Search Results (357)

Accelerated urbanisation and associated land-use conversion are reshaping the composition and functions of terrestrial ecosystems globally. In Southeast Asia, ecological change is increasingly mediated not only by demographic urbanisation but also by urban expansion, peri-urban development, and the conversion of agricultural, coastal, and forest land into built-up surfaces. This study integrates multi-source geographical information from 2014 to 2024 to examine 351 provincial-level units in 11 Southeast Asian nations. To describe the spatial-material dimension of urbanisation and ecological conditions, two indices were created: the Composite Nighttime Light Index (CNLI), used as a proxy for urban expansion and built-up development intensity, and the Improved Remote Sensing Ecological Index (IRSEI), which is tailored to tropical coastal locations. The development of human-environment interactions was measured using the Coupling Coordination Degree (CCD) model. Pathways of synergy and trade-off were found using an incremental four-quadrant framework, and nonlinear causes of spatial differentiation were investigated using Spearman correlation and the Optimal Parameter-based Geographical Detector (OPGD). Uncertainty was addressed through data-quality masking, annual compositing, consistent index-construction rules, and cautious interpretation of CCD and driver results as relative provincial-scale patterns. The regional mean CCD rose from 0.250 to 0.314 during the decade, showing a slow improvement; nevertheless, most places still have low to moderate levels of coordination. There is clear pathway divergence, with 38.7% of locations enduring trade-offs where built-up development happens at the price of ecological quality and 58.4% of regions seeing synergistic improvement. The coupling pattern is primarily driven by built-up area expansion, with multiple factors jointly producing strong nonlinear enhancement effects. Climate conditions and forest disturbance further strengthen these effects. This study extends beyond single-country analyses by situating remote-sensing coupling results within land-use transition, peri-urbanisation, urban–rural linkage, and regional-governance perspectives. It provides quantitative evidence to support differentiated policy strategies in rapidly urbanising places. Full article

Clarifying the dynamic changes in vegetation cover and the driving mechanisms under the combined influence of the natural environment and human activities is a crucial foundation for understanding the evolutionary processes of ecosystems in arid and semi-arid regions and for improving the effectiveness of ecological restoration. Taking the Kuye River Basin, a typical resource exploitation zone in the middle reaches of the Yellow River, as the research area, this study retrieved 30 m resolution annual maximum NDVI datasets from 1986 to 2020 to calculate the Fractional Vegetation Cover (FVC). Utilizing methods such as Theil–Sen slope analysis, Mann–Kendall significance test, Hurst exponent, stability analysis, geographical detector, and sensitivity index, this study systematically revealed the spatiotemporal patterns of vegetation change, future evolution trends, and the response mechanisms of FVC dynamics to multiple factors including climate, topography, and land use. The results indicated that from 1986 to 2020, FVC in the study area exhibited an overall increasing trend (0.0105 a⁻¹), with the average FVC rising from 0.21 to 0.61. Regions with very low and low vegetation coverage continued to decrease, while areas with high and very high vegetation coverage showed significant increases, particularly in the very high vegetation coverage category, which experienced the largest growth (CV = 179.32%). The regions with moderate vegetation coverage demonstrated the highest stability (CV = 48.42%). Analysis of the driving mechanisms revealed that precipitation and land use types were the primary factors influencing changes in FVC, with land use demonstrating a more stable explanatory power (CV = 3.63%). Furthermore, the interaction between these two factors significantly enhanced the explanatory power related to vegetation changes. Sensitivity analysis indicated that the increase in forest and grassland effectively mitigated the negative impact of cropland on moderate to high coverage areas; industrial and mining land had a notable impact on very low coverage areas. It can be inferred that the Grain for Green program and the expansion of industrial and mining lands might generate differentiated impacts across diverse vegetation coverage classes. Future projections indicate that 91.19% of the region exhibits potential for FVC improvement in the future. However, a risk of sustained vegetation degradation exists in densely populated areas and regions with concentrated industrial and mining land. The study demonstrates that under the combined influences of climate change and land use adjustments, optimizing land use structures and coordinating ecological restoration with resource development are critical approaches to enhancing the stability of ecosystems in arid and semi-arid regions, as well as promoting sustainable regional ecological development. Full article

Clarifying how ecosystem services (ESs) change over time and space, and how their trade-offs and synergies evolve, is essential for regional ecological protection and high-quality development. Using Shandong Province as a case study, this research quantified carbon storage (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ) with the InVEST model. GeoDetector, geographically weighted regression (GWR), XGBoost-SHAP, Spearman’s rank correlation, bivariate spatial autocorrelation, and spatial overlay analysis were then combined to examine ES patterns, driving mechanisms, and interaction relationships. The main findings are as follows. (1) During 2000–2020, the most evident land-use changes occurred in cropland, grassland, built-up land, and water bodies. (2) The dominant drivers varied markedly among services: CS and HQ were mainly shaped by land-use type and human activity, WY was chiefly controlled by precipitation, and SC was most sensitive to topographic conditions. Factor interactions were generally stronger than single-factor effects, with two-factor enhancement being the prevailing interaction type. (3) ES trade-off/synergy relationships were relatively stable through time. A strong synergy persisted between CS and HQ, whereas CS and SC exhibited a moderate synergistic relationship. By contrast, WY showed evident trade-offs with both HQ and CS, with the WY–HQ trade-off being particularly pronounced. (4) Spatial overlay results showed that the overall ES synergy level remained low. Low-synergy areas accounted for 69.23–70.94% of the study area across the study period. Although strong-trade-off areas expanded overall, high-synergy areas remained limited, indicating considerable room to improve the coordinated provision of ESs in Shandong Province. Full article

Against the backdrop of global climate change and carbon neutrality strategies, land use carbon emissions have become a prominent topic amid regional efforts toward low-carbon transformation. However, existing studies on land-use carbon emissions have predominantly focused on humid and economically developed regions, while the unique carbon metabolism pathways of arid oasis–desert ecosystems, which are characterized by extremely low environmental carrying capacity and high sensitivity to land-use disturbance, remain largely unexplored. This study takes the oasis urban cluster in the Tarim Basin in southern Xinjiang Uygur Autonomous Region as the research object. This region belongs to a typical oasis–desert composite ecosystem, with a simple structure and low environmental carrying capacity (reflected by sparse vegetation cover < 20%, annual precipitation < 100 mm, extremely limited water resources, and high sensitivity to land disturbance). Its carbon metabolism pathway (i.e., the dynamic balance between carbon sources and sinks induced by land-use change) is fundamentally different from that in humid areas, and thus merits dedicated investigation. This study selects the period from 2000 to 2020 as the research period, which completely covers the acceleration period of urbanization and agricultural expansion in the Tarim Basin oasis urban cluster since the advancement of China’s Western Development Initiative. The data have a temporal resolution of 5 years (samples in 2000, 2005, 2010, 2015, 2020) and a spatial resolution of 30 m for land use and prefecture level for socio-economic indicators. Based on this, to fill the above-mentioned research gap, a research framework integrating the carbon emission coefficient accounting method, landscape pattern index, spatial autocorrelation analysis and geographic detector is adopted. Specifically, this study aims to systematically quantify the spatio-temporal evolution of land use carbon emissions and identify the most robust driving factors in the Tarim Basin oasis urban cluster by integrating multiple models, an approach that has not been previously applied to arid oasis regions. The research results show: (1) Based on the carbon emission coefficient method, total carbon emissions increased from 1.4455 million tons to 22.364 million tons, following a ‘slow-then-fast’ trajectory. In terms of temporal evolution, the study period can be further divided into three sub-stages: 2000–2005 (slow diffusion, with emission center skewed toward the northern energy-intensive zone), 2005–2015 (rapid restructuring, characterized by a ‘unipolar surge’ in Aksu and spread to the central oasis belt), and 2015–2020 (high-intensity stabilization, forming a cross-regional emission belt). Meanwhile, the land use structure has undergone a significant transformation. Construction land and cultivated land have continued to expand, while ecological land has significantly shrunk, resulting in a complex transformation pattern of oasis–desert ecotone. (2) The overall landscape became increasingly fragmented and diversified, the integrity of ecological space was damaged, and the regional carbon sink function was weakened. (3) The spatial autocorrelation analysis indicates that the spatial distribution of carbon emissions shows a heterogeneous pattern, forming a high-emission concentration area centered around Aksu-Bayingol. However, the global Moran’s I index is negative (such as −0.171 in 2020, p > 0.05), suggesting that carbon emissions have not formed a significant spatial clustering. (4) Carbon emissions are dominated by human and economic factors, and the interaction of factors is significant. The geographic detector identifies population density (average q value 0.904) and the proportion of construction land (average q value 0.858) as the key determinants of spatial variation in carbon emissions, reflecting the sensitive response of the human-nature system of arid zones to the urbanization process. These findings not only clarify the spatio-temporal features and driving forces of land use carbon emissions in the Tarim Basin oasis urban cluster, but also provide a replicable analytical framework for carbon-emission research in other arid and semi-arid regions worldwide. Based on these findings, we discuss the unique driving mechanisms of carbon emissions in arid regions, conclude that construction land expansion and population density are the dominant factors, and recommend a three-tier zoning governance system (carbon source control zone, carbon sink enhancement zone, coordinated development zone) for low-carbon spatial planning in arid areas. Full article

The Ningxia Yellow River Urban Agglomeration, located in the ecologically fragile arid and semi-arid zone of the upper Yellow River, serves as a critical spatial carrier for maintaining the ecological security of the Yellow River Basin and supporting the regional economy and population agglomeration in Ningxia. Driven by rapid urbanization, intensified human–land conflicts have induced widespread ecological degradation and unbalanced water–soil resource allocation across the region. Based on land use data from 2010, 2015, 2020 and 2023, we applied the land use transition matrix, land use dynamic degree and standard deviational ellipse to characterize the spatiotemporal patterns of spatial expansion of the Ningxia Yellow River Urban Agglomeration over the past decade. The Patch-generating Land Use Simulation (PLUS) model was further employed to predict the land use demand and spatial distribution of the study area under diverse scenarios in 2035. The research results reveal three key findings. First, grassland, cropland and unused land constitute the dominant land use types across the study region, jointly occupying more than 90% of the total territorial area. Over the past decade, regional land use has undergone noticeable changes: grassland area has continuously declined, cropland and built-up land have sustained steady expansion, and water areas have experienced a mild reduction. Land use conversions mainly occur among grassland, cropland and built-up land. Second, driving factors vary substantially in their spatial contributions to the expansion of different land use types. The spatial growth of cropland and built-up land is comprehensively shaped by terrain conditions, economic development and transportation location superiority. In comparison, the distribution and dynamic changes in forestland, grassland and water areas are predominantly restricted by natural elements, including precipitation, temperature and soil characteristics. Third, multi-scenario simulation results verify that differentiated territorial spatial planning and regulatory policies profoundly affect the evolutionary trajectory of regional territorial patterns. The natural development scenario experienced the most intensive expansion of built-up land, with a newly increased area of 181.11 km². The ecological protection scenario can effectively curb the loss of ecological land and minimize the shrinkage of grassland resources. The cropland protection scenario is conducive to stabilizing cropland scale to the greatest extent and restraining the disorderly sprawl of urban land. The sustainable development scenario realizes coordinated and balanced changes in all land use types and delivers mutually beneficial progress between regional ecological conservation and socioeconomic development. 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

Climate change is reshaping the epidemiology of vector-borne diseases in Europe by altering the ecological conditions that determine vector survival, seasonal activity and pathogen transmission. Rising temperatures, milder winters, prolonged warm seasons and changing precipitation patterns are increasing the suitability of parts of Europe for competent mosquito, tick and sandfly vectors. These changes, combined with human mobility and land-use change, increase the probability that imported pathogens encounter permissive conditions for local transmission. This Opinion article examines autochthonous vector-borne disease transmission in Europe, using dengue as a sentinel example of a wider climate-sensitive transition. We discuss how imported viraemic cases, established competent vectors, vector–host contact and delayed clinical recognition can converge to enable local outbreaks. Beyond dengue, we consider West Nile virus, chikungunya, tick-borne encephalitis, leishmaniasis and Crimean–Congo haemorrhagic fever as examples of a broader and increasingly heterogeneous European risk landscape. We argue that the public-health impact of this transition is shaped not only by vector expansion, but also by gaps in surveillance integration, diagnostic readiness, workforce preparedness and One Health coordination. Strengthening climate-informed surveillance, rapid laboratory capacity, frontline clinical awareness and cross-sectoral response systems will be essential to prevent repeated introductions from becoming sustained public-health challenges. Full article

Open-access satellite data from major Earth observation (EO) missions, including Landsat, Sentinel, and MODIS, have transformed environmental monitoring globally, yet in most least developed countries (LDCs) this data abundance has not translated into operational decisions or policy impact. This review argues that the dominant narrative in the remote sensing community, that open data leads to democratized impact, is fundamentally incomplete. Using Nepal as an illustrative case study, we demonstrate that legal openness alone is insufficient without parallel advances in technical usability and institutional accessibility, the two layers of EO accessibility that the community has largely overlooked. Through a cross-sectoral synthesis spanning forests, agriculture, disaster management, and land cover monitoring, we identify a persistent “last-mile problem”: the systematic gap between data availability and operational governance integration. Systemic barriers including limited internet infrastructure, skills gaps compounded by brain drain, fragmented institutional mandates, and the absence of a national EO coordination mechanism collectively prevent technically sound EO outputs from informing routine planning and policy decisions. Nepal’s small geographic extent, growing digital literacy, and ongoing governance reforms create strategic opportunities for transition, but realizing these requires a functioning geospatial ecosystem integrating data systems, technical infrastructure, human capital, and institutional frameworks. We propose the “Pixels to Policy” framework to operationalize this ecosystem and identify three priority research directions for the global remote sensing community: lightweight data formats for low-bandwidth settings, capacity-aware tool design, and implementation science for EO uptake. These directions reframe the community’s responsibility from delivering open data to ensuring it can be used. Full article

Cropland and rural settlements are core components of rural human–environment systems, and their coordinated development is crucial for regional sustainability, particularly in China’s major agricultural production regions. Taking the Huang-Huai-Hai region as the study area, this study systematically investigates the spatiotemporal evolution of cropland and its coupling relationship with rural settlements using land use data from 1990 to 2020. Grid-based analysis and multiple spatial modeling methods were employed. The results show that: (1) From 1990 to 2020, the cropland in the region decreased by a net total of 21,021.94 km², with annual dynamic degrees ranging from −0.13% to −0.28%. Cropland conversion to other land uses far exceeded conversion from others, with construction land being the primary destination. Among these, rural settlements and urban construction land accounted for 43.75% and 55.58% of the total cropland loss, respectively. (2) The spatial distribution of cropland exhibited a distinct pattern of “hot in the center and south, cold in the periphery and north” (Moran’s I = 0.232, p < 0.001), indicating significant positive spatial autocorrelation. Hot spot areas clustered in the North China Plain and the Huang-Huai Plain, while cold spot areas were distributed in the Yanshan–Taihang mountains and the hilly regions of the Shandong Peninsula, clearly controlled by topography. (3) Cropland change exhibited stage-specific characteristics. The pattern was relatively stable during 1990–2000. During 2000–2010, cropland conversion to other uses intensified, with high-value conversion areas concentrated around urban agglomerations. In the 2010–2020 period, these high-value conversion areas diffused from the core plain areas to urban fringe zones. (4) The spatial coupling between cropland and rural settlements was predominantly characterized by the Moderately Coordinated Type (MCT), accounting for 48.38–58.44% of the area. However, the proportion of Rural Settlement-Dominant Type (RC) increased from 15.51% to 21.58%, indicating a trend toward intensifying human–environment conflicts. Overall, the Huang-Huai-Hai region experienced significant cropland changes. While its spatial pattern remains relatively stable, the coupling relationship between cropland and rural settlements is deteriorating, posing challenges to regional food security and rural sustainable development. Full article

Urban mangroves are increasingly recognized for their important blue-carbon functions, yet their long-term aboveground carbon dynamics under climate extremes and human disturbances remain poorly understood. Here, we developed an integrated framework that combines multi-source satellite observations, field survey and LiDAR-constrained modeling to reconstruct annual species composition, canopy structure, and aboveground carbon dynamics from 1990 to 2022 in Shenzhen Bay, which is the only mangrove ecosystem within a megacity in China. Total aboveground carbon increased from 1820 (95% CI: 1386–2199) Mg C in 1990 to 6006 (95% CI: 5280–6618) Mg C in 2022, with habitat expansion accounting for most of the increase. Aboveground carbon accumulation was affected by coastal reclamation, estuarine engineering, and management-driven removal of introduced stands. Species composition emerged as a key determinant of ecosystem response to disturbance and long-term carbon dynamics. Native mangroves remained dominant and exhibited relatively stable canopy greenness during the 2008 extreme cold event. But the introduced Sonneratia apetala experienced a 42.9% drop in greenness and then took about five years to return to the level before the disturbance. By linking long-term changes in species composition, canopy structure, and aboveground carbon storage, this study provides a transferable foundation for monitoring urban blue-carbon ecosystems and evaluating the long-term consequences of disturbance, restoration, and management under accelerating urbanization and climate change. Full article

Against the backdrop of the transition of new-type urbanization towards high-quality development, the triple contradictions of population agglomeration, land constraints, and housing supply-demand imbalance have become increasingly prominent. The conventional binary framework of human–land relations can no longer meet the requirements of coordinated development within human settlement systems, creating an urgent need to examine the multi-system interactions among population, land, and housing in order to resolve spatial mismatch. Taking Guangzhou as a case study, this research integrates 2020 population census data, land-use data from the European Space Agency (ESA), housing-price data from the Anjuke platform, and multi-source data on related influencing factors, and conducts a systematic empirical analysis by combining coupling coordination analysis, a relative development model, and the geographical detector. The findings reveal that the coupling coordination level of population, land and housing in Guangzhou exhibits a concentric, ring-shaped distribution pattern with central agglomeration and peripheral decline. The relative development among the three systems can be classified into matching types including the core-differentiated type, the peripheral-imbalanced type, and the surrounding-equilibrium type. With respect to influencing factors, all pairwise interactions are of the bi-factor enhancement type, and the driving mechanism displays a three-stage dynamic evolution. This study enriches research on human–land relations, provides precise guidance for optimizing spatial allocation and alleviating housing mismatch conflicts in Guangzhou, and offers transferable practical experience for comparable cities in China seeking to advance the high-quality development of new-type urbanization. Full article

Monitoring urban biodiversity is essential for designing resilient and sustainable cities. Urban trees provide a wide range of ecosystem services (ESs), including air pollution reduction, urban heat island mitigation, and psychological benefits for citizens. Accurate and updated tree inventories are therefore essential tools for urban environmental monitoring. However, existing urban tree inventories are often incomplete or outdated, especially in private areas, limiting accurate ES assessment and urban planning. Earth observation satellite missions, particularly very-high-resolution multispectral (VHR-MS) imagery, offer a valuable alternative to field surveys for gathering information on urban environments. This work proposes a deep learning (DL) framework based on VHR-MS satellite imagery for the automatic generation of accurate urban tree inventories. DL models reduce human effort and save operational time by automatically learning complex representations and patterns from satellite imagery. The proposed encoder–decoder architecture extends prior point-based detection approaches by integrating a ResNet-50 backbone and a percentile-based threshold calibration procedure. Given the lack of suitable training data covering heterogeneous and densely vegetated urban environments, a dedicated dataset was constructed from VHR-MS satellite imagery acquired over the Lombardy region (Italy). The dataset encompasses a wide range of land uses and land covers, including residential and industrial zones, public parks, private gardens, and agricultural areas. Through the photointerpretation of more than 2800 images, precise coordinates for more than 50,000 manually annotated trees were obtained. The DL model is trained with point-level annotations, enabling precise localization of individual trees while reducing annotation ambiguity in dense urban contexts. On the Lombardy dataset at 30 cm/px resolution, the proposed framework achieves 86.72% Precision, 66.92% Recall, an F1-score of 75.54%, and a localization error of 1.473 m. Full article

Understanding how ecological quality and human activity co-evolve in densely populated watersheds is essential for sustainable land management, yet spatially explicit long-term evidence remains limited. This study investigated the spatiotemporal dynamics and coupling coordination between ecological quality and multi-dimensional human activity intensity in the Huai River Basin (approximately 269,000 km²) from 2012 to 2024. An Improved Remote Sensing Ecological Index (IRSEI) was constructed by integrating EVI, wetness, dryness, land surface temperature, and a salinity index through annual principal component analysis. A composite Human Activity Intensity (HAI) index combining nighttime light, built-up intensity, and population density was derived with objectively determined weights. The coupling coordination degree (CCD) model and a pixel-level four-quadrant classification were then applied to characterize the human–environment interaction. Results showed that the basin-wide mean IRSEI declined from 0.564 in 2012 to 0.516 in 2020, before recovering to 0.566 in 2024, while HAI increased moderately by 16.9%. CCD improved slightly from 0.451 to 0.480, indicating limited but positive coordination gains. Four-quadrant transitions revealed that high-ecology, low-activity areas expanded, low-ecology, low-activity areas contracted, whereas low-ecology, high-activity zones persisted as stable pressure cores. These findings demonstrate that ecological recovery and human activity intensification can coexist spatially, but persistent high-pressure areas require targeted management interventions. Full article

A biomechanical model of the flight phase in the long jump was constructed to analyze the factors influencing performance. Time-series coordinate data of key joints, obtained through AI-based human pose estimation, were incorporated into the model. For Problem 1, vertical velocity and acceleration of the joints were calculated using the dynamic parameter framework, and reference values with adaptive thresholds were applied to precisely identify take-off and landing moments. For Problem 2, joint information was used to derive arm swing amplitude, take-off angle, and joint rate of change as metrics to characterize athletes’ movement patterns and enable comparison before and after training. For Problem 3, physical and kinematic features were integrated, and Random Forest, multiple linear regression, and Recursive Feature Elimination were employed to evaluate key determinants of long jump performance and provide targeted training recommendations. The first two models achieved R² of 0.9772 and 0.9526, respectively, indicating excellent predictive accuracy. Finally, for Problem 4, the Random Forest and regression models developed in Problem 3 were applied to predict the performance of an athlete following posture optimization training. Full article

The human–environment interaction on the Qinghai–Xizang Plateau determines the direction of global human sustainable development, making it necessary to propose a refined prediction for this relationship. Currently, there is a lack of a predictive method for human–environment relationships, especially at the grid scale. This study focuses on Qinghai Province and proposes a human–environment relationship simulation method based on cellular automata (CA), utilizing land-use data and a remote sensing-based ecological (RSEI) index. The method enables grid-scale explicit predictions of human–environment relationships. The results show that by 2030, the human–environment relationship in Qinghai Province will become more diverse, with the coordination ratio rising to 11% and the degradation ratio to 7%. The ecological protection scenario serves a defensive role, preventing 3835 km² of land from degradation. In contrast, the urban development scenario plays a revitalizing role, achieving a coordinated area 2% larger than the business-as-usual scenario. By 2030, about 8956 km² of land in Qinghai will be suitable for agricultural revitalization, and 54,340 km² must be reserved for ecological protection. Due to the high-altitude environment, the human–environment relationship aligns only with the right half of the Environmental Kuznets Curve, namely, development brings greater harmony. We further discover the lag in the natural system’s response, for artificially increasing vegetation cover will not quickly improve habitat quality. Likewise, leapfrogging expansion in the urban development scenario may conceal long-term ecological risks behind short-term coordination. For stakeholders and policymakers, this study provides refined and differentiated governance measures at the grid scale, while highlighting the need to focus on underdeveloped regions and remain vigilant about the lag in human–environment relationship responses. Full article