Search Results (1,165)

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

Accurate mapping of land use and land cover (LULC) is crucial for various applications such as urban planning, environmental management, and sustainable development, particularly in rapidly growing urban areas. African cities such as Yaoundé, Cameroon, are particularly affected by this rapid and often uncontrolled urban growth with complex spatio-temporal dynamics. Effective modeling of LULC indicators in such areas requires robust algorithms for high-resolution images segmentation and classification, as well as reliable data with great spatio-temporal distributions. Among the most suitable data sources for these types of studies, Sentinel-2 image time series, thanks to their high spatial (10 m) and temporal (5 days) resolution, are a valuable source of data for this task. However, for an effective LULC modeling purpose in such dynamic areas, many challenges remain, including spectral confusion between certain classes, seasonal variability, and spatial heterogeneity. This study proposes a hybrid deep learning architecture combining U-Net and Convolutional Long Short-Term Memory (ConvLSTM) layers, allowing the spatial structures and temporal dynamics of the Sentinel-2 series to be exploited jointly. Applied to the Yaoundé region (Cameroon) over the period 2018–2025, the hybrid model significantly outperforms the U-Net and ConvLSTM models alone. It achieves a macro-average F1 score of 0.893, an accuracy of 0.912, and an average IoU of 0.811 on the test set. These segmentation performances reached up to 0.948, 0.953, and 0.910 for precision, F1-score, and IoU, respectively, on the built-up areas class. Moreover, despite its better performance, in terms of complexity, the figures confirm that the hybrid does not significantly penalize evaluation speed. These results demonstrate the relevance of jointly integrating space and time for robust LULC classification from multi-temporal satellite images. Full article

Fucheng-1 is China’s first commercial synthetic aperture radar (SAR) satellite equipped with interferometric capabilities. Since its launch in 2023, it has demonstrated strong potential across a range of application domains. However, a comprehensive and systematic evaluation of its overall performance, including its time-series monitoring capability, is still lacking. This study applies the Small Baseline Subset (SBAS-InSAR) method to conduct the first systematic processing and evaluation of 22 Fucheng-1 images acquired between 2023 and 2024. A total of 45 potential landslides were identified and subsequently validated through field investigations and UAV-based LiDAR data. Comparative analysis with Sentinel-1 and ALOS-2 indicates that Fucheng-1 demonstrates superior performance in small-scale deformation identification, temporal-variation characterization, and maintaining a high density of coherent pixels. Specifically, in the time-series InSAR-based potential landslide identification, Fucheng-1 identified 13 small-scale potential landslides, whereas Sentinel-1 identified none; the number of identifications is approximately 2.17 times that of ALOS-2. For time-series subsidence monitoring, the deformation magnitudes retrieved from Fucheng-1 are generally larger than those from Sentinel-1, mainly attributable to finer spatial sampling enabled by its higher spatial resolution and a higher maximum detectable deformation gradient. Moreover, as landslide size decreases, the advantages of Fucheng-1 in deformation identification and subsidence estimation become increasingly evident. Interferometric results further show that the number of high-coherence pixels for Fucheng-1 is 7–8 times that of co-temporal Sentinel-1 and 1.1–1.4 times that of ALOS-2, providing more high-quality observations for time-series inversion and thereby supporting a more detailed and spatially continuous reconstruction of deformation fields. Meanwhile, the orbital stability of Fucheng-1 is comparable to that of Sentinel-1, and its maximum detectable deformation gradient in mountainous terrain reaches twice that of Sentinel-1. Overall, this study provides the first systematic validation of the time-series InSAR capability of Fucheng-1 under complex terrain conditions, offering essential support and a solid foundation for the operational deployment of InSAR technologies based on China’s domestic SAR satellite constellation. Full article

Information on crop planting structure serves as a key reference for crop growth monitoring and agricultural structural adjustment. Mapping the spatial distribution of crops through feature-based classification serves as a fundamental component of sustainable agricultural development. However, current crop classification methods often face challenges such as the discontinuity of optical data due to cloud cover and the limited discriminative capability of traditional SAR backscatter intensity for spectrally similar crops. In this case study, we assess multi-temporal Sentinel-1 and Sentinel-2 Satellite images for crop classification in a paddy rice growing region in Helonghu Town, located in the central region of Xiangyin County, Yueyang City, Hunan Province, China (28.5° N–29.0° N, 112.8° E–113.2° E). We systematically investigate three key aspects: (1) the classification performance using optical time-series Sentinel-2 imagery; (2) the time-series classification performance utilizing polarimetric SAR decomposition features from Sentinel-1 dual-polarimetric SAR images; and (3) the classification performance based on a combination of Sentinel-1 and Sentinel-2 images. Optimal classification results, with the highest overall accuracy and Kappa coefficient, are achieved through the combination of Sentinel-1 (SAR) and Sentinel-2 (optical) data. This case study evaluates the time-series classification performance of Sentinel-1 and Sentinel-2 data to determine the optimal approach for crop classification in Helonghu Town. Full article

The ongoing advance of urbanization has increased the need for accurate monitoring of urban green space (UGS). However, existing remote-sensing UGS mapping still struggles with inconsistent data quality, diverse urban forms, and limited cross-city generalization. This study focuses on China’s Guangdong-Hong Kong-Macao Greater Bay Area as its research region, establishing a fully automated UGS mapping framework based on Sentinel-2 time-series imagery and standardized OpenStreetMap (OSM) data. This process achieves UGS mapping at 10 m resolution for 16 cities within the metropolitan area through a dynamic standardized OSM tagging system, a Sentinel-2 satellite image sample generation mechanism integrating spectral and textural features, multidimensional sample quality assessment and weighting strategies, as well as balanced cross-city sampling and weighted SVM classification. The results demonstrate that this method exhibits stable performance across multiple urban environments, achieving an average overall accuracy of approximately 0.83 and an average F1 score of approximately 0.82. The highest recorded F1 score reaches 0.96, highlighting the method’s strong generalization capability under diverse urban conditions. The mapping results reveal significant disparities in UGS distribution within the Guangdong-Hong Kong-Macao Greater Bay Area, reflecting the combined effects of varying urban development patterns and ecological contexts. The unified workflow proposed in this study demonstrates strong applicability in handling heterogeneous urban structures and enhancing cross-regional comparability. It provides consistent, transparent, and reusable foundational data for regional eco-urban planning, urban green infrastructure development, and policy evaluation. Full article

Incremental computing is becoming increasingly important for processing large-scale datasets. In satellite imagery, spatial resolution, temporal depth, and large files pose significant computational challenges, requiring efficient architectures to manage processing time and resource usage. Accordingly, in this study, we propose a dynamic architecture, termed Multi-Cube, for optical satellite time series. The framework introduces a modular and baseline-aware approach that enables scalable subdivision, incremental growth, and consistent management of spatiotemporal data. Built on NetCDF, xarray, and Zarr, Multi-Cube automatically constructs stable multidimensional data cubes while minimizing redundant reprocessing, formalizing automated internal decisions governing cube subdivision, baseline reuse, and incremental updates to support recurrent monitoring workflows. Its performance was evaluated using more than 83,000 Sentinel-2 images (covering 2016–2024) across multiple areas of interest. The proposed approach achieved a 5.4× reduction in end-to-end runtime, decreasing execution time from 53 h to 9 h, while disk I/O requirements were reduced by more than two orders of magnitude compared with a traditional sequential reprocessing pipeline. The framework supports parallel execution and on-demand sub-cube extraction for responsive large-area monitoring while internally handling incremental updates and adaptive cube management without requiring manual intervention. The results demonstrate that the Multi-Cube architecture provides a decision-driven foundation for integrating dynamic Earth observation workflows with analytical modules. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

This work presents an automated method for detecting and monitoring volcanic islands in the Tonga archipelago using Sentinel-2 satellite imagery. The method is able to detect newly created islands, as well as an increase in island size, a possible precursor to an explosion due to magma chamber inflation. At its core, the method combines a U-Net-type convolutional neural network (CNN) for semantic segmentation with a custom change detection algorithm, enabling the identification of land–water boundaries and the tracking of volcanic island dynamics. The algorithm analyzes morphological changes through image comparison and Intersection over Union (IoU), capturing the emergence, disappearance, and evolution of volcanic islands. The segmentation model, trained on a custom dataset of Pacific Ocean imagery, achieved an IoU score of 97.36% on the primary test dataset and 83.54% on a subset of challenging cases involving small, recently formed volcanic islands. Generalization capability was validated using the SNOWED dataset, where the segmentation model attained an IoU of 81.02%. Applied to recent volcanic events, the workflow successfully detected changes in island morphology and provided time-series analyses. Practical feasibility of the methodology was assessed by testing it on a large region in Tonga, using an HPC cluster. This system offers potential applications for geophysical studies and navigation safety in volcanically active regions. Full article

Accurate identification and monitoring of rice cultivation areas are essential for food security and sustainable agricultural development. However, regions with frequent cloud cover, high rainfall, and fragmented fields often face challenges due to the absence of temporal features caused by cloud and rain interference, as well as spectral confusion from scattered plots, which hampers rice recognition accuracy. To address these issues, this study employs a Satellite Image Time Series Transformer (SITS-Former) model, enhanced with the integration of diverse phenological features to improve rice phenology representation and enable precise rice identification. The methodology constructs a rice phenological feature set that combines temporal, spatial, and spectral information. Through its self-attention mechanism, the model effectively captures growth dynamics, while multi-scale convolutional modules help suppress interference from non-rice land covers. The study utilized Sentinel-2 satellite data to analyze rice distribution in Wuxi City. The results demonstrated an overall classification accuracy of 0.967, with the estimated planting area matching 91.74% of official statistics. Compared to traditional rice distribution analysis methods, such as Random Forest, this approach outperforms in both accuracy and detailed presentation. It effectively addresses the challenge of identifying fragmented rice fields in regions with persistent cloud cover and heavy rainfall, providing accurate mapping of cultivated areas in difficult climatic conditions while offering valuable baseline data for yield assessments. Full article

Rangeland degradation poses a serious challenge for the sustainable management of free-ranging livestock in Mediterranean wetlands. In Doñana National Park, Spain, the endangered Marismeño horse depends exclusively on natural forage, making it essential to monitor vegetation productivity and grazing suitability under increasing climate variability. This study presents a satellite-based assessment of rangeland carrying capacity to support the adaptive management of this iconic breed. A six-year time series (2015–2020) of 1242 images from Landsat 8 OLI/TIRS and Sentinel-2 (L1C/L2A) was processed using ILWIS and Python-based workflows to derive vegetation indices (GNDVI, NDMI) and model aboveground biomass, forage energy, and grazing pressure across five grazing units. Results revealed strong seasonal cycles, with biomass and nutritive value peaking in spring and declining sharply in summer. Ecotonal zones such as La Vera y Sotos acted as crucial refuges during drought-induced resource shortages. The harmonized multi-sensor approach demonstrated high reliability for mapping forage dynamics and assessing carrying capacity at fine scales. This remote sensing framework offers an effective, scalable tool for sustainable livestock management in Doñana, directly supporting biodiversity conservation and the long-term resilience of Mediterranean rangeland ecosystems. Full article

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation. Full article

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations $({\sigma }_r,{\sigma }_a,{\sigma }_c)$ are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows. Full article

This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R²) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article

Abandoned cropland has been expanding due to complex socio-economic factors such as urbanization, demographic shifts, and declining agricultural profitability. As abandoned cropland simultaneously brings ecological, environmental, and social risks and benefits, quantitative monitoring is essential to assess its overall impact. Satellite image-based spatial data are suitable for identifying spectral characteristics related to crop phenology, and recent research has advanced in detecting large-scale abandoned cropland through changes in time-series spectral characteristics. However, frequent cloud covers and highly fragmented croplands, which vary across regions and climatic conditions, still pose significant challenges for satellite-based detection. This study combined Harmonized Landsat and Sentinel-2 (HLS) imagery, offering high temporal (2–3 days) and spatial (30 m) resolution, with the eXtreme Gradient Boosting (XGBoost) algorithm to capture seasonal spectral variations among rice paddy, upland fields, and abandoned croplands. An XGBoost model with a Balanced Bagging Classifier (BBC) was used to mitigate class imbalance. The model achieved an accuracy of 0.84, $Cohen’s kappa$ 0.71, and $F2 score$ 0.84. SHapley Additive exPlanations (SHAP) analysis identified major features such as NIR (May–June), SWIR2 (January), MCARI (September), and BSI (January–April), reflecting phenological differences among cropland types. Overall, this study establishes a robust framework for large-scale cropland monitoring that can be adapted to different regional and climatic settings. Full article