Search Results (345)

In densely vegetated and topographically complex mountainous areas, the applicability of SAR data for potential landslide hazard identification depends not only on whether slopes are visible to the radar, but also on whether stable interferometric coherence can be preserved under vegetation and terrain constraints. To clarify the applicability differences between L-band Lutan-1 and C-band Sentinel-1 in such environments, this study focused on Hanyuan County, Sichuan Province, China. Ascending and descending SAR images acquired by the two satellite systems from 2024 to 2025 were processed using stacking-based Interferometric Synthetic Aperture Radar (Stacking-InSAR) and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to extract regional deformation anomalies and time-series deformation characteristics of representative landslides. DEM, LiDAR, optical imagery, fractional vegetation cover (FVC) derived from Sentinel-2, and field investigation data were further integrated to establish a comparative framework linking geometric visibility, interferometric coherence, and landslide identification results. The results show that both Lutan-1 and Sentinel-1 provided favorable geometric observation conditions after combining ascending and descending tracks, with joint visibility proportions of 98.48% and 97.76%, respectively, indicating limited differences in geometric coverage within the study area. However, at a unified grid scale, the mean coherence and valid grid-cell proportion of Lutan-1 reached 0.564 and 72.49%, respectively, substantially higher than those of Sentinel-1, which were 0.320 and 24.26%. As FVC increased, coherence decreased for both datasets, but Lutan-1 maintained higher coherence in densely vegetated areas, suggesting stronger adaptability to vegetation-induced decorrelation. Based on integrated interpretation of multi-source remote sensing data, 77 potential landslide hazards were identified in the study area, including 74 detected by Lutan-1, 17 detected by Sentinel-1, and 14 jointly detected by both datasets. Comparisons of representative landslides further show that Lutan-1 provided a higher density of valid deformation points in densely vegetated and small-scale landslides, with deformation patterns corresponding well to slope geomorphic boundaries and local deformation zones. Sentinel-1, with its higher temporal sampling density, can provide complementary information for time-series verification and multi-source cross-validation of key landslides. These results indicate that Lutan-1 is more suitable for spatial identification of potential landslide hazards in densely vegetated, topographically complex mountainous areas, while the joint use of Lutan-1 and Sentinel-1 can better balance landslide identification detail and time-series monitoring continuity. Full article

Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and planner-ready map. This study presents a satellite–unmanned aerial vehicle (UAV) workflow for constructing and incrementally maintaining an off-road traversability map for mission-level global planning. A common H3 index organizes satellite imagery, terrain, soil, road evidence, and local UAV semantic observations while retaining their native spatial support and provenance. The map separates environmental-prior, semantic, and traversability-cost layers to support interpretable fusion and independent updating. A confidence-hierarchical conflict resolution mechanism resolves inconsistencies in the regional prior, while an observer-agnostic interface projects UAV semantic observations onto local map cells. RGB imagery is used by the primary UAV observer, and digital surface model (DSM) is evaluated as an optional semantic-observation modality. Evaluation included a manually reviewed regional benchmark, a unified buffered spatial holdout, cell-level update assessment, and 40 fixed replanning tasks. Conflict resolution reduced high-risk omissions. RGB-only SegFormer-B2 achieved the highest semantic accuracy with moderate computational complexity. UAV override achieved a cell-level F1 score of 96.96% and limited the false-positive accumulation associated with conservative union. Replanning further revealed a trade-off between hazardous-cell avoidance and search-graph connectivity. The proposed workflow provides a maintainable interface between multi-source remote sensing and global UGV planning rather than a replacement for onboard perception, local obstacle avoidance, or vehicle control. Full article

Atmospheric effects substantially influence remote-sensing reflectance retrieval in optically complex inland waters. This study evaluated seven atmospheric correction approaches (QUAC, FLAASH, Sen2Cor, LaSRC, 6S, C2RCC, and ACOLITE) for Sentinel-2 MSI and Landsat-8/9 OLI imagery over the Danjiangkou and Luhun reservoirs. The evaluation used 67 quality-controlled, temporally matched in situ spectral observations and satellite matchups. Performance was quantified using the squared Pearson correlation coefficient (r²), root mean square error (RMSE), and average unsigned relative error (AURE). Laboratory-measured chlorophyll-a (Chl-a) concentrations were used to develop sensor-specific retrieval models and to examine how atmospheric-correction differences propagated into Chl-a estimates and spatial patterns. Because residual aerosol and sun-glint effects may remain after atmospheric correction, an exploratory SWIR-based adjustment was evaluated for the C2RCC visible-band outputs. In the pooled-band analysis, C2RCC yielded the most favorable balance of the evaluated metrics for both sensor datasets. However, performance varied among bands, and the Landsat-8/9 B5 output showed very weak covariation with the in situ measurements. Within the model-development dataset, Sen2Cor achieved the highest Sentinel-2 r² (0.762), whereas C2RCC achieved the lowest Sentinel-2 RMSE (2.30 mg/m³). C2RCC achieved both the highest Landsat-8/9 r² (0.689) and the lowest RMSE (3.18 mg/m³). Independent temporal validation used 14 Luhun observations from 2024. Sen2Cor yielded the lowest Sentinel-2 RMSE and AURE (1.658 mg/m³ and 29.28%). For Landsat-8/9 OLI, C2RCC yielded the highest r² (0.536), lowest RMSE (3.468 mg/m³), and lowest AURE (44.08%). Relative errors increased in weak-signal near-infrared bands, underscoring the need for band-specific interpretation. The SWIR-based adjustment improved both RMSE and AURE for Sentinel-2 MSI but did not provide a consistent improvement for Landsat-8/9 OLI. An exploratory comparison of quality-screened imagery from 2016 to 2025 showed broadly similar reservoir-scale Chl-a patterns in C2RCC-derived products from the two sensors. These results provide reservoir-specific evidence for atmospheric-correction selection and Chl-a retrieval under the sampled conditions. Full article

Tropical restoration areas present high spectral heterogeneity and spatial fragmentation, making land use and land cover (LULC) classification challenging, particularly when using medium-resolution satellite imagery. Although spectral mixture analysis, object-based image analysis, and machine learning approaches have been widely explored individually, their integrated application using freely available China–Brazil Earth Resources Satellite 4A Wide-field Panchromatic and Multispectral (CBERS-4A/WPM) imagery for monitoring Environmental Restoration Commitment Agreements (TCRAs) remains limited. This study investigated whether the integration of Linear Spectral Mixture Model (LSMM) fractions, Geographic Object-Based Image Analysis (GEOBIA), and machine learning algorithms could improve LULC classification for mapping TCRAs in the state of São Paulo, Brazil. Spectral, textural, and sub-pixel attributes derived from pan-sharpened CBERS-4A/WPM imagery were combined into a unified feature dataset, and Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Machine classifiers were evaluated. Random Forest achieved the best classification performance, with an overall accuracy of approximately 0.66, demonstrating superior predictive capability compared with the other tested algorithms. Feature importance analysis indicated that LSMM-derived vegetation and shadow fractions contributed significantly to class discrimination. The integration of spectral, spatial, and machine learning approaches provides an interpretable and cost-effective framework for LULC mapping in highly fragmented tropical restoration landscapes, supporting environmental monitoring and restoration assessment. Full article

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable. Full article

Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation and fine canopy structure, which can reduce vegetation-index reliability. Most super-resolution (SR) research targets RGB or satellite imagery and emphasizes perceptual or pixel-wise quality, leaving the spectral fidelity of reconstructed UAV multispectral imagery under-examined. This study benchmarked an SR evaluation framework for UAV-based five-band crop imagery (Blue, Green, Red, Red-edge, and near-infrared) using the open-source AI Hub cabbage dataset, with low-resolution inputs generated by controlled downsampling at ×2, ×3, and ×4. Nine methods (bicubic, SRCNN, EDSR, RCAN, SwinIR-based, ESRGAN-based, HAT-based, DAT-based, and DRCT-based SR) were compared under joint five-channel and band-wise reconstruction on 2170 test scenes using image-quality, spectral-angle, vegetation-index (NDVI, GNDVI, NDRE), band-wise, and efficiency metrics. EDSR and RCAN gave the most balanced performance. At ×4, band-wise reconstruction was strongest for per-band spatial fidelity, where EDSR reduced RMSE by 12.6%, and RCAN lowered near-infrared RMSE by about 21% relative to bicubic, whereas joint reconstruction with its spectral-angle and vegetation-index losses best preserved spectral relationships (spectral angle and vegetation-index errors). Learning-based gains were clearest at ×4. The recently proposed HAT-based, DAT-based, and DRCT-based attention models achieved the strongest pixel-wise RMSE and PSNR but did not surpass EDSR or RCAN on spectral angle or vegetation-index preservation under the equalized training budget. These results indicate that UAV multispectral SR should be assessed by spatial fidelity together with spectral consistency and agricultural index reliability. Full article

Warsaw experiences significant urban heat island (UHI) effects driven by low-albedo surfaces and urban geometry, which pose ongoing challenges for public health and climate adaptation. This study evaluates daytime SUHI intensity at satellite acquisition time across the entire city to provide a comparison of two acquisition dates during heatwaves. Utilizing Landsat 7 and Landsat 9 satellite imagery from July 2015 and July 2022, the research compares six distinct SUHII calculation methods, including spectral indices, statistical normalizations, and area-based temperature differences, as minimum SUHII values differed significantly between the two observations (shifting from approximately −13.8 °C to −7.7 °C). This indicates that suburban areas can become thermally similar to the city due to rapid land conversion and decreased evaporative cooling of vegetation during severe heat. Average intensities calculated via the SUHII 4 method reached 5.80 °C in 2015 and 2.07 °C in 2022. Under the criteria considered in this case study—interpretability, explicit physical units, treatment of water bodies, data requirements, and spatial consistency—SUHII 4 was the most suitable of the six tested formulations for the Warsaw analysis. Conversely, dimensionless spectral indices and purely statistical approaches are not recommended due to interpretative limitations. Full article

Coastal erosion is commonly assessed from shoreline-position change using methods such as the Digital Shoreline Analysis System (DSAS). However, on microtidal sandy beaches with strong seasonal wave variability, shoreline retreat may reflect temporary sediment redistribution rather than permanent erosion. This study evaluates the morphodynamic behaviour of a ~280 km sandy coastline along the Mexican Caribbean (Isla Blanca–Punta Allen) between 2009 and 2024 using high-resolution RapidEye and PlanetScope satellite imagery. Beach-area changes were quantified for 160 sedimentary cells and analysed with the Mann–Kendall test to identify long-term morphological trends, while shoreline mobility was assessed separately using the DSAS End Point Rate (EPR) and Linear Regression Rate (LRR). The results indicate that the coastline is predominantly characterised by dynamic equilibrium and seasonal sediment redistribution: 64.4% of the cells remained stable, 15.0% exhibited redistributive behaviour, and only 12.5% showed persistent beach-area loss. In contrast, the DSAS analysis based on Total EPR showed shoreline retreat (EPR < 0) in 78.1% of the cells, whereas only 12.5% showed persistent beach-area loss, indicating that shoreline-position change alone may overestimate chronic erosion along this seasonally dynamic coast. Although DSAS rates were moderately to strongly correlated with beach-area trends (r = 0.59–0.64), the two approaches led to markedly different management interpretations. These findings demonstrate that multi-temporal beach-area analysis provides a valuable complement to shoreline-based methods, enabling a clearer distinction between seasonal variability and chronic erosion on highly dynamic microtidal coasts. Full article

The construction of the Hainan Free Trade Port (FTP) is guided by the core philosophy of “ecological priority and green development.” To meet the practical requirements of building a “Green and Beautiful FTP,” this study constructed a kernel Normalized Difference Vegetation Index (kNDVI) dataset using Landsat series satellite imagery. By integrating methods including the Mann–Kendall (MK) trend test, Hurst exponent, and coefficient of variation (CV), an in-depth analysis was conducted on the spatiotemporal evolution characteristics and trends of growing-season vegetation in Hainan Island from 1994 to 2023. Additionally, the Extreme Gradient Boosting (XGBoost) model and the SHapley Additive exPlanations (SHAP) interpretation method were employed to quantitatively unravel the driving mechanisms of climatic factors and human activities on kNDVI variations. The results indicate that: (1) Over the past three decades, the overall kNDVI of Hainan Island has exhibited a significant upward trend, characterized spatially by an evolution pattern of “stable recovery in the central region and localized degradation along the coast.” (2) The vegetation evolution demonstrates strong persistence and is highly consistent with community stability. The high stability in the central mountainous areas stems from a superior natural background and strict ecological protection; the transition zone is jointly influenced by vegetation types and anthropogenic management; meanwhile the coastal areas exhibit significant degradation characteristics driven by high-intensity human disturbances. (3) The analysis of driving mechanisms reveals a complex control logic of “topographical foundation—human reshaping—extreme climate triggering.” Static topographical factors, such as elevation and slope, occupy an absolute dominant position; human activities, represented by rubber plantation expansion and urbanization, exert a bidirectional reshaping effect characterized by “inland greening and coastal suppression”; furthermore, extreme drought and high temperatures in the later stages of the study demonstrated a significant pulse-like impact, exacerbating the risks of short-term climatic stress. This study clarifies the core patterns of vegetation evolution in Hainan Island, validates the effectiveness of ecological policies, and provides a quantitative scientific basis for ecological conservation and sustainable development in tropical island regions. Full article

High-resolution and regularly updatable land cover maps are essential for local-scale environmental monitoring, water resource management, and territorial governance, yet existing global and regional products fail to provide the spatial detail and thematic richness required for operational applications in complex Mediterranean landscapes. This study presents an operational workflow for high-resolution LULC mapping and its application to Sardinia for the reference year 2020, developed within the Sardinia Land Cover Mapping Project in collaboration with the Agenzia del Distretto Idrografico della Sardegna (ADIS). The workflow integrates multi-temporal SAR and multispectral satellite imagery with high-resolution ancillary geospatial vector datasets through a semi-automatic pipeline combining hierarchical cascade pixel-based classification, multi-resolution image segmentation, geometric overlay of infrastructure vector layers, and an iterative accuracy-driven reclassification cycle. The classification combines automated rule-based procedures, semi-automatic threshold-based methods, and expert photo-interpretation to address the high thematic and spatial complexity of the Sardinian landscape. The resulting map comprises 35 land cover classes at the third and selected fourth CORINE levels, with a minimum mapping unit of 400 m² and an overall weighted accuracy of 82.4%. Designed as a dynamic product updatable on an annual basis, it represents an operational tool for local environmental governance, spatial planning, and resource management. Full article

This study presents a comparative analysis of urban land-use sustainability in Barcelona and Izmir using a four-tier Decision Support Framework (DSF) supported by automated Google Earth Engine workflows. The framework integrates open satellite data, cloud-based spatial analysis, and policy-oriented indicators derived from Sentinel-2 and Landsat imagery. Outputs include LULC classification, NDVI, NDBI, a Sentinel-2-derived relative thermal proxy (sLST), the Composite Environmental Biophysical Index (CEBI), and distance-weighted urban expansion pressure. Classification accuracy reached 86.85% for Barcelona and 91.03% for Izmir. The cities exhibited distinct morphological and environmental profiles. Izmir had higher vegetation density than Barcelona (NDVI: 0.320 vs. 0.142), while harmonized Landsat summer LST values were similar in 2023, with Barcelona slightly warmer than Izmir (39.343 °C vs. 39.146 °C). Accordingly, sLST was used for relative intra-urban thermal assessment rather than absolute inter-city comparison. CEBI indicated higher environmental biophysical performance in Izmir (0.526) than Barcelona (0.446), interpreted strictly within the vegetation, thermal, and built-up components included in the index. Barcelona’s compact urban fabric exhibited stronger expansion pressure, whereas Izmir’s geographically constrained morphology indicated a more controlled growth dynamic. The findings demonstrate the value of transparent cloud-based spatial analytics for reproducible, evidence-based urban planning. Full article

Forests cover nearly one-third of the Earth’s land surface and are subject to increasing anthropogenic pressure, including unauthorised construction, infrastructure expansion, and habitat fragmentation. Detecting buildings concealed beneath forest canopy is essential for environmental monitoring and territorial surveillance, yet optical satellite imagery fails under persistent cloud cover and dense vegetation, and no existing approach provides reliable detection across contrasting forest ecosystems. We address this gap by proposing a hybrid SAR-based detection framework that combines twelve physically interpretable Scattering-Informed SAR Features (SISF)—derived from electromagnetic scattering theory across amplitude, polarimetric, temporal, and texture dimensions—with a universal Convolutional Neural Network, integrated through probability-level fusion with isotonic regional calibration. Rather than relying on individual feature thresholds, the framework identifies buildings through their characteristic multidimensional scattering signature—a combination that remains discriminative across biomes where any single SAR descriptor would fail. The framework was evaluated on a novel 7721-object multi-biome benchmark spanning forest-steppe (Kazakhstan), boreal forest (Komi Republic, Russia), and tropical rainforest (Brazil, Pará). The final Fusion + Regional Calibration model achieved an overall F1-score of 0.803 on the held-out test set (N = 1545), outperforming single-model baselines by up to 17 percentage points. Regional F1-scores ranged from 0.727 (Kazakhstan) to 0.944 (Komi Republic), with detection performance remaining robust under partial canopy occlusion (F1 = 0.911, versus 0.903 for unobscured buildings). An empirical inverse relationship between hard negative proportion and detection F1-score was identified within each biome, with the 28–35% proportions present in our sampled regions reported as a preliminary observation rather than a generally optimal range—a dataset design finding not previously reported in the literature. The proposed framework provides a physically interpretable solution for SAR-based building detection under forest canopy, demonstrating consistent performance across three contrasting forest biomes, with direct applications to environmental monitoring and territorial surveillance in forested regions. Full article

Accurate discrimination between rice and artificial grassland remains challenging in regional agricultural monitoring because both herbaceous types share similar spectral signatures during vegetative growth, and existing land-cover products do not treat artificial grassland as a separate class. Using Jurong City, Jiangsu Province, as the study area, we propose a framework that optimizes red-edge vegetation index selection within crop-specific phenological windows to separate rice from grassland. Using Unmanned Aerial Vehicle (UAV) multispectral imagery and Sentinel-2 satellite data, we quantified spectral separability across eight phenological stages using Fisher ratios. We identified two optimal discrimination windows: early tillering (mid-June) and heading–flowering (early September). Within the heading–flowering window, a dual-index classification rule combining Normalized Difference Red-Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI) was transferred from UAV to Sentinel-2 and used to produce a 10 m rice–grassland map for the entire city. Spatial agreement with two publicly available rice datasets reached 75.2% and 79.5% for rice pixels, reflecting differences in spatial resolution, reference year, and class definition rather than classification error. Independent field validation using 200 samples yielded an overall accuracy of 92.50% (F1-score = 0.93), confirming the effectiveness of the VI–window optimization strategy. The framework offers an interpretable, physiology-driven alternative for crop-type mapping that relies solely on widely available multispectral bands. Full article

This study presents a multidisciplinary investigation of the fluvial evolution of the northern meander of the Calore River at Cellarulo locality, near Benevento (southern Italy). The research integrates archaeological evidence, geological and geomorphological data, historical cartography, remote sensing imagery and geophysical surveys in a Geographic Information System (GIS) environment. Multi-temporal analysis of historical maps, aerial photographs and satellite images from 1824 to 2022 allowed the reconstruction of channel migration patterns and the identification of abandoned meanders and paleochannel traces. Stratigraphic data derived from boreholes revealed the presence of a channel of the Calore River dated at least in the Bronze Age (3900 years ago), abandoned in the nineteenth century. Geoelectrical investigations provided detailed information on subsurface resistivity anomalies, highlighting the presence of buried structures and possible ancient anthropogenic features located at shallow depths between 1 and 1.5 m. The combined interpretation of geomorphological, archaeological and geophysical data demonstrates significant data on the unveiling of an ancient river channel and its abandonment during the last 150 years, suggesting a strong interaction between natural fluvial dynamics and human occupation. The results confirm the effectiveness of an integrated multidisciplinary approach for reconstructing fluvial landscape evolution and for identifying buried archaeological and geomorphological features in complex floodplain environments. Full article

This study assessed environmental changes observed during an integrated programme of microbiological bioremediation and macrophyte management in the Pasternik Reservoir in Starachowice, Poland. The monitoring programme included water and sediment analyses, repeated measurements of soft organic fraction thickness, observations of macrophyte management, Sentinel-2 Maximum Chlorophyll Index mapping, and historical catchment modelling. During the monitoring period, the mean thickness of soft organic fractions decreased by 78%, sediment dry matter increased, and several water quality variables showed favourable temporal changes. Rapid macrophyte regrowth required repeated cutting and increased the practical demands of vegetation management. Sentinel-2 imagery revealed marked spatial and seasonal variation in the red edge optical signal within the reservoir. The Maximum Chlorophyll Index was interpreted as a relative optical indicator rather than as a quantitative chlorophyll a product. Nutrient Delivery Ratio modelling was used only to provide historical catchment context for 1990–2018. Because the study involved one reservoir and did not include an untreated reference site, the observed changes cannot be attributed exclusively to the management programme. The study shows the value of combining field measurements, satellite observations, and catchment information in the adaptive monitoring of small eutrophic reservoirs. Full article