Search Results (172)

Global climate change and rapid urbanization have intensified ecological security risks in arid oasis regions, creating an urgent need for reliable ecological security assessment and spatial zoning to support regional sustainable development. Taking the Kashgar Oasis as the study area, this study combined the pressure-state-response (PSR) framework with the Mazziotta–Pareto Index (MPI) to construct a Multivariate Ecological Security Index (MESI), with the aim of reducing excessive compensation among different ecological dimensions and highlighting ecological weaknesses. Based on multisource spatial data from 2010 to 2025, we further examined the spatiotemporal evolution of ecological security, spatial relationships among ecological dimensions, and ecological security zoning. The main results were as follows: (1) Ecological security exhibited a pronounced spatial gradient, with higher levels in mountainous areas, intermediate levels in oasis areas, and lower levels in desert areas. High ecological security areas were concentrated mainly in mountainous regions, river corridors, and natural oasis areas, whereas low ecological security areas occurred primarily in urban expansion areas, agricultural development zones, and desert margins. (2) Ecological security changed substantially over the study period, following three successive stages: expansion of moderate-security areas, increasing spatial differentiation, and overall recovery, with the recovery trend becoming increasingly apparent after 2020. (3) The ecological dimensions exhibited marked spatial heterogeneity. Mountainous areas and river corridors were generally characterized by lower human disturbance, lower land desertification sensitivity, and higher ecosystem service capacity, whereas oasis margins and urban expansion fronts showed more complex spatial mismatches and potential trade-offs. (4) Six ecological security functional zones were delineated based on multidimensional ecological characteristics and the overall ecological security state, and differentiated management strategies were proposed for the respective zones. Overall, by jointly considering multiple ecological dimensions and their imbalances, this study links ecological security assessment, spatial relationship identification, and spatially continuous zoning, thereby providing an integrated analytical framework for identifying ecological weaknesses and supporting differentiated ecological management in arid oasis regions. Full article

Mineral resource-based cities face long-term conflicts among resource exploitation, urban expansion, and ecological conservation, making it necessary to clarify how long-term land-use change affects ecosystem services and ecological security patterns. Taking Chenzhou, China, as a case study, this study analyzed land-use change from 1990 to 2024 and quantified four ecosystem services—water yield, carbon storage, habitat quality, and soil conservation—using the InVEST model. Ecological sources were identified based on an integrated ecosystem service index, while ecological corridors, ecological pinch points, and ecological barrier points were identified by integrating an ecological resistance surface, least-cost path analysis, and circuit theory. On this basis, an ecological restoration spatial pattern was delineated. The results showed that land-use change in Chenzhou was mainly characterized by stage-specific transitions between cropland and forest land and the continuous expansion of impervious surfaces. The integrated ecosystem service index generally declined before gradually stabilizing and recovering, with high-value areas concentrated in the mountainous forest regions of eastern, southern, and southwestern Chenzhou. Forest restoration improved ecosystem services, whereas the conversion of ecological land to impervious surfaces caused pronounced losses of ecological functions. The regional ecological network remained relatively stable overall, while ecological sources, corridors, pinch points, and barrier points of different levels exhibited clear spatial differentiation and differences in relative importance. Based on these characteristics, an ecological restoration spatial pattern was developed, comprising the Luoxiao and Nanling mountain ecological barriers, major ecological corridors, the Dongjiang Lake core ecological source, three ecological restoration zones, and priority ecological restoration nodes. The results indicate that integrating long-term land-use change, ecosystem services, and ecological network analysis can help identify key ecological security spaces and priority areas for ecological restoration, providing a spatial basis for differentiated ecological conservation and restoration in Chenzhou and a reference for mineral resource-based cities with similar development backgrounds. Full article

Identifying favourable zones for geothermal exploration at the regional scale remains challenging, particularly in areas where conventional geophysical surveys are spatially limited or economically unfeasible. This study presents an integrated framework for delineating geothermal favourability across the Iberian Peninsula using global gravity and magnetic products combined with subsurface thermal information. EGM2008, WGM2012, EMAG2, and WDMAM2 were compared and harmonized through geostatistical modelling, anisotropic ordinary kriging, and common-grid processing. Potential-field transformations and spectral coherence analysis were used to derive a Geophysical Favourability Index (FI_geof). This index was integrated with temperature at 100 m depth using weighted fuzzy logic, applying FuzzyLinear and FuzzyLarge membership functions with a 60% FI_geof and 40% temperature weighting, to obtain the Geothermal Favourability Index (FI_geot). Quantitative comparison and cross-validation indicated that EGM2008 and EMAG2 were the most suitable primary reference products within their respective datasets, whereas WGM2012 and WDMAM2 provided complementary regional-scale information. The fuzzy integration identified the highest favourability mainly in Galicia and the Levante–Betic sector, where elevated FI_geot values coincide with heat-flow values of approximately 96–154 mW m⁻² and comparatively high geothermal gradients. Around 20% of the study area was classified within the highest favourability category. The resulting FI_geot should be interpreted as a regional screening and prioritization tool rather than as direct evidence of an exploitable geothermal resource. Overall, the proposed methodology provides a reproducible approach for identifying priority areas for further geothermal investigation in large and incompletely characterized regions. Full article

Land subsidence in soft-soil urban transport hubs arises from the complex coupling of natural geology and intensive anthropogenic activities, yet its spatial differentiation mechanisms and seasonal drivers remain poorly understood in high-development core areas. This study develops a progressive analytical framework integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), GeoDetector, Gaussian Mixture Model (GMM), and Singular Spectrum Analysis (SSA) to investigate spatiotemporal deformation patterns and driving mechanisms in the Shanghai Hongqiao Transport Hub Core Area from 2015 to 2024 using 209 Sentinel-1A images. Validation against official subsidence contours yields a Pearson correlation coefficient of 0.697 (p < 0.001) and an RMSE of 4.23 mm, confirming good spatial pattern agreement. Urban functional zones and construction stages are identified as the dominant influencing factors, with their interaction exhibiting notable bi-factor enhancement. Six distinct deformation response types are delineated via GMM, and two opposing seasonal signals are distinguished: near-instantaneous precipitation-driven surface loading on shallow soft soil and temperature-driven thermoelastic expansion of built structures. These findings may inform differentiated subsidence management and offer a transferable workflow for analogous soft-soil urban areas. Full article

Coal mining landscapes require restoration strategies that account not only for where ecological risk is concentrated but also for how risk constrains landscape connectivity. However, landscape ecological risk assessment is still commonly used as a zoning tool, with weak links to resistance surface parameterization and node-level restoration. Using the Shenmu coal mining area in northern China as a case study, we developed a risk-informed ecological network framework based on multi-source spatial data from 1995 to 2020. The framework combined landscape ecological risk assessment, GeoDetector-based driver analysis, ecological source screening, resistance surface construction, minimum cumulative resistance modeling, a gravity model, and circuit theory-based node diagnosis. Landscape dominance showed the highest explanatory power within the tested factor set (q = 0.06083), followed by land use type, water body proximity, and landscape fragmentation, while most factor interactions showed bivariate or nonlinear enhancement. Risk zoning delineated ecological conservation (467.62 km²), enhancement (1434.86 km²), and restoration areas (2566.49 km²). The framework identified 10 ecological sources; 18 potential corridors with a total length of 213.18 km; and 89 key nodes, including 52 pinch points, 4 barrier points, and 33 fracture points. The main contribution of this framework lies not in combining established ecological network tools, but in transferring ecological risk information into resistance surface parameterization and linking different types of critical nodes to differentiated restoration priorities. These outputs should be interpreted as model-based structural and potential functional connectivity priorities, rather than as direct evidence of realized species movement. Full article

Metropolitan fringe areas are transitional spaces where urban expansion, industrial relocation, residential spillover, transport infrastructure, and ecological recreation jointly reshape land-use functions. However, urban vitality research has mainly focused on central urban areas, leaving limited evidence on how activity intensity is organized in metropolitan fringe spaces. Taking the Chengdu Metropolitan Area, China, as a case, this study delineates metropolitan fringe areas using POI kernel density analysis and density–distance relationships, calibrated with impervious-surface and remote-sensing evidence, measures village-level relative activity intensity using Baidu heatmap data, and examines nonlinear associations between built-environment variables and urban vitality using LightGBM and SHAP. The results show that vitality across the delineated fringe does not form a uniform core–periphery gradient. Instead, high-vitality units are clustered around industrial parks, residential spillover zones, transport corridors, public-service nodes, and ecological recreation spaces. Population density, nighttime light, gross domestic product, building density, and commercial POI density are the main predictors in both weekday and weekend models. Several variables show nonlinear and saturation-like associations rather than simple linear effects. These findings suggest that higher predicted fringe vitality is more likely to occur where population concentration, development intensity, service provision, transport linkage, and socioeconomic activity coexist, rather than where densification occurs alone. The study extends urban vitality research to metropolitan fringe areas and provides evidence for differentiated land-use planning in urban-rural transition zones. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Ion-adsorption rare earth deposits (IADs) constitute the primary global source of medium-to-heavy rare earth elements. The sustainable exploitation of these resources hinges on the precise characterization of the weathered crust architecture and its hydrogeological properties. Conventional drilling often fails to resolve the complex vertical heterogeneity of regolith profiles, thereby limiting orebody delineation and the optimization of in situ leaching (ISL). To address this, this study integrates Electrical Resistivity Tomography (ERT) with Archie’s Law and the Kozeny–Carman equation. Focusing on the YZK1 and YZK2 survey lines in the Dabu mining area, southern Jiangxi, we derived 2D distributions of porosity and permeability from resistivity inversions. Our results delineate the weathered crust into four distinct vertical strata: a surface accumulation layer, a completely-to-highly weathered granite layer, a moderately-to-slightly weathered layer, and fresh bedrock. Quantitatively, the completely-to-highly weathered layer exhibits a “low resistivity (10–700 Ω·m)–high porosity (8.7%–17%)–high permeability (0.04–1.2 mD)” mode, serving as the primary leachable reservoir. In contrast, the surface and moderately weathered layers display “high resistivity (>800 Ω·m)–low porosity (3.4%–6.4%)–low permeability (0.01–0.04 mD)” characteristics. Crucially, although the fractured bedrock zones on both YZK1 and YZK2 profiles exhibit similar absolute permeability values (~0.016 mD), they represent two distinct hydraulic architectures. On the YZK1 profile, the fractured zone acts as a relatively homogeneous potential leakage pathway. Conversely, the YZK2 profile displays significant vertical segmentation: the upper and lower margins function as permeable pathways, while the central core, likely infilled with fault gouge, acts as a sealing zone that impedes fluid flow. Consequently, resistivity data alone are insufficient to accurately assess hydraulic conductivity; joint interpretation incorporating both permeability and porosity is essential to definitively determine whether a fault zone acts as a “conduit” or a “barrier.” By transitioning from qualitative imaging to quantitative parameter evaluation, this study provides a robust technical paradigm for fine-scale exploration and ISL optimization in IADs. Full article

Karst river basins require restoration approaches that identify vulnerable connections while distinguishing ecological constraints from governance implementation conditions. This study developed a sequential Structure–Resistance–Connectivity–Governance (SRCG) framework for the Chengjiang River Basin, Guangxi, China. Ecological sources were identified from vegetation vitality, habitat quality, landscape integrity, and hydrological proximity. Natural landscape resistance and anthropogenic disturbance were integrated to delineate potential source-pair corridors. Bottleneck Intensity combined corridor load, minimum corridor width, and high-resistance overlap, and was integrated with ecological movement resistance to delineate Ecological Restoration Priority Zones (ERPZs). Governance Mismatch Index and Administrative Boundary Proximity Index values were applied only after ERPZ delineation. The analysis identified 28 ecological sources covering 312.4 km², 76 potential connections, three principal bottleneck clusters, and six ERPZs covering 118.5 km². The upper basin retained a relatively continuous network; whereas, the middle and lower reaches were increasingly constrained by roads, settlements, quarry disturbance, fragmented cropland, and discontinuous riparian vegetation. ERPZ-A, D, E, and F were resistance-dominated, ERPZ-B was bottleneck-dominated, and ERPZ-C was compound-priority. Governance assessment differentiated four implementation contexts. Overall land-cover accuracy was 0.896, Cohen’s kappa was 0.874, and 14 of 16 field sections were concordant with mapped landscape conditions; sensitivity tests retained the principal sources, bottlenecks, and ERPZ cores. The framework separates ecological restoration urgency from implementation difficulty, while its outputs represent potential structural rather than confirmed functional connectivity. Full article

Anthropogenic pressures increasingly threaten ecological connectivity and basin-scale ecological sustainability in peri-urban landscapes. This study proposes a Hybrid Ecological Typology framework for the Büyükçekmece Lake Basin (Istanbul, Türkiye), integrating Morphological Spatial Pattern Analysis (MSPA), Analytic Hierarchy Process (AHP), and Minimum Cumulative Resistance (MCR) analysis within a unified spatial decision-support system. The framework is applied to a 67,627.06 ha basin area, including a 25,976.99 ha terrestrial focus area. The results indicate a structurally heterogeneous landscape dominated by interior habitat zones (85.94%) distributed across 93 core patches. Despite this dominance, ecological connectivity is maintained through a highly fragmented network of 484 landscape elements, where limited bridge (0.08%) and branch (0.62%) structures highlight structural vulnerability. Edge-dominated zones (12.87%) further reflect strong anthropogenic fragmentation pressures. Connectivity analysis identifies 10 key habitat patches with dPC (Probability of Connectivity index) values exceeding 5% and 12 strategic ecological corridors supporting basin-scale ecological flows. The proposed hybrid typology delineates five functional planning categories: conservation areas (22.72%), ecological corridors (1.91%), restoration areas (1.63%), sustainable use areas (0.51%), and controlled development areas (8.11%). Although high-quality habitat cores dominate the basin, ecological connectivity remains spatially constrained, with bottleneck zones (0.89%) concentrated along transportation corridors that significantly reduce landscape permeability. Overall, the findings demonstrate that basin-scale ecological sustainability in peri-urban environments is governed not only by habitat quantity but also by the interaction between spatial configuration and resistance structures. The framework provides a transferable decision-support tool that bridges landscape ecology theory with spatial planning practice for basin management and ecological network design. Full article

The formation mechanisms of Urban Vitality have been constrained by the limitations of traditional linear driving hypotheses, and the fragmented analysis of subjective and objective factors. Integrating the explainable machine learning model XGBoost-SHAP with multi-source geospatial data, this study constructs a systematic analysis framework at the community scale, comprising 275 community units across five administrative districts in Changsha. It constructs a systematic analysis framework that unifies objective environmental conditions and subjective perceptions—a deliberate departure from the fragmented approaches dominant in previous vitality research. Through this objective-subjective integrated lens, it explores the spatial patterns, non-linear driving mechanisms, and variable interaction effects of Urban Vitality. The XGBoost model achieves a cross-validated R² of 0.794 and an RMSE of 0.031, ensuring interpretative reliability for exploring non-linear mechanisms. The results indicate that Urban Vitality exhibits a spatial pattern characterized by “high-value aggregation in the core, gradient decay in the periphery, and local fragmentation,” with a significant siphon effect observed in the core area. Partial Dependence Plot (PDP) analysis reveals that the impacts of variables on vitality can be categorized into four patterns: continuous upward, threshold leap, inverted U-shaped, and weak or sample-concentrated. Univariate dependence plots further delineates fine-grained threshold effects, including “threshold triggering, nterval suitability, high-value suppression, and co-occurrence signals.” Furthermore, bivariate interactions reveal four synergistic mechanisms: Building Density must match road network support; Functional Aggregation should synergize with locational value; transportation nodes must integrate with activity-support capacity; and Street View quality and human demand mutually regulate each other. The conclusion asserts that urban stock renewal must transcend the mindset of single-factor maximization and shift towards a precision governance approach of “threshold activation, synergistic matching, and zoning intervention,” thereby providing a quantitative decision-making basis for human-oriented, fine-grained urban regulation. Full article

Unplanned urban expansion accompanied by a decline in green infrastructure poses significant challenges for sustainable land-use planning in semi-arid, water-constrained secondary cities. Quetta, Pakistan, exemplifies these challenges due to rapid population growth, ecological degradation, water scarcity, and the absence of an updated master plan. This study develops a GIS-based spatial decision-support framework to evaluate residential development and green infrastructure priorities and to identify areas of conflict, synergy, and balanced planning opportunities. Sentinel-2A imagery acquired in May 2023 was used to generate a land-use/land-cover map, while residential and green-infrastructure suitability factors were standardized using fuzzy membership functions and integrated through an AHP Weighted Linear Combination approach. The resulting Residential Suitability Index (RSI) and Green-Infrastructure Suitability Index (GSI) were normalized and combined through a rule-based Residential–Green Infrastructure Trade-off Index (RGTI). Unlike conventional suitability assessments that evaluate development and ecological priorities independently, the proposed framework explicitly identifies zones of residential dominance, ecological dominance, and shared planning potential. Five planning-priority categories were delineated, comprising Very High Green Infrastructure Priority, Moderate Green Infrastructure Priority, Shared Zone, Moderate Residential Expansion Priority, and Very High Residential Expansion Priority. A spatial consistency assessment demonstrated that the identified planning zones correspond closely with existing land-use patterns and available land resources, supporting the plausibility of the proposed framework. The results provide a practical basis for delineating ecological conservation areas, residential development zones, and integrated planning zones capable of balancing urban growth and environmental sustainability. The framework offers a transparent and transferable approach for supporting land-allocation decisions in arid, data-scarce, and rapidly urbanizing cities facing competing development and ecological pressures. Full article

Urban functional zones (UFZ) serve as fundamental units of human activity, and their spatial configurations significantly influence urban carbon emissions. However, current research often overlooks the heterogeneity of UFZ forms and the complex, multi-scale relationships underlying their impact on emissions. To address this gap, this study investigates the Yangtze River Delta (YRD) urban agglomeration by delineating UFZ from the dual perspectives of block function and form. By integrating multi-scale spatial analysis with explainable machine learning, this study establishes a morphological indicator system covering three dimensions: density, morphology, and structure. The results reveal a pronounced scale dependency in the effects of UFZ form on carbon emission intensity (CEI), with optimal analytical scales identified at 14 km for density, 5 km for morphology, and 7 km for structure. Notably, variations within these dimensions lead to distinct patterns of impact intensity, even within the same functional category. Furthermore, most indicators exhibit nonlinear, threshold-dependent effects on CEI. These findings provide actionable guidance for fine-grained urban planning and carbon mitigation strategies. Full article

With the continuing advancement of global environmental change and rapid urbanization, rural human settlements are facing multiple pressures, including ecological degradation, spatial decline, population outflow, and functional weakening. Based on the vulnerability analysis framework, studies on rural vulnerability provide an important perspective for assessing villages’ risk exposure, disturbance response, and functional degradation when coping with internal and external disturbances. However, existing studies often rely on single-dimensional or linearly weighted evaluations, making it difficult to comprehensively reveal the coupling relationships among multiple discriminative variables and the spatial differentiation patterns of vulnerability. Taking rural areas in Hancheng City, Shaanxi Province, as the research object, this study selects 12 indicators from three dimensions—natural ecological constraints, settlement spatial organization, and public service support—to provide proxy representations of conditions related to potential rural vulnerability. K-means clustering was used to identify potential vulnerability configuration types under multidimensional indicator combinations. A Python-based XGBoost model was then employed as an interpretable surrogate model to assist in characterizing the clustering boundaries, while SHAP analysis was used to explain the key discriminative variables associated with type membership. The results show that the potential rural vulnerability configurations in Hancheng City present a significant west–central–east spatial differentiation pattern. Elevation, village core density, topographic wetness index, distance to town centers, accessibility of daily service facilities, distance to major roads, and normalized difference vegetation index are the main discriminative variables distinguishing different potential vulnerability configuration types. Among them, village core density shows a particularly strong explanatory role. Different key discriminative variables also exhibit evident nonlinear response characteristics across different potential types. Under the indicator system and the $K$ = 4 clustering scheme adopted in this study, the potential rural vulnerability configurations in Hancheng City can be summarized into four types: service-concentrated settlement type, complex terrain-constrained type, human–land coupling transitional type, and natural ecological isolation type. The findings reveal the spatial differentiation characteristics, variable combination relationships, and typological discriminative features of potential rural vulnerability configurations in Hancheng City. They can provide a case-based reference for identifying potential vulnerability, conducting spatial zoning diagnosis, and supporting classified governance in similar county-level rural areas within the loess hilly and gully region. In practical terms, this framework can serve as a diagnostic tool for local governments and planners in classified rural governance. It can be used to identify priority areas for public service and infrastructure investment, review key risk-control areas in complex terrain zones, delineate low-intensity use and protection boundaries in ecologically isolated areas, and guide differentiated resource allocation for different types of villages. Full article

Direct sea discharge outlets served as critical conduits for urban sewage and industrial wastewater disposal, playing dual roles as pollutant dilution channels and hotspots for pathogens and antibiotic resistance genes. Traditional monitoring approaches relying on physicochemical parameters and fecal indicator bacteria failed to capture the latent and cumulative risks posed by complex microbial communities. In this review, a holistic microbiome perspective was adopted to systematically synthesize current knowledge on the bacterial community dynamics, assembly mechanisms, resistome–virulome coupling patterns, mobilome-associated risk characteristics, and emerging biomonitoring strategies in direct sea discharge outlets. By integrating high-throughput multi-omics technologies with ecological network analysis and machine learning, we delineated a paradigm shift from cataloging microbial presence to deciphering functional interactions, risk propagation dynamics, and proactive surveillance strategies. Furthermore, under the “One Health” framework, we discussed emerging research frontiers and future challenges in managing pollution at discharge outlets, aiming to provide a scientific basis for environmental risk management in coastal zones. Full article