Search Results (938)

Coordinating the synergistic development of ecological protection and recreational utilization is a core issue for the high-quality development of green spaces in metropolitan areas. Using the coupling coordination degree model (CCDM) and Moran’s I Outlier Clustering, this study spatially assesses the coupling coordination degree (CCD) between the ecological quality and recreation service intensity of the green spaces in the Suzhou-Wuxi-Changzhou region. It further employs the Geodetector model to identify the influencing factors affecting the CCD. The results show the following: (1) The overall regional coordination is low, with 83.96% of green spaces being in moderate-to-severe imbalance and only 16.04% reaching primary-to-intermediate coordination, highlighting a prominent supply–demand imbalance. (2) The spatial pattern exhibits a structure whereby values are “high in the center, low in the east and west”, showing significant spatial differentiation. (3) Among the influencing factors, socio-economic elements such as cultural attractiveness (q = 0.433) and economic development (q = 0.148) play a dominant role, indicating that imbalance is mainly driven by socio-economic factors. Accordingly, this study proposes spatial optimization strategies based on zonal management, balanced layout, and multi-dimensional drivers to promote balanced recreational supply, facilitate the synergy between ecological protection and recreational utilization, and achieve the sustainable development of regional green spaces. Full article

The rapid development of airport economic zones has significantly reshaped regional land-use structures and industrial spatial organization. Taking the Nanjing Airport Economic Zone as the study area, this study integrates multi-source geospatial data, including land-use data, enterprise registration records, Points of Interest (POIs), transportation networks, nighttime light intensity, population, topography, and ecological-environmental variables for 2013, 2018, and 2023. Land-use transition matrices, spatial autocorrelation analysis, standard deviation ellipse analysis, Geodetector, and Multiscale Geographically Weighted Regression (MGWR) models were employed to examine land-use transition, industrial spatial restructuring, and their influencing factors from 2013 to 2023. The results show that: (1) Land-use change in the study area was mainly characterized by the decline of cropland, the expansion of impervious surfaces, and the shrinkage of water bodies. From 2013 to 2023, cropland decreased from 81.07 km² to 70.12 km², impervious surfaces increased from 10.98 km² to 25.65 km², and water bodies decreased from 5.50 km² to 1.79 km². The conversion from cropland to impervious surfaces was the dominant transition pathway, covering 14.67 km². (2) Industrial space exhibited significant spatial clustering, with a Moran’s I value of 0.9639 in 2023. The standard deviation ellipse results indicate that industrial space expanded during 2013–2018 and contracted during 2018–2023, suggesting a shift from extensive outward expansion to relative agglomeration around the core area and major transport corridors. (3) Nighttime light intensity and distance to major transport access points were important explanatory factors for industrial spatial distribution, with q-values of 0.396 and 0.310, respectively. The interaction between slope and metro accessibility showed the strongest explanatory power, with a q-value of 0.6967. The MGWR results further revealed the spatial heterogeneity of the effects of transportation, economic activity, population concentration, and ecological constraints. Overall, land-use transition and industrial spatial restructuring in the Nanjing Airport Economic Zone were jointly shaped by transportation accessibility, economic vitality, population agglomeration, and ecological constraints. These findings provide a reference for land-use optimization and industrial spatial governance in airport economic zones. Full article

Frequent extreme climate events pose severe threats to human health. Existing studies mainly focused on summer thermal environments, while few compared summer and winter extreme climate risks from the perspective of urban functional zones (UFZs). This study classified more precise UFZs using the machine-learning method and constructed heat and cold exposure indicators. GeoDetector was adopted to analyze driving factors and interactions of both types of exposure across UFZs. The results showed that UFZ classification achieved an overall accuracy of 81.8% and a Kappa coefficient of 0.75. High heat exposure concentrated in core public, residential, and commercial zones, while high cold exposure occurred in peripheral industrial and greenspace zones. Dual high exposure zones lay between the 3rd and 5th Ring Roads. Industrial zones positively contributed to both exposures, while commercial, public, and residential zones showed positive heat but negative cold exposure contributions, and greenspace zones presented opposite effects. Vulnerable population ratios had a strong explanatory power. Heat exposure interactions were dominated by vulnerable populations, building morphology, and landscape patterns, while cold exposure was also affected by the degree of facility agglomeration and human activities with varied mechanisms across UFZs. This study advanced single-season thermal research to multi-season exposure and zoned governance for climate-adaptive renewal. Full article

Accurately assessing the spatiotemporal evolution of ecological environment quality (EEQ) on the Loess Plateau of Northern Shaanxi is of great significance for consolidating the ecological security barrier of the Yellow River Basin. Most of the existing research focuses on a single ecological theme, which does not reflect the overall ecological status of the region. In this study, a remote sensing ecological index (RSEI) model was constructed to systematically assess the EEQ from 2000 to 2024. The Theil–Sen estimator, Mann–Kendall test, and Hurst exponent were jointly employed to detect change significance and predict future trends, while the Geodetector model was applied to explore driving factors. The results were as follows: (1) EEQ exhibited a fluctuating but overall upward trend, with the mean RSEI rising from 0.376 in 2000 to 0.545 in 2024—an average annual increase of approximately 0.00569. (2) Spatially, a distinct pattern of “higher in the south, lower in the north and the lowest in the northwest” was observed. Over the 25-year period, the combined proportion of “excellent” and “good” grades increased by roughly 20 percentage points, and the “moderate” grade expanded from 13.61% to 47.12%. (3) Areas showing an improving trend accounted for 91.21% of the total area and highly overlapped with those projected to improve in the future. (4) Single-factor detection revealed that geomorphological type exerted the greatest influence on the spatial heterogeneity of EEQ, with a multi-year mean q-value of 0.701. Interaction detection further indicates that the geomorphology–land use interaction may continue to shape the regional EEQ’s spatial distribution. These findings provide a scientific basis for precise ecological restoration planning and spatial optimization on the Loess Plateau of Northern Shaanxi. Full article

Habitats are fundamental for maintaining biodiversity, supporting ecological processes, and delivering essential ecosystem services such as carbon sequestration, water regulation, and soil conservation. Habitat degradation has become an increasingly critical environmental concern, particularly in coastal regions where anthropogenic pressures intersect with natural dynamics under the accelerating impacts of climate change. (1) This study explores the spatially stratified heterogeneity and underlying driving factors of habitat quality and degradation in Çeşme, a rapidly developing coastal district in western Türkiye. (2) The InVEST Habitat Quality model was applied to assess both habitat quality and habitat degradation across the study area for the years 2017 and 2024. The GeoDetector method was applied to analyze the spatial heterogeneity in habitat quality and degradation, enabling the assessment of dominant environmental and anthropogenic drivers, including urban development pressure, tourism activities, energy-related infrastructure, road density, and vegetation conditions. (3) Night-time light intensity showed the highest explanatory power among the tested variables, although its absolute explanatory power for habitat degradation remained limited, while protection status represented a contrasting human-related factor associated with higher habitat quality. (4) These findings underscore the importance of carefully directing human interventions to balance development pressures with effective conservation strategies. Full article

Vegetation net primary productivity (NPP) is a key indicator of terrestrial carbon sequestration and ecological restoration effectiveness. The karst mountainous region of Southwest China is characterized by fragmented terrain and high ecological vulnerability, making quantification of NPP dynamics and drivers essential for regional management. Using MOD17A3 NPP data (2000–2020), this study applied trend analysis, Hurst exponent analysis, partial correlation analysis, residual trend analysis, and Geodetector to investigate NPP spatiotemporal patterns and driving mechanisms in Guizhou Province. Results show a significant increasing trend in NPP (3.653 gC·m⁻²·a⁻¹, p < 0.01), with 78.61% of the area exhibiting growth and a spatial pattern of higher values in the south and lower values in the north. NPP shows persistence, indicating a continued increasing tendency. Along elevation gradients, NPP exhibits a unimodal pattern, peaking at 1000–1200 m, while growth rates increase with elevation and slope, with greater variability at higher altitudes. Temperature exerts a stronger and more extensive influence on NPP than precipitation, with significant correlations over 34.35% and 10.16% of the study area, respectively (p < 0.05). Residual trend analysis indicates that non-climatic factors accounted for a larger share of NPP variation (64.49%) than climatic factors (35.51%), with ecological restoration likely the leading non-climatic driver. Geomorphological type is the primary driver of spatial heterogeneity (q = 0.220), followed by precipitation, temperature, and land use, with interaction effects mainly showing nonlinear enhancement. These findings provide insights for ecological restoration and vegetation management in karst regions. Full article

Soil pH is a fundamental geochemical parameter with direct implications for environmental quality, but its spatial drivers in geologically complex mountain regions remain poorly understood. This study investigated surface soil pH across 452 sites in Pingquan City, a semi-arid, lithologically heterogeneous mountainous area of Hebei Province, China. The results show that the soil in Pingquan City is predominantly alkaline, with higher pH in southwestern and northeastern areas and lower pH in the northwest. Soil pH ranged from 4.62 to 9.98, with strong positive spatial autocorrelation. Comprehensive quality assessment indicated that the overall soil quality is moderately low. GeoDetector analysis identified average annual temperature, soil texture, elevation, and bedrock lithology as dominant structural drivers, with bi-factor enhancement interactions. GeoSHAP further uncovered two local effects: precipitation exerts a positive influence on pH in carbonate-rock-dominated areas, reversing the leaching–acidification pattern; and temperature functions as a proxy variable integrating co-varying topography, parent material, and texture rather than a direct thermal driver. The combined application of spatial autocorrelation, GeoDetector, and GeoSHAP provides an effective framework for identifying spatial phenomena, discriminating dominant drivers, and explaining local variations. These findings support regional soil quality assessment and land management, and provide a geochemical baseline for safeguarding groundwater resources in mountainous regions. Full article

The traditional urbanization path based on scale expansion is unsustainable in karst mountainous regions due to fragmented topography and ecological fragility. Taking Guiyang City as a case study, this paper constructs two evaluation indicator systems for urban–rural development and environmental support. Employing the entropy method, coupled coordination degree model, Grey relational analysis, Geodetector, and multi-source spatial analysis methods to examine the evolutionary trajectory, driving mechanisms, and spatial responses of the human–land system from 2000 to 2024. The results show three main findings. First, the comprehensive score of Guiyang’s urban–rural human–land system increased from 0.054 to 0.826, and the coupling coordination degree rose from 0.223 (relative imbalance) in 2000 to 0.903 (high-quality coordination) in 2024, while the environmental support system deviated from the classic environmental Kuznets curve. Second, the driving force has shifted from economic scale to green well-being. The interaction analysis using Geodetector shows that all interaction types fall under the category of two-factor enhancement, among which the interaction coefficient between the number of broadband internet subscribers and other driving factors has the highest explanatory power, with a q-value of 0.949. Third, spatially, the light center distribution stabilized after 2015, and the land use ecological transition index dropped from 0.162 to 0.050 while the D-value continued rising, showing a significant negative correlation (r = −0.89, p < 0.05). Construction land was concentrated in low-slope (0–6°) and mid-elevation (1000–1400 m) basin areas, overlapping with high-quality farmland, and the synchronization rate between economically active areas and construction expansion was 50%. These findings reveal a digital–ecological co-evolution path in karst regions and provide an empirical basis for urban–rural integration governance. Full article

Understanding vegetation sustainability is crucial for ensuring ecological security in dryland interior river systems. Focusing on the Manas River Basin in Xinjiang, our research extracted Landsat time-series data from 2000 to 2024 via Google Earth Engine, employing statistical approaches alongside Geodetector modeling to quantitatively evaluate the spatiotemporal dynamics of vegetation sustainability and its influencing factors. Our findings reveal that the basin’s Normalized Difference Vegetation Index (NDVI) displayed a significant upward trajectory (Sen’s slope = 0.010/yr, R² = 0.95, p < 0.01), with distinct temporal phases: the period 2000–2013 was characterized by rapid oasis expansion driven by cultivated land, while the period 2014–2024 was characterized by systematic vegetation improvement with a stabilizing land use pattern. Spatially, areas exhibiting extremely significant improvement accounted for 56.24% of the total basin area (concentrated mainly in artificial oases and the mid-mountain zone), and non-significant degradation accounted for only 1.89%. Land use type and soil texture were identified as the dominant spatial differentiation factors, followed by annual precipitation, with all pairwise factor interactions exhibiting enhancement effects. By identifying the optimal thresholds for vegetation growth (annual average temperature of 0.82–3.96 °C, elevation of 1826–2598 m, and loamy sand), this study defines the boundaries for sustainable vegetation development. These findings deliver a theoretical foundation for zonation management and habitat rehabilitation planning, supplying decision-making support for safeguarding regional ecological security and fostering sustainable development of oasis systems in arid Central Asia. Full article

Understanding the spatiotemporal dynamics and driving mechanisms of ecosystem services in response to land use change is critical for regional ecological security and sustainable development, especially under the combined pressure of intensive human activities and future climate change in tropical regions. Existing studies often lack an integrated framework for multi-scenario simulation, multi-dimensional ecosystem service quantification, and spatial driving factor identification. To support sustainable management, this study focused on Hainan Island and utilized land use data from 2000 to 2025. The Markov-Patch-generating Land Use Simulation (PLUS) model was employed to simulate land use patterns for 2050 under historical trend, SSP1-1.9, and SSP5-8.5 scenarios, incorporating future climate data. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to quantify habitat quality, carbon storage, water yield, and soil conservation. The Multi-weighted Entropy Ecosystem Service Index (MEESI) was established to evaluate ecosystem service performance. Furthermore, the GeoDetector model was applied to assess the explanatory power of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), and Bare Soil Index (BSI) on ecosystem service dynamics. The results indicated that: (1) during 2000–2025, land use change in Hainan Island was dominated by forest-to-cropland conversion and impervious surface expansion, while future suggestions included stronger ecological protection under SSP1-1.9 and greater ecological pressure under SSP5-8.5; (2) during 2000–2025, habitat quality and carbon storage generally declined, whereas water yield and soil conservation increased, and SSP1-1.9 maintained higher overall ecosystem service performance (habitat quality = 0.6207, carbon storage = 327.89 × 10⁶ t, and MEESI = 0.3509) than the historical trend and SSP5-8.5 scenarios in 2050; and (3) NDVI exhibited the strongest explanatory power for ecosystem service variation, whereas NDBI showed the weakest. These findings suggest that ecosystem management should consider the trade-offs and synergies among multiple ecosystem services rather than focusing on a single service. This study provides a systematic and spatially explicit framework for ecosystem service assessment under future scenarios. The study can also support scientific land use optimization, ecological conservation, and sustainable management decisions in tropical island regions. Full article

Street space quality in historic cities has become a central concern in heritage conservation and urban renewal. Nevertheless, existing evaluation frameworks often overlook the historical dimension and insufficiently address the interaction effects among influencing factors. Taking the historic urban area of Xi’an as a case study, this study constructed a comprehensive assessment framework for street space quality comprising five dimensions: accessibility, comfort, convenience, safety, and historicity. A total of 15 indicators were quantified for 404 street segments across four street typologies—commercial, residential, historic, and mixed-use—using multi-source data and Baidu Street View image analysis based on a fully convolutional neural network. The GeoDetector model was then applied to identify key influencing factors and explore their interaction effects. The results reveal that comfort and convenience are the dominant dimensions affecting street space quality. Among all indicators, street interface permeability and facility density show the strongest explanatory power. Furthermore, all pairs of influencing factors exhibit either bi-factor enhancement or nonlinear enhancement, highlighting the synergistic effects of multiple variables in shaping street quality. Based on these findings, this study proposes differentiated renewal strategies for the four street types and offers a transferable methodological framework for data-driven assessment and targeted intervention in the renewal of historic urban streets. Full article

Against the backdrop of coordinated development in the Beijing–Tianjin–Hebei region, Hebei Province serves as an ecological safety barrier for the Beijing–Tianjin–Hebei urban agglomeration. Conducting research on land use and land cover change (LUCC) and ecosystem service value (ESV) holds significant theoretical and practical value for elucidating the mechanisms underlying ESV evolution under the combined effects of rapid urbanization and major ecological engineering projects, and for applying these findings to regional land-use planning and ecological conservation and restoration efforts. This research aligns with the United Nations Decade on Ecosystem Restoration (2020–2030). Based on land-use data from 2000, 2010, and 2020, along with 11 categories of natural and socio-economic drivers, this study systematically analyses regional LUCC and calculates ESV using locally adjusted equivalence factors. It examines the spatiotemporal evolution patterns of ESV through the analysis of local spatial autocorrelation indices (LISAs), centroid, and standard deviation ellipses, and employs a GeoDetector to measure ESV drivers. Three scenarios—a natural evolution scenario (NES), economic development scenario (EDS), and ecological protection scenario (EPS)—were established. The patch-generating Land use simulation (PLUS) model was employed to simulate LUCC for 2030 (Kappa = 0.840) and calculate ESV. Results show that from 2000 to 2020, forest land and impervious surfaces in Hebei Province continued to expand, while cropland and grassland decreased. The cumulative ESV increased by 4.85 billion yuan. Slope was the primary driver of spatial variation in ESV, and the interaction between natural and socioeconomic factors demonstrated significantly stronger explanatory power. In 2030, the total ESV under all three scenarios was lower than in 2020. The EPS reached an ESV of 344.72 billion yuan, representing a relatively suitable model that balances development and conservation. Full article

In the context of global rural sustainable development, tourist perceptions play a crucial role in rural tourism development. This study employs GIS, Geodetector, and LDA topic modeling approaches, taking key tourism villages in Beijing as the research object, to analyze the characteristics of tourist perceptions within the rural tourism resource supply system in the suburban areas of Beijing. The results indicate that, regarding homogeneous supply, tourists exhibit strong perceptions of the Great Wall Cultural Belt, elevation, distance to the city center, and intangible cultural heritage. These perceptions are influenced by visitor origins, coverage range, and the well-established experience model of “regional culture + landscape.” Concerning heterogeneous supply, tourists develop perceptions of landscape and geomorphology, historic sites and relics, pastoral landscapes and folk customs, outdoor recreation, leisure and consumption, and comprehensive categories, mainly shaped by the complementary cognition of rural authenticity and modernity. In terms of their relationship, homogeneous supply provides the foundational basis for the region, whereas heterogeneous supply contributes to the formation of distinctive village characteristics. Based on these findings, strategies are proposed to optimize rural tourism development in suburban Beijing, offering guidance for its sustainable development. Full article

Taking 563 national key rural tourism villages across 12 provinces, autonomous regions, and municipalities in western China as the research object, this study integrates multi-source data on physical geography, transportation location, socioeconomic conditions, and historical culture based on the ArcGIS platform. It comprehensively applies kernel density analysis, spatial autocorrelation analysis, buffer analysis, Spearman correlation analysis, Geodetector, and the relative enrichment index to examine the spatial distribution characteristics of these villages and their associated spatial factors. The results show that key rural tourism villages in western China exhibit an overall clustered and uneven distribution, forming a spatial pattern characterized by “high concentration in core areas, extension along secondary corridors, and sparse distribution across vast hinterlands.” The core agglomeration areas are mainly located in the Sichuan Basin, the Chongqing metropolitan area, and the Guanzhong Plain. In terms of physical geography, the distribution of key villages shows certain spatial associations with major river basins, low-slope areas, and low-relief terrain. In terms of human factors, population density and road network density are important associated factors, and the combined population–transportation conditions have strong explanatory power for the spatial differentiation of key village density. With regard to historical culture, folk-custom inheritance villages and red-culture heritage villages account for relatively high proportions, while different cultural types show certain regional agglomeration or corridor-like distribution characteristics. The findings can provide references for zoned optimization, transportation connectivity, cultural resource integration, and coordinated regional development of key rural tourism villages in western China. Full article

Traditional Chinese villages are important carriers of rural heritage, collective memory, vernacular landscapes, and living cultural traditions. However, rapid urbanization, agricultural modernization, climate change, and tourism development have increasingly threatened their spatial integrity and cultural continuity, highlighting the need for evidence-based conservation and adaptive management. This study examines the spatial distribution patterns and associated factors of 8155 national-level traditional villages in China. An integrated spatial analytical framework was developed by combining kernel density estimation, spatial autocorrelation analysis, Geodetector, and multiscale geographically weighted regression (MGWR). The results show that: (1) traditional villages are unevenly distributed across China and form a distinct “three-core and multi-node” spatial pattern, with major high-density clusters concentrated in several cross-provincial regions and secondary clusters distributed in other heritage-rich areas; (2) the spatial differentiation of traditional village density is statistically associated with natural, cultural, and socioeconomic factors, among which temperature and precipitation show the strongest explanatory power, while cultural endowment, ecological quality, and socioeconomic variables show more context-dependent associations; and (3) compared with OLS and conventional GWR, MGWR improves model performance by capturing spatially heterogeneous and scale-dependent relationships through variable-specific bandwidths. These findings provide national-scale empirical evidence for differentiated conservation planning and support the integration of traditional village protection with rural revitalization, cultural heritage conservation, and sustainable regional development. Full article