Search Results (224)

This study develops an interpretable and validated XGBoost–SHAP framework integrating multisource geospatial data and historical flood observations, with model performance evaluated using independent validation and future projections driven by bias-corrected CMIP6 climate scenarios to characterize the spatiotemporal variations and contributions of flood driving factors across three regions of Kazakhstan (2000–2025). The results demonstrate pronounced spatial differences in flood-driving factors: delayed snowmelt coupled with orographic rainfall dominates flood variability in the mountainous Almaty Region; hydrological memory effects regulate flood responses in the Akmola plains; and socioeconomic exposure shows an increasing contribution to flood risk evolution in Turkestan. Future multi-scenario simulations indicate that the flood-affected area in the Almaty Region is projected to increase by 10.8% under SSP2-4.5, which is associated with enhanced snowmelt processes, whereas the Akmola Region may experience a 23.2% reduction under SSP5-8.5, which is associated with changes in evaporation–soil moisture interactions. The interaction between socioeconomic development and natural hazards results in divergent risk trajectories: urban expansion in Akmola and Turkestan may offset declining hydroclimatic hazards, creating a potential risk paradox, whereas mountainous regions remain sensitive to concurrent increases in hazard intensity and exposure. These findings indicate that flood risk evolution in the studied regions of arid Central Asia is being increasingly influenced by socioeconomic dynamics in addition to natural hazards, highlighting the importance of differentiated adaptive planning strategies. Full article

Understanding intra-urban spatial interaction is essential for context-sensitive urban planning. While classical spatial interaction theories provide strong conceptual foundations, systematically translating these theoretical concepts into quantifiable indicators remains a significant methodological challenge. Furthermore, capturing the complex, non-linear dynamics driving urban mobility requires analytical approaches that balance predictive power with interpretability. To address this gap, this study develops a feasible, theory-informed analytical framework that bridges classical spatial interaction theory with interpretable machine learning to quantify the predictive patterns underlying intra-urban mobility. In a case study of Changchun, China, Ullman’s three core concepts, together with fundamental measures of urban scale, were systematically operationalized as a set of quantitative proxy variables based on multi-source geospatial big data. XGBoost was then used to model grid-level origin-destination flows at multiple spatial resolutions, and the SHapley Additive exPlanations (SHAP) was used to assess the contributions and dependence patterns of the theory-driven indicators. The results demonstrate the framework’s predictive robustness, with the XGBoost model consistently outperforms the traditional parametric benchmark across all evaluated spatial resolutions. The 1000 m resolution provided the best balance between predictive performance and spatial detail, yielding an R² of 0.696, compared with 0.611 for the benchmark. The explanatory analysis indicates that functional complementarity is the most critical predictive dimension overall. It also identifies distinct nonlinear patterns, including negative associations between transfer impedance and predicted mobility flows beyond critical thresholds, positive associations between built-environment scale indicators and predicted flows only above minimum intensity thresholds, and diminishing marginal associations between intervening opportunities and predicted flows. This study provides a scalable and transferable approach for diagnosing spatial interactions in data-rich urban contexts, providing an empirical basis for calibrating future micro-level urban simulations. Full article

Flash floods are among the most damaging hydrometeorological hazards in the Western Balkans (WB), yet regionally consistent, cross-border susceptibility assessments remain scarce because of fragmented national datasets and differing methodological standards. This study develops a harmonized, cloud-based flash-flood susceptibility framework for the WB (208,052 km²) by implementing a physiography-based modified Flash-Flood Potential Index (FFPI) in Google Earth Engine (GEE) at 30 m resolution. The modified FFPI integrates slope, land cover, soil texture, vegetation exposure (Bare-Soil Index), and soil erodibility, and is aggregated across 9524 EU-Hydro sub-basins to produce an operational catchment-level ranking. Additionally, CHIRPS-derived maximum daily precipitation is used to derive a rainfall-triggered hotspot layer that highlights sub-basins where terrain-controlled susceptibility coincides with strong observed rainfall extremes over the 2001–2025 period. Enhanced susceptibility is concentrated in Adriatic and Aegean-facing mountain basins of Albania, Montenegro, and North Macedonia, with 44.2% of sub-basins classified as high or very-high susceptibility. Multi-source validation against inventoried torrential catchments, published GIS-based susceptibility maps, and flood records yielded moderate to very strong agreement (68.6–92.0%), together with an AUC-ROC of 0.79 and F1-score of 0.77 for the pooled orthophoto-based validation dataset (n = 336 sub-basins). The framework provides a reproducible transboundary tool for regional flood-risk screening and demonstrates the potential of cloud-based geospatial platforms to overcome cross-border data fragmentation in hazard assessment. Its main limitations are the static physiographic nature of the FFPI, the coarser resolution of CHIRPS and SoilGrids relative to small sub-basins, and possible overestimation in karst terrains where subsurface drainage reduces surface runoff. Full article

Recreation suitability in national parks refers to the capacity of a place to support low-impact recreation while maintaining ecological integrity and complying with conservation objectives. In alpine protected areas, high landscape attractiveness does not necessarily indicate feasible recreation space because ecological fragility, remoteness, and strict protection requirements may constrain use. Using Sanjiangyuan National Park, China, as a case study, we integrated multi-source geospatial data to construct an Ecological Recreation Suitability Index from Ecological Recreation Provisioning, the Landscape Attractiveness Index, and the Wilderness Degree Index. WDI was transformed into a nonlinear compatibility function that constrained the realization of landscape attractiveness, and ecological support was then integrated with wilderness-compatible attractiveness. Relatively high- and high-suitability zones accounted for 30.88% of the valid assessment area and were mainly concentrated in the eastern and southeastern parts of the park, where higher ecological recreation provisioning and landscape attractiveness under suitable wilderness constraints jointly contributed to greater recreation suitability. Western and northwestern areas were constrained chiefly by weak ecological support and high wilderness conditions. These findings show that recreation suitability is not a direct expression of landscape attractiveness. The principal scientific contribution is a condition-dependent coupling structure that distinguishes potential attraction from conservation-compatible recreation suitability. Full article

Typhoons often cause severe casualties, property losses, and infrastructure damage, and high-resolution spatial risk assessment is an important basis for developing effective disaster prevention and mitigation strategies. However, most existing typhoon risk assessments are conducted at relatively coarse spatial scales and provide limited representation of intra-urban differences in mitigation capacity. To address this gap, this study takes Haikou, China, as the study area and develops a spatially detailed mitigation-capacity indicator system. Mitigation capacity is incorporated as a key dimension into the conventional hazard–exposure–vulnerability framework. Based on multi-source geospatial data, data mining, and spatial analysis, a risk assessment system comprising 23 indicators was established. Indicator weights were determined using the analytic hierarchy process, and all indicator layers were harmonized to generate a typhoon risk map on a 30 m analytical grid. The results show that the high-resolution risk maps can effectively characterize the spatial extent and level differentiation of typhoon risk while revealing significant spatial heterogeneity in risk at the fine grid scale. In Haikou, 18.99% of the area is classified as being at high and very high risk levels, mainly distributed along the coastal zones of Shishan Town, Xixiu Town, Changliu Town, Lingshan Town, and Yanfeng Town. A preliminary plausibility check was conducted using eight georeferenced typhoon-related fatality locations recorded from 2015 to 2024. Five were located in high- or very-high-risk zones. Given the limited sample size, this comparison does not constitute formal statistical validation, but the observed spatial correspondence provides preliminary support for the plausibility of the assessment results. This study provides spatially explicit decision support for identifying intra-urban variations in typhoon risk, delineating priority areas for disaster mitigation, and optimizing the allocation of mitigation resources. Full article

Rapid urban development has intensified conflicts between land development and ecological conservation, making spatially explicit land-use planning increasingly important for resource-depleted cities. This study develops a geoinformation-based decision-support framework for Huangshi, China, by integrating multi-scenario land-use modeling, production–living–ecological space analysis, landscape pattern assessment, and SDG 15 diagnostics. Four 2035 policy-oriented scenarios were compared: Business-as-Usual (BAU), Ecological Restoration Priority (ERP), Economic Development Priority (EDP), and Sustainable Development (SD). The results show that ERP delivers the strongest ecological performance, with ecological space reaching 46.47%, forest cover increasing from 35.40% to 36.80%, water area rising to 9.65%, net land degradation declining to −2.04%, and mean habitat quality reaching 0.484. SD provides a more balanced pathway, with ecological space of 44.80%, living space of 8.93%, a land-use stability rate of 96.36%, and a relatively low net degradation rate of 1.33%. BAU and EDP show higher ecological risks. The framework demonstrates how multi-source geospatial data and spatially explicit SDG diagnostics can support adaptive planning in resource-depleted cities. Full article

To support groundwater protection and sustainable utilization in southern Sichuan, this study aims to clarify the multi-year variation characteristics of groundwater levels (GWLs) and identify their main driving factors in the Yibin region. In this paper, 2019–2024 GWL monitoring records, hydrometeorological data, and multi-source geospatial datasets were integrated. Trend analysis, centroid migration modeling, continuous wavelet transform, Geodetector, and Fast Fourier Transform-based cross-correlation analysis were used to examine GWL dynamics and their controlling factors. The results show that GWL depth exhibits a distinct “shallow-northwest to deep-southeast” pattern, which is closely associated with regional aquifer lithology and hydrogeological conditions, with the most pronounced fluctuations occurring in the northwest. From 2019 to 2024, GWLs showed multi-scale periodic oscillations, with dominant periods of 50–64 months. GWLs in the red-bed region showed a continuous and slow decline, whereas those in the carbonate rock region remained relatively stable with a slight decreasing trend. Among the 13 hydrometeorological, geographic, and human activity factors, cropland area and precipitation had the strongest individual explanatory power. Their interactions with other factors produced nonlinear or bi-factor enhancement effects. The sustained expansion of cropland, together with declining precipitation, suggests that the observed phased and gradual decline in GWLs during 2019–2024 may be associated with a combined climate–human activity forcing mechanism. Annual GWL peaks were weakly and positively correlated with rainfall and temperature, while the lag between rainfall infiltration and GWL response varied with lithology. Full article

Snow disasters constitute a major natural hazard in Northwestern China, where heavy snowfall, blowing snow, and avalanches pose significant threats to infrastructure and socioeconomic activities. A scientific assessment of regional snow disaster risk is therefore critical for disaster prevention, spatial planning, and sustainable development. This study integrates multi-source remote sensing and geographic data to develop a comprehensive risk assessment method. By entropy weight method (EWM), we construct an assessment model that quantifies the combined hazard potential of heavy snowfall, blowing snow, and avalanches. The results reveal a significant spatial correlation among the three primary hazard types. The average potential hazard intensity of heavy snowfall is greater than that of blowing snow, which in turn exceeds that of avalanches. Spatially, the comprehensive snow disaster risk is most severe in the Altai Mountains, the Ili River valley, and the Tacheng region. A moderate-to-high risk level is distributed across the southwestern valleys and the foothills of the northeastern mountains. In contrast, the lowest risk areas are concentrated in certain interior valleys and the leeward slopes of the Junggar Basin. The resulting regional risk zoning was evaluated using receiver operating characteristic (ROC) curve analysis and disaster records. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.871 and an overall accuracy of 84.3%, indicating good spatial discrimination between high- and low-risk zones. These metrics support the application of the framework to regional snow disaster risk assessment. Full article

How the diffusion of green innovation technologies translates into regional landscape sustainability is still poorly resolved, in part because most studies rely on a single data source that cannot separate an adoption signal from confounding climatic and terrain influences. To make progress on this identification problem, an empirical framework is assembled that fuses openly licensed observations, Landsat and Sentinel-2 imagery, OpenStreetMap layers, NPP-VIIRS nighttime lights, and public statistical yearbooks, and embeds them in a spatial econometric design, so that the adoption–pattern–service–sustainability chain can be traced across 72 county-level units spanning Ningxia, eastern Gansu, and northern Shaanxi over 2013–2022. Pixel- and object-level integration, entropy weighting, and principal component reduction jointly deliver a fused representation whose coefficient of determination against held-out reference data exceeds 0.85 while the reconstruction error falls by roughly a third relative to single-source baselines. A spatial Durbin specification then decomposes adoption’s association with sustainability into a dominant direct component and a smaller, distance-bounded spillover, and roughly one-quarter of the total travels through landscape reconfiguration; the result survives the placebo, subsample, and variable-substitution checks, and is strongly conditioned by the terrain and local economic capacity. These findings favour a spatially coordinated, capacity-targeted transition policy rather than uniform deployment. Two caveats should be read alongside them: adoption is measured through proxies whose validity, though corroborated against county-level green-patent and installed-capacity records, is not perfect, and external validation across contrasting landscapes remains outstanding. Full article

Artificial Light at Night (ALAN) is increasingly recognized as an environmental exposure in dense urban areas, where conventional two-dimensional nighttime-light remote sensing cannot adequately represent vertical illumination on building facades, while detailed three-dimensional simulations remain computationally expensive for wide-area application. To address this limitation, we developed the Physics-Informed Residual Cascade Framework (PIRCF) for estimating individual-building vertical light exposure from two-dimensional multisource geospatial data. Here, “physics-informed” refers to the incorporation of exposure-related geometric features, distance-related attenuation, spatial-topological relationships, and environmental occlusion priors as inductive biases, rather than the direct enforcement of physical governing equations in the loss function. PIRCF combines graph-based neighborhood inference with residual correction to represent both broad spatial relationships and localized environmental variation. In Guangzhou, the framework achieved $R^2$ values of 0.78 for panchromatic exposure and 0.85 for blue-light exposure, outperforming the selected statistical baselines. In a zero-shot transfer experiment—that is, direct application of the Guangzhou-trained model to Shanghai without additional training or parameter adjustment—the corresponding $R^2$ values were 0.70 and 0.73. The predicted patterns further indicated distinct spectral organizations: panchromatic exposure exhibited broader and more continuous gradients associated with the road network, whereas blue-light exposure showed more fragmented local clustering near commercial and vertically developed urban areas. These findings demonstrate the potential of PIRCF as a scalable screening tool for building-level urban light-exposure assessment and for prioritizing locations requiring more detailed field investigation. Full article

Point cloud fusion is crucial in geospatial analysis, combining data from multiple sources (e.g, LiDAR and photogrammetry) to provide a more complete and accurate environmental representation. However, integrating airborne hybrid sensors or cross-source point clouds remains challenging due to variations in geometric accuracy, data precision, gaps, and sensor attributes. Despite recent advancements, these challenges remain and are among the most demanding aspects in geospatial data processing for remote sensing applications. We propose a new point cloud fusion algorithm that leverages local plane constraints to achieve advanced semantic consistency. The proposed method dynamically fits local planes to the target point clouds, enabling robust alignment of source points to these planes. Evaluation on two real-world datasets demonstrates significant gains in accuracy and preservation of geometric details. Our algorithm also improves the accuracy of downstream tasks such as semantic segmentation. In our experiment, the overall accuracy for the Dudelange dataset increases from 48.5% to 80.1%, and that for the Dublin dataset increases from 72.9% to 88.0%. While challenges persist with sparse and noisy datasets, experimental results highlight the effectiveness of the proposed method, offering valuable insights for maximizing the potential of cross-source point cloud data. Full article

Understanding the spatiotemporal dynamics of carbon emissions and developing differentiated governance strategies for urban agglomerations are essential for achieving regional low-carbon transformation. This study aims to identify the spatiotemporal patterns, driving mechanisms, and development-stage differences of carbon emissions across China’s urban agglomerations and to establish a type-specific governance framework. Based on multi-source geospatial and socioeconomic data from 19 urban agglomerations for the period 2006–2023, this study integrates spatial autocorrelation analysis, standard deviation ellipse analysis, hotspot analysis, random forest regression with SHAP interpretation, K-medoid clustering, and the Environmental Kuznets Curve (EKC) model to systematically examine emission evolution, influencing factors, and governance pathways. The results indicate the following: (1) carbon emissions in China’s urban agglomerations increased continuously during the study period and exhibited significant spatial heterogeneity, characterized by a “high east–low west” pattern, expanding eastern emission hotspots, and a gradual southwest shift in the emission centroid; (2) industrial structure and economic development level were identified as the dominant factors associated with carbon-emission differences, while energy efficiency, urbanization, and population density showed heterogeneous relationships across regions; (3) five carbon-emission development types were identified, including high-carbon high-development, transition-pressure, resource-dependent, stable-development, and low-carbon potential agglomerations, each exhibiting distinct development characteristics and governance requirements; and (4) EKC analysis revealed differentiated development stages among these types, suggesting that carbon governance should be tailored according to regional development conditions, dominant drivers, and emission-transition stages. This study provides an integrated geospatial modeling framework for understanding carbon-emission heterogeneity and offers scientific support for differentiated low-carbon planning and collaborative governance of urban agglomerations. Full article

In high-density built environments, traditional single-source street-level data are often limited by spatial blind spots when measuring the Green View Index (GVI). Street-view images (SVIs) have been widely used for extracting GVI, but their coverage is often discontinuous. To achieve a more continuous estimation of GVI in complex urban blocks, this study proposes an estimation framework that combines SVI-derived GVI extraction with ensemble learning and multi-source geospatial data. Shichahai Subdistrict in Beijing is used as a case study. Spatial morphology and vegetation-related factors are quantified within multi-scale buffers, and a Stacking ensemble model combining Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) is used to infer missing GVI values. The Stacking model achieved the best overall validation performance among the tested models, with an MAE of 0.0651 and an R² of 0.81, outperforming the single models and simpler benchmark models. Environmental factors also exhibited scale sensitivity, with vegetation factors showing stronger explanatory power at the 25 m micro-scale and building morphology factors showing negative associations at larger scales. In this case study, the framework mitigates discontinuities caused by gaps in street-view data and provides quantitative evidence for refined green renewal in high-density historic urban areas, but the results should be interpreted as site-specific interpolation rather than general cross-city prediction. Full article

Accurate land-use polygon mapping for forest-oriented natural resource monitoring requires both image-based class prediction and the reconciliation of heterogeneous geospatial semantics. In operational mapping, land survey, forestry survey, and natural resource monitoring datasets often differ in classification systems, management objectives, boundary rules, and mapping scales, causing semantic conflicts when overlaid or forced into one-to-one categories. To address this issue, this study proposes a topology-aware land-use polygon-mapping framework that integrates multi-source semantic representation learning, semantically guided relation learning, and topology-constrained polygon optimization. In the experiments, remote sensing imagery provides visual evidence, the Third National Land Survey (TNLS) and forestry survey (FS) datasets are encoded as source-specific auxiliary semantic priors, and the natural resource integrated monitoring (NRIM) data serve only as reference labels for training and evaluation. Rather than resolving cross-source conflicts using predefined rules, the framework learns a unified land-use representation, transforms semantic boundary cues into a vertex-edge topology graph, and reconstructs GIS-compatible polygons through topology-constrained polygon optimization. In a representative forest–agricultural landscape, the method achieved an mIoU of 79.86%, an APLS of 56.82%, and a TOPO-F1 of 54.56%. These results suggest that learnable semantic harmonization and topology-aware polygon generation can improve the semantic consistency and vector reliability of land-use products for forest and natural resource monitoring. Full article

Earthquake surface ruptures provide critical geological evidence for identifying seismogenic structures, reconstructing rupture processes of strong earthquakes, and assessing seismic hazards associated with active faults. The Mainland China Surface Rupture Database (MCSRD) has compiled surface rupture records from 632 data sources and 72 earthquake events, providing an important data foundation for investigating strong-earthquake surface ruptures in mainland China. However, offline data products are limited in supporting online visualization, interactive querying, and cross-platform sharing, which restricts the broader reuse of surface rupture data. In this study, we developed a WebGIS-based data-sharing system for earthquake surface ruptures in mainland China using MCSRD as the core data source and an open-source geospatial technology stack comprising PostgreSQL/PostGIS, GeoServer, Django, and Leaflet. The system enables the unified organization, online publication, interactive querying, result export, and Open Geospatial Consortium (OGC)-compliant service access of multi-source thematic data. Unlike platforms primarily designed for the management of basic active-fault information, the proposed system focuses on event-based surface rupture data from documented strong earthquakes, with emphasis on rupture geometry, coseismic displacement, reliability assessment, and source traceability. Application and performance testing results show that the system can transform offline MCSRD thematic data into standardized spatial information services, enhancing the online accessibility, interoperability, and reusability of earthquake surface rupture data. This study provides a practical implementation framework for the Web-based publication and open sharing of earthquake geology thematic data. Full article