Search Results (10,989)

High-spatiotemporal-resolution evapotranspiration (ET) is critical for precision irrigation management and water resource regulation. Regarding the existing spatiotemporal fusion methods suffering from sparse high-resolution observations and coarse land surface temperature (LST), this study took winter wheat in Luancheng District, Hebei Province, as the research object, and proposed a remote sensing ET retrieval method based on the LST sharpening model. The Data Mining Sharpener (DMS) algorithm combined with Sentinel-2 multispectral data was used to downscale MODIS LST from 1000 m to 10 m, with auxiliary variables (DEM, albedo, NDVI, land cover) integrated into the Cubist regression tree to improve the physical rationality and spatial details of MODIS LST. The 10 m resolution ET was estimated from 10 m sharpened LST and Sentinel-2 multispectral data using the surface energy balance model, and the unmixing–weight ET image fusion model (UWET) was adopted to fuse the 10 m resolution ET with MODIS low-resolution ET to generate a daily 10 m ET dataset covering the entire winter wheat growing season. Validation with eddy covariance flux measurements showed that the correlation coefficient R = 0.921, RMSE = 0.779 mm/day during 2019–2020, and R = 0.900, RMSE = 0.831 mm/day during 2020–2021. The results demonstrate that auxiliary variables significantly enhance the spatial reality of LST, LST sharpening effectively improves the spatial heterogeneity of ET, and Sentinel-2 data compensates for the temporal deficiency of Landsat, thereby greatly promoting the accuracy of spatiotemporal fusion. This method can provide reliable high-spatiotemporal-resolution data support for refined farmland irrigation management and water resources regulation. Full article

Understanding how lightning interacts with environmental conditions to ignite wildfires is key to improving fire risk assessments. While previous studies have explored the role of lightning characteristics and environmental conditions in fire ignition on regional scales, a comprehensive analysis across the contiguous United States (CONUS) is lacking. This study investigates the influence of both lightning characteristics and environmental conditions on lightning-ignited wildfires from 2016 to 2020 using high-resolution lightning, precipitation, meteorological, and land cover datasets. By matching over 25,000 lightning-ignited wildfires (LIWs) to nearby lightning events, we distinguish between fire-initiating (FL) and non-fire lightning (NFL) and analyze key variables including lightning peak current, flash multiplicity, precipitation, fuel moisture, vapor pressure deficit, and vegetation type. Results show that fire lightning tends to have lower multiplicity and slightly higher peak current, and occurs under drier atmospheric and fuel moisture environments. Precipitation accumulated over 1 h and 24 h prior to LIWs shows the largest effect-size difference between FL and NFL, followed by fuel moisture and energy release component. Among vegetation types, evergreen needleleaf forests and grasslands show the highest relative ignition efficiency. Regional analysis across National Climate Assessment regions reveals notable variation in the relative importance of these factors, with eastern regions showing stronger sensitivity to precipitation and fuel moisture compared to western regions. We also find that holdover fires, discovered more than 24 h after ignition, occur under wetter conditions compared to promptly detected fires. These findings highlight the dominant role of environmental conditions in lightning fire ignition and emphasize the need for region-specific fire management strategies. Full article

Urban pluvial flooding shows clear temporal accumulation, delayed response, and spatial heterogeneity. Better flood-depth prediction from a spatiotemporal coupling perspective can support urban flood risk identification and refined management. This study develops a spatiotemporal prediction model that integrates a Transformer and graph neural network (GNN). The Transformer module captures temporal dependencies in rainfall processes and flood-depth evolution. The graph attention network (GAT) represents spatial associations constrained by terrain, drainage networks, and neighboring spatial relationships. A fusion attention mechanism then adaptively couples temporal and spatial features. This study uses multi-source data, including hourly meteorological observations, terrain, land cover, drainage networks, and water-system data. It selects the heavy rainfall event caused by Typhoon In-Fa in Huai’an City in July 2021 as a typical case. The study analyzes the temporal evolution of regional average flood depth and the spatial differentiation of inundated grid cells at the municipal scale. The results show three main findings. First, during the typical heavy rainfall event, regional average flood depth follows a continuous process of low-level stability, sustained rise, rapid increase, delayed peak, slow recession at a high level, and rapid recession. The flood peak lags behind the rainfall peak by about 3 h. This result indicates clear accumulation and delayed response in urban pluvial flooding. Second, at the municipal scale, inundated grid cells show a pattern of concentrated distribution in urban built-up areas, secondary distribution in county-level built-up areas, and scattered distribution in non-construction land. Different depth grades also show clear hierarchical differentiation. Mild and moderate inundation covers a wider area. Medium-high inundation concentrates locally. High-grade inundation appears as a small number of nested high-value cells. Third, the spatial differentiation of medium- and high-grade inundated grid cells does not result from low-lying terrain or construction land alone. It forms under the combined effects of low-lying terrain, local relative depressions, and impervious surfaces in construction land. This pattern shows clear built-up-area clustering, grade differentiation, and land-cover correspondence. The results provide methodological support and decision references for urban flood risk identification, grid-based risk management, and emergency dispatch during extreme rainfall. Full article

Reliable aquaculture monitoring requires spatial, administrative, and environmental evidence to be integrated without treating these sources as equivalent. This study characterized mapped aquaculture land-cover dynamics in six coastal provinces of Ecuador from 1985 to 2024, assessed their 2023 correspondence with authorized or concessioned shrimp-farm area, quantified 2023–2024 land-cover transitions, and described climate exposure over a fixed historical aquaculture footprint. Annual MapBiomas Ecuador Collection 3.0 classifications were aggregated by province, canton, and parish; MPCEIP records were harmonized with INEC administrative codes; and CHIRPS v3 precipitation and ERA5-Land temperature were summarized over the union of all pixels mapped as aquaculture at least once during the study period. Mapped class 31 area increased from 58,198.792 ha in 1985 to 138,200.970 ha in 2024, with a maximum of 153,516.642 ha in 2022. In 2023, mapped area represented 74.508% of 202,114.900 ha of authorized or concessioned area, with strong canton-level rank correspondence. Between 2023 and 2024, persistence reached 135,774.430 ha, gross loss 14,816.766 ha, gross gain 2426.130 ha, and net change −12,390.636 ha. Climate exposure was wettest in 1998 and warmest in 2023. Together, the results show that coastal aquaculture development cannot be inferred reliably from a single territorial measure: long-term mapped expansion, administrative extent, recent classification changes, and climate exposure describe complementary but non-equivalent dimensions of the system. This distinction provides a more defensible basis for interpreting aquaculture land-use change and for targeting territorial monitoring and administrative verification. Full article

Wildfire impact assessment requires information on both burned areas and the biomass exposed within burned landscapes. We developed a field-calibrated, multi-source remote-sensing framework to estimate above-ground biomass (AGB) and quantify annual wildfire-related potential AGB exposure across the boreal forests of Quebec and Ontario, Canada, during 2018–2024. The dataset comprised 3725 plot-year AGB observations linked to optical, Sentinel-1 C-band, ALOS L-band synthetic aperture radar, environmental, and geographic predictors. Product-wise screening reduced the 91 candidate predictors to 28. An optimized extreme gradient boosting (XGBoost) model was evaluated using five-fold grouped cross-validation, with repeated observations from each plot assigned to a single fold. The model achieved an RMSE of 25.08 ± 0.36 t ha⁻¹, an MAE of 20.89 ± 0.39 t ha⁻¹, and an R² of 0.53 ± 0.02. The full multi-source configuration outperformed all reduced-source and source-only configurations, while removing ALOS L-band SAR or environmental/geographic predictors produced among the largest performance declines. The model was applied to 9937 land-cover-stratified points within wildfire polygons using predictors from the year preceding each fire. Under the complete-loss assumption, cumulative potential AGB exposure was 269.20 Mt across 6.66 Mha of effective burned area, with a 95% bootstrap interval of 254.19–284.06 Mt reflecting finite-point sampling uncertainty and an area-weighted mean exposure intensity of 40.43 t ha⁻¹. The 2023 fire season accounted for 206.44 Mt, representing 76.7% of cumulative exposure and 73.2% of effective burned area. Effective burned area and total potential exposure were strongly correlated (r = 0.99), whereas exposure intensity followed a distinct pattern and peaked in 2022 at 46.86 t ha⁻¹. Thus, burned area was the primary correlate of regional potential biomass exposure, whereas exposure intensity reflected variation in pre-fire biomass among burned landscapes. These estimates represent potential exposure rather than measured combustion, mortality, or carbon emissions and demonstrate the value of integrating spatially explicit pre-fire AGB with wildfire perimeters. Full article

LULCC is a major driver of habitat fragmentation, biodiversity loss, and declining landscape connectivity, particularly in rapidly urbanizing regions. Although Islamabad has experienced substantial urban expansion and forest-cover change, long-term city-scale assessments linking land-cover dynamics with landscape connectivity remain limited. This study examined spatiotemporal LULCC in Islamabad from 1991 to 2021 and assessed whether recent forest recovery improved landscape structural connectivity. Landsat images acquired in 1991, 2001, 2011, and 2021 were classified into five land-cover categories: water, forest, built-up area, bare land, and agricultural land. Classification was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE). Landscape composition and spatial configuration were quantified using FRAGSTATS 4.3, while forest fragmentation was evaluated using the Landscape Fragmentation Tool v2.0 (LFT) with a 100 m edge threshold. The classifications achieved overall accuracies above 90%, with Kappa coefficients (K) greater than 0.85. Built-up area increased from 76.31 km², representing 7.55% of the study area, in 1991, to 258.62 km², or 25.60%, in 2021, demonstrating rapid urban expansion and associated habitat conversion. Forest cover increased to 340.86 km² in 2001, declined to 271.96 km² in 2011, and subsequently recovered to 409.22 km² in 2021. Despite this increase in forest extent, fragmentation metrics indicated persistent spatial subdivision and limited structural connectivity. High patch density (PD), reduced landscape aggregation, and changes in the largest patch index (LPI) indicated persistent spatial subdivision and limited habitat continuity. These findings highlight the value of integrating RF-based land-cover classification, multitemporal remote sensing, and landscape metrics for urban environmental monitoring. The findings suggest that future land-use planning should consider landscape connectivity, protection of existing forest patches, and spatially coordinated restoration alongside continued reforestation. Full article

Ecotourism, as one of the most important forms of sustainable tourism, plays a significant role in the conservation of natural resources, the economic development of local communities, and the achievement of sustainable development goals. However, the sustainable development of this sector requires the accurate identification of suitable areas and the simultaneous assessment of the ecological capacities and limitations arising from anthropogenic activities, an issue that has received less attention at the national scale, particularly in countries with high environmental diversity such as Iran. Therefore, the present study was conducted with the aim of assessing the potential for ecotourism development in Iran based on Geographic Information System (GIS) and Spatial Multi-Criteria Decision Analysis (SMCDA). In line with the scope of the sub-factors evaluated, the assessed construct is referred to throughout this study as Environmental–Anthropogenic Ecotourism Suitability (EAES), reflecting an environmental-quality and anthropogenic-pressure perspective rather than a comprehensive assessment of ecotourism sustainability. The main innovation of this study lies in the simultaneous integration of natural and anthropogenic factors at the national scale and the sensitivity assessment of the results through the design of different development scenarios. In this study, a set of natural sub-factors including protected areas, vegetation cover, slope, precipitation, land use, and natural attraction density, as well as anthropogenic sub-factors including settlements, roads, accommodations, mines, industrial parks, dams, power transmission lines, dust, and the Global Human Modification (GHM) index were used. The weighting of the sub-factors was carried out using the Best–Worst Method (BWM), and spatial suitability maps were subsequently generated through the Weighted Linear Combination (WLC) method in the GIS environment. To evaluate uncertainty and examine the effect of the relative importance of sub-factors, three scenarios including natural factor dominance, anthropogenic factor dominance, and a balanced scenario were designed and analyzed. The results showed that protected areas (0.19), vegetation cover (0.17), and natural attraction density (0.16) were the most important natural sub-factors, while the GHM index (0.16), roads (0.15), and settlements (0.14) were the most important anthropogenic sub-factors affecting ecotourism development. The spatial pattern of the results indicated the concentration of areas with good potential in the Alborz and Zagros mountain ranges, the Hyrcanian forests, and parts of the protected areas of Iran. In the balanced scenario, approximately 24.8% of Iran’s area was classified within the suitable and highly suitable classes, while 46.3% was classified within the moderately suitable class. Furthermore, comparison of the scenarios showed that the use of one-dimensional approaches may lead to unrealistic estimates of ecotourism capacity. Overall, the results of this study indicate that the sustainable development of ecotourism in Iran requires the adoption of an integrated approach in which the conservation of natural assets and the management of anthropogenic interventions are simultaneously considered. The proposed framework can serve as a decision-support tool for spatial planning, investment prioritization, and sustainable ecotourism development policymaking in Iran and other similar regions. Full article

Agricultural non-point source pollution (ANPSP) presents a critical threat to water security in the upper reaches of the Yangtze River Economic Belt (UYREB). Traditional export coefficient models (ECMs) often fail to capture the spatially heterogeneous processes of pollutant transport and ecological attenuation in complex mountainous terrains. Here, we constructed an improved ANPSP risk assessment framework by integrating ecosystem regulating functions (water yield, soil retention, and habitat quality) into the ECM. By coupling the Patch-generating Land Use Simulation (PLUS) model and Geographically and Temporally Weighted Regression (GTWR), we evaluated the spatiotemporal risk evolution from 2000 to 2020, projected risk configurations for 2030 under three scenarios (Natural Development, ND; Cropland Protection, CP; Ecological Protection, EP), and identified the spatiotemporal non-stationary drivers. The key findings are: (1) Independent validation using observed river water-quality data further demonstrated that the modeled risk index was significantly positively correlated with contemporaneous TN and TP concentrations (Pearson’s r = 0.673, p < 0.05), supporting its ability to identify the spatial distribution of relative ANPSP risk while accounting for regional differences in ecosystem regulation. (2) Historically, the integrated ANPSP risk index exhibited a “rise-then-decline” pattern, peaking in 2010 with extreme risk areas covering 12.1% of the region. Under the 2030 EP scenario, high-risk areas contract by 15.3% compared to 2020, demonstrating superior mitigation compared to ND and CP scenarios. (3) GTWR reveals that cropland proportion is the dominant positive spatial associate (0.800 − 0.852), while GDP shows a negative spatial association in economically developed sub-regions, reflecting potential environmental management co-benefits rather than direct causation. This framework provides an effective relative risk assessment tool for mountainous watersheds, highlighting that land use optimization under ecological regulation may contribute to reducing potential pollutant transport risk. Full article

High-resolution remote sensing semantic segmentation requires the joint modeling of local details, global semantics, and height-derived geometric structures, and it provides an important basis for urban object mapping, land-cover analysis, and fine-grained spatial understanding. However, in complex urban scenes, fine-grained boundaries, small objects, inter-class similarity, and spectral confusion can still weaken the stability of pixel-level prediction. To enhance discriminative dense feature representations in high-resolution remote sensing images, we propose HDSMNet, a dual-branch multimodal semantic segmentation network designed for optical–nDSM data. The network separately extracts appearance and semantic features from optical imagery and height–structural features from nDSM, and introduces a Height-Guided Sparse Cross-Modal Fusion (HGSCF) module. Rather than treating nDSM as an additional feature source for generic fusion, HGSCF derives contextual representations, local feature contrasts, and structural-discontinuity cues from encoded nDSM features and uses them to guide sparse anchor-based interaction between optical and height features. This design enhances discriminative dense feature representations through interaction with a compact set of geometry-guided anchors. To complement HGSCF at the output stage, HDSMNet further adapts a Context-Guided Refinement (CGR) path that combines intermediate-response-guided contextual aggregation with dynamic feature modulation. This supplementary path recalibrates decoder features for output refinement. Experiments on the ISPRS Potsdam and Vaihingen datasets show that HDSMNet achieves mIoU values of 86.57% and 84.22%, respectively; ablation results further identify HGSCF as the main contributor to the observed improvement. Full article

Geological hazards pose persistent challenges to sustainable land use planning, infrastructure safety, and community resilience in alpine valley regions. Taking Tongren City on the northeastern margin of the Qinghai–Tibet Plateau as the study area, this study assessed composite susceptibility to landslides, rockfalls, and debris flows using 18,136 slope units, 189 historical hazard points, and 92 independent verification points, with the three hazard types combined into a single positive class for regional screening. Twelve conditioning factors were retained after collinearity testing, and negative-sample buffer distances of 200, 500, 1000, and 1500 m were compared before applying Bayesian-optimized random forest (Bo-RF), Bayesian-optimized CatBoost (Bo-CatBoost), and TabPFN models. Model performance was evaluated through repeated buffered spatial block cross-validation, probability calibration, spatial error analysis, independent verification, and SHAP interpretation. The 1000 m buffer produced the best negative-sample performance, with accuracy of 0.905 and an ROC-AUC of 0.972. Under spatial validation, the three models showed comparable discrimination, with ROC-AUC values of 0.9152–0.9161 and accuracy values of 0.8509–0.8549; Bo-CatBoost performed better in probability calibration, whereas TabPFN exhibited weaker spatial clustering of residuals. The TabPFN very-high-susceptibility zone covered 17.03% of the study area and contained 84.66% of historical hazard points, while the corresponding Bo-CatBoost zone captured 72.83% of independent verification points. SHAP analysis identified the mean annual rainfall, distance to water systems, distance to roads, NDVI, and lithology as the principal factors shaping the composite susceptibility pattern. Overall, the proposed framework provides spatial decision support for sustainable land use planning, resilient infrastructure management, targeted hazard investigation, and the efficient allocation of disaster prevention resources in alpine valley regions. Full article

Accurate mapping of rubber plantations is essential for sustainable land management and forest conservation in tropical regions. However, existing methods face two major challenges: persistent cloud cover limits the effectiveness of optical remote sensing in tropical areas, and regional phenological heterogeneity hinders the transferability of fixed-parameter approaches. This study proposes a Phenology-Adaptive Rubber Plantation Mapping (PARM) framework that integrates Sentinel-1 SAR time-series data with optimally selected spectral indices through a cascading constraint architecture. The framework operates as a structurally coherent system wherein SAR-derived phenological anchors explicitly govern downstream optical analysis across three internally dependent modules. First, three key phenological nodes—leaf-off start (LOS), fastest greening point (FGP), and full canopy point (FCP)—are extracted directly from SAR VH-polarization backscatter time series, enabling cloud-independent extraction of phenological temporal anchors. Second, the Jeffries–Matusita (JM) distance, evaluated within SAR-constrained phenological windows, is employed to identify the optimal vegetation and water indices for each region from six candidate spectral indices. Third, a time-weighted Rubber Plantation Discrimination Index (RPDI) is constructed using the selected indices and locally extracted phenological nodes, thereby amplifying the coupled signals of canopy greenness and moisture dynamics during critical phenological transitions. The framework was validated in Hainan Island and Vietnam, two regions with contrasting phenological regimes, using a spatial-block partitioning protocol (leave-one-subregion-out combined with DBSCAN-based clustering) designed to prevent samples from the same plantation from occurring in both training and test subsets. Within the Dynamic World forest mask, PARM achieved overall accuracies of 92.04% and 91.24%, respectively (93.57% and 91.00% on the fully held-out Qionghai City and Gia Lai province subregions), with Kappa coefficients exceeding 0.81 in both regions, consistently outperforming schemes based on raw spectral bands, individual spectral indices or direct multi-source time-series stacking. Error structure analysis revealed that residual classification failures are primarily associated with landscape fragmentation, stand immaturity, and residual cloud contamination, delineating the generalizability boundaries of the framework. These results demonstrate that tightly coupling SAR-based phenological characterization with adaptive optical index selection through a cascading constraint architecture provides a reliable foundation for rubber plantation mapping in cloud-prone tropical regions. Full article

Long-term monitoring of land cover dynamics is essential for evaluating conservation outcomes in World Heritage sites, yet remains challenging in conflict-affected regions with limited field observations. This study aims to reconstruct annual land cover dynamics and assess forest sustainability in the Okapi Wildlife Reserve (OWR), a World Heritage site in the Democratic Republic of Congo. Using Landsat dense time series observations (2000–2023), the Continuous Change Detection and Classification (CCDC) algorithm, landscape metrics, and the Sustainable Development Goal (SDG) 15.1.1 indicator, we developed an annual scale framework to quantify land cover changes and conservation status. The results showed that forest loss was the dominant land cover change process, with degradation accelerating after 2013. Forest disturbances were spatially heterogeneous, mainly concentrated in the boundary buffer zone and along National Road No. 4, while localized disturbances also occurred within the core protected area. Landscape fragmentation increased during the study period, and SDG 15.1.1 trends revealed differentiated conservation pressures among management zones. This study provides an annual-scale land cover history of the OWR and demonstrates the potential of Earth observation for monitoring endangered World Heritage sites. Full article

Protegira songi Chen and Zhang is a monophagous defoliating pest that feeds exclusively on Eucommia ulmoides Oliv., an economically and medicinally important tree species widely cultivated in China. Climate change may influence the potential suitable habitat of this pest, with implications for future monitoring and management. In this study, a Maximum Entropy (MaxEnt) model integrating occurrence records and environmental variables was used to evaluate current habitat suitability and project future changes under four Shared Socioeconomic Pathway (SSP) scenarios. The minimum temperature of the coldest month (Bio6) and precipitation of the driest month (Bio14) were identified as key climatic factors associated with potential habitat suitability. Currently, suitable habitats were estimated to cover approximately 20.58% of China’s land area, with highly suitable regions mainly distributed in central and southwestern China. Future projections indicated that potential suitable habitats would undergo spatial redistribution, with core suitable areas remaining relatively stable while peripheral regions showed more variation. Under SSP245, the suitable habitat area exhibited the largest increase by the 2090s, accompanied by an overall southward shift in the centroid of suitable habitat. These findings improve understanding of climate-driven changes in the potential suitable habitat of P. songi and provide a scientific basis for climate-informed monitoring and adaptive pest management. Full article

Mega-events can accelerate land–use/land cover change (LULCC) through infrastructure development and ecological interventions, but the carbon consequences of different land transition pathways remain unclear. Using Yanqing District, a host region of the Beijing 2022 Winter Olympics, as a case study, this study investigates LULC transitions, carbon storage dynamics, and future land management. A coupled PLUS–InVEST framework integrating transition attribution and trajectory analysis was applied to 10-m LULC datasets (2018, 2021, and 2024) to reconstruct historical transitions and simulate four land management scenarios for 2035. Results indicated that total carbon storage increased continuously from 41.19 × 10⁶ t C in 2018 to 42.14 × 10⁶ t C in 2024 despite concurrent urban expansion. This increase resulted from distinct transition processes across the two periods: cropland-to-rangeland conversion contributed most to carbon gains during 2018–2021, whereas rangeland-to-trees conversion dominated gains during 2021–2024. Pixel-level trajectory analysis further revealed that these dominant transitions rarely formed a continuous restoration sequence at the same locations, with rangeland–rangeland–trees trajectories accounting for 76.2% of trajectories leading to trees-class gains. Future simulations showed that the Urban Development Scenario would reduce carbon storage by 5.6%, whereas the Ecological Protection Scenario would increase carbon storage by 2.3% relative to the 2024 baseline. These results indicate that regional carbon dynamics depend not only on LULC composition but also on transition pathways, providing insights into how transition pathways can inform ecological restoration and sustainable land management. Full article

Land-cover (LC) and land-use (LU) mapping is essential for environmental monitoring, yet supervised multi-decadal classification remains constrained by heterogeneous agroforestry mosaics, uneven observation quality, and limited consistent reference data. This study presents a Landsat-based workflow for robust multi-temporal LC/LU classification, combining quality-focused preprocessing, radiometric harmonization, predictor-set evaluation, and Random Forest (RF) hyperparameter-stability assessment. Implemented in south-central Chile, the workflow combines masks with a locally calibrated cloud–snow index and applies per-band histogram matching for color balancing. It also tests progressive predictor designs (seasonal spectral bands, spectral indices, and territorial variables) under sample scarcity. Classification used Google Earth Engine and an RF grid search over hyperparameter ranges. Performance was evaluated through validation-kappa (κ) distributions and a hyperparameter-dispersion metric assessing accuracy and stability. Color balancing improved cross-period consistency and can partly offset the absence of spectral indices, but high performance was achieved only with the full predictor set, even under limited observations. High-performing treatments showed lower hyperparameter dispersion, supporting model selection that jointly considers accuracy and stability rather than one tuned configuration. Under limited reference data, classification depends more on predictor design and robustness-based selection than on increasing sample size alone. Full article