Search Results (324)

The complex topography and harsh natural environment of the Loess Plateau in Longzhong have been suffering from an undefined living circle structure, which has hindered rural planning and development. A rural community living circle is a spatial unit centered on meeting the needs of villagers, within which various service facilities are rationally allocated within a specific spatial scope. To refine its spatial patterns, the concept of living circles was introduced to address travel challenges. The extent of these living circles is affected by the accessibility of public service facilities and barriers to travel. Using land use data, DEM, population density, and road networks, this study employed the MCR model, gravity model, and ArcGIS spatial analysis to examine the patterns of rural community living circles. The focus was on analyzing the living circle structure of rural communities on the Loess Plateau in Longzhong, considering both natural and artificial environmental constraints. The results show: (1) Rural community living circles present multi-scale spatial features. The basic living circle covers a 15 min slow-travel area. The central living circle corresponds to village-level needs, accessible within 35 min by both slow and motorized travel. The town living circle covers a 10 km radius, reachable within 60 min by a mix of transport modes. The county living circle, dominated by motorized travel, represents the top tier of public service configuration. (2) Quantitatively, the delineation identified 2753 basic, 444 central, 19 township, and 1 county-level living circles in the Anding District of Dingxi City. The Northern, Eastern, and Southwest Zones suffer from fragmented mountainous landscapes, limiting mobility and accessibility. The Central Zone, however, benefits from a combination of mountainous terrain and river valley plains, offering superior service accessibility. (3) The analysis results based on the MCR model and gravity model aligned more closely with reality, reflecting the scale patterns of rural community living circles. The results of this study can provide theoretical guidance for rural planning, construction, and management in the hilly and gully areas of the Loess Plateau. Full article

Quantitative assessment of ecosystem health (EH) effectively provides a scientific reference for regional landscape ecological development and socio-ecological system coordination. This study combined the VORSH framework and the XGBoost-SHAP model to assess EH and its spatiotemporal driving factors in the Pinglu Canal Economic Zone. The results show that the comprehensive ecosystem health index (EHI) generally remained at a moderate level during this period, exhibiting a pattern of initial decline followed by recovery, resulting in an overall improving trend. The period from 2005 to 2010 was identified as a critical transitional phase, during which EH began to recover and gradually improve. The Pinglu Canal Economic Zone exhibits distinct spatial heterogeneity in EH. Areas with poor and unhealthy grades are primarily distributed around urban peripheries, plain regions, and near certain water bodies. In contrast, healthy and relatively healthy areas are predominantly located in the densely vegetated mountainous regions of the southwest, north, and east. Between 2000 and 2020, the EH status demonstrated a significant overall upward trend, with most areas experiencing slight improvement and only a few regions exhibiting significant degradation. Topography and temperature were the primary factors driving the spatiotemporal variations in EH, while the influence of human activities continued to intensify with ongoing socioeconomic development. Full article

Seismic disasters pose major challenges to urban resilience, particularly in high-density cities where the concentration of people, buildings, and infrastructure amplifies disaster risk. This study establishes a GIS-based analytical framework to evaluate the spatial distribution and fairness of seismic emergency shelters in Seoul, using built-up neighborhoods (called dongs in Korean) as the basic analytical unit. Three dimensions are assessed: (1) 500 m walking accessibility based on the road network; (2) redundancy, representing the number of shelters simultaneously reachable; and (3) fairness analysis, integrating spatial and population-based dimensions to reveal disparities between shelter provision and population demand. The results indicate that overall accessibility in Seoul is relatively high, with more than 50% of dongs achieving coverage levels above 50%. However, distinct spatial disparities remain. Central and mountainous areas, such as Jung-gu, Jongno-gu, and southern Seocho-gu, show coverage rates below 20%, while districts in the southwest and northeast exhibit higher redundancy. Fairness analysis further reveals inequality in shelter capacity relative to population: excluding null values, the median coverage ratio is 0.92 and the mean is 1.29, with only 44.97% of dongs achieving sufficient or surplus capacity (coverage ≥ 1). Notably, 44 dongs fall into the Low–High category, representing areas with large populations but limited shelter access, mainly concentrated in Jungnang-gu, Gangbuk-gu, and Yangcheon-gu. These dongs should be prioritized in future planning. Policy implications highlight strengthening shelter provision in high-population but low-coverage zones, incorporating evacuation functions into urban redevelopment, promoting inter-district resource sharing, and improving public awareness. The proposed framework provides a transferable model for optimizing seismic shelter systems in other high-density urban contexts. Full article

With global warming, tropical islands, as sensitive areas to climate change, exhibit new and significant temperature variation characteristics. Using the high-resolution Hainan Island Regional Reanalysis (HNR) dataset and multi-source data, this study analyzes temperature changes on Hainan Island from 1900 to 2022, focusing on spatiotemporal trends, diurnal patterns, and probability distribution shifts. The findings reveal significant periodic temperature changes: weak warming (0.02–0.08 °C/decade) from 1900 to 1949, a temperature hiatus from 1950 to 1979, and accelerated warming (0.14–0.28 °C/decade) from 1979 to 2022. Coastal plains (0.11 °C/decade) warm faster than inland mountains (0.08 °C/decade), reflecting oceanic and topographic effects. Diurnal temperature variations show topographic dependence, with a maximum range (8–9 °C) in the north during the warm season, and a southwest–northeast gradient in the cold season. Probability density function analysis indicates that the curves for transitional and cold seasons show a noticeable widening and rightward shift, reflecting the increasing frequency of extreme temperature events under the trend of temperature rise. The study also finds that the occurrence time of daily maximum temperature over coastal plains is advancing (−0.05 to −0.1 h/decade). This study fills gaps in understanding tropical island climate responses under global warming and provides new insights into temperature changes over Hainan Island. Full article

Rural areas are complex multi-level regional systems comprising multiple elements such as natural resources, human resources, social systems, and economic elements. Drawing on the socio-ecological system framework, we develop a new evaluation system to better understand rural sustainable development and the interactions between economic, social, and natural factors. Applying this system to the case of Guizhou Province reveals the following: First, the overall level of sustainable development of rural communities is low. Furthermore, the development gap between communities is significant, mainly driven by differences in the resource system and economic outcomes. Second, the overall coupling and coordination level among the rural sustainable development subsystems is low, and they are all in the grinding and less coordinated stage. Compared with communities with lower sustainable development, those with higher sustainable development levels exhibit higher coupling and coupling coordination. Third, the obstacles to sustainable development in rural communities are mainly concentrated in the resource systems and economic outcomes, including construction land, housing, government funding, asset growth, income growth, profitability, and bonus sharing. Full article

Rock glaciers have been recognized as key indicators of geomorphic and climatic processes in high mountain environments. In this study, Sentinel-2 MSI imagery and topographic data were integrated to construct enhanced feature sets for rock glacier identification. Three state-of-the-art deep learning models (U-Net, DeepLabV3+, and HRnet) were employed to perform semantic segmentation for extracting rock glacier boundaries in the Hunza River Basin, located in the eastern Karakoram Mountains. The combination of spectral and terrain features significantly improved the differentiation of rock glaciers from surrounding landforms, establishing a robust basis for model training. A series of comparative experiments were conducted to evaluate the performance of each model. The HRnet model achieved the highest overall accuracy, exhibiting superior capabilities in high-resolution feature representations and generalization. Using the HRnet framework, a total of 597 rock glaciers were identified, covering an area of 183.59 km². Spatial analysis revealed that these rock glaciers are concentrated between elevations of 4000 m and 6000 m, with maximum density near 5000 m, and a predominant south and southwest orientation. These spatial patterns reflect the combined influences of topography, thermal conditions, and snow accumulation on the formation and preservation of rock glaciers. The results confirm the effectiveness of deep learning-based semantic segmentation for large-scale rock glacier mapping. The proposed framework establishes a technical foundation for automated monitoring of alpine landforms and supports future assessments of rock glacier dynamics under climate variability. Full article

This study investigates the changes and driving mechanisms of agritourism landscapes in mountainous regions of Southwest China, providing a scientific basis for sustainable landscape management. We analyzed Hanyuan County (2013–2023) using remote images, POI data, terrain niche index, distribution index, landscape transition matrix, and logistic regression model from a landscape function perspective. These analyses reveal that the landscape pattern maintains overall stability with local fluctuations, with ecologically oriented landscapes being consistently dominant (>76% coverage). The primary conversion direction of development-potential landscapes shifted from ecological to agricultural dominance after 2018. All landscape types have shown more distinct distribution advantages in the fifth-level terrain gradient, with intensified fluctuations in low-gradient areas after 2018. Location factors were the most common driving force, but their effects differ: production-oriented landscapes shifted from location–climate correlation to location–socioeconomic–terrain correlation; living-oriented landscapes remain influenced by slope and location accessibility; ecological-oriented landscapes shifted from a location–climate correlation to location–tourism correlation; development-potential landscapes were positively influenced by multiple factors. This study suggests implementing zoned management based on functions and terrain gradients through policy guidance and technological intervention. The findings of this study can provide a reference for the comprehensive revitalization of rural areas and the sustainable development of landscapes in similar areas. Full article

Climate change intensifies ecosystem vulnerability in mountainous regions, particularly in Northwestern Sichuan’s Terrestrial Ecosystems (TENS), where complex terrain amplifies impacts on biodiversity and carbon dynamics. This study assesses spatiotemporal ecological vulnerability using the IPCC exposure-sensitivity-resilience framework. We applied autoregressive modeling and a 5-year moving window to monthly NDVI, temperature, and precipitation data from 1983 to 2022. Results show vulnerability index (VI) increases latitudinally from south to north, driven by inverse temperature correlations. Longitudinally, VI forms a V-shaped pattern due to topographic and monsoon influences. Wetlands are most vulnerable (VI ≈ 0.48) from precipitation sensitivity, while forests show lowest vulnerability (VI ≈ 0.43) due to high resilience. Temporally, VI fluctuates nonlinearly with decline (1985–1994) under cool-humid conditions, increase (1994–2008) amid warmer-drier El Niño effects, and sharp decline (2008–2011) from La Niña and sand control initiatives. Spatially, 34.6% of areas exhibit decline-increase-decline-increase trends. Centroids of decreasing VI shift southwest-to-north, indicating recovery diffusion. Increasing VI centroids move northwest-central-north. These findings underscore ecosystem-specific adaptive management and conservation policies, especially in northern TENS, to mitigate accelerating climate pressures. Full article

Soil erosion poses a significant threat to the sustainability of land systems in karst mountainous regions, where steep slopes, shallow soils, and intensive human activities exacerbate land degradation, undermining both the productive functions and ecological services of land resources. This study evaluated soil erosion susceptibility in the karst-dominated Qingshui River watershed, Southwest China, and identified key drivers of land degradation to support targeted land management strategies. Four machine learning models, BPANN, BRTs, RF, and SVR were trained using twelve geo-environmental variables representing lithological, topographic, pedological, hydrological, and anthropogenic factors. Variable importance analysis revealed that annual precipitation, land use type, distance to roads, slope, and aspect consistently had the greatest influence on soil erosion patterns. Model performance assessment indicated that BRTs achieved the highest predictive accuracy (RMSE = 0.161, MAE = 0.056), followed by RF, BPANN, and SVR. Spatial susceptibility maps showed that high and very high erosion risk zones were mainly concentrated in the central and southeastern areas with steep slopes and exposed carbonate rocks, while low-risk zones were located in flatter, vegetated southwestern regions. These results confirm that hydrological conditions, topography, and anthropogenic activities are the primary drivers of soil erosion in karst landscapes. Importantly, the findings provide actionable insights for land and landscape management—such as optimizing land use, restoring vegetation on steep slopes, and regulating human activities in sensitive areas—to mitigate erosion, preserve land quality, and enhance the sustainability of karst land systems. Full article

Under the context of global climate change and China’s dual carbon strategy (DCS), the impact of land use/land cover change (LULCC) on regional carbon stocks has garnered increasing attention. As a key economic and ecological hub in Southwest China, Chengdu has undergone significant urbanization over the past two decades, and it is necessary to quantitatively assess how shifts in land use affect its carbon stock function. This study integrates multi-period remote sensing data from 2000 to 2020, combining socioeconomic and natural environmental drivers. The PLUS model was employed to simulate land use in 2030 under four scenarios: Natural Development Scenario (NDS), Urban Development Scenario (UDS), Conservation of Cropland Scenario (CPS), and Ecological Protection Scenario (EPS). The InVEST model was then used to calculate changes in carbon stocks and their spatial distribution characteristics. The results indicate the following: (1) From 2000 to 2020, Chengdu’s cropland decreased by 1188.6174 km², while built-up land increased by 1006.5465 km², resulting in a net carbon stock decrease of approximately 3.25 × 10⁶ t, with carbon gains from forest restoration offsetting part of the cropland-to-built-up loss; (2) Under all scenarios, built-up land exhibited an expansion trend, with the UDS showing the most significant increase, reaching 1919.2455 km². In the EPS, the forest increased to 4035.258 km², achieving the largest carbon stock increase of 8.5853 × 10⁶ t. (3) Chengdu’s carbon stock exhibits a spatial distribution pattern characterized by “high in the northwest, low in the center”. High-value areas are concentrated in the ecologically sound Longmen Mountains and Longquan Mountains, while low-value areas are primarily located in urban built-up zones and their peripheries. The study indicates that rationally controlling the expansion of Built-up land, strengthening ecological restoration, and protecting forests can effectively enhance Chengdu’s carbon sink capacity and achieve regional low-carbon and sustainable development. This study aims to address the gap in carbon stock assessments under different development scenarios at the urban scale in Southwest China, and to provide a scientific basis for Chengdu’s regional spatial planning, ecological conservation, low-carbon development, and sustainable land management. Full article

Under the dual pressures of rapid urbanization and intense human socioeconomic activities, habitat fragmentation and poor landscape connectivity have become critical issues in cities. Constructing ecological networks is essential for maintaining urban ecosystem health and promoting sustainable environmental development. It represents an effective approach to balancing regional economic growth with ecological conservation. This study focused on the Shenzhen Special Economic Zone. Ecological sources were identified using Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity assessment. Circuit theory was applied to extract ecological corridors, ecological pinch points, and ecological barriers. The importance levels of ecological corridors were classified to form an ecological network. The network was optimized by adding ecological sources, stepping stones, and restoring breakpoints. Its structure and functionality were evaluated before and after optimization. The results indicate the following: (1) The core area in Shenzhen City Area covers 426.67 km², the largest proportion among landscape types. It exhibits high fragmentation, low connectivity, and a spatial pattern characterized as “dense in the east and west, sparse in the center.” (2) Seventeen ecological sources were identified, consisting of 8 key sources, 5 important sources, and 4 general sources, accounting for 17.62% of the total area. Key sources are mainly distributed in forested regions such as Wutong Mountain, Maluan Mountain, Paiya Mountain, and Qiniang Mountain in the southeast. (3) Twenty-six ecological corridors form a woven network, with a total length of 127.44 km. Among these, 13 key corridors are concentrated in the eastern region, while 7 important corridors and 6 general corridors are distributed in the western and central parts. Few corridors exist in the southwest and southeast, leading to ecological flow interruption. (4) The optimized ecological network includes 12 newly added ecological source areas, 20 optimized ecological corridors, 120 ecological pinch points, and 26 ecological barriers. The maximum current value increased from 10.60 to 20.51, indicating significantly enhanced connectivity. The results provide important guidance for green space planning, biodiversity conservation, and ecosystem functionality enhancement in Shenzhen City Area. Full article

The social-ecological resilience of rural settlements refers to their ability to resist and mitigate the risks posed by internal and external disturbances, and to utilize the external environment to achieve a new equilibrium state. Amid rapid urbanization, it is of great significance for mountainous settlements to improve their risk resistance and development ability. Taking Dong’an Town in Chengkou County, located in the eastern part of Qinling–Bashan Mountains in southwestern China, as the research object, this study constructs an evaluation index system for rural residential resilience based on social-ecological resilience theory. It explores the resilience level of rural residences in mountainous areas from the dimensions of internal resilience and external environmental resilience and scientifically proposes an optimization path for the spatial layout of rural residences. This study provides a reference for optimizing the rural living environment, promoting spatial equity, and improving people’s livelihood according to local conditions. The results showed that: (1) The overall level of security resilience of rural settlements in Dong’an Town was relatively high, with 221 patches above the security level, accounting for 19.53% of the total area of the town. (2) The rural residents in Dong’an Town can be categorized into three types: core structure optimization, peripheral system upgrading, and relocation and withdrawal. Different types of rural settlements adapt to internal and external resource conditions and select optimal spatial layout paths according to local conditions. Full article

Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article

Improving the precision of forest vegetation carbon stock estimation is essential for scientifically evaluating its economic value and ecological benefits. This study aims to investigate the impact of different estimation methods on carbon stock and its economic value. Taking the forest vegetation of the Zixi Mountain Nature Reserve as the research object, the carbon stock of the arbor layer was estimated using four approaches: the variable biomass expansion factor method, the biomass expansion factor method, the volume conversion method, and the continuous function method of the biomass conversion factor. The carbon stocks of economic forests and shrublands were estimated using the average biomass method. The economic value of forest carbon storage was then evaluated through the market value method and the optimal pricing approach for forest carbon sinks. The results revealed no significant differences among the four estimation methods. The estimated arbor forest carbon stocks were 692,548.39 tC, 672,599.83 tC, 673,161.07 tC, and 400,369.17 tC, respectively, with an overall average of 609,669.62 tC. The biomass expansion factor method and the volume conversion method produce the most consistent results. The corresponding relative errors were 13.59%, 10.32%, 10.41%, and −34.33%, respectively. The continuous function method of the biomass conversion factor exhibited the greatest variability, mainly due to the influence of Pinus yunnanensis parameters. Among all methods, the biomass expansion factor method yielded the smallest relative error, making it the most suitable for estimating arbor carbon stocks in the study area. The total average economic value of forest carbon storage in the region was estimated at CNY 58.09 million. Among all forest types, Pinus yunnanensis contributed the highest carbon value, totaling CNY 50.48 million. In terms of economic value per unit area, Pinus armandii ranked first, with CNY 11,418.92 per hectare. Among different age groups of arbor forests, middle-aged stands had the highest carbon sequestration value, reaching CNY 36.87 million. Across all functional zones, the core zone showed the greatest economic value at CNY 29.34 million. Enhancing forest resource protection to maximize both carbon sink capacity and economic returns, as well as promoting forest carbon trading, can bring additional economic benefits to Southwest China while contributing to the achievement of the national “dual carbon” goals. Full article

Rapid urbanization within ecologically fragile mountainous regions exacerbates tensions between development needs and land use sustainability, yet few studies have systematically quantified long-term land use/land cover (LULC) dynamics in large-scale mountainous urban agglomerations. Focusing on the Chengdu–Chongqing Urban Agglomeration (CCUA) in Southwest China—an archetypal mountainous megaregion undergoing accelerated development—this study analyzed LULC evolution from 1985 to 2019 using multi-period data, identified dominant driving factors through logistic regression, and projected future LULC patterns under various scenarios via the Future Land Use Simulation (FLUS) model. The outcomes indicate that (1) over the past decades, construction land expanded by over 4000 km², an increase of about 318%, while cultivated land decreased by nearly 8600 km², a reduction of 6.86%; (2) the dominant transformation type was the conversion of cultivated land to forest, followed by its conversion to construction land; (3) elevation, slope, and average annual temperature emerged as significant predictors of LULC change, highlighting the critical influence of topographical and climatic conditions; and (4) natural development scenarios (NDS) and ecology and cultivated protection scenarios (ECPS) represent suitable development pathways. These findings contribute to evidence-based spatial governance and provide policy guidance for ecological protection in the CCUA and other similarly vulnerable areas. Full article