Search Results (807)

This study establishes an integrated “Detection–Decomposition–Interpretation” framework for geohazard assessment, with Tangshan City serving as a representative case. Using Sentinel-1 SAR images from 2020 to 2024, regional surface deformation was derived via the Small Baseline Subset InSAR (SBAS-InSAR) technique. Six categories of geohazards were systematically identified and classified: landslides, open-pit slope deformation, mining-induced subsidence, spoil heap deformation, tailings pond deformation, and reclamation settlement. A total of 115 potential hazards were spatially cataloged, revealing distinct zonation characteristics: the northern mountainous area is predominantly affected by landslides and open-pit mining hazards; the central plain exhibits concentrated mining subsidence; and the southern coastal zone is marked by large-scale reclamation settlement. For the southern reclamation area, where settlement mechanisms are complex, the Independent Component Analysis (ICA) method was applied to successfully decompose the deformation signals into three independent components: IC1, representing the dominant long-term irreversible settlement driven by fill consolidation, building loads, and groundwater extraction; IC2, reflecting seasonal deformation coupled with groundwater level fluctuations; and IC3, comprising residual noise. Time series analysis further reveals the coexistence of “decelerating” and “accelerating” settlement trends across different zones, indicative of their respective evolutionary stages—from decaying to actively progressing settlement. This study not only offers a scientific basis for geohazard prevention and control in Tangshan, but also provides a transferable framework for analyzing hazard mechanisms in other complex geographic settings. Full article

Vegetation is a fundamental component of ecosystems and plays a vital role in maintaining ecological processes. It contributes to soil conservation, climate regulation, and landscape quality. Kunming, widely known as the “Spring City,” relies heavily on vegetation to sustain its ecological and social environment. This study employs moderate resolution imaging spectroradiometer (MODIS) and Normalized Difference Vegetation Index (NDVI) data in combination with temperature, precipitation, population, and gross domestic product (GDP) records to analyze the spatiotemporal dynamics and driving factors of NDVI-derived vegetation index in Kunming from 2000 to 2020 using trend and correlation analyses. We derived fractional vegetation coverage (FVC) from MODIS NDVI using the pixel dichotomy model, analyzed its temporal trends with linear regression, and applied pixel-wise Pearson correlation analysis to identify the spatial relationship between FVC and precipitation. The main findings can be summarized as follows: (1) The NDVI-derived vegetation index pattern in Kunming is generally higher in the west than in the east and higher in mountainous areas than in plains and basins. From 2000 to 2020, overall NDVI-derived vegetation index increased, with the mean NDVI rising from 0.48 to 0.545. Notably, the NDVI values in 2010 and 2012 declined sharply, likely due to drought conditions caused by reduced rainfall in the preceding years. (2) During the study period, 26.86% of the area showed moderate (NDVI slope: 0.005–0.016) improvement and 10.35% showed significant (NDVI slope: 0.016–0.063) improvement, while 10.28% exhibited degradation. Spatially, improvements were concentrated in Xundian County, parts of Dongchuan District, northern Luquan County, and northern border areas adjoining Yiliang and Shilin Counties. Areas with clear degradation were primarily located in Kunming’s main urban area and along the corridor from the airport to Songming. (3) Correlation analysis revealed that 53.3% of areas exhibited a positive relationship between temperature and NDVI-derived vegetation index, while 18.6% showed a significant negative correlation, mainly in the lower Pudu River basin, the Fumin–Luquan border, and the basin areas of Songming and Shilin Counties. This negative relationship may be attributed to increased evapotranspiration under higher temperatures, which exacerbates soil moisture loss and imposes drought stress on vegetation, thereby inhibiting plant growth. Similarly, 53% of areas showed a positive correlation between precipitation and FVC, whereas only 8.3% showed a significant negative correlation, underscoring the strong influence of precipitation on vegetation dynamics in Kunming. (4) Over the past two decades, Kunming’s GDP increased tenfold. In comparison with NDVI-derived vegetation index data for the same period, this indicates that areas of higher GDP are often associated with lower NDVI-derived vegetation index. Full article

The Sichuan–Yunnan region, a primary seismic-prone zone on the Qinghai–Tibet Plateau, has experienced heightened seismic exposure due to rapid urbanisation. In order to address the issue of disaster risks and to promote sustainable urban development, this study establishes an integrated urban seismic resilience evaluation framework based on the DPSIR (Driving–Pressure–State–Impact–Response) model. The CRITIC–AHP combined weighting method was utilised to determine indicator weights, and data from 37 prefecture-level cities (2010, 2015, 2020) were analysed to reveal spatial–temporal evolution patterns and correlations. The results demonstrate a consistent improvement in regional seismic resilience, with the overall index increasing from 0.501 in 2010 to 0.526 in 2020. Sichuan exhibited a “decline-then-rise” trend (0.570 to 0.566 to 0.585), while Yunnan demonstrated continuous growth (0.517 to 0.557). The spatial pattern underwent an evolution from “west–low, central–eastern–high” to “south–high, north–low”, with over half of the cities attaining relatively high resilience by 2020. Chengdu and Kunming have been identified as dual high-resilience cores, diffusing resilience outward to neighbouring regions. In contrast, mountainous areas such as Garze and Aba have been found to exhibit low resilience levels, primarily due to high seismic stress and limited socioeconomic capacity. Subsystem analysis has revealed divergent resilience pathways across provinces, while spatial autocorrelation has demonstrated fluctuating global Moran’s I values and temporary local clustering. This research provides a scientific foundation for seismic disaster mitigation and offers a transferable analytical framework for enhancing urban resilience in earthquake-prone regions globally. Full article

Prefabricated technology addresses inefficiencies and quality variations in traditional construction but exhibits insufficient adaptability to pipeline systems in mountain rail transit equipment rooms (PPMRR), with obvious gaps in schedule and full-life-cycle economic quantification. This study aims to quantify the schedule and economic advantages of prefabrication for PPMRR and explore mountain-adapted implementation pathways. Taking Chongqing Rail Transit as the empirical context, this study employs case studies, field investigations, and semi-structured interviews as research methods. Results show that compared with the traditional mode, the prefabricated mode shortens the total project duration of PPMRR by 62–70% and reduces the 30-year full-life-cycle cost by 38.6%. This study addresses the research gap in schedule–economic quantification for the prefabricated mode of pipeline systems in mountain rail transit equipment rooms. It can provide critical references for construction planning, cost management and control, and the formulation of local prefabrication technical standards for mountain rail transit projects, thereby promoting the scalable application of prefabricated technology in complex terrain infrastructure. Full article

Promoting sustainable mobility and socio-economic sustainability through demand management is critical for mountainous urban rail systems. This study investigates urban rail transit in mountainous cities, focusing on how passenger travel behavior responds to time-based pricing policies across different station types, with the aim of informing differentiated fare policy design. Using Chongqing—a city with pronounced mountainous terrain—as a case study, we classified stations into 12 categories based on 11 indicators, including road slope, bus transfer density, average housing price, and peak-hour train crowding within a 500 m radius. This classification was then combined with questionnaire data to quantify fare elasticity of departure time. The results show that high-value bus-transfer congested stations are concentrated in central urban clusters with dense bus networks, mitigating terrain constraints and encouraging active travel. In contrast, low-value pedestrian-transfer comfort-oriented stations are predominantly located on the urban periphery, where sparse road networks and steep terrain exert greater influence. Low-value pedestrian-transfer congested stations exhibit the highest fare elasticity across all periods, indicating greater sensitivity to fare changes, while high-value bus-transfer comfort-oriented stations demonstrate the lowest elasticity, with passengers more likely to maintain existing travel patterns. Multiple linear regression identifies six significant determinants of fare elasticity, including section-level passenger crowding, average housing price, and bus route density. Sensitivity analysis using multinomial logistic regression further reveals that increasing bus route availability enhances the stability of low-value balanced-transfer comfort-oriented stations, whereas improving walkability can shift stations toward pedestrian-transfer types. By tailoring time-of-day pricing to station heterogeneity, policymakers can achieve equitable and environmentally friendly demand management, enhance operational efficiency and support sustainable urban development in mountainous regions. Full article

Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high computational cost or yield statistically accurate but physically inconsistent results. Urban air quality in mountain–valley cities is strongly shaped by thermal inversions and weak nocturnal ventilation that trap pollutants close to the surface. We present a hybrid physics–machine-learning framework that combines a Navier–Stokes surface-layer model with data-driven post-processing to produce short-term forecasts of wind, temperature, and particulate matter while preserving physical consistency. The approach captures diurnal ventilation patterns and the well-known negative linkage between near-surface wind and particulate loadings during wintertime inversions. Compared with purely statistical baselines, the hybrid system improves short-range forecast skill and maintains interpretability through physically grounded diagnostics. Beyond Almaty, the workflow is transferable to other mountain–valley environments and is directly actionable for early warning, traffic and heating-related emission management, and health-risk communication. By uniting physically meaningful fields with lightweight Machine Learning correction, the method offers a practical bridge between computational fluid dynamics and operational decision support for cities facing recurrent stagnation episodes. Aim: Develop and verify a method for the diagnostics and short-term forecasting of surface circulation and particle concentrations in Almaty (2024), ensuring physical consistency of fields, increased forecast accuracy on 6–24 h horizons, and interpretability of risk factors. Compared to purely statistical baselines (R² ≈ 0.55 for PM forecasts), our hybrid framework achieved a 16% gain in explained variance and reduced RMSE by 25%. This improvement was most evident during winter inversion episodes. Methods: This study introduces a hybrid modeling framework that integrates the Navier–Stokes equations with machine-learning algorithms to diagnose and forecast surface air circulation and particulate matter concentrations. The approach ensures both physical consistency and improved predictive accuracy for short-term horizons (6–24 h). The Navier–Stokes equations in the Boussinesq approximation, the energy equation, and K-closure particulate matter transport were used. The numerical solution is based on the projection method (convection—TVD/QUICK, pressure—Poisson equation). The ML module is gradient boosting and decision trees for meteorological parameters, lags, and diagnostic quantities. The 2024 data are cleaned, normalized, and visualized. Results: The hybrid model reproduces the diurnal cycle of ventilation and concentrations, especially during winter inversions. For 6 h: wind RMSE ≈ 1.2 m/s (R² ≈ 0.71), temperature RMSE ≈ 1.8 °C (R² ≈ 0.78), and particles RMSE ≈ 0.012 mg/m³ (R² ≈ 0.64). Errors are higher for 24 h. A negative relationship between wind and concentration was established: +1 m/s reduces the median by 10–15% during winter nights. Conclusions: The approach can be generalized to other mountain–valley cities beyond Almaty. Combining the physical model and ML correction improves short-term predictive ability and maintains physical consistency. The method is applicable for air quality risk assessment and decision support; further clarification of emissions and consideration of urban canyon geometry are required. The results support early-warning systems, health risk communication, and urban planning. Full article

To reveal the cross-scale trade-offs and synergies of ecosystem services (ESs) in resource-based cities, this study took Xingtai City, Hebei Province, as a case. Six ESs—water yield (WY), soil retention (SDR), habitat quality (HQ), urban cooling (UC), net primary productivity (NPP), and PM_2.5 removal—were quantified at the 1 km grid, township, and county scales. Using Spearman correlation, geographically weighted regression (GWR), and the XGBoost-SHAP framework, we analyzed the spatiotemporal evolution of the ecosystem service supply–demand ratio (ESDR) from 2000 to 2020 and identified the dominant driving mechanisms. The results indicate the following: (1) The mean ESDR in Xingtai decreased sharply from 0.14 in 2000 to 0.008 in 2020, a decline of 94.3%, showing a pronounced “high in the western mountains–low in the eastern plains” gradient pattern and an increasingly severe supply–demand imbalance. (2) Synergistic relationships dominated among the six ESs, accounting for over 80%. Strong synergies were observed between supply-related services such as WY–SDR and HQ–NPP, with correlation coefficients ranging from 0.65 to 0.88, whereas weak trade-offs (<20%) occurred between UC and PM₂.₅ removal in urbanized areas, which diminished with coarser spatial scales. (3) Population density (Pop), elevation (DEM), cropland proportion (Crop), and vegetation index (NDVI) were identified as the key driving factors, with a combined contribution of 71.4%. NDVI exhibited the strongest positive effect on ecosystem service supply (mean SHAP value = 0.24), while Pop and built-up land proportion showed significant negative effects once exceeding the thresholds of 400 persons/km² and 35%, respectively, indicating nonlinear and threshold-dependent responses. This study quantitatively reveals the spatiotemporal synergy patterns and complex driving mechanisms of ecosystem services in resource-based cities, providing scientific evidence for differentiated ecological restoration and multi-scale governance, and offering essential insights for enhancing regional sustainability. Full article

The sustainable development of resource-based cities faces challenges due to the imbalance between ecosystem service supply and demand. This study examines Xingtai, a typical resource-based city located in northern China, using ecosystem service bundle theory to analyze the supply–demand relationships of six ecosystem services—water yield, soil retention, habitat quality, urban cooling, PM_2.5 removal, and carbon sequestration—from 2000 to 2020. Based on the ratio of supply–demand, we identify ecosystem service bundles and explore their driving factors using redundancy analysis (RDA) and the geographically and temporally weighted regression (GTWR) model. Results show a clear “mountain–plain” supply gradient, with high supply in the western Taihang Mountains and low supply in urbanized eastern plains. Demand follows a “center-high, periphery-low” pattern, with urban centers showing higher demand for urban cooling and PM_2.5 removal. A severe supply–demand imbalance exists: soil retention, PM_2.5 removal, habitat quality, and carbon sequestration are undersupplied in urbanized areas, while water yield and urban cooling are oversupplied in the western mountains. Natural factors (precipitation and temperature) shape western mountain supply, while human activities (GDP and nighttime light) drive demand polarization in the east. GTWR results reveal that urban GDP growth and land expansion intensify demand, while stable supply in mountain areas relies on precipitation and forest cover. This study provides scientific support for the sustainable development of resource-based cities. Full article

Waterfront wilderness landscapes in mountainous cities, such as Chongqing, play a vital role in sustaining urban biodiversity and human well-being amid steep topography and hydrological variations that create unique habitats. However, public recognition of their ecological values and potential ecological–aesthetic conflicts remain underexplored. This study investigated biodiversity features and public preferences in Chongqing’s central urban waterfront wilderness through field surveys of 218 quadrats for biodiversity assessment (e.g., Shannon–Wiener and Simpson indices, cluster analysis identifying 12 typical communities) and two questionnaire surveys (N = 260 and 306) evaluating spatial features and plant attributes, with correlation and regression analyses examining relationships between ecological indices and preference scores. Results recorded 116 plant species from 41 families, dominated by herbaceous plants (77.6%), with herbaceous, shrub-herbaceous, and tree-herbaceous communities prevalent. No significant correlations existed between objective diversity indices and preference scores; instead, structure (β = 0.444, p < 0.001) and color (β = 0.447, p < 0.001) drove preferences (explaining 96.7% variance), favoring accessible mid-successional shrub-herbaceous structures over dense, low-diversity evergreen types. These findings reveal ecological–aesthetic conflicts in mountainous settings where aesthetic dominance limits biodiversity recognition. Implications include user-centered zoning: restrict access in low-preference steep areas with buffers for conservation, while enhancing high-preference flat zones via selective pruning and native colorful species introduction, supplemented by educational signage. This research provides a mountainous city archetype, enriching global urban wilderness studies and informing sustainable management in rapidly urbanizing regions. Full article

The Loess Plateau (China) is an ecologically fragile region where understanding the impact of forest naturalness on soil carbon content is critical for ecological restoration and enhancing carbon sequestration. This study investigates this relationship in the Cuiying Mountain area (Yuzhong County, Lanzhou City), a representative landscape of the semi-arid Loess Plateau. The Cuiying Mountain ecosystem is characterized by coniferous forests and Gray-cinnamon soils. We assessed forest naturalness using several key indicators: herb coverage, shrub coverage, tree biodiversity, and stand structural attributes. The results revealed a generally low level of forest naturalness at Cuiying Mountain. Although herb coverage was high, shrub coverage was minimal (2.1%), and tree biodiversity was low (Shannon index = 0.09). The stand structure was simple, characterized by considerable variation in individual tree sizes and a single canopy layer (mean mingling degree = 0.14). This structural simplicity aligns with the area’s history of plantation management. Furthermore, analysis of soil physicochemical properties and their relationship with plant diversity identified plant diversity as a significant factor influencing soil carbon content. The strongest correlation was observed between plant species number and topsoil organic carbon (r = 0.77), indicating a particularly pronounced effect of plant diversity on surface soil organic carbon. In summary, while forest naturalness at Cuiying Mountain is generally low, increased plant diversity enhances the accumulation of litter/root exudates and carbonates, suggesting that enhancing plant diversity is an effective strategy for increasing total soil carbon content. This study provides valuable insights for refining ecological restoration practices and strengthening the soil carbon sink function in forest ecosystems across the Loess Plateau and similar semi-arid regions. Full article

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects. Full article

Global ecosystems have undergone significant degradation and deterioration, making the identification of ecosystem changes essential for promoting sustainable development and enhancing quality of life. Hami City, a representative region characterized by the complex “desert–oasis–mountain” ecosystem in Xinjiang, China, provides a critical context for examining ecosystem changes in extremely arid environments. This study utilizes remote sensing data alongside the Revised Wind Erosion Equation and Revised Universal Soil Loss Equation models to analyze the transformations within the desert–oasis ecosystems of Hami City and their driving forces. The findings reveal that (1) over the past 24 years, there have been substantial alterations in the ecosystem patterns of Hami City, primarily marked by an expansion of cropland and grassland ecosystems and a reduction in desert ecosystems. (2) Between 2000 and 2023, there has been an upward trend in Fractional Vegetation Cover, Net Primary Productivity, and windbreak and sand fixation amount in Hami City, whereas soil retention has shown a declining trend. (3) The overall ecosystem change in Hami City is moderate, encompassing 61.85% of the area, with regions exhibiting positive change comprising 16.79% and those with negative change comprising 21.33%. (4) Temperature, precipitation, and evapotranspiration are the primary drivers of ecosystem change in Hami City. Although the overall changes in ecosystems in Hami City have shown an improving trend, significant spatial heterogeneity still exists. The natural climatic conditions of Hami City constrain the potential for further ecological improvement. This study enhances the understanding of ecosystem change processes in extremely arid regions and demonstrates that strategies for mitigating or adapting to climate change need to be implemented as soon as possible to ensure the sustainable development of ecosystems in arid areas. Full article

The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R² = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R² = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities. Full article

Rural tourism in China is advancing rapidly, with cultural and tourism integration (CTI) becoming a vital pathway for sustainability. Mountain–water towns, given their special geographical conditions, face numerous challenges in CTI development, which need to enhance landscape resilience. This study proposes the theoretical framework of landscape resilience in mountain–water towns from the perspective of CTI. Taking Yinji Town of Wugang City as an example, it constructs a resilience evaluation system including three dimensions: cultural landscape, natural landscape, and social systems. The study uses the AHP–Entropy Weight combined method to determine indicator weights. Indicator scores are obtained through field research and GIS analysis, which are substituted into the preparedness–vulnerability resilience model to calculate resilience level, and the Jenks Natural Breaks method is used for level classification. Finally, the Obstacle Degree Model is applied to identify the primary obstacle factors affecting landscape resilience. The results indicate the following: (1) The average landscape resilience (RI) score of the 19 villages in Yinji Town is 0.84 (RI < 1), indicating a generally low level. Two villages are in the high-level range, while four villages are in the low-level range. (2) Cultural landscape resilience is the primary weakness, with the lowest average score (0.70), while natural landscape resilience is the highest (1.03). (3) Major obstacles include such as the number of cultural inheritors, the degree of susceptibility to natural disasters, and the distance to core mountain–water resources. The study contributes a CTI-based evaluation framework and methodology for assessing landscape resilience, offering enhancement strategies through increased preparedness and reduced vulnerability. Full article

The rational management of vacant rural housing is critical for optimization of Territorial Spatial Patterns. Although the issue of rural housing vacancy (RHV) has attracted widespread attention, systematic investigations in mountainous regions remain limited. This study is based on census data covering 3039 rural houses across six villages in Jinshan Town, Nanjing County, Zhangzhou City, Fujian Province, China. Using binary logistic regression and the XGBoost machine learning model, it systematically identifies the dominant determinants of rural housing vacancy in mountainous areas and evaluates their relative importance. The results show that the relative importance of the influencing factors is ranked as follows: locational conditions, physical housing characteristics, and topographic features. Specifically, among locational factors, the distances to the national road, county government, township government, and village committee centers are the most critical determinants of housing vacancy. In terms of physical attributes, the number of stories, the structural type, the floor area per story, and the orientation of the house are key variables. Regarding topographic factors, slope and aspect have limited overall influence. The two models yielded consistent directions and magnitudes of the key predictors, confirming the robustness and reliability of the results. The findings of this study help address the existing gaps in research regions, influencing factors, and methodological approaches, thereby contributing to the promotion of sustainable rural development. Full article