Search Results (6,620)

Shifting cultivation (SC) is a predominant land use across the tropics, feeding hundreds of millions of marginalized people, causing significant deforestation in tropical regions. A key question is how to realize rapid and large-scale identification of the spatial distribution, cycle numbers, and fallow periods of SC. Building the LandCycler algorithm that fully considers the inter-annual cycle of SC based on Landsat imagery from 1988 to 2020, we identify the distribution and fallow period of SC in Southeast Asia, including Assam in India and Yunnan Province in China. The results show that the LandCycler for the identification of SC is satisfactory (producer’s accuracy 82.12% and user’s accuracy 81.37%), and the accuracy in detecting the average cycle number, and calculating the average fallow period is 83.71%, and 96%, respectively. We found that the total area of SC is as high as 16.79 × 10⁴ km² in Southeast Asia, which uses almost 10% of the total forests. Meanwhile, the average cycle number and the average fallow period of SC are two times and 10 years, respectively. More than 98% of SC has repeated deforestation four times or less. The shorter the distance from settlements and the distance from roads, the larger the cycle number of SC. Although there was no significant correlation between elevation and slope and the cycle number of SC, SCs were mainly distributed at slopes of 18 ± 5° and elevations of 800 ± 300 m. These findings provide effective tools for sustainable agroforestry management as well as for global SC mapping. Full article

Urban green spaces are increasingly threatened by rapid urban expansion, making their continuous monitoring and prediction essential for sustainable urban management. This study investigates the spatiotemporal dynamics of urban garden landscapes in Shiraz, Iran, by integrating multi-temporal Landsat imagery, GIS analysis, and CA–Markov modeling. Landsat data from 2003, 2013, and 2023 were processed to derive the Normalized Difference Vegetation Index (NDVI), which was classified into four vegetation-density categories to quantify land-cover transitions. A CA–Markov framework implemented in IDRISI TerrSet (Version 20.0) was then employed to simulate spatial dynamics and predict vegetation changes for 2033. Results reveal a significant expansion of non-vegetated areas from 711.93 ha in 2003 to 976.66 ha in 2023, accompanied by a decline in dense vegetation from 403.68 ha to 382.64 ha. Model projections indicate a further reduction in dense vegetation to 239.35 ha by 2033, suggesting ongoing fragmentation of urban green infrastructure driven by development pressures. By combining time-series remote sensing, GIS-based spatial analysis, and predictive modeling, this study provides an integrative framework for detecting, interpreting, and forecasting urban land-cover change. The findings offer evidence-based insights to support sustainable urban planning, green infrastructure protection, and climate-resilient city management in rapidly growing urban environments. Full article

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based on the fine-tuned Prithvi foundation model for direct LAI retrieval from cloud-contaminated 30 m Harmonized Landsat and Sentinel-2 imagery. By mapping inputs directly to Hi-GLASS reference labels, the proposed architecture processes cloud contamination and vegetation signals simultaneously and circumvents the error propagation inherent in cascaded retrieval pipelines. Results demonstrate that the end-to-end LAI retrieval model significantly outperforms cascaded variants, achieving a superior R² (0.78) and lower RMSE (0.57). Furthermore, predictive accuracy exhibits a distinct U-shaped trajectory relative to the temporal mean cloud fraction, reaching an inflection point at 50–60% occlusion, which highlights the model’s implicit regularization capacity under severe atmospheric interference. This work establishes that direct feature learning with foundation models offers a more robust and streamlined pathway for generating continuous biophysical products from imperfect optical observations, prioritizing quantitative fidelity over artificial perceptual sharpness. Full article

Topsoil sand content (TSC) is a critical indicator of soil degradation in black soil regions, yet its long-term dynamics remain poorly quantified. To address this, we developed an automated Landsat-based framework on Google Earth Engine (GEE) for mapping cropland TSC across the Northeast China Black Soil Region (NCBSR) from 1984 to 2023. The methodology integrates a hierarchical bare-soil extraction strategy using the Normalized Difference Bare Soil Index (NDBSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Tillage Index (NDTI) with a Random Forest (RF) model optimized by three-band spectral indices and a “prediction-first” compositing workflow. Results demonstrate that the bare-soil extraction achieved an overall accuracy of 96%, while the TSC retrieval model maintained robust performance with a coefficient of determination (R²) of 0.80 and a root mean square error (RMSE) of 9.68%, together with satisfactory temporal transferability. Long-term mapping revealed a significant biphasic evolutionary trajectory: 23.4% of croplands experienced soil coarsening predominantly before 2000, followed by a partial reversal and stabilization in later decades. This framework provides a high-resolution, multi-decadal baseline for monitoring soil physical degradation and supports sustainable agricultural management in global black soil regions. Full article

Understanding how urban morphology regulates Land Surface Temperature (LST) is important in the context of rapid urbanization and increasingly frequent extreme climate events. Although both two-dimensional (2D) and three-dimensional (3D) morphological factors are known to affect urban thermal environments, their relative explanatory roles, factor-specific optimal scales, and nonlinear responses are still insufficiently quantified within a unified multi-scale framework. This study focuses on the area within Beijing’s Fifth Ring Road and applies an interpretable LightGBM-SHAP framework to examine the multi-scale relationships between integrated 2D/3D urban morphology and LST using a Landsat 8 image acquired during a typical summer daytime heatwave event. Five analytical scales (150, 300, 600, 900, and 1200 m) are evaluated to compare factor importance, identify optimal explanatory scales, and characterize threshold-like response patterns. The LightGBM models maintained relatively strong predictive performance across all scales under spatial cross-validation, with the highest mean R² observed at 600 m, followed closely by 300 m. The results indicate a clear scale-dependent contrast in explanatory dominance: 2D factors show stronger associations with LST at fine-to-medium scales, whereas 3D factors become more influential at coarser scales. From a process perspective, this contrast is consistent with differences in surface-cover-related and vertical-structure-related thermal regulation, although the underlying physical mechanisms are not directly tested in this study. SHAP analysis further identifies factor-specific nonlinear response intervals for several key indicators under the selected extreme-heat condition. For example, a cooling tendency is observed when Mean Building Height (MBH) exceeds 15 m at the 150 m scale. These findings provide scale-explicit and context-specific evidence for interpreting urban morphology–LST relationships and support heat-mitigation strategies that combine micro-scale surface-cover optimization with larger-scale regulation of building height variation and urban roughness. The identified response intervals should be interpreted as empirical references under a typical daytime heatwave condition rather than as universally transferable climatological thresholds. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

This study analyses the influence of three-dimensional (3D) urban form on intra-urban thermal variability and its long-term evolution in Zagreb, Croatia. The research focuses on four residential districts (Špansko sjever, Dugave, Lanište, and Novi Jelkovec) representing different development periods. The central hypothesis is that differences in the development period and urban compactness are associated with differences in summer thermal patterns, with more open spatial configurations generally exhibiting weaker thermal responses than more compact developments. The methodology integrates LiDAR-derived building morphology with a decade-long Landsat time series (2015–2024), including land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The results indicate a consistent increase in summer LST across all analysed neighbourhoods, with warming rates ranging from approximately 2.00 to 2.83 °C per decade. Built-up intensity shows a positive association with temperature, while vegetation trends are generally weak. A multiple linear regression model explains 47% of the variance in LST (R² = 0.47), with NDBI identified as a significant predictor (p < 0.01), whereas NDVI and volumetric building density are not statistically significant. Despite this, neighbourhoods with higher volumetric building density (up to ≈2.96 m³/m²) tend to exhibit stronger warming trends compared to lower-density areas (≈1.69 m³/m²), indicating the additional explanatory value of three-dimensional urban morphology. These findings support the concept of a volumetric expression of urban thermal processes, while highlighting that 3D urban morphology contributes to the interpretation of the long-term thermal patterns when considered alongside other factors. They also emphasize the importance of integrating 3D spatial metrics into climate-sensitive urban planning and mitigation strategies. Full article

Net primary production (NPP) is a key indicator of the terrestrial carbon cycle, and its response to disturbance and subsequent recovery is important for understanding regional carbon sink dynamics. Conventional region-based statistical approaches have limitations in capturing localized heterogeneous changes. In this study, a typical ecologically fragile region on the northwestern Sichuan Plateau was selected as the study area. Using the Google Earth Engine (GEE) platform, Landsat time-series imagery (2001–2020) and MOD17A3HGF NPP data were integrated. The LandTrendr algorithm was applied to identify vegetation disturbance patches, and two representative disturbance years (2008 and 2014) were selected for long-term analysis. Trend analysis, coefficient of variation, and the Hurst exponent were used to characterize the spatiotemporal dynamics and stability of NPP in disturbed areas. The results show that: (1) NPP declined after disturbance and then exhibited a recovery trend, with significant spatial heterogeneity in recovery rates; (2) recovery trajectories differed between disturbance years, indicating combined effects of disturbance intensity and environmental conditions; and (3) Hurst exponent analysis suggests that although recovery trends are persistent in most areas, some disturbed patches show potential instability. This study establishes an analytical framework integrating disturbance detection and recovery tracking, which improves the representation of NPP dynamics in heterogeneous regions and provides a basis for assessing ecosystem recovery and carbon sink dynamics. Full article

Urban remote sensing provides an efficient and accessible way to monitor and assess the urban environment. However, the difficulty in classifying bare soil and built-up land is exacerbated in desert landscapes, due to the spectral confusion of bare soil and impervious surfaces. Therefore, urban remote sensing research in desert environments employs complex and time-consuming classification techniques, which cause difficulties in reliability when transferring these methods to other desert cities. This paper describes two new index-based approaches that can successfully detect and classify urban areas without the disruption of bare soil influences in desert environments using Landsat 8–9 satellite imagery. They are called the desert urban landscape index (DULI) and the isoline impervious surface index (IISI). The desert cities of Phoenix, Ciudad Juárez, and Riyadh were used as study areas for the development of these indices. The two proposed indices outperformed the dry built-up index (DBI), with overall accuracy rates of 85% in Phoenix using DULI, 87% in Ciudad Juárez using DULI, and 90% in Riyadh using IISI. DULI also demonstrates the ability to suppress landscape features such as bare soil, mountains, and canyons. Full article

Soil erosion assessment in Southeast Europe’s mountainous regions often lacks systematic field validation, limiting confidence in model-based predictions. This study integrates the Erosion Potential Model (EPM) with remote sensing and field verification across 26,570 km² in the Federation of Bosnia and Herzegovina (FBiH) and Brčko District (BD). We developed a two-stage framework: initial GIS-based assessment using digital elevation models, soil maps, climate data, CORINE Land Cover, and Landsat imagery, followed by field calibration at 190 representative sites. Spectral indices (NDVI, BSI) provided dynamic corrections for vegetation cover and visible erosion features. Field validation significantly improved model performance; the erosion coefficient increased from Z = 0.21 to Z = 0.24, while discriminatory power improved AUC from 0.82 to 0.85, with corresponding gains in overall accuracy from 0.78 to 0.84 and F1-score from 0.78 to 0.85. The field-validated model estimated mean annual sediment production of 546.60 m³·km⁻²·year⁻¹, with total erosion material production of 14,074,940.2 m³·year⁻¹. Field calibration revealed substantial spatial redistribution, with medium-to-excessive erosion categories expanding by 30.37%, affecting 1319.12 km² requiring priority intervention. The Kappa coefficient (0.81) confirms high classification reliability. This field-validated framework enables evidence-based identification of degradation hotspots and provides actionable guidance for soil conservation planning in geomorphologically heterogeneous, data-limited regions. Full article

Urban districts along major rivers are undergoing rapid transformation, yet long-term evidence on how redevelopment reshapes land cover and vegetation structure remains limited in post-socialist cities. This study examines the spatio-temporal evolution of land use and land cover (LULC) and vegetation dynamics along the Sava River corridor in Belgrade from 1990 to 2024. CORINE Land Cover (CLC) datasets were combined with Landsat-derived NDVI and MSAVI time series, while high-resolution Esri Wayback imagery was used for visual interpretation and qualitative corroboration of the detected land-cover and vegetation patterns. Beyond conventional NDVI/LULC assessments, the study integrates multi-decadal spectral trends with functional vegetation structure classification to evaluate canopy continuity and ecological configuration under contrasting redevelopment models. Results reveal a pronounced divergence between the two riverbanks. The left bank (New Belgrade) maintains stable land-cover composition and consistently higher NDVI and MSAVI values, indicating preserved green infrastructure and sustained canopy continuity. In contrast, the right bank (Belgrade Waterfront) experienced substantial land-cover conversion after 2006, with a statistically significant decline in vegetation greenness (NDVI −0.020 dec⁻¹, p < 0.001) and a marked increase in impervious surfaces. MSAVI-based functional classes indicate a shift from mixed low vegetation to predominantly sealed land, while tree canopy remained persistently low throughout redevelopment. The findings demonstrate measurable ecological simplification and canopy loss, even where nominal green areas remain present. By providing a rare multi-decadal, spatially explicit comparison of two contrasting planning paradigms within the same river corridor, the study contributes new empirical evidence on how governance and redevelopment models shape riparian ecological trajectories and sustainable urbanism in post-socialist cities. Strengthening blue-green infrastructure and restoring native riparian vegetation are essential for enhancing climate resilience and ensuring long-term riverfront sustainability. Full article

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

Urbanisation reshapes Land Cover and Land Use (LCLU) by driving deforestation, wetland loss, and the conversion of natural and agricultural areas into built environments. However, integrated analyses of LCLU change in response to climate variability in topographically complex, rapidly urbanising African cities remain limited. Therefore, this study examined 2000–2024 LCLU changes in hilly Gasabo District (Kigali, Rwanda) using 30 m Landsat imagery and a Random Trees classifier (92.7% accuracy, 70/30 train-test split), with 2032 projections via a population-driven hybrid trend model. Population estimates/projections 320,516 in 2002 to 967,512 in 2024, 1.41 million by 2032, were derived from Rwanda’s census data and exponential growth modelling (calibrated to 5.05% annual growth). Rapid population growth has driven a 539% expansion of Built-up Areas, accompanied by notable declines in cropland and Forest. Local climate trends (Meteo Rwanda stations) aligned with global datasets (ERA5-Land and CHIRPS): rainfall fluctuation and temperature rose, with strong correlations between population-driven Built-up Areas expansion. From 2024 to 2032, LCLU projections indicate that Built-up Areas will continue to expand by 29.5%. Cropland was forecast to decline to 15.9%, while Forest loss slowed to 5.7%. MLR analysis revealed strong correlations between population-driven expansion of Built-up Areas, cropland/forest loss, warming, and rainfall fluctuations in Gasabo. An ARDL model was applied to address multicollinearity among LCLU predictors, which limited the interpretation of individual coefficients, and confirmed the core MLR correlation trends, with statistically significant (p < 0.05) coefficients. The results highlight the need for data-driven spatial planning in Gasabo (stricter zoning, high-rise buildings, targeted reforestation, climate-resilient green infrastructure) to mitigate population and urbanisation-driven environmental degradation. Full article

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article