Search Results (216)

Water, land, energy, and food are fundamental resources for human survival and ecological stability, yet they face intensifying pressure from surging demands and spatial mismatches. Integrated governance of their interconnected nexus is pivotal to achieving sustainable development. In this study, we analyze the water-land-energy-food (WLEF) nexus synergies in China’s North China Plain, a vital grain base for China’s food security. We develop a city-level WLEF evaluation framework and employ a coupling coordination model to assess spatiotemporal patterns of the WLEF system from 2010 to 2022. Additionally, we diagnose critical internal and external influencing factors of the WLEF coupling system, using obstacle degree modeling and geographical detectors. The results indicate that during this period, the most critical internal factor was per capita water resource availability. The impact of the external factor—urbanization level—was characterized by fluctuation and a general upward trend, and by 2022, it had become the dominant influencing factor. Results indicated that the overall development of the WLEF system exhibited a fluctuating trend of initial increasing then decreasing during the study period, peaking at 0.426 in 2016. The coupling coordination level of the WLEF system averaged around 0.5, with the highest value (0.526) in 2016, indicating a marginally coordinated state. Regionally, a higher degree of coordination was presented in the southern regions of the North China Plain compared with the northern areas. Anhui province achieved the optimal coordination, while Beijing consistently ranked lowest. The primary difference lies in the abundant water resources in Anhui, in contrast to the water scarcity in Beijing. Internal diagnostic analysis identified per capita water availability as the primary constraint on system coordination. External factors, including urbanization rate, primary industry’s added value, regional population, and rural residents’ disposable income, exhibited growing influence on the system over time. This study provides a theoretical framework for WLEF system coordination and offers decision-making support for optimizing resource allocation and promoting sustainable development in comparable regions. Full article

Revealing the trade-offs, synergies, and driving mechanisms among land use functions is essential for mitigating conflicts between functions, optimizing territorial spatial patterns, and providing policy support for regional sustainable development. Taking the Central Yunnan Urban Agglomeration as a case study, this study adopts a grid-based evaluation unit and employs a multi-model fusion approach to systematically analyze the interaction mechanisms among land use functions. By integrating the Pearson correlation method and root mean square deviation (RMSD) model, the trade-off and synergy relationships and their spatiotemporal evolution were quantitatively assessed. The XGBoost–SHAP model and optimized parameter-based geographical detector (OPGD) were introduced to identify the nonlinear characteristics and interaction effects of influencing factors on land use function trade-offs and synergies. In addition, a geographically weighted regression (GWR) model was used to explore spatial heterogeneity in these effects. The results indicate that (1) from 2010 to 2020, the overall synergy between production and ecological functions (PF&EF) in the urban agglomeration was enhanced, while trade-offs between production and living functions (PF&LF) intensified, and the trade-off intensity between living and ecological functions (LF&EF) decreased. Significant spatial heterogeneity exists among land use function interactions: PF&EF and PF&LF trade-offs are concentrated in the central and eastern parts of the urban agglomeration, while LF&EF trade-offs are more scattered, mainly occurring in highly urbanized and ecologically sensitive areas; (2) the dominant factors influencing land use function trade-offs and synergies include precipitation, slope, land use intensity, elevation, NDVI, Shannon diversity index (SHDI), distance to county centers, and distance to expressways; (3) these dominant factors exhibit strong nonlinear effects and significant threshold responses in shaping trade-offs and synergies among land use functions; and that (4) compared with the OLS model, the GWR model demonstrated higher fitting accuracy. This reveals that the impacts of natural, socio-economic, and landscape pattern factors on land use function interactions are characterized by pronounced spatial heterogeneity. Full article

The trade-offs and synergies among ecosystem services can provide clues for understanding the mechanisms of regional ecological evolution. Previous studies have mainly concentrated on administrative divisions to characterize ecosystem services trade-offs and synergies within specific regions. However, ambiguity persists regarding the spatial diversity and scale dependency of regional ecosystem services, along with the degree to which human activity and climatic variation influence the relationships of multiscale ecosystem services. This study focuses on the Yangtze River Delta Urban Agglomeration in China. Based on grid, county-level, and city-level scales, it analyzes five ecosystem services, namely habitat quality, carbon storage, food production, soil conservation, and water yield, from 2000 to 2020. By using correlation analysis and spatial autocorrelation methods, this study explores the intensity of the trade-offs and synergies among ecosystem services and their spatial patterns. Then, combined with the Optimal Parameters-based Geographical Detector, it identifies the dominant driving factors, quantifies their degree of contribution, and reveals the multiscale differentiation of ecosystem service relationships and their causes. The results show that the five ecosystem services all exhibit significant spatiotemporal heterogeneity. At the grid scale, there is a trade-off relationship between food production and the other four services, while a strong synergistic effect exists among the remaining four services. At the county scale, the synergistic association between habitat quality and carbon storage is the most significant, with the highest contributions from the average annual precipitation and average annual temperature (q-values 0.893 and 0.782, respectively). At the prefecture-level city scale, the intensity of the ecosystem services trade-offs and synergies shows an increasing trend, and the impact of interactions between socio-ecological elements is significantly higher than that at the grid and county scales. This research provides an evidence-based foundation for decision makers to devise suitable strategies that support the coordinated advancement of ecology and the economy across various spatial scales. It is crucial for promoting precise ecosystem regulation and the sustainability of the Yangtze River Delta Urban Agglomeration in China. Full article

This research has been performed to optimize the management of cultivated land fragmentation in the Henan Section of the Yellow River Basin, (“the research area”), coordinate the contradiction between increasing food demand and environmental constraints, maintain regional food security, and promote agricultural and rural modernization. The spatial and temporal evolution characteristics have been summarized by calculating the fragmentation index of the cultivated land landscape, and the driving factors explored with geographical detectors. Results show the following: (1) between 2000 and 2023, the landscape fragmentation index of cultivated land in the research region exhibited a pattern of initial decline followed by a subsequent rise. It decreased by 69.33% from 2000 to 2015 and increased by 138.42% from 2015 to 2023. Over the period from 2000 to 2023, the cultivated land landscape fragmentation index in the study area saw an overall reduction of 26.87%. (2) ”The reduction in cultivated land area and the decrease in landscape fragmentation” index accounted for 82.46% in the county unit. (3) The kernel density curve of the cultivated land landscape fragmentation index showed a unimodal distribution, but the shape was flat. The regions with a fragmentation index mainly range from 4 to 6. The regional cultivated land fragmentation distribution was more dispersed. (4) The average altitude, the distance from the Yellow River, the proportion of the construction land area and population density are the main driving factors. The combined impact of the proportion of the construction land area and population density contributes more than 46% to the cultivated land landscape fragmentation index. The interaction among various factors exerts a more pronounced effect than any individual factor alone. The intensity of the main interaction factors reaches above 0.67. The findings of this study can serve as a theoretical foundation for the sustainable utilization and development of cultivated land resources, as well as for ecological protection and construction in the Henan segment of the Yellow River Basin. Full article

Agricultural land consolidation (ALC) is a critical instrument for protecting the environment and expanding cropland. However, implementing different consolidation methods, scales, and technologies may have adverse effects on ecological and environmental factors. The ecological effects of ALC are evaluated in this investigation, with the Manas River Basin in China as the research object. Initially, the research examined the changes in land use that occurred during various periods of ALC in the basin using land cover data (CLCD). Secondly, an enhanced water benefit-based ecological index (SWBEI) for arid regions was developed using the Google Earth Engine (GEE) platform. The spatiotemporal variations in ecological environment quality (EEQ) during various ALC periods were analysed. Ultimately, the effects of a variety of factors on EEQ were disclosed. The research results show that: (1) The principal land-use types in the Manas River Basin are barren land, grassland, and cropland, with substantial fluctuations in area. Cropland area is increasing, with the majority being converted from grassland and desolate land. During the initial phase of farmland consolidation, the most rapid growth was observed, with expansion occurring both inward and outward from existing cropland. (2) The SWBEI outperforms the water benefit-based ecological index (WBEI) in arid regions. (3) The EEQ of the basin and cropland typically exhibits an “increasing–decreasing–increasing trend”, with deterioration predominantly occurring during early-stage ALC and a gradual improvement in EEQ during late-stage ALC. The Gobi Desert belt at the foothills of mountains and high-altitude frigid regions exhibit a deteriorating trend in the EEQ, whereas the oasis areas in the middle reaches of the basin exhibit an improving trend. (4) The most significant explanatory power for the basin’s EEQ is attributed to climate factors, followed by topographic factors, hydrological factors, and human factors. The influence of human factors and hydrological factors on the basin’s EEQ is increasing. The primary factors that influence the EEQ of a basin are the actual evapotranspiration, temperature, and elevation. The explanatory power of these two factors for the basin’s EEQ is augmented by their interaction. In the long term, ALC helps improve the EEQ of the basin and cropland. This study provides a reference for improving ALC methods and approaches, enhancing the ecological environment of river basins, and balancing agricultural production efficiency. Full article

The long-term sustainability of conservation efforts in critical reforestation regions requires timely, spatiotemporal assessments of ecological quality. In alignment with China’s environmental initiatives, this study integrates Google Earth Engine (GEE) and MODIS data to construct an enhanced Remote Sensing Ecological Index (RSEI) for two decades of ecological monitoring. Hotspot analysis (Getis-Ord Gi*) revealed concentrated high-quality zones, particularly in Xinjiang’s Altay Prefecture, with ‘Good’ and ‘Excellent’ areas increasing from 21.64% in 2000 to 31.30% in 2020. To uncover driving forces, partial correlation and geographic detector analyses identified a transition in the Three-North Shelter Forest Program (TNSFP) from climate–topography constraints to land use–climate synergy, with land use emerging as the dominant factor. Socioeconomic influences, shaped by policy interventions, also played an important but fluctuating role. This progression—from natural constraints to active human regulation—underscores the need for climate-adaptive land use, balanced ecological–economic development, and region-specific governance. These findings validate the effectiveness of current conservation strategies and provide guidance for sustaining ecological progress and optimizing future development in the TNSFP. Full article

The red soil region in southern China is an ecologically fragile area. Although ecological engineering construction has achieved phased results, there are still obvious gaps in research on the mechanisms underlying vegetation dynamics in response to natural and anthropogenic variables. Changting County (CTC) serves as a typical case of vegetation degradation and restoration in the region. We examined the vegetation dynamics in CTC with the fraction vegetation cover (FVC) based on kernel normalized difference vegetation index-based dimidiate pixel model (kNDVI-DPM) and employed the optimal parameter-based geographical detector (OPGD), multiscale geographically weighted regression (MGWR), and partial least square structural equation modeling (PLS-SEM) to analyze interaction mechanisms between vegetation dynamics and underlying factors. The FVC showed a fluctuating upward trend at a rate of 0.0065 yr⁻¹ (p < 0.001) from 2000 to 2020. The spatial distribution pattern was high in the west and low in the east. Soil and terrain factors were the primary factors dominating the spatial heterogeneity of FVC, soil organic matter and elevation showing the most significant influence, with annual mean q-values of 0.4 and 0.3, respectively. Climate, terrain, and soil properties positively and anthropogenic activities negatively impacted vegetation. From 2000 to 2020, the path coefficient of anthropogenic activities to FVC decreases from −0.152 to −0.045, the adverse effects of human activities are diminishing with ongoing ecological construction efforts. Climate and anthropogenic activities act indirectly on vegetation through negative effects on soils and terrain. The impact of climate on soils and terrain is gradually lessening, whilst the influence of anthropogenic activities continues to grow. This study provides an analytical framework for understanding the complex interrelationships between vegetation changes and the underlying factors. Full article

The Loess Plateau frequently endures droughts, and the propagation process has grown more intricate due to the interplay of climate change and human activities. This study developed the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSMI) on a 3-month scale and examined the spatiotemporal characteristics and driving mechanisms of drought propagation from meteorological to agricultural drought utilizing cross-wavelet analysis, grey relational analysis, and the optimal parameter-based geographical detector (OPGD) model. The results demonstrate a substantial seasonal correlation between meteorological and agricultural droughts in spring, summer, and autumn, as evidenced by cross-wavelet coherence analysis (wavelet coherence > 0.8, p < 0.05). Lag analysis utilizing grey relational degree (>0.8) indicates that drought propagation generally manifests with a temporal delay of 1–3 months, with the shortest lag observed in spring (average 1.2 months) and the longest in winter (average 3.1 months). Distinct spatial heterogeneity is seen within geomorphological divisions: the loess wide valley hills and loess beam hills divisions exhibit the highest propagation rates (0.64 and 0.59), whereas the loess tableland and soil–stone hills divisions have lower propagation (around 0.50). The OPGD results reveal that precipitation, soil moisture, and temperature are the principal contributing factors, although their effects differ among geomorphological types. Interactions among components exhibit synergistic enhancement effects. This study improves our comprehension of seasonal and geomorphological heterogeneity in drought propagation from meteorological to agricultural droughts and provides quantitative evidence to support early drought warnings across various divisions, agricultural risk assessment, and water security strategies in the Loess Plateau. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article

Forest spatial structure is of significant importance for studying forest biomass accumulation and management. However, above-ground biomass (AGB) estimation based on satellite remote sensing struggles to capture forest spatial structure information, which to some extent affects the accuracy of AGB estimation. To address this issue, this study focused on Chinese fir (Cunninghamia lanceolata) plantations in Zhejiang Province. Using UAV-LiDAR (unmanned aerial vehicle light detection and ranging) data and a seed-point-based individual tree segmentation algorithm, information on individual fir trees was obtained. Building on this foundation, structural parameters such as neighborhood comparison (U), crowding degree (C), uniform angle index (W), competition index (CI), and canopy openness (K) were calculated, and their distribution characteristics analyzed. Finally, these parameters were integrated with UAV-LiDAR point cloud features to build machine learning models, and a geographical detector was used to quantify their contribution to AGB estimation. The research findings indicate the following: (1) The studied stands exhibited a random spatial pattern, moderate competition, and sufficient growing space. (2) A significant correlation existed between the U and AGB (r > 0.6), followed by CI. The optimal stand structure for AGB accumulation was C = 0.25, U < 0.5, CI in (0, 0.8], and K > 0.3. (3) The four machine learning models constructed by coupling spatial structure with point cloud features all improved the accuracy of AGB estimation for the fir forest to some extent. Among them, the XGBoost model performed best, achieving a model accuracy (R²) of 0.92 and a relatively low error (RMSE = 14.02 kg). (4) Geographical detector analysis indicated that U and CI contributed most to AGB estimation, with q-values of 0.44 and 0.37, respectively. Full article

The Ecological Green Heart Area of the Chang–Zhu–Tan Urban Agglomeration in Central China faces increasing forest health threats due to rapid urbanization and land use change. This study assessed the spatiotemporal dynamics and drivers of forest health from 2005 to 2023 using a multi-dimensional framework based on vitality, organizational structure, and anti-interference capacity. A forest health index (FHI) was constructed using multi-source data, and the optimal parameter geographic detector (OPGD) model was applied to identify dominant and interacting factors. The results show the following: (1) FHI declined from 0.62 (2005) to 0.55 (2015) and rebounded to 0.60 (2023). (2) Healthier forests were concentrated in the east and center, with degradation in the west and south; (3) Topography was the leading driver (q = 0.17), followed by climate, while socioeconomic factors gained influence over time. (4) Interactions among factors showed strong nonlinear enhancement. This research demonstrates the effectiveness of the OPGD model in capturing spatial heterogeneity and interaction effects, underscoring the need for differentiated, spatially informed conservation and land management strategies. This research provides scientific support for integrating ecological protection with urban planning, contributing to the broader goals of ecosystem resilience, sustainable land use, and regional sustainability. Full article

Net primary productivity (NPP) reflects the carbon sequestration capacity of terrestrial ecosystems and it is used as an important indicator for measuring ecosystem quality. However, due to the effects of “warming and humidification” and “oasisization”, the spatiotemporal evolution and driving mechanisms of the NPP of vegetation in the northern slope of the Tianshan Mountains (NSTM), a typical arid area in China, are still unclear. Thus, in this study, we used remote sensing data and meteorological data to construct a Carnegie–Ames–Stanford–Approach (CASA) model for estimating the NPP of vegetation in the study area. Trend analysis, partial correlation analysis, and optimal parameter-based geographic detector (OPGD) methods were combined to explore the spatiotemporal evolution and driving mechanisms to changes in the NPP. The results showed that from 2001 to 2020, the annual average NPP on the NSTM exhibited an overall significant upward trend, increasing from 107.33 gC⋅m⁻²⋅yr⁻¹ to 156.77 gC⋅m⁻²⋅yr⁻¹, with an increase of 2.47 gC⋅m⁻² per year and 46.06% year-on-year. Over the past 20 years, climate change and human activities generally positively affected the changes in NPP in the study area. Human activities in the study area are mainly manifested in the large-scale conversion of other land use types into farmland, with a total increase of 16,154 km² in farmland area, resulting in a net increase of 6.01 TgC in NPP. Precipitation has the strongest correlation with NPP in the study area, with a partial correlation coefficient of 0.30, temperature and solar radiation have partial correlation coefficients with NPPs of 0.17 and 0.09, respectively. Therefore, increases in precipitation, temperature, and solar radiation have a promoting effect on the growth of NPP on the NSTM. During the study period, the land use type and soil moisture were the main factors that affected the spatial differentiation of vegetation NPP, and the effects of human interference on natural environmental conditions had significant impacts on vegetation NPP in the area. Therefore, in this study, we accurately determined the spatiotemporal variations in the NPP on the NSTM and comprehensively explored the driving mechanisms to provide a theoretical basis for sustainable development in arid areas and achieving carbon neutrality goals. Full article

Rapid urbanization has altered the land use pattern, reducing urban green space and increasing carbon emissions, and it is critical to scientifically examine the interaction mechanism between green space and carbon emissions in order to drive low-carbon urban development. Using Nanjing as an example, this study examined the spatiotemporal evolution characteristics of urban green space patterns and carbon emissions between 2000 and 2020. Carbon emissions at the city and county levels were estimated with great precision using a gray BP neural network model and a downscaling decomposition method. Using urban green space landscape pattern indices and geographic detectors, significant driving factors were discovered and their impact on carbon emissions examined. The results show the following: (1) Carbon emissions are mostly influenced by socioeconomic factors, and the gray BP neural network model (R² = 0.9619, MAPE = 1.68%) can predict outcomes accurately. (2) Between 2000 and 2020, Nanjing’s overall carbon emissions increased by 118.9%, demonstrating a “core–periphery” pattern of spatial divergence, with significant emissions from industrial districts and emission reductions in the central urban region. (3) The urban green space exhibits “quantity decreasing and quality increasing” characteristics, with the total area falling by 4.84% but the structure optimized to form a networked pattern with huge ecological patches as the backbone. (4) The primary drivers are the LPI, COHESION, and AI. This study reveals the complex relationship mechanism between the spatial configuration of urban green space and carbon emissions and, based on the results, proposes a green space optimization framework with three dimensions, protection of core ecological patches, enhancement of connectivity through ecological corridors, and implementation of low-carbon maintenance measures, which will provide a scientific basis for the planning of urban green space and the construction of low-carbon cities in the Yangtze River Delta region. Full article

Urban vitality serves as an important indicator for evaluating the level of urban quality development and sustainability. In response to a series of urban challenges arising from rapid urban expansion, enhancing urban quality and fostering urban vitality have become key objectives in contemporary urban planning and development. This study summarizes the spatial distribution patterns of urban vitality at the street and neighborhood levels in the central area of Qingdao, and analyzes their spatial characteristics. A 5D built environment indicator system is constructed, and the effects of the built environment on urban vitality are explored using the Optimal Parameter Geographic Detector (OPGD) and the Multi-Scale Geographically Weighted Regression (MGWR) model. The aim is to propose strategies for enhancing spatial vitality at the street and neighborhood scales in central Qingdao, thereby providing references for the optimal allocation of urban spatial elements in urban regeneration and promoting sustainable urban development. The findings indicate the following: (1) At both the subdistrict and block levels, urban vitality in Qingdao exhibits significant spatial clustering, characterized by a pattern of “weak east-west, strong central, multi-center, cluster-structured,” with vitality cores closely aligned with urban commercial districts; (2) The interaction between the three factors of functional density, commercial facilities accessibility and public facilities accessibility and other factors constitutes the primary determinant influencing urban vitality intensity at both scales; (3) Commercial facilities accessibility and cultural and leisure facilities accessibility and building height exert a positive influence on urban vitality, whereas the resident population density appears to have an inhibitory effect. Additionally, factors such as building height, functional mixing degree and public facilities accessibility contribute positively to enhancing urban vitality at the block scale. (4) Future spatial planning should leverage the spillover effects of high-vitality areas, optimize population distribution, strengthen functional diversity, increase the density of metro stations and promote the coordinated development of the economy and culture. Full article

Ecological security evaluation serves as the cornerstone for ecological management decision-making and spatial optimization. This study focuses on the Dongping Lake Basin. Based on the Pressure–State–Response (PSR) model framework, it integrates ecological risk, ecosystem health, and ecosystem service indicators. Utilizing methods including Local Indicators of Spatial Association (LISA), Transition Matrix, and GeoDetector, it analyzes the spatio-temporal evolution characteristics and driving mechanisms of watershed ecological security from 2000 to 2020. The findings reveal that the Watershed Ecological Security Index (WESI) exhibited a trend of “fluctuating upward followed by periodic decline”. In 2000, the status was “relatively unsafe”. It peaked in 2015 (index 0.332, moderately safe) and experienced a slight decline by 2020. Spatially, a significantly clustered pattern of “higher in the north and lower in the south, higher in the east and lower in the west” was observed. In 2020, “High-High” clusters of ecological security aligned closely with Shandong Province’s ecological conservation red line, concentrating in core protected areas such as the foothills of the Taihang Mountains and Dongping Lake Wetland. Level transitions were characterized by “predominant continuous improvement in low levels alongside localized reverse fluctuations in middle and high levels,” with the “relatively unsafe” and “moderately safe” levels experiencing the largest transfer areas. Geographical detector analysis indicates that the Human Interference Index (HI), Ecosystem Service Value (ESV), and Annual Afforestation Area (AAA) were key drivers of watershed ecological security change, influenced by dynamic interactive effects among multiple factors. This study advances watershed-scale ecological security assessment methodologies. The revealed spatio-temporal patterns and driving mechanisms provide valuable insights for protecting the ecological barrier in the lower Yellow River and informing ecological security strategies within the Dongping Lake Watershed. Full article