Prevailing coupled human-natural systems theory assumes ecological responses to anthropogenic disturbances lag by years to decades due to ecosystem inertia-a foundational assumption underlying Environmental Kuznets Curve models. However, this framework inadequately captures dynamics in rapidly developing economies with abrupt policy transitions and satellite-based monitoring. We tested whether synchronous coupling-concurrent human-ecological changes-dominates such contexts by constructing 1-km resolution indices (Anthropogenic Activity Magnitude Index, AAMI; Ecological Environment Condition Index, EECI) for China (2000-2020) and applying a nine-quadrant trajectory framework with provincial quantile thresholds. Three mechanisms explain synchronous coupling: direct biophysical coupling (land-use events simultaneously modifying human and ecological indicators), policy-driven transitions (top-down regulations generating systemic adjustments within 1-2 years), and climate-mediated forcing (hydrometeorological extremes constraining both human activities and ecosystems). Results reveal 69.94% of China exhibited synchronous AAMI-EECI changes, fundamentally challenging lag assumptions. Spatially, the Hu Line functions as a governance capacity threshold: eastern regions achieved coordination despite higher pressure through effective institutions, while conflict zones expanded 24.3% in central-western cities where development outpaced governance. Temporal analysis shows ecological turning points varied systematically (2010-2011 eastward vs. 2013-2015 westward), reflecting governance differentials rather than uniform trajectories. These findings establish that in policy-driven economies, governance regime type-not only development stage-restructures coupling timescales, necessitating spatially differentiated sustainability frameworks and trajectory-based analytical approaches for satellite-observable human-nature systems.

The Lower Jinsha River Basin (LJRB) has experienced decades of concurrent cascade hydropower construction, mineral exploitation and large-scale Grain for Green (GFG) restoration. Comprehensive long-term multi-dimensional assessments linking these overlapping human activities to basin ecological sustainability remain limited in existing research. This study develops an integrated evaluation framework built on a 35-year (1991–2025) Modified Remote Sensing Ecological Index (MRSEI) dataset, Hurst exponent persistence analysis, a composite Ecological Stability Index (ESI) developed for this study, and Convergent Cross Mapping (CCM) causal attribution to quantify long-term ecological trajectories and identify the ecological regime shift. Results show sustained basin-wide ecological improvement with the mean MRSEI rising from 0.201 to 0.386, with Sen's slope = 0.0055 yr−1 and Kendall's τ = 0.476 (p < 0.01). Three phases of uninterrupted MRSEI growth are distinguished throughout the study timeline: slow baseline improvement from 1991 to 2000, moderate steady gains between 2001 and 2012, and notably accelerated ecological recovery after 2012 coinciding with reservoir impoundments where EVI and WET increased markedly after reservoir impoundment, by 0.456 and 0.567, respectively. While 93.08% of the basin presents positive MRSEI trends, only 42.51% corresponds to stable long-term recovery; 45.21% displays anti-persistent trajectories (Hurst <0.5), a latent vulnerability that single-indicator trend analysis may not identify. Mining disturbance follows a clear distance-decay gradient (R2 = 0.63), with functional ecosystem recovery observable 8–10 km from open-pit mine sites. CCM analysis confirms reservoir impoundment as the primary trigger of post-2012 ecological improvement (ρ = 0.617), with cross-map skill 39.3% stronger than GFG revegetation (ρ = 0.443) and 22.7% greater than the reverse feedback pathway (ρ = 0.503). Geodetector modelling shows the interaction between land cover and evapotranspiration delivers the highest explanatory power (q = 0.534), exceeding all single independent variables and reflecting coupled land-water-climate feedbacks shaping regional ecological conditions. The integrated MRSEI–ESI workflow supports delineation of spatial management zones, including stable conservation areas, mining degradation corridors, and empirical 2 km disturbance buffers around reservoirs and mine sites. This analytical framework may be adapted to assess ecological sustainability in other mountain river basins undergoing rapid anthropogenic transformation.

The Jinsha River Basin (JRB) has a good vegetation coverage and plays an important role in maintaining the ecological environment in the upper reaches of the Yangtze River. In this study, the effectiveness of ecological engineering implementation in the Yunnan section of the JRB was analysed from the three aspects of fractional vegetation cover (FVC), vegetation restoration potential achievement degree (VRPAD) and ecological land, and residual analysis was used to analyse the effects of ecological engineering and climate on vegetation. The results revealed that: (1) From 1990 to 2023, the interannual FVC and VRPAD were 0.65 and 0.5, respectively, and the growth rates of VFPAD and FVC were 0.32 % and 0.39 %, respectively. (2) The average annual area of ecological land was 82.05 x 10 (3) km(2), with a mean annual growth rate of 0.04 %, and an overall increase of 1.18 x 10( 3) km(2). The forestland and wetland areas both tended to increase, whereas the grassland area continued to decrease. (3) Spatially, the area of increased FVC was 70.52 % and the area of decreased FVC was 25.22 %. Compared with the period from 1990 to 2000, the VRPAD from 2000 to 2023 showed a transition from nonsignificant increase to a significant increase. (4) From 1990 to 2000, the area of vegetation positively affected by ecological engineering accounted for 57.39 %, and the area of vegetation negatively affected by anthropogenic activities accounted for 40.24 %. From 2000 to 2023, the area of vegetation positively affected by ecological engineering accounted for 68.28 %, and the area of vegetation negatively affected by anthropogenic activities accounted for 31.02 %. (4) In areas where vegetation improved, ecological restoration policies promoted the recovery of vegetation, which accounted for the largest area (51.88 %). In areas where vegetation has degraded, anthropogenic activities have led to vegetation degradation, accounting for the largest area (54.21 %). In the future, the influence of natural factors and human activities should be incorporated into the formulation of ecological policies.

Vegetation in Karst regions is highly sensitive to climate change, yet vegetation-climate relationships remain poorly quantified across spatial scales in these complex landscapes. Using 2000-2022 MODIS data, we apply kernel Normalized Difference Vegetation Index (kNDVI)-which reduces soil/rock background effects-to examine climate-vegetation dynamics at pixel, vegetation-type, and regional scales in Southwest China Karst Typical Region (SWCKTR). Multi-method correlation analyses (Pearson, detrended, and moving-window partial correlations) reveal scale-dependent patterns. Regional analysis shows persistent greening (0.0048 yr-1) despite warming (0.028 °C/year) and drying (-5.19 mm/year), with temperature as the dominant driver (R = 0.3289, p < 0.01). At the pixel scale, 61.32% of areas show positive temperature correlations, while 92.65% show negative precipitation correlations, reflecting karst hydrological constraints. Vegetation-type analysis reveals divergent sensitivities: Northern Tropical Humid Semi-Evergreen Seasonal Rainforest exhibits the fastest spring greening (0.0086 yr-1, p < 0.01) and positive autumn precipitation correlation (0.3705, p < 0.01), while Subtropical Mountain Coniferous Forest shows negative precipitation responses across all seasons (summer: 0.5808, p < 0.01) and autumn degradation (-0.0014 yr-1). Seasonally, spring shows the fastest regional greening (0.0069 yr-1, p < 0.01) with positive climate correlations, while summer exhibits the slowest growth (0.0039 yr-1) despite strongest warming. Residual analysis indicates climate factors dominate (>80% of pixels), though human activities contribute significantly near urban centers (>10% positive residuals). Multi-scale integration reveals hierarchical climate-vegetation coupling, with temperature effects consistent across scales while precipitation effects vary by scale, season, and vegetation type.

To elucidate the impacts of multi-dimensional droughts on vegetation carbon sinks in the Jinsha River Basin（JRB）, this study utilized multi-source spatiotemporal data from 2001 to 2020 to quantify the dynamics of Annual Gross Primary Productivity (AGPP) across the entire basin and its four eco-geographical zones, as well as its response mechanisms to meteorological, hydrological, and atmospheric droughts. The results indicate that: (1) Over the past two decades, the basin-wide AGPP exhibited a significant upward trend (with a total increment of 134.80 gC/m²/a), and both its spatial distribution and growth magnitude increased stepwise along the “semi-arid to humid” moisture gradient. (2) The long-term evolution of different drought types diverged significantly: Standardized Precipitation Evapotranspiration Index (SPEI) remained stable; Soil Moisture (SM) underwent a spatial reshaping characterized; whereas Vapor Pressure Deficit (VPD) experienced widespread and significant alleviation (with a decreasing trend covering 32.97% of the basin). (3) The drought driving mechanisms exhibited a prominent “shift in dominance” along the hydroclimatic gradient. In the semi-arid zone, SM was the dominant factor; however, as underlying moisture conditions improved, the dominance of VPD gradually climbed, ultimately replacing SM as the core limiting factor in the humid zone. Furthermore, non-linear Generalized Additive Models (GAMs) confirmed that VPD acts as the core constraint regulating AGPP, featuring an optimal suitability range of 0.4–0.8 kPa and a “cliff-like” drop threshold that triggers stomatal closure, while the critical stress baseline for SM was identified at 0.15.

Abstract Ecological security exhibits pronounced nonlinear responses and spatial heterogeneity to natural conditions and human activities, yet the spatial differentiation of these nonlinear thresholds across contrasting ecological contexts remains poorly understood. Focusing on Central Yunnan, China, this study developed a data-driven framework integrating the Driver–Pressure–State–Ecosystem services–Response framework with explainable machine learning and multi-source remote sensing data to assess spatiotemporal changes in ecological security from 2000 to 2020 and identify the nonlinear threshold responses of key drivers across ecological zones. The results showed that the regional mean ecological security index (ESI) increased from 0.3533 in 2000 to 0.3798 in 2020, indicating an overall fluctuating upward trend. Areas with improved ecological security accounted for 71.56% of the study area, whereas 28.44% showed decline, with degraded areas mainly concentrated in impervious surfaces their surrounding regions. Ecological security displayed marked spatial differentiation, with relatively high ESI values in Zones 1, 3 and 5, whereas Zone 2 remained persistently low. The magnitude of improvement also varies significantly among different ecological zones, with Zone 1 showing the largest increase (0.0681) and Zone 8 the smallest (0.0138). Among the examined drivers, slope consistently emerged as the most stable and influential factor at the regional scale, followed by precipitation and elevation. SHAP-based analysis further revealed a pronounced nonlinear threshold effect of slope on ecological security, with global thresholds persistently concentrated within 13°–15°, yet showing clear spatial heterogeneity among ecological zones. These findings provide new insight into ecological security formation mechanisms and support differentiated ecological governance.

Reforestation is crucial for ecological restoration in mining areas, and precise classification of tree species is an important prerequisite for evaluating the effectiveness of ecological restoration. Unmanned aerial vehicles (UAVs) hyperspectral imagery enables highly accurate classification of tree species, leveraging its superior spectral and spatial resolutions. However, the challenge of dimensionality escalation, caused by hundreds of spectral features across numerous bands, introduces new hurdles for conventional classification methods. Deep learning provides a new solution for automatic feature extraction and tree classification and mapping. However, there are problems such as single scale of extracted features, static model inference, and weak model generalization ability. Especially in the ecological restoration area of the mining area, where the terrain environment is complex and tree species of multiple forest age levels coexist, and the classification accuracy needs to be improved. Therefore, this study uses UAV-based hyperspectral imagery acquired from the Jianshan mining area in Kunming, Yunnan Province, as the primary data source, and proposes a novel three-dimensional convolutional neural network (SDTA-3DCNN) incorporating a separable depth transposed attention mechanism. The model enhances the extraction of high-dimensional sparse spectral features through a cascaded 3DCNN architecture, while effectively resolving complex spectral patterns by leveraging an attention mechanism to fuse local features with high-level global representations. The final tree classification accuracy reaches F1-score = 0.9814, OA = 0.9903 and Kappa = 0.9868. The proposed method is also well applied in other tree classification and crop classification scenarios, with OA and Kappa of 0.9871 and 0.9688 in other tree classification scenarios, respectively, and the highest classification accuracy in crop classification scenarios reaches OA = 0.9712, Kappa = 0.9635. The method can provide scientific basis and technical support for the fine classification of tree species in mining areas, the monitoring of ecological restoration status in mining areas, the evaluation of effects, and the decision-making.

Investigating the connection between urbanization and vegetation is crucial for sustainable urban development and the study of urban ecological environments. The urbanization process in Russia is distinctive, and the evolving relationship between urban expansion and vegetation cover remains largely unexplored. We integrated 10-year urban physical boundary data with a 30-m resolution vegetation index to introduce the urbanization-vegetation relationship index (UVRI). This index assesses the conflict or coordination between urbanization and vegetation cover across 74 federal subjects in Russia from 1990 to 2020. The results show that (1) over the past three decades, Russia's 74 federal subjects have undergone swift and extensive yet uneven urban expansion, with urban areas increasing by 38,526.71 km2. (2) In general, the vegetation coverage of cities in various federal areas has increased, and urban vegetation has shown an apparent greening trend. However, the changes in vegetation cover vary across cities at different stages of development. (3) UVRIs are coordinated in 62 of Russia's 74 federal subjects. This indicates that the relationship between urbanization and vegetation cover in most Russian cities is coordinated. However, cities in the east and west display evident spatial heterogeneity.

Water conservation is a key ecosystem service of tropical forests, crucial for regional water security and ecological balance. This study assessed the spatiotemporal dynamics of water conservation in the Annamite Range Moist Forests using the InVEST water yield model and 2000–2020-time series data. Results indicated that: (1) Temporal changes: Average water conservation decreased from 1,337.51 mm in 2000 to 1,209.85 mm in 2020, a decline of 127.66 mm (9.55%) equivalent to 14.275 billion m³ of water. Distinct phases were observed: growth (2000–2005, +6.64%), sharp decline (2005–2010, -23.46%), continuous decline (2010–2015, -45.09%), and strong recovery (2015–2020, +106.10%), with high inter-annual variability (CV = 29.31%); (2) Spatial patterns: While water conservation intensity fluctuated markedly, the spatial distribution remained stable, with each grade area averaging 22,364 km². Over 20 years, 61.51% of the area declined, 36.90% increased, and 1.59% remained stable; (3) Differential responses: Very high-grade areas increased by 13.79%, demonstrating strong resilience, whereas low and very low-grade areas declined by 32.15% and 28.01%, respectively, indicating higher vulnerability. These findings provide a scientific basis for targeted conservation strategies and adaptive management of the Annamite Range Moist Forests.

Accurate vegetation type information is essential for the scientific assessment of ecological restoration in open pit mining areas. However, many existing mine-mapping studies tend to classify all vegetation into a single class, overlooking inter-species differentiation and thereby limiting the precision of ecological evaluations. To address this issue, this study proposed a multi-scale hierarchical classification (MSHC) method for fine-scale mapping of vegetation types in open phosphate mining areas by combining unmanned aerial vehicle (UAV) RGB remote sensing imagery and light detection and ranging (LiDAR) data with the support of object-oriented methods. The results showed that (1) The all features (AF) scheme achieved an overall accuracy (OA) of 97.04% and a Kappa coefficient of 0.98. (2) The proposed MSHC method reached an OA of 97.69% and a Kappa of 0.97. (3) The vegetation in the study area was predominantly grassland (approximately 29.57% of the total area), with Alnus nepalensis being the dominant tree species (16.07% of the area). Overall, this study provides a detailed classification of vegetation types, offering a valuable dataset for ecological monitoring and assessment. The proposed method also presents a transferable and effective approach for the fine-scale vegetation mapping of other open-pit mining environments.

Understanding and quantifying the dynamic features of local ecosystem services (ESs) and integrating diverse ecosystem assessment results form crucial foundations for regional ES management. However, existing methods for integrating and objectively evaluating multiple ESs remain limited. Consequently, this research evaluates four key services based on the InVEST and RUSLE models in the Central Yunnan Province (CYP)-from 2000 to 2020: water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC). It then constructs an Integrated Ecosystem Service Index (IESI) using principal component analysis (PCA). Additionally, this study explores the factors driving the spatial divergence of ESs by employing the optimal parameter-based geographical detector model (OPGD) at the optimal spatial scale. The results indicated that (1) the IESI was effectively applied in the CYP and could quantitatively and comprehensively integrate the assessment results of the four key ESs. (2) During the study period, the ESs in the CYP showed increasing trends for WY, HQ, and SC, while CS showed a decreasing trend. (3) The IESI during the study period exhibited a trend of initially decreasing and then increasing. The average IESI values for CYP were 0.7338 in 2000, 0.6981 in 2005, 0.6947 in 2010, 0.6650 in 2015, and 0.6992 in 2020. (4) A 4500 m × 4500 m grid was identified as the optimal spatial scale for detecting the spatial divergence of comprehensive ecosystem service (CES) in CYP, and relief degree of land surface (RDLS), slope, and the NDVI were the top three drivers based on q-values. This study offers a more scientific and effective method for evaluating regional CES. It also provides a comprehensive analytical tool for balancing land use competition and assessing the effectiveness of policy implementation.

Yunnan Province, a typical mountainous region in southwestern China, acts as a vital water source and ecological barrier for China as well as South/Southeast Asia. However, the province faces growing water security threats from intensifying extreme weather, increasing human water demand, and persistent water quality challenges. To address these issues, this study developed a comprehensive water security assessment framework integrating water quality constraints and water-related ecosystem services (WES) flows. Applying this framework, we systematically evaluated the spatiotemporal dynamics of water security in Yunnan Province from 2000 to 2023. Key findings indicate that: (1) The framework evaluates the level of Effective Water Yield (EWY) services by integrating the Comprehensive Water Quality Index (CWQI), providing an accurate characterization of water security in mountainous regions; (2) The influence of water quality on EWY has increased significantly. EWY accounted for approximately one-third of total water yield (WY), and its decline rate (about -2.05 %/a) exceeded that of total WY (about -1.49 %/a); (3) WES inflow effectively mitigated water stress in an average of 37.83 % of static water security-deficit areas annually; (4) Although Yunnan Province's dynamic water security was generally dominated by high surplus, approximately 15 % of its regions remained in deficit; (5) Average potential evapotranspiration (APet), average temperature (ATmp), and average precipitation (APre) were the dominant climatic drivers of water security variations in Yunnan Province, collectively accounting for 56.19 % of the relative contribution. These findings provide a theoretical foundation for developing more scientifically robust and effective water security mitigation strategies to assist Yunnan Province in responding to future water security risks.

Vegetation net primary productivity (NPP) serves as a critical indicator of ecosystem health and carbon sequestration capacity in ecologically fragile mountainous regions. This study analyzes NPP spatiotemporal dynamics in Yunnan Province, China from 2005-2020 using MODIS NPP products (MOD17A3HGF V6.1) combined with trend analysis, empirical orthogonal functions (EOF), and structural equation models (SEMs). Results show: (1) NPP exhibited higher values in west and south regions, with forestland averaging 1091.07 gC/m(2)/year, followed by cultivated land (942.49 gC/m(2)/year) and grassland (936.17 gC/m(2)/year). (2) EOF analysis revealed the first two modes explained 55.13% of total variance, with NPP patterns shifting every five years. The northwest region showed highest sensitivity, with the first mode (39.64% variance) having temporal coefficients ranging from -217.40 to +431.36. (3) SEM analysis demonstrated climate change as the primary NPP driver (standardized path coefficient: 0.87), while topography (-0.51) and socioeconomic activities (-0.63) showed significant negative effects. Region-specific SEMs revealed distinct driving mechanisms across ecogeographical zones. These findings provide critical insights for sustainable ecosystem management and regional policy development in Yunnan Province's complex mountainous environment.

Regional ecosystem service value (ESV) is significantly influenced by factors such as land use/cover change (LUCC). In this study, from the perspective of spatio-temporal heterogeneity, we constructed a dynamic and zonal equivalence table of ecosystem service values using the equivalence factor method and analyzed the spatio-temporal changes in ecosystem service values of different agricultural plantation regions of the karst mountainous areas of southwestern China (Yunnan Province, YP) in the years from 1990 to 2020. Also, the ESV of YP in 2030 was simulated using the Patch-generating Land Use Simulation (PLUS) model. The results showed the following: (1) land use/land cover (LULC) in YP from 1990 to 2020 was dominated by needle-leaved forestland, broadleaved forestland, grassland, and rainfed cropland. (2) The total ESV in YP fluctuated between CNY 876.74 and 1323.68 B from 1990 to 2020, expanding at a rate of 50.98%. The largest portion of the total ESV comes from climate regulation. The ESV increased from east to west, and the positive spatial correlation of the ESV gradually weakened. (3) The ESV in YP was projected to reach CNY 1320.70 B by 2030, representing a decrease of ~CNY 2.98 B since 2020. The results showed a decline in the ecological environment’s quality in YP.

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric mapping tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China’s activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development.

Land use and land cover changes significantly affect the function and value of ecosystem services (ES). Exploring the spatial correspondence between changes in land cover and ES is conducive to optimizing the land use structure and increasing regional coordinated development. Thus, this study aimed to examine changes in land use and land cover (30 × 30 m) in Laos between 2000 and 2020 and their effects on ecosystem services value (ESV) using the Global Surface Cover Database land use data for 2000 to 2020, ArcGIS technology, and the table of Costanza’s value coefficients. The study results indicated that forest (79.5%), cultivated land (10.6%), and grassland (8.3%) were the dominant land use types in Laos over the past two decades. The forest area decreased significantly, while there were increases in other land types, and the forest was transformed into cultivated land and grassland. ES in Laos was valued at about USD 140–150 billion, with forest contributing the most, followed by cultivated land and grassland. ESV over the last two decades in Laos has increased by USD 3.94 million. Large values were assigned to regulating services (40%) and supporting services (14%). The ESV of food production, soil formation, and water supply increased, and the ESV of climate regulation, genetic resources, and erosion control decreased. In addition, the elasticity value of artificial surfaces was more prominent, with a more evident impact on ESV. For future development, Laos should rationally plan land resources, develop sustainable industries, maintain the dynamic balance of second-category ESV, and achieve sustainable economic and ecological development. This study provides a scientific basis for revealing changes in ESV in Laos over the past two decades, maintaining the stability and sustainable development of the environment in Laos, and realizing the sustainable use and efficient management of the local environmental resources.

The frequent occurrence of extreme weather events is one of the future prospects of climate change, and how ecosystems respond to extreme drought is crucial for response to climate change. Taking the extreme drought event in the Tropic of Cancer (Yunnan section) during 2009-2010 as a case study, used the standardized precipitation evapotranspiration index to analyse the impact of extreme drought on enhanced vegetation index (EVI), leaf area index (LAI) and gross primary productivity (GPP), and to analyzed the post extreme drought vegetation recovery status. The results indicate the following: (1) Due to the cumulative effects of drought and vegetation phenology, vegetation growth in the months of March to May in 2010 was more severely affected. (2) Compared to EVI and LAI, GPP is more sensitive to drought and can accurately indicate areas where drought has impacted vegetation. (3) Following an extreme drought event, 70% of the vegetation can recover within 3 months, while 2.87-6.57% of the vegetation will remain unrecovered after 6 months. (4) Cropland and grassland show the strongest response, with longer recovery times, while woodland and shrubland exhibit weaker responses and shorter recovery times. This study provides a reference for the effects of extreme drought on vegetation.

Urbanization affects vegetation distribution by changing land cover. However, it also significantly changes urban water and heat conditions, affecting vegetation growth and development. Vegetation coverage is an effective indicator of vegetation growth. This study analyzed trends in vegetation coverage over the past 35 years, taking the Yunnan Central Urban Economic Circle as the study area. The study was based on applying the pixel dichotomy model and correlation statistical analysis on joint Landsat 5, 7, and 8 long- term remote sensing data, meteorological, and land use data. The direct and indirect effects of urbanization on vegetation coverage were also further explored by constructing an urbanization impact framework. The results revealed that: (1) The urban area during urbanization from 1986 to 2021 increased by 720.29 km2. There was a continuous decline in vegetation cover in and around urban areas, which intensified with accelerating urbanization, with the effect being more pronounced in suburban areas. (2) There were consistent increasing trends in urbanization's direct and indirect effects on vegetation over the last 35 years, with average negative and positive effects of - 0.41 and 1.59, respectively. (3) Direct effects could mainly be attributed to the expansion of impervious surfaces, whereas the main indirect effect during the late urbanization period (2011-2020) was increasing average temperature. The average temperature showed a correlation coefficient with urbanization of 0.7767, and this relationship showed seasonal heterogeneity due to the significant growth of urban vegetation in summer and winter. (4) Cities that developed faster showed better environmental planning and construction. The direct and indirect effects of urbanization on vegetation during the early and middle stages were higher in cities developing at slow and moderate rates, with this trend reversing only in the later stages of urbanization. The results of this study can increase understanding of the effect of urbanization on vegetation coverage in the Yunnan Central Urban Economic Circle. They can assist in improving urban green spaces and urban ecological resilience.