays to control the Navile canal water flow rate. The initial step was the definition of a 3D topographic frame, including geodetic measurements, which served as a reference for the complete 3D survey integrating Terrestrial Laser Scanning (TLS), Structured Light Projection scanning, and the photogrammetric processing of Unmanned Aircraft System (UAS) imagery through a Structure from Motion (SfM) approach. The resulting 3D point cloud has supported as-built parametric modelling (Scan-to-BIM) with the consequent extraction of plans and sections. Finally, the Heritage/Historic Building Information Modelling (HBIM) model generated was rendered and tested for a VR-based immersive experience. Building Information Modelling (BIM) and virtual reality (VR) applications were tested as a support for the management of the building, the maintenance of the hydraulic system, and the training of qualified technicians. In addition, considering the historical value of the surveyed building, the methodology was also applied for dissemination purposes. Full article
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25 pages, 8439 KiB  
Article
On Unsupervised Multiclass Change Detection Using Dual-Polarimetric SAR Data
by Minhwa Kim, Seung-Jae Lee and Sang-Eun Park
Remote Sens. 2024, 16(15), 2858; https://doi.org/10.3390/rs16152858 - 5 Aug 2024
Viewed by 459
Abstract
Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the [...] Read more.
Change detection using SAR data has been an active topic in various applications. Because conventional change detection identifies signal changes in single-pol radar observations, they cannot separately detect different kinds of change associated with different ground parameters. In this study, we investigated the comprehensive use of dual-pol parameters and proposed a novel dual-pol-based change detection framework utilizing different dual-pol scatter-type indicators. To optimize the exploitation of dual-pol change information, we presented a two-step processing strategy that divides the multiclass change detection process into a binary detection step that identifies the presence of changes and the classification step that distinguishes the types of change. In the detection stage, each dual-pol parameter was considered as an independent information source. Assuming potential conflict between dual-pol parameters, a disjunctive combination of detection results from different dual-pol parameters was applied to obtain the final detection result. In the classification step, an unsupervised change classification strategy was proposed based on the change direction and magnitude of the dual-pol parameters within the change class. Experimental results exhibited significantly improved detectability across a wide change spectrum compared with previous dual-pol-based change detection approaches. They also demonstrated the possibility of distinguishing different semantic changes without in situ ground data. Full article
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17 pages, 10926 KiB  
Article
Hybrid CMOD-Diffusion Algorithm Applied to Sentinel-1 for More Robust and Precise Wind Retrieval
by Qi Zhou, Huiming Chai and Xiaolei Lv
Remote Sens. 2024, 16(15), 2857; https://doi.org/10.3390/rs16152857 - 5 Aug 2024
Viewed by 363
Abstract
Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model [...] Read more.
Synthetic Aperture Radar (SAR) imagery presents significant advantages for observing ocean surface winds owing to its high spatial resolution and low sensitivity to extreme weather conditions. Nevertheless, signal noise poses a challenge, hindering precise wind retrieval from SAR imagery. Moreover, traditional geophysical model functions (GMFs) often falter, particularly in accurately estimating high wind speeds, notably during extreme weather phenomena like tropical cyclones (TCs). To address these limitations, this study proposes a novel hybrid model, CMOD-Diffusion, which integrates the strengths of GMFs with data-driven deep learning methods, thereby achieving enhanced accuracy and robustness in wind retrieval. Based on the coarse estimation of wind speed by the traditional GMF CMOD5.N, we introduce the recently developed data-driven method Denoising Diffusion Probabilistic Model (DDPM). It transforms an image from one domain to another domain by gradually adding Gaussian noise, thus achieving denoising and image synthesis. By introducing the DDPM, the noise from the observed normalized radar cross-section (NRCS) and the residual of the GMF methods can be largely compensated. Specifically, for wind speeds within the low-to-medium range, a DDPM is employed before proceeding to another CMOD iteration to recalibrate the observed NRCS. Conversely, a posterior-placed DDPM is applied after CMOD to reconstruct high-wind-speed regions or TC-affected areas, with the prior information from regions characterized by low wind speeds and recalibrated NRCS values. The efficacy of the proposed model is evaluated by using Sentinel-1 SAR imagery in vertical–vertical (VV) polarization, collocated with data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Experimental results based on validation sets demonstrate significant improvements over CMOD5.N, particularly in low-to-medium wind speed regions, with the Structural Similarity Index (SSIM) increasing from 0.76 to 0.98 and the Root Mean Square Error (RMSE) decreasing from 1.98 to 0.63. Across the entire wind field, including regions with high wind speeds, the validation data obtained through the proposed method exhibit an RMSE of 2.39 m/s, with a correlation coefficient of 0.979. Full article
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21 pages, 10907 KiB  
Article
A Point Cloud Improvement Method for High-Resolution 4D mmWave Radar Imagery
by Qingmian Wan, Hongli Peng, Xing Liao, Weihao Li, Kuayue Liu and Junfa Mao
Remote Sens. 2024, 16(15), 2856; https://doi.org/10.3390/rs16152856 - 4 Aug 2024
Viewed by 948
Abstract
To meet the requirement of autonomous driving development, high-quality point cloud generation of the environment has become the focus of 4D mmWave radar development. On the basis of mass producibility and physical verifiability, a design method for improving the quality and density of [...] Read more.
To meet the requirement of autonomous driving development, high-quality point cloud generation of the environment has become the focus of 4D mmWave radar development. On the basis of mass producibility and physical verifiability, a design method for improving the quality and density of point cloud imagery is proposed in this paper, including antenna design, array design, and the dynamic detection method. The utilization of apertures is promoted through antenna design and sparse MIMO array optimization using the genetic algorithm (GA). The hybrid strategy for complex point clouds is adopted using the proposed dynamic CFAR algorithm, which enables dynamic adjustment of the threshold by discriminating and calculating different scanning regions. The effectiveness of the proposed method is verified by simulations and practical experiments. Aiming at system manufacture, analysis methods for the ambiguity function (AF) and shooting and bouncing rays (SBR) tracing are introduced, and an mmWave radar system is realized based on the proposed method, with its performance proven by practical experiments. Full article
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17 pages, 11574 KiB  
Article
Assessing Habitat Suitability: The Case of Black Rhino in the Ngorongoro Conservation Area
by Joana Borges, Elias Symeonakis, Thomas P. Higginbottom, Martin Jones, Bradley Cain, Alex Kisingo, Deogratius Maige, Owen Oliver and Alex L. Lobora
Remote Sens. 2024, 16(15), 2855; https://doi.org/10.3390/rs16152855 - 4 Aug 2024
Viewed by 713
Abstract
Efforts to identify suitable habitat for wildlife conservation are crucial for safeguarding biodiversity, facilitating management, and promoting sustainable coexistence between wildlife and communities. Our study focuses on identifying potential black rhino (Diceros bicornis) habitat within the Ngorongoro Conservation Area (NCA), Tanzania, [...] Read more.
Efforts to identify suitable habitat for wildlife conservation are crucial for safeguarding biodiversity, facilitating management, and promoting sustainable coexistence between wildlife and communities. Our study focuses on identifying potential black rhino (Diceros bicornis) habitat within the Ngorongoro Conservation Area (NCA), Tanzania, across wet and dry seasons. To achieve this, we used remote sensing data with and without field data. We employed a comprehensive approach integrating Sentinel-2 and PlanetScope images, vegetation indices, and human activity data. We employed machine learning recursive feature elimination (RFE) and random forest (RF) algorithms to identify the most relevant features that contribute to habitat suitability prediction. Approximately 36% of the NCA is suitable for black rhinos throughout the year; however, there are seasonal shifts in habitat suitability. Anthropogenic factors increase land degradation and limit habitat suitability, but this depends on the season. This study found a higher influence of human-related factors during the wet season, with suitable habitat covering 53.6% of the NCA. In the dry season, browse availability decreases and rhinos are forced to become less selective of the areas where they move to fulfil their nutritional requirements, with anthropogenic pressures becoming less important. Furthermore, our study identified specific areas within the NCA that consistently offer suitable habitat across wet and dry seasons. These areas, situated between Olmoti and the Crater, exhibit minimal disturbance from human activities, presenting favourable conditions for rhinos. Although the Oldupai Gorge only has small suitable patches, it used to sustain a large population of rhinos in the 1960s. Land cover changes seem to have decreased the suitability of the Gorge. This study highlights the importance of combining field data with remotely sensed data. Remote sensing-based assessments rely on the importance of vegetation covers as a proxy for habitat and often overlook crucial field variables such as shelter or breeding locations. Overall, our study sheds light on the imperative of identifying suitable habitat for black rhinos within the NCA and underscores the urgency of intensified conservation efforts. Our findings underscore the need for adaptive conservation strategies to reverse land degradation and safeguard black rhino populations in this dynamic multiple land-use landscape as environmental and anthropogenic pressures evolve. Full article
(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation (Second Edition))
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19 pages, 10716 KiB  
Article
Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
by Jenniffer Carolina Triana-Martinez, Andrés Marino Álvarez-Meza, Julian Gil-González, Tom De Swaef and Jose A. Fernandez-Gallego
Remote Sens. 2024, 16(15), 2854; https://doi.org/10.3390/rs16152854 - 4 Aug 2024
Viewed by 631
Abstract
To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within [...] Read more.
To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. Full article
(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)
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26 pages, 5746 KiB  
Article
A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability
by Jingjing Ti, Zhiyong Suo, Yi Liang, Bingji Zhao and Jiabao Xi
Remote Sens. 2024, 16(15), 2853; https://doi.org/10.3390/rs16152853 - 3 Aug 2024
Viewed by 614
Abstract
The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic [...] Read more.
The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. Full article
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21 pages, 7377 KiB  
Article
A Novel Sample Generation Method for Deep Learning Lithological Mapping with Airborne TASI Hyperspectral Data in Northern Liuyuan, Gansu, China
by Huize Liu, Ke Wu, Dandan Zhou and Ying Xu
Remote Sens. 2024, 16(15), 2852; https://doi.org/10.3390/rs16152852 - 3 Aug 2024
Viewed by 432
Abstract
High-resolution and thermal infrared hyperspectral data acquired from the Thermal Infrared Airborne Spectrographic Imager (TASI) have been recognized as efficient tools in geology, demonstrating significant potential for rock discernment. Deep learning (DL), as an advanced technology, has driven substantial advancements in lithological mapping [...] Read more.
High-resolution and thermal infrared hyperspectral data acquired from the Thermal Infrared Airborne Spectrographic Imager (TASI) have been recognized as efficient tools in geology, demonstrating significant potential for rock discernment. Deep learning (DL), as an advanced technology, has driven substantial advancements in lithological mapping by automatically extracting high-level semantic features from images to enhance recognition accuracy. However, gathering sufficient high-quality lithological samples for model training is challenging in many scenarios, posing limitations for data-driven DL approaches. Moreover, existing sample collection approaches are plagued by limited verifiability, subjective bias, and variation in the spectra of the same class at different locations. To tackle these challenges, a novel sample generation method called multi-lithology spectra sample selection (MLS3) is first employed. This method involves multiple steps: multiple spectra extraction, spectra combination and optimization, lithological type identification, and sample selection. In this study, the TASI hyperspectral data collected from the Liuyuan area in Gansu Province, China, were used as experimental data. Samples generated based on MLS3 were fed into five typical DL models, including two-dimensional convolutional neural network (2D-CNN), hybrid spectral CNN (HybridSN), multiscale residual network (MSRN), spectral-spatial residual network (SSRN), and spectral partitioning residual network (SPRN) for lithological mapping. Among these models, the accuracy of the SPRN reaches 84.03%, outperforming the other algorithms. Furthermore, MLS3 demonstrates superior performance, achieving an overall accuracy of 2.25–6.96% higher than other sample collection methods when SPRN is used as the DL framework. In general, MLS3 enables both the quantity and quality of samples, providing inspiration for the application of DL to hyperspectral lithological mapping. Full article
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17 pages, 4776 KiB  
Article
Optimization of Remote-Sensing Image-Segmentation Decoder Based on Multi-Dilation and Large-Kernel Convolution
by Guohong Liu, Cong Liu, Xianyun Wu, Yunsong Li, Xiao Zhang and Junjie Xu
Remote Sens. 2024, 16(15), 2851; https://doi.org/10.3390/rs16152851 - 3 Aug 2024
Viewed by 404
Abstract
Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly, to tackle the issues of foreground–background imbalance and scale [...] Read more.
Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly, to tackle the issues of foreground–background imbalance and scale variation, a module based on multi-dilated rate convolution fusion was integrated into a decoder. This module extended the receptive field through multi-dilated convolution, enhancing the model’s capability to capture global features. Secondly, to address the diversity of scenes and background interference, a hybrid attention module based on large-kernel convolution was employed to improve the performance of the decoder. This module, based on a combination of spatial and channel attention mechanisms, enhanced the extraction of contextual information through large-kernel convolution. A convolution kernel selection mechanism was also introduced to dynamically select the convolution kernel of the appropriate receptive field, suppress irrelevant background information, and improve segmentation accuracy. Ablation studies on the Vaihingen and Potsdam datasets demonstrate that our decoder significantly outperforms the baseline in terms of mean intersection over union and mean F1 score, achieving an increase of up to 1.73% and 1.17%, respectively, compared with the baseline. In quantitative comparisons, the accuracy of our improved decoder also surpasses other algorithms in the majority of categories. The results of this paper indicate that our improved decoder achieves significant performance improvement compared with the old decoder in remote-sensing image-segmentation tasks, which verifies its application potential in the field of land-cover segmentation. Full article
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20 pages, 7333 KiB  
Article
Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
by Zilin Ran, Chao Lu, Yunpeng Hu, Dehe Yang, Xiaoying Sun and Zeren Zhima
Remote Sens. 2024, 16(15), 2850; https://doi.org/10.3390/rs16152850 - 3 Aug 2024
Viewed by 354
Abstract
The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales [...] Read more.
The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection. Full article
(This article belongs to the Section Environmental Remote Sensing)
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17 pages, 16804 KiB  
Article
Land Cover Mapping in a Mangrove Ecosystem Using Hybrid Selective Kernel-Based Convolutional Neural Networks and Multi-Temporal Sentinel-2 Imagery
by Seyd Teymoor Seydi, Seyed Ali Ahmadi, Arsalan Ghorbanian and Meisam Amani
Remote Sens. 2024, 16(15), 2849; https://doi.org/10.3390/rs16152849 - 3 Aug 2024
Viewed by 572
Abstract
Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we [...] Read more.
Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts. Full article
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20 pages, 7699 KiB  
Article
SSANet-BS: Spectral–Spatial Cross-Dimensional Attention Network for Hyperspectral Band Selection
by Chuanyu Cui, Xudong Sun, Baijia Fu and Xiaodi Shang
Remote Sens. 2024, 16(15), 2848; https://doi.org/10.3390/rs16152848 - 3 Aug 2024
Viewed by 496
Abstract
Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based [...] Read more.
Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS. Full article
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22 pages, 5881 KiB  
Article
An Improved Multi-Target Tracking Method for Space-Based Optoelectronic Systems
by Rui Zhu, Qiang Fu, Guanyu Wen, Xiaoyi Wang, Nan Liu, Liyong Wang, Yingchao Li and Huilin Jiang
Remote Sens. 2024, 16(15), 2847; https://doi.org/10.3390/rs16152847 - 2 Aug 2024
Viewed by 501
Abstract
Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the [...] Read more.
Under space-based observation conditions, targets are subject to a large number of stars, clutter, false alarms, and other interferences, which can significantly impact the traditional Gaussian mixture probability hypothesis density (GM-PHD) filtering method, leading to tracking biases. To enhance the capability of the traditional GM-PHD method for multi-target tracking in space-based platform observation scenarios, in this article, we propose a GM-PHD algorithm based on spatio-temporal pipeline filtering and enhance the conventional spatio-temporal pipeline filtering method. The proposed algorithm incorporates two key enhancements: firstly, by adaptively adjusting the pipeline’s central position through target state prediction, it ensures continuous target tracking while eliminating noise; secondly, by computing trajectory similarity to distinguish stars from targets, it effectively mitigates stellar interference in target tracking. The proposed algorithm realizes a more accurate estimation of the target by constructing a target state pipeline using the time series and correlating multiple frames of data to achieve a smaller optimal sub-pattern assignment (OSPA) distance and a higher tracking accuracy compared with the traditional algorithm. Through simulations and real-world data validation, the algorithm showcased its capability for multi-target tracking in a space-based context, outperforming traditional methods and effectively addressing the challenge of stellar interference in space-based multi-target tracking. Full article
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17 pages, 23072 KiB  
Article
Fire-Net: Rapid Recognition of Forest Fires in UAV Remote Sensing Imagery Using Embedded Devices
by Shouliang Li, Jiale Han, Fanghui Chen, Rudong Min, Sixue Yi and Zhen Yang
Remote Sens. 2024, 16(15), 2846; https://doi.org/10.3390/rs16152846 - 2 Aug 2024
Viewed by 392
Abstract
Forest fires pose a catastrophic threat to Earth’s ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle [...] Read more.
Forest fires pose a catastrophic threat to Earth’s ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model’s ability to perceive features at various scales, particularly small-scale fire sources in wild forest scenes. Through training and testing on a real-world dataset, various lightweight convolutional neural networks were evaluated on embedded devices. The experimental outcomes indicate that Fire-Net attained an accuracy of 98.18%, a precision of 99.14%, and a recall of 98.01%, surpassing the current leading methods. Furthermore, the model showcases an average inference time of 10 milliseconds per image and operates at 86 frames per second (FPS) on embedded devices. Full article
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11 pages, 2354 KiB  
Article
Influence of Abnormal Eddies on Seasonal Variations in Sonic Layer Depth in the South China Sea
by Xintong Liu, Chunhua Qiu, Tianlin Wang, Huabin Mao and Peng Xiao
Remote Sens. 2024, 16(15), 2845; https://doi.org/10.3390/rs16152845 - 2 Aug 2024
Viewed by 386
Abstract
Sonic layer depth (SLD) is crucial in ocean acoustics research and profoundly influences sound propagation and Sonar detection. Carrying 90% of oceanic kinetic energy, mesoscale eddies significantly impact the propagation of acoustic energy in the ocean. Recent studies classified mesoscale eddies into normal [...] Read more.
Sonic layer depth (SLD) is crucial in ocean acoustics research and profoundly influences sound propagation and Sonar detection. Carrying 90% of oceanic kinetic energy, mesoscale eddies significantly impact the propagation of acoustic energy in the ocean. Recent studies classified mesoscale eddies into normal eddies (warm anticyclonic and cold cyclonic eddies) and abnormal eddies (cold anticyclonic and warm cyclonic eddies). However, the influence of mesoscale eddies, especially abnormal eddies, on SLD remains unclear. Based on satellite altimeter and reanalysis data, we explored the influence of mesoscale eddies on seasonal variations in SLD in the South China Sea. We found that the vertical structures of temperature anomalies within the eddies had a significant impact on the sound speed field. A positive correlation between sonic layer depth anomaly (SLDA) and eddy intensity (absolute value of relative vorticity) was investigated. The SLDA showed significant seasonal variations: during summer (winter), the proportion of negative (positive) SLDA increased. Normal eddies (abnormal eddies) had a more pronounced effect during summer and autumn (spring and winter). Based on mixed-layer heat budget analysis, it was found that the seasonal variation in SLD was primarily induced by air–sea heat fluxes. However, for abnormal eddies, the horizontal advection and vertical convective terms modulated the variations in the SLDA. This study provides additional theoretical support for mesoscale eddy–acoustic coupling models and advances our understanding of the impact of mesoscale eddies on sound propagation. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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18 pages, 7349 KiB  
Article
Temporal Patterns of Vegetation Greenness for the Main Forest-Forming Tree Species in the European Temperate Zone
by Kinga Kulesza and Agata Hościło
Remote Sens. 2024, 16(15), 2844; https://doi.org/10.3390/rs16152844 - 2 Aug 2024
Viewed by 364
Abstract
In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in [...] Read more.
In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones. Full article
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26 pages, 12204 KiB  
Article
The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area
by Zhichao Chen, He Qin, Xufei Zhang, Huazhu Xue, Shidong Wang and Hebing Zhang
Remote Sens. 2024, 16(15), 2843; https://doi.org/10.3390/rs16152843 - 2 Aug 2024
Viewed by 403
Abstract
The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of [...] Read more.
The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought. Full article
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28 pages, 20313 KiB  
Article
SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye
by Muzaffer Can Iban and Oktay Aksu
Remote Sens. 2024, 16(15), 2842; https://doi.org/10.3390/rs16152842 - 2 Aug 2024
Viewed by 679
Abstract
Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, [...] Read more.
Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability. Full article
(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))
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18 pages, 1320 KiB  
Article
Polarimetric Adaptive Coherent Detection in Lognorm-Texture-Distributed Sea Clutter
by Jian Xue, Jiali Yan, Shuwen Xu and Jun Liu
Remote Sens. 2024, 16(15), 2841; https://doi.org/10.3390/rs16152841 - 2 Aug 2024
Viewed by 391
Abstract
This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric [...] Read more.
This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data’s polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix. Full article
(This article belongs to the Topic Radar Signal and Data Processing with Applications)
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17 pages, 1421 KiB  
Technical Note
Angle Estimation Using Learning-Based Doppler Deconvolution in Beamspace with Forward-Looking Radar
by Wenjie Li, Xinhao Xu, Yihao Xu, Yuchen Luan, Haibo Tang, Longyong Chen, Fubo Zhang, Jie Liu and Junming Yu
Remote Sens. 2024, 16(15), 2840; https://doi.org/10.3390/rs16152840 - 2 Aug 2024
Viewed by 405
Abstract
The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of [...] Read more.
The measurement of the target azimuth angle using forward-looking radar (FLR) is widely applied in unmanned systems, such as obstacle avoidance and tracking applications. This paper proposes a semi-supervised support vector regression (SVR) method to solve the problem of small sample learning of the target angle with FLR. This method utilizes function approximation to solve the problem of estimating the target angle. First, SVR is used to construct the function mapping relationship between the echo and the target angle in beamspace. Next, by adding manifold constraints to the loss function, supervised learning is extended to semi-supervised learning, aiming to improve the small sample adaptation ability. This framework supports updating the angle estimating function with continuously increasing unlabeled samples during the FLR scanning process. The numerical simulation results show that the new technology has better performance than model-based methods and fully supervised methods, especially under limited conditions such as signal-to-noise ratio and number of training samples. Full article
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22 pages, 27810 KiB  
Article
Spatio-Temporal Dynamics of Vegetation and Its Driving Mechanisms on the Qinghai-Tibet Plateau from 2000 to 2020
by Changhui Ma, Si-Bo Duan, Wenhua Qin, Feng Wang and Lei He
Remote Sens. 2024, 16(15), 2839; https://doi.org/10.3390/rs16152839 - 2 Aug 2024
Viewed by 703
Abstract
Revealing the response of vegetation on the Qinghai-Tibet Plateau (QTP) to climate change and human activities is crucial for ensuring East Asian ecological security and regulating the global climate. However, the current research rarely explores the time-lag effects of climate on vegetation growth, [...] Read more.
Revealing the response of vegetation on the Qinghai-Tibet Plateau (QTP) to climate change and human activities is crucial for ensuring East Asian ecological security and regulating the global climate. However, the current research rarely explores the time-lag effects of climate on vegetation growth, leading to considerable uncertainty in analyzing the driving mechanisms of vegetation changes. This study identified the main driving factors of vegetation greenness (vegetation index, EVI) changes after investigating the lag effects of climate. By analyzing the trends of interannual variation in vegetation and climate, the study explored the driving mechanisms behind vegetation changes on the QTP from 2000 to 2020. The results indicate that temperature and precipitation have significant time-lag effects on vegetation growth. When considering the lag effects, the explanatory power of climate on vegetation changes is significantly enhanced for 29% of the vegetated areas. About 31% of the vegetation on the QTP exhibited significant “greening”, primarily in the northern plateau. This greening was attributed not only to improvements in climate-induced hydrothermal conditions but also to the effective implementation of ecological projects, which account for roughly half of the significant greening. Only 2% of the vegetation on the QTP showed significant “browning”, sporadically distributed in the southern plateau and the Sanjiangyuan region. In these areas, besides climate-induced drought intensification, approximately 78% of the significant browning was due to unreasonable grassland utilization and intense human activities. The area where precipitation dominates vegetation improvement was larger than the area dominated by temperature, whereas the area where precipitation dominates vegetation degradation is smaller than that where temperature dominates degradation. The implementation of a series of ecological projects has resulted in a much larger area where human activities positively promoted vegetation compared to the area where they negatively inhibited it. Full article
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