Search Results (2,914)

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article

Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation as its core component. Matching strategies in radargrammetry typically follow local, global, or semi-global methodologies. Local methods, while having higher accuracy, especially in low-texture SAR images, require larger kernel sizes, leading to quadratic computational complexity. Conversely, global and semi-global models produce more consistent and higher-quality disparity maps but are computationally more intensive than local methods with small kernels and require more memory (RAM). In this study, inspired by the advantages of local matching algorithms, a computationally efficient and novel model is proposed for extracting corresponding pixels in SAR-intensity stereo images. To enhance accuracy, the proposed two-stage algorithm operates without an image pyramid structure. Notably, unlike traditional local and global models, the computational complexity of the proposed approach remains stable as the input size or kernel dimensions increase while memory consumption stays low. Compared to a pyramid-based local normalized cross-correlation (NCC) algorithm and adaptive semi-global matching (SGM) models, the proposed method maintains good accuracy comparable to adaptive SGM while reducing processing time by up to 50% relative to pyramid SGM and achieving a 35-fold speedup over the local NCC algorithm with an optimal kernel size. Validated on a Sentinel-1 stereo pair with a 10 m ground-pixel size, the proposed algorithm yields a DEM with an average accuracy of 34.1 m. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. Full article

Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential. Full article

The equator’s unique combination of high humidity and temperature renders optical satellite imagery highly susceptible to persistent cloud cover. In contrast, synthetic aperture radar (SAR) offers a robust alternative due to its ability to penetrate clouds with microwave imaging. This study addresses the challenges of cloud-induced data gaps and cross-sensor geometric biases by proposing an advanced optical and SAR image-matching framework specifically designed for cloud-prone equatorial regions. We use a prompt-driven visual segmentation model with automatic prompt point generation to produce cloud masks that guide cross-modal feature-matching and joint adjustment of optical and SAR data. This process results in a comprehensive digital orthophoto map (DOM) with high geometric consistency, retaining the fine spatial detail of optical data and the all-weather reliability of SAR. We validate our approach across four equatorial regions using five satellite platforms with varying spatial resolutions and revisit intervals. Even in areas with more than 50 percent cloud cover, our method maintains sub-pixel edging accuracy under manual check points and delivers comprehensive DOM products, establishing a reliable foundation for downstream environmental monitoring and ecosystem analysis. Full article

The Medium Earth Orbit (MEO) spaceborne Synthetic Aperture Radar (SAR) has great coverage ability, which can improve maritime ship target surveillance performance significantly. However, due to the huge computational load required for imaging processing and the severe defocusing caused by ship motions, traditional ship recognition conducted in focused image domains cannot process MEO SAR data efficiently. To address this issue, a multi-level focusing-classification strategy for MEO SAR ship recognition is proposed, which is applied to the range-compressed ship data domain. Firstly, global fast coarse-focusing is conducted to compensate for sailing motion errors. Then, a coarse-classification network is designed to realize major target category classification, based on which local region image slices are extracted. Next, fine-focusing is performed to correct high-order motion errors, followed by applying fine-classification applied to the image slices to realize final ship classification. Equivalent MEO SAR ship images generated by real LEO SAR data are utilized to construct training and testing datasets. Simulated MEO SAR ship data are also used to evaluate the generalization of the whole method. The experimental results demonstrate that the proposed method can achieve high classification precision. Since only local region slices are used during the second-level processing step, the complex computations induced by fine-focusing for the full image can be avoided, thereby significantly improving overall efficiency. Full article

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types. Full article

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. Full article

Due to the sparse distribution of ship targets in wide-area offshore scenarios, the typical cascade mode of imaging and detection for space-borne Synthetic Aperture Radar (SAR) echo data would consume substantial computational time and resources, severely affecting the timeliness of ship target information acquisition tasks. Therefore, we propose a ship target integrated imaging and detection framework (ST-IIDF) for SAR oceanic region data. A two-step filtering structure is added in the SAR imaging process to extract the potential areas of ship targets, which can accelerate the whole process. First, an improved peak-valley detection method based on one-dimensional scattering characteristics is used to locate the range gate units for ship targets. Second, a dynamic quantization method is applied to the imaged range gate units to further determine the azimuth region. Finally, a lightweight YOLO neural network is used to eliminate false alarm areas and obtain accurate positions of the ship targets. Through experiments on Hisea-1 and Pujiang-2 data, within sparse target scenes, the framework maintains over 90% accuracy in ship target detection, with an average processing speed increase of 35.95 times. The framework can be applied to ship target detection tasks with high timeliness requirements and provides an effective solution for real-time onboard processing. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Natural gas storage is an effective solution to address the energy supply–demand imbalance, and underground gas storage (UGS) is a primary method for storing natural gas. The overarching goal of this study is to monitor and analyze surface deformation at the Hutubi underground gas storage facility in Xinjiang, China, which is the largest gas storage facility in the country. This research aims to ensure the stable and efficient operation of the facility through long-term monitoring, using remote sensing data and advanced modeling techniques. The study employs the SBAS-InSAR method, leveraging Synthetic Aperture Radar (SAR) data from the TerraSAR and Sentinel-1 sensors to observe displacement time series from 2013 to 2024. The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. The key focus is the development of a high-precision predictive model using a Gated Recurrent Unit (GRU) network, referred to as GWO-VMD-GRU, to accurately predict surface deformation. The results show periodic surface uplift and subsidence at the facility, with a notable net uplift. During the period from August 2013 to March 2015, the maximum uplift rate was 6 mm/year, while from January 2015 to December 2024, it increased to 12 mm/year. The surface deformation correlates with gas injection and extraction periods, indicating periodic variations. The accuracy of the InSAR-derived displacement data is validated through high-precision GNSS data. The GWO-VMD-GRU model demonstrates strong predictive performance with a coefficient of determination (R²) greater than 0.98 for the gas well test points. This study provides a valuable reference for the future safe operation and management of underground gas storage facilities, demonstrating significant contributions to both scientific understanding and practical applications in underground gas storage management. Full article

Mining activities can trigger geological disasters, including slope instability and surface subsidence, posing a serious threat to the surrounding environment and miners’ safety. Consequently, the development of reasonable, effective, and rapid deformation monitoring methods in mining areas is essential. Traditional synthetic aperture radar(SAR) satellites are often limited by their revisiting period and image resolution, leading to unwrapping errors and decorrelation issues in the central mining area, which pose challenges in deformation monitoring in mining areas. In this study, persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology is used to monitor and analyze surface deformation of the Jinchuan mining area in Jinchang City, based on SAR images from the small satellites “Fucheng-1” and “Shenqi”, launched by the Tianyi Research Institute in Hunan Province, China. Notably, the dual-star constellation offers high-resolution SAR data with a spatial resolution of up to 3 m and a minimum revisit period of 4 days. We also assessed the stability of the dual-star interferometric capability, imaging quality, and time-series monitoring capability of the “Fucheng-1” and “Shenqi” satellites and performed a comparison with the time-series results from Sentinel-1A. The results show that the phase difference (SPD) and phase standard deviation (PSD) mean values for the “Fucheng-1” and “Shenqi” interferograms show improvements of 21.47% and 35.47%, respectively, compared to Sentinel-1A interferograms. Additionally, the processing results of the dual-satellite constellation exhibit spatial distribution characteristics highly consistent with those of Sentinel-1A, while demonstrating relatively better detail representation capabilities at certain measurement points. In the context of rapid deformation monitoring in mining areas, they show a higher revisit frequency and spatial resolution, demonstrating high practical value. Full article