###### Abstract  
Vegetation is a crucial component of terrestrial ecology and plays a significant role in carbon sequestration. Monitoring changes in vegetation ecological quality has important guidance value for sustainable development. In this study, we investigated the spatial and temporal variation characteristics of Ecological Quality Index of Terrestrial Vegetation (EQI) in Anhui Province during the growing season from 2000 to 2020 using trend analysis, partial correlation analysis and bivariate spatial autocorrelation analysis. Based on the Multiscale Geographically Weighted Regression (MGWR), the spatial heterogeneity of the effects of average temperature, precipitation, elevation, slope, and human activity factors on EQI was explored. Our results showed an increasing trend in EQI during the growing season in Anhui Province from 2000 to 2020. The significantly increasing areas accounted for 43.49%, while the significantly decreasing areas accounted for 3.60%. EQI had a mostly positive correlation with precipitation and a negative correlation with average temperature (\(p<0.1\)), showing a higher sensitivity to precipitation than to temperature. Additionally, EQI tended to increase initially and then decrease with increasing elevation and slope. Furthermore, our analysis revealed a significant negative spatial correlation between human activity intensity and EQI (\(p<0.01\)). The bivariate global autocorrelation Moran index between EQI and human activity in 2000, 2005, 2010, 2015, and 2018 were \(-0.418\), \(-0.427\), \(-0.414\), \(-0.487\), and \(-0.470\), respectively. We also found that the influencing factors explain 63-83% of the spatial variation of EQI, and the order of influence of factors on EQI is elevation \(>\) human activity \(>\) slope \(>\) average temperature \(>\) precipitation. MGWR results indicated that human activities and topographic factors had a stronger impact on EQI at the local scale, while climate factors tended to influence EQI at the global scale. vegetation ecological quality; spatial and temporal variation; Google Earth Engine; human activities; bivariate spatial autocorrelation; multiscale geographic weighted regression +
Footnote †: journal: Applied Sciences
sensing data processing cloud platform launched by Google in 2000 to enable online visualization, computation and analysis of large, long time series and multi-scale Earth data (mainly satellite data) [9]. Compared to traditional desktop remote sensing processing platforms (such as ENVI and ERDAS software), GEE has two major advantages: (1) it provides online access to satellite remote sensing data from different sources and scales, and users can also upload their own data, breaking the limitation of local data storage; and (2) it provides a variety of algorithms and API interfaces, such as cloud masking and radiation correction, which allow users to process remote sensing images flexibly and autonomously [10]. This has led to an increasing number of studies based on the GEE platform in recent years [11]. Taking advantage of the convenience provided by the GEE platform, this study integrates the primary data acquisition and processing steps into the platform. Temperature and precipitation are the main climatic factors affecting vegetation growth [12], but the effects of topographic factors and human activities on vegetation growth should not be neglected. Most previous studies have directly and quantitatively assessed the relationship between vegetation indices and climate or topography [6; 13], and fewer studies have assessed the impact of human activities on vegetation ecology. Some characterize human activities with a single data, such as [PERSON] et al. [14], who used crop yield and afforestation to explain the ecological disturbance caused by human activities, and [PERSON] et al. [15], who used population data as a proxy for human activities.