Remote Sensing

62 pages, 28364 KB

Open AccessReview

Surface Roughness in Geomorphometry: From Basic Metrics Toward a Coherent Framework

by Sebastiano Trevisani and Peter L. Guth

Remote Sens. 2025, 17(23), 3864; https://doi.org/10.3390/rs17233864 (registering DOI) - 28 Nov 2025

Surface roughness (SR), most often computed from a digital elevation model (DEM), is a fundamental concept in geomorphometry, with significant applications across the earth sciences and ecology. However, its analysis remains fragmented, lacking a unified conceptual and methodological framework within geomorphometry. This review [...] Read more.

Surface roughness (SR), most often computed from a digital elevation model (DEM), is a fundamental concept in geomorphometry, with significant applications across the earth sciences and ecology. However, its analysis remains fragmented, lacking a unified conceptual and methodological framework within geomorphometry. This review synthesizes the current state of surface roughness research, highlighting persistent challenges that stem from this disunity. Key issues include a pervasive lack of consensus on terminology and definitions, frequent misuse of standardized indices, and difficulty in selecting appropriate analytical scales and metrics for specific landscapes and research questions. A major impediment to progress is the absence of benchmark datasets, which are crucial for the rigorous evaluation and comparison of both established and novel roughness metrics. Furthermore, we argue that in geomorphometry, roughness is best conceptualized as surface texture (ST), encompassing a multitude of terrain patterns across scales. Consequently, effective analysis often requires multiscale approaches and the development of new indices capable of quantifying specific textural features. We emphasize, for instance, the need for metrics based on robust statistical estimators, such as MAD or the Radial Roughness Index (RRI), to reliably characterize complex, heterogeneous terrain derived from high-resolution DEMs. These arguments are substantiated with computational examples comparing a range of metrics, from popular basic indices to more complex alternatives. This review aims to consolidate discourse on surface roughness and chart a path toward more robust, standardized, and interpretative analytical practices. Full article

24 pages, 11772 KB

Open AccessArticle

Effective SAR Image Despeckling Using Noise-Guided Transformer and Multi-Scale Feature Fusion

by Linna Zhang, Le Zheng, Yuxin Wen, Fugui Zhang, Fuyu Bo and Yigang Cen

Remote Sens. 2025, 17(23), 3863; https://doi.org/10.3390/rs17233863 (registering DOI) - 28 Nov 2025

Abstract

Speckle noise is a significant challenge in synthetic aperture radar (SAR) images, severely degrading the visual quality and compromising subsequent image interpretation tasks. While existing despeckling methods can reduce noise, they often fail to strike a appropriate balance between noise suppression and the [...] Read more.

Speckle noise is a significant challenge in synthetic aperture radar (SAR) images, severely degrading the visual quality and compromising subsequent image interpretation tasks. While existing despeckling methods can reduce noise, they often fail to strike a appropriate balance between noise suppression and the preservation of fine image details. To address this issue, in this paper, we propose a novel SAR image despeckling method that leverages both structural image priors and noise distribution characteristics in an end-to-end framework. Our approach consists of two key components: a dual-branch subnet for coarse despeckling and noise estimation, and a noise-guided Transformer-based subnet for final image refinement. The dual-branch subnet decouples the tasks of noise estimation and despeckling, improving both noise suppression accuracy and structural detail preservation. Furthermore, a combination of grouped pooling attention (GPA) and context-aware fusion (CAF) modules enables effective multi-scale feature fusion by jointly capturing local details and global contextual information. The noise estimation branch generates adaptive priors that guide the Transformer refinement, which incorporates deformable convolutions and a masked self-attention mechanism to selectively focus on relevant image regions. Extensive experiments conducted on both synthetic and real SAR datasets demonstrate that the proposed method consistently outperforms current state-of-the-art methods, achieving superior speckle suppression while preserving fine details more effectively. Full article

(This article belongs to the Special Issue SAR Images Processing and Analysis (3rd Edition))

20 pages, 24227 KB

Open AccessArticle

Quantifying the Contribution of Forest Restoration to Wind Erosion Control Using RWEQ—A Case Study of Duolun County in Inner Mongolia, China

by Yan Xin, Huirong Li, Linli Sun, Songqing Zhou, Yongming Xu, Zheng Lin and Yuchen Yuan

Remote Sens. 2025, 17(23), 3861; https://doi.org/10.3390/rs17233861 (registering DOI) - 28 Nov 2025

Abstract

Wind erosion is one of the most severe environmental problems in arid and semi-arid regions, posing a serious threat to ecological security and human settlements. Afforestation is widely acknowledged as a practical strategy for mitigating wind erosion. However, quantitative assessments of the relationship [...] Read more.

Wind erosion is one of the most severe environmental problems in arid and semi-arid regions, posing a serious threat to ecological security and human settlements. Afforestation is widely acknowledged as a practical strategy for mitigating wind erosion. However, quantitative assessments of the relationship between forest restoration and wind erosion control remain limited, particularly over long temporal scales and at fine spatial resolutions. This study takes Duolun County, Inner Mongolia, as a representative case to examine the role of large-scale forest restoration in controlling wind erosion. Specifically, land use dynamics from 1985 to 2024 were mapped using a time series of Landsat imagery to identify forest expansion. Then, the Revised Wind Erosion Equation (RWEQ) was applied to simulate the spatiotemporal variations in wind erosion and sand fixation. Finally, a scenario-based framework contrasting forested and non-forested conditions was used to isolate and quantify the contribution of forest restoration to wind erosion control. Results showed that forest cover increased significantly from 3.95% to 36.19% over the past 40 years, with expansion primarily concentrated in the central desertified regions and the northern hilly areas. Sand fixation increased from

8.70 \times 10^{5}

t to

8.20 \times 10^{6}

t, with an average annual growth of

9.06 \times 10^{4}

t/year. Spatially, growth rates were more pronounced in the central and northern regions than in the south. Ecological restoration programs contributed substantially to wind erosion control, with their attributable sand fixation increasing from near zero to

6.61 \times 10^{5}

t, with an average annual rate of

8.21 \times 10^{3}

t/year. These findings provide new insights into the role of large-scale forest restoration in enhancing sand fixation and mitigating wind erosion. Full article

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26 pages, 26439 KB

Open AccessArticle

Impact of Joint Assimilating AWS and Radar Observations on the Analysis and Forecast of a Squall Line with Complex Terrain

by Ruonan Zhao, Dongmei Xu, Cong Li and Zhixin He

Remote Sens. 2025, 17(23), 3860; https://doi.org/10.3390/rs17233860 (registering DOI) - 28 Nov 2025

Abstract

Based on the WRF-3DVar system, this study investigates the impacts of assimilating radar and automatic weather station (AWS) observations, both independently and jointly, for a squall line case that occurred over complex terrain in China on 30 May 2024. It is found that [...] Read more.

Based on the WRF-3DVar system, this study investigates the impacts of assimilating radar and automatic weather station (AWS) observations, both independently and jointly, for a squall line case that occurred over complex terrain in China on 30 May 2024. It is found that radar data assimilation with spatial truncation significantly enhances the representation of convective structures while reducing false echoes by about 40%. However, when the variance and correlation length scales are enlarged, reflectivity intensity is increased by 5–10 dBZ with false signals and positional errors also introduced, while a balanced scheme is observed to yield the highest skill scores. Assimilation of AWS alone provides limited improvements, whereas radar assimilation introduces localized structures that rapidly decay within 1–2 h due to the absence of boundary-layer constraints. The benefits of joint assimilation are clearly demonstrated in terms of spatial continuity and vertical consistency, with the assimilation order being identified as a decisive factor. When AWS is assimilated prior to radar, low-level thermodynamic and dynamic conditions are optimized, leading to strengthened cold pool structures by around 2 K, enhanced updrafts by over 20%, and improved wind distribution. The critical role of AWS-radar joint assimilation in capturing the dynamical characteristics of squall lines is thus highlighted. Detailed examination of the forecast and analysis indicates that assimilating AWS before radar not only optimizes boundary-layer conditions but also enhances the coupling between cold pools and updrafts, resulting in improved simulation accuracy in both horizontal and vertical structures. These findings provide valuable insights for advancing the prediction of severe convective systems. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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28 pages, 132469 KB

Open AccessArticle

Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas

by Jinghan Wang, Yuefei Zhou, Miaohang Zhou, Zengjing Song, Xiangyu Ji and Xujun Han

Remote Sens. 2025, 17(23), 3859; https://doi.org/10.3390/rs17233859 (registering DOI) - 28 Nov 2025

Abstract

Land cover products are essential tools in environmental and ecological research. However, limited attention has been paid to their data quality issues. Many existing products suffer from pronounced spatiotemporal inconsistencies, characterized by frequent and repetitive classification fluctuations in specific regions and years, which [...] Read more.

Land cover products are essential tools in environmental and ecological research. However, limited attention has been paid to their data quality issues. Many existing products suffer from pronounced spatiotemporal inconsistencies, characterized by frequent and repetitive classification fluctuations in specific regions and years, which substantially compromise the accuracy of analyses and models that rely on them. To address these challenges, this study introduces a method for deriving spatiotemporally stable samples to support high-precision land cover classification. The approach integrates national and regional land-use policies to assess temporal stability and incorporates advanced time-series processing techniques together with innovative vegetation indices to facilitate effective sample reuse. Experimental results show that this method markedly improves classification accuracy across vegetation types and reduces the extent of areas prone to frequent land-cover changes by 22.64%. Compared with existing products of similar spatial resolution, our approach achieves an overall classification accuracy of 91.1%, providing stable, high-quality input data that underpin precise and reliable regional-scale environmental and ecological modeling. Full article

(This article belongs to the Special Issue Intelligent Image Analysis: Advancing Remote Sensing with Artificial Intelligence)

26 pages, 1699 KB

Open AccessArticle

Ship Target Feature Detection of Airborne Scanning Radar Based on Trajectory Prediction Integration

by Fan Zhang, Zhenghuan Xia, Shichao Jin, Xin Liu, Zhilong Zhao, Chuang Zhang, Han Fu, Kang Xing, Zongqiang Liu, Changhu Xue, Tao Zhang and Zhiying Cui

Remote Sens. 2025, 17(23), 3858; https://doi.org/10.3390/rs17233858 (registering DOI) - 28 Nov 2025

Abstract

In order to address the challenges faced by airborne scanning radars in detecting maritime ship targets, such as low signal-to-clutter ratios and the strong spatio-temporal non-stationarity of sea clutter, this paper proposes a multi-feature detection method based on trajectory prediction integration. First, the [...] Read more.

In order to address the challenges faced by airborne scanning radars in detecting maritime ship targets, such as low signal-to-clutter ratios and the strong spatio-temporal non-stationarity of sea clutter, this paper proposes a multi-feature detection method based on trajectory prediction integration. First, the Margenau–Hill Spectrogram (MHS) is employed for time–frequency analysis and uniformization processing. The extraction of features is conducted across three dimensions: energy intensity, spatial clustering, and distributional disorder. The metrics employed in this study include ridge integral (RI), maximum size of connected regions (MS), and scanning slice time–frequency entropy (SSTFE). Feature normalization is achieved via reference units to eliminate dynamic range variations. Secondly, a trajectory prediction matrix is constructed to correlate target cross-scan distance variations. When combined with a scan weight matrix that dynamically adjusts multi-frame contributions, this approach enables effective accumulation of target features across multiple scans. Finally, the greedy convex hull algorithm is used to complete target detection with a controllable false alarm rate. The validation process employs real-world data from a C-band dual-polarization airborne scanning radar. The findings indicate a 36.11% enhancement in the number of successful detections in comparison to the conventional single-frame three-feature detection method. Among the extant scanning algorithms, this approach evinces optimal feature space separability and detection performance, thus offering a novel pathway for maritime target detection using airborne scanning radars. Full article

21 pages, 30242 KB

Open AccessArticle

A Fast Collaborative Representation Algorithm Based on Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection

by Fang He, Shuanghao Fan, Haojie Hu, Jianwei Zhao, Jiaxin Dong and Weimin Jia

Remote Sens. 2025, 17(23), 3857; https://doi.org/10.3390/rs17233857 (registering DOI) - 28 Nov 2025

Abstract

As one of the vital research directions in hyperspectral image (HSI) processing, anomaly detection is dedicated to identifying anomalous pixels in HSIs that have significant spectral differences from the surrounding background, and it has attracted extensive attention from numerous scholars in recent years. [...] Read more.

As one of the vital research directions in hyperspectral image (HSI) processing, anomaly detection is dedicated to identifying anomalous pixels in HSIs that have significant spectral differences from the surrounding background, and it has attracted extensive attention from numerous scholars in recent years. Anomaly detectors based on collaborative representation have achieved favorable performance in this field. Based on CRD, scholars have proposed many different variants. However, most of these methods only focus on the spectral information of HSIs, and they suffer from slow detection speed and poor robustness. In this paper, we combine the Extended Multi-Attribute Profile (EMAP) with the CRD algorithm, propose a fast collaborative representation anomaly detection algorithm based on the extended multi-attribute profile. First, we use EMAP to extract the spatial structural information of the HSI. Then, before the anomaly detection, we employ the k-means clustering algorithm to separate anomalous pixels with similar features, and obtain a reconstructed background dictionary matrix. This further separates the background from anomalies and improves the robustness of anomaly detection. Finally, we apply a collaborative representation-based anomaly detector to detect anomalies. The proposed method is compared with other algorithms through experiments on four real HSI datasets and one synthetic HSI dataset. The experimental simulation results verify the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling)

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29 pages, 43939 KB

Open AccessArticle

GPRNet: A Geometric Prior-Refined Semantic Segmentation Network for Land Use and Land Cover Mapping

by Zhuozheng Li, Zhennan Xu, Runliang Xia, Jiahao Sun, Ruihui Mu, Liang Chen, Daofang Liu and Xin Li

Remote Sens. 2025, 17(23), 3856; https://doi.org/10.3390/rs17233856 (registering DOI) - 28 Nov 2025

Abstract

Semantic segmentation of high-resolution remote sensing images remains a challenging task due to the intricate spatial structures, scale variability, and semantic ambiguity among ground objects. Moreover, the reliable delineation of fine-grained boundaries continues to impose difficulties on existing CNN- and transformer-based models, particularly [...] Read more.

Semantic segmentation of high-resolution remote sensing images remains a challenging task due to the intricate spatial structures, scale variability, and semantic ambiguity among ground objects. Moreover, the reliable delineation of fine-grained boundaries continues to impose difficulties on existing CNN- and transformer-based models, particularly in heterogeneous urban and rural environments. In this study, we propose GPRNet, a novel geometry-aware segmentation framework that leverages geometric priors and cross-stage semantic alignment for more precise land-cover classification. Central to our approach is the Geometric Prior-Refined Block (GPRB), which learns directional derivative filters, initialized with Sobel-like operators, to generate edge-aware strength and orientation maps that explicitly encode structural cues. These maps are used to guide structure-aware attention modulation, enabling refined spatial localization. Additionally, we introduce the Mutual Calibrated Fusion Module (MCFM) to mitigate the semantic gap between encoder and decoder features by incorporating cross-stage geometric alignment and semantic enhancement mechanisms. Extensive experiments conducted on the ISPRS Potsdam and LoveDA datasets validate the effectiveness of the proposed method, with GPRNet achieving improvements of up to 1.7% mIoU on Potsdam and 1.3% mIoU on LoveDA over strong recent baselines. Furthermore, the model maintains competitive inference efficiency, suggesting a favorable balance between accuracy and computational cost. These results demonstrate the promising potential of geometric-prior integration and mutual calibration in advancing semantic segmentation in complex environments. Full article

(This article belongs to the Special Issue Multi-Task Remote Sensing Image Analysis: Classification, Segmentation, and Change Detection)

27 pages, 13327 KB

Open AccessArticle

Boosting SAR ATR Trustworthiness via ERFA: An Electromagnetic Reconstruction Feature Alignment Method

by Yuze Gao, Dongying Li, Weiwei Guo, Jianyu Lin, Yiren Wang and Wenxian Yu

Remote Sens. 2025, 17(23), 3855; https://doi.org/10.3390/rs17233855 - 28 Nov 2025

Abstract

Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) methods exhibit a tendency to overfit specific operating conditions—such as radar parameters and background clutter—which frequently leads to high sensitivity against variations in these conditions. A novel electromagnetic reconstruction feature alignment (ERFA) method [...] Read more.

Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) methods exhibit a tendency to overfit specific operating conditions—such as radar parameters and background clutter—which frequently leads to high sensitivity against variations in these conditions. A novel electromagnetic reconstruction feature alignment (ERFA) method is proposed in this paper, which integrates electromagnetic reconstruction with feature alignment into a fully convolutional network, forming the ERFA-FVGGNet. The ERFA-FVGGNet comprises three modules: electromagnetic reconstruction using our proposed orthogonal matching pursuit with image-domain cropping-optimization (OMP-IC) algorithm for efficient, high-precision attributed scattering center (ASC) reconstruction and extraction; the designed FVGGNet combining transfer learning with a lightweight fully convolutional network to enhance feature extraction and generalization; and feature alignment employing a dual-loss to suppress background clutter while improving robustness and interpretability. Experimental results demonstrate that ERFA-FVGGNet boosts trustworthiness by enhancing robustness, generalization and interpretability. Full article

(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)

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22 pages, 14869 KB

Open AccessArticle

WMFA-AT: Adaptive Teacher with Weighted Multi-Layer Feature Alignment for Cross-Domain UAV Object Detection

by Gui Cheng, Hao Yang, Yan Tian, Meilin Xie, Chaoya Dang, Qing Ding and Xubin Feng

Remote Sens. 2025, 17(23), 3854; https://doi.org/10.3390/rs17233854 (registering DOI) - 28 Nov 2025

Abstract

Unmanned Aerial Vehicle (UAV) object detection has witnessed rapid progress in recent years. However, its heavy reliance on labeled data and the assumption of consistent data distributions between training and deployment domains limit its generalization ability, leading to significant performance degradation under domain [...] Read more.

Unmanned Aerial Vehicle (UAV) object detection has witnessed rapid progress in recent years. However, its heavy reliance on labeled data and the assumption of consistent data distributions between training and deployment domains limit its generalization ability, leading to significant performance degradation under domain shifts. To address this challenge arising from substantial discrepancies in feature distributions across UAV images captured under diverse conditions, we propose a novel framework: Adaptive Teacher with Weighted Multi-layer Feature Alignment (WMFA-AT) for cross-domain UAV object detection. WMFA-AT adopts a teacher–student mutual learning paradigm, integrating domain adversarial learning with weighted multi-layer feature alignment and strong-weak data augmentation to effectively mitigate domain discrepancies. Specifically, the student model performs adversarial alignment using multiple domain discriminators applied to different feature layers, where layer-wise transferability is quantitatively estimated and used to adaptively weight the alignment process. This strategy ensures that features from the source and target domains are aligned in a distribution-aware manner. Meanwhile, the teacher model benefits from the student model via mutual learning, incorporating knowledge from both source and target domains while avoiding overfitting to the source. To comprehensively evaluate the proposed approach, we construct four challenging cross-domain UAV object detection benchmarks covering cross-time, cross-camera, cross-view, and cross-weather scenarios. Experimental results demonstrate that WMFA-AT consistently improves detection accuracy across diverse domain shifts, highlighting its robustness, generalization capability, and practical applicability in real-world UAV deployment settings. Full article

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27 pages, 5060 KB

Open AccessArticle

A High-Fidelity Star Map Simulation Method for Airborne All-Time Three-FOV Star Sensor Under Dynamic Conditions

by Jingsong Zhou, Hui Zhang, Liang Fang, Xiaodong Gao, Kaili Lu, Wei Sun and Rujin Zhao

Remote Sens. 2025, 17(23), 3853; https://doi.org/10.3390/rs17233853 - 28 Nov 2025

Abstract

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, [...] Read more.

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, and detector noise models was developed to accurately model star trailing effects under dynamic conditions. First, a stellar position calculation model incorporating atmospheric refraction correction and platform motion parameters was established through coordinate transformations between the Geocentric Celestial Reference System (GCRS) and FOV coordinate system. Next, a complete energy transfer chain was constructed by combining star catalog data, atmospheric radiative properties, and detector noise characteristics. Finally, a quantitative evaluation system was introduced, employing metrics such as signal-to-noise ratio (SNR), total grayscale value (G_total), grayscale concentration index (GCI), and dynamic star displacement (DSD). Field experiments at 2388 m altitude (100.23°E, 26.86°N) demonstrated the average relative error of all evaluation metrics below 9% for static conditions and approximately 8% for dynamic scenarios between simulated and real star maps. The method effectively reproduces stellar radiation, atmospheric noise, and dynamic degradation, providing reliable simulation conditions for airborne star sensor testing and star trailing restoration algorithm development. Full article

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19 pages, 6278 KB

Open AccessArticle

Selecting the Optimal Approach for Individual Tree Segmentation in Euphrates Poplar Desert Riparian Forest Using Terrestrial Laser Scanning

by Asadilla Yusup, Xiaomei Hu, Ümüt Halik, Abdulla Abliz, Maierdang Keyimu and Shengli Tao

Remote Sens. 2025, 17(23), 3852; https://doi.org/10.3390/rs17233852 - 28 Nov 2025

Abstract

Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and [...] Read more.

Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and conducting forest inventories due to its complex stand structure and overlapping crowns. To determine the most effective ITS approach for P. euphratica, we benchmarked six commonly used tree segmentation approaches for terrestrial laser scanning (TLS) data: canopy height model segmentation (CHMS), point cloud segmentation (PCS), comparative shortest-path algorithm (CSP), stem location seed point segmentation (SPS), deep-learning trunk-based segmentation (TBS), and leaf–wood separation-based segmentation (LWS). All methods followed a unified preprocessing and tuning protocol. We evaluated these methods based on tree-count accuracy, crown delineation, and structural attributes such as tree height (H), diameter at breast height (DBH), and crown diameter (CD). The results indicated that the TBS and LWS methods performed the best, achieving a mean tree-count accuracy of 98%, while the CHMS method averaged only 46%. These two methods provide the basic branch structure within the tree crown, reducing the likelihood of incorrect segmentation. Validation against field-measured values for H, DBH, and CD showed that both the TBS and LWS methods achieved accuracies exceeding 80% (RMSE = 0.8 m), 86% (RMSE = 0.02 m), and 73% (RMSE = 0.7 m), respectively. For TLS data in P. euphratica desert riparian forests, these two methods provide the most reliable results, facilitating rapid plot-scale inventory and monitoring. These findings establish a practical basis for conducting high-accuracy inventories of Euphrates poplar desert riparian forests. Full article

(This article belongs to the Special Issue Close-Range LiDAR for Forest Structure and Dynamics Monitoring)

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27 pages, 9531 KB

Open AccessArticle

Assessing Wildfire Impacts from the Perspectives of Social and Ecological Remote Sensing

by Xiaolin Wang and Shaoyang Liu

Remote Sens. 2025, 17(23), 3851; https://doi.org/10.3390/rs17233851 - 27 Nov 2025

Abstract

Wildfires in the Wildland–Urban Interface (WUI) pose escalating threats to socio-ecological systems, challenging regional resilience and sustainable recovery. Understanding the compound impacts of such fires requires an integrated, data-driven assessment of both ecological disturbance and social response. This study develops a multi-dimensional framework [...] Read more.

Wildfires in the Wildland–Urban Interface (WUI) pose escalating threats to socio-ecological systems, challenging regional resilience and sustainable recovery. Understanding the compound impacts of such fires requires an integrated, data-driven assessment of both ecological disturbance and social response. This study develops a multi-dimensional framework combining multisource remote sensing data (Landsat/Sentinel-2 NDVI and VIIRS nighttime light) with socio-structural indicators. A Composite Disturbance Index (ImpactIndex) was constructed to quantify ecological, population, and socioeconomic disruption across six fire clusters in the January 2025 Southern California wildfires. Mechanism analysis was conducted using Fixed-Effects OLS (M2) and Geographically Weighted Regression (GWR, M3) models. The ImpactIndex revealed that Eaton and Palisades experienced the most severe compound disturbances, while Border 2 showed purely ecological impacts. During-disaster CNLI signals were statistically decoupled from ecological disturbance (ΔNDVI) and dominated by site-specific effects (p < 0.001). GWR results (Adj. R² = 0.354) confirmed asymmetric spatial heterogeneity: high-density clusters (Palisades, Kenneth) exhibited a significant “Structural Burden” effect, whereas low-density areas showed weak, nonsignificant recovery trends. This “Index-to-Mechanism” framework redefines the interpretation of nighttime light in disaster contexts and provides a robust, spatially explicit tool for targeted WUI resilience planning and post-fire recovery management. Full article

(This article belongs to the Special Issue Remote Sensing and GIScience for Natural Hazard Mitigation and Resilience)

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29 pages, 26792 KB

Open AccessArticle

Hail Damage Detection: Integrating Sentinel-2 Images with Weather Radar Hail Kinetic Energy

by Adrian Ursu, Vasilică Istrate, Vasile Jitariu and Ionuț-Lucian Lazăr

Remote Sens. 2025, 17(23), 3850; https://doi.org/10.3390/rs17233850 - 27 Nov 2025

Abstract

Hailstorms represent one of the most damaging convective hazards for agriculture, yet quantifying their impacts at a landscape scale remains challenging due to their localized and short-lived nature. In this study, we combine weather radar parameters and Sentinel-2 multispectral imagery to assess vegetation [...] Read more.

Hailstorms represent one of the most damaging convective hazards for agriculture, yet quantifying their impacts at a landscape scale remains challenging due to their localized and short-lived nature. In this study, we combine weather radar parameters and Sentinel-2 multispectral imagery to assess vegetation damage caused by two major hail events in northeastern Romania: Rădăuți (17 July 2016) and Dolhasca (30 July 2020). Radar-derived hail kinetic energy (HKE) was used as a rapid temporal indicator of hail occurrence, with a threshold of 300 J m⁻² applied to delineate potentially affected areas. Sentinel-2 Level-1C imagery, selected under strict temporal and cloud cover criteria, was processed to generate pre- and post-event Normalized Difference Vegetation Index (NDVI) maps, from which NDVI differences (ΔNDVI) were computed. Thresholds of 0.10 and 0.20 were applied to identify moderate and severe vegetation stress, respectively. The results demonstrate strong spatial correspondence between radar-derived HKE cores and Sentinel-2 ΔNDVI reductions. In Rădăuți, where only one post-event image was available, ΔNDVI thresholds identified between 2236 and 5856 ha of affected vegetation within the HKE > 300 J m⁻² zone. In Dolhasca, where three post-event images were available (5, 8, and 15 days), the analysis revealed 6200–9100 ha affected at 5 days, decreasing to 4800–7200 ha at 8 days, and further to 3100–5600 ha at 15 days post-event. This temporal gradient highlights both the recovery of vegetation and the diminishing sensitivity of the ΔNDVI signal with increasing time elapsed since the event. Analysis by land use classes showed arable fields to be the most sensitive, followed by orchards and pastures, while forests exhibited smaller but persistent declines. This study demonstrates the robustness of integrating radar-derived hail kinetic energy with Sentinel-2 NDVI differencing for the spatiotemporal assessment of hail damage. The approach provides both rapid detection and temporally resolved mapping of hail damage, underlining the critical role of time as a determining factor in impact assessments. These findings have strong implications for operational crop monitoring, disaster response, and risk management in hail-prone regions. Full article

(This article belongs to the Special Issue Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources)

23 pages, 46843 KB

Open AccessArticle

A Sentinel-1 Based Hybrid Interferometric Approach to Complement EGMS for Landslides Identification

by Matteo Mantovani, Federica Ceccotto, Angelo Ballaera, Emilia Bertorelle, Giulia Bossi, Gianluca Marcato and Alessandro Pasuto

Remote Sens. 2025, 17(23), 3849; https://doi.org/10.3390/rs17233849 - 27 Nov 2025

Abstract

This study introduces a Hybrid Interferometric Approach (HIA) tailored for the detection, mapping, and measurement of landslides using Sentinel-1 satellite data. The HIA is specifically designed to identify ground displacements that exceed the detection thresholds of the European Ground Motion Service (EGMS), offering [...] Read more.

This study introduces a Hybrid Interferometric Approach (HIA) tailored for the detection, mapping, and measurement of landslides using Sentinel-1 satellite data. The HIA is specifically designed to identify ground displacements that exceed the detection thresholds of the European Ground Motion Service (EGMS), offering an enhanced capacity for monitoring faster-moving landslides. The methodology integrates multi-baseline interferometric analysis, utilizing backscattered signals from both point-like and distributed radar targets at full spatial resolution. The approach utilizes ten interferometric datasets acquired between 2017 and 2021 from both ascending and descending orbits. Each annual dataset is restricted to a six-month observation window to reduce temporal decorrelation effects. The HIA was implemented in a landslide-prone sector of the Dolomites, a UNESCO World Heritage Site located in the Eastern Italian Alps. Comparative evaluation against EGMS ground motion products demonstrates that the HIA significantly broadens the range of detectable slope instabilities, thus providing a valuable supplement to existing ground motion monitoring services and contributing meaningfully to landslide hazard assessment and risk reduction efforts. Full article

30 pages, 3480 KB

Open AccessArticle

MFE-STN: A Versatile Front-End Module for SAR Deception Jamming False Target Recognition

by Liangru Li, Lijie Huang, Tingyu Meng, Cheng Xing, Tianyuan Yang, Wangzhe Li and Pingping Lu

Remote Sens. 2025, 17(23), 3848; https://doi.org/10.3390/rs17233848 - 27 Nov 2025

Abstract

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and [...] Read more.

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

20 pages, 15632 KB

Open AccessArticle

Investigating an Earthquake Surface Rupture Along the Kumysh Fault (Eastern Tianshan, Central Asia) from High-Resolution Topographic Data

by Jiahui Han, Haiyun Bi, Wenjun Zheng, Hui Qiu, Fuer Yang, Xinyuan Chen and Jiaoyan Yang

Remote Sens. 2025, 17(23), 3847; https://doi.org/10.3390/rs17233847 - 27 Nov 2025

Abstract

As direct geomorphic evidence and records of earthquakes on the surface, coseismic surface ruptures have long been a key focus in earthquake research. However, compared with strike-slip and normal faults, studies on reverse-fault surface ruptures remain relatively scarce. In this study, surface rupture [...] Read more.

As direct geomorphic evidence and records of earthquakes on the surface, coseismic surface ruptures have long been a key focus in earthquake research. However, compared with strike-slip and normal faults, studies on reverse-fault surface ruptures remain relatively scarce. In this study, surface rupture characteristics of the most recent earthquake on the Kumysh thrust fault in eastern Tianshan were investigated using high-resolution topographic data, including 0.5 m- and 5 cm-resolution Digital Elevation Models (DEMs) generated from the WorldView-2 satellite stereo image pairs and Unmanned Aerial Vehicle (UAV) images, respectively. We carefully mapped the spatial geometry of the surface rupture and measured 120 vertical displacements along the rupture strike. Using the moving-window method and statistical analysis, both moving-mean and moving-maximum coseismic displacement curves were obtained for the entire rupture zone. Results show that the most recent rupture on the Kumysh Fault extends ~25 km with an overall NWW strike, exhibits complex spatial geometry, and can be subdivided into five secondary segments, which are discontinuously distributed in arcuate shapes across both piedmont alluvial fans and mountain fronts. Reverse fault scarps dominate the rupture pattern. The along-strike coseismic displacements generally form three asymmetric triangles, with an average displacement of 0.9–1.1 m and a maximum displacement of 2.8–3.2 m, yielding an estimated earthquake magnitude of M_w 6.6–6.7. This study not only highlights the strong potential of high-resolution remote sensing data for investigating surface earthquake ruptures, but also provides an additional example to the relatively underexplored reverse-fault surface ruptures. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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25 pages, 10906 KB

Open AccessArticle

Correction of Refraction Effects on Unmanned Aerial Vehicle Structure-from-Motion Bathymetric Survey for Coral Reef Roughness Characterisation

by Marion Jaud, Mila Geindre, Stéphane Bertin, Yoan Benoit, Emmanuel Cordier, France Floc’h, Emmanuel Augereau and Kévin Martins

Remote Sens. 2025, 17(23), 3846; https://doi.org/10.3390/rs17233846 - 27 Nov 2025

Abstract

Coral reefs play a crucial role in tropical coastal ecosystems, even though these environments are difficult to monitor due to their diversity and morphological complexity and due to their shallowness in some cases. This study used two approaches for acquiring very-high-resolution bathymetric data: [...] Read more.

Coral reefs play a crucial role in tropical coastal ecosystems, even though these environments are difficult to monitor due to their diversity and morphological complexity and due to their shallowness in some cases. This study used two approaches for acquiring very-high-resolution bathymetric data: underwater structure-from-motion (SfM) photogrammetry collected from a low-cost platform and unmanned/uncrewed aerial vehicle (UAV)-based SfM photogrammetry. While underwater photogrammetry avoids the distortions caused by refraction at air/water interface, it remains limited in spatial coverage (about 0.04 ha in 1 h of survey). In contrast, UAV photogrammetry allows for covering extensive areas (more than 20 ha/h) but requires applying refraction correction in order to accurately compute bathymetry and roughness values. An analytical approach based on Snell laws and an empirical approach based on linear regression (calibrated using a batch of points whose depths are representative of the depth range of the surveyed areas) are tested to correct the apparent depth on the raw UAV digital elevation model (DEM). Comparison to underwater photogrammetry shows that correcting refraction reduces the root mean square error (RMSE) by more than 50% (up to 62%) on bathymetric models, with RMSE lower than 0.13 m for the analytical approach and down to 0.09 m for the regression method. The linear-regression-based refraction correction proved most effective in restoring accurate seabed roughness, with a mean error on roughness lower than 17% (vs. 30% for analytical refraction correction and 48% for apparent bathymetry). Full article

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19 pages, 4950 KB

Open AccessArticle

Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions

by Shaoning Lv, Edward Ayres and Yin Hu

Remote Sens. 2025, 17(23), 3845; https://doi.org/10.3390/rs17233845 - 27 Nov 2025

Abstract

Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as [...] Read more.

Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as the τ-z model interpret microwave emissions to estimate soil moisture, taking into account the complex interactions between soil and radiation. However, in validating these models against high-temporal-resolution, ground-based measurements, especially from extensive networks like the Terrestrial National Ecological Observatory Network (NEON), further research and validation efforts are needed. This study comprehensively validates the τ-z model’s ability to estimate the soil temperature sensing depth (z_Teff) using data from the NEON and Soil Moisture Active Passive (SMAP) satellite missions. A harmonization process was conducted to align the spatial and temporal scales of the two datasets, enabling rigorous validation. We compared soil optical depth (τ)—a parameter capable of theoretically unifying sensing depth representations across wet soil (~0.05 m) to extreme dry/frozen conditions (e.g., up to ~1500 m in ice-equivalent scenarios)—and geometric depth (z) frameworks against outputs from the τ-z model and NEON’s in situ profiles. The results show that: (1) for the profiles that satisfy the monotonic assumption by the τ-z model, z_Teff fits the prediction well at about 0.2 τ for the average; (2) Combining SMAP’s soil moisture, the τ-z model achieves high accuracy in estimating z_Teff, with RMSD (0.05 m) and unRMSD (0.03 m), and correlations (0.67) between estimated and observed values. The findings are expected to advance remote sensing techniques in various fields, including agriculture, hydrology, and climate change research. Full article

(This article belongs to the Special Issue Root-Zone Soil Moisture Retrieval and Applications from Remote Sensing Measurements)

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