Applied Sciences, Vol. 13, Pages 6359: Analyzing Spatial-Temporal Change of Vegetation Ecological Quality and Its Influencing Factors in Anhui Province, Eastern China Using Multiscale Geographically Weighted Regression

Applied Sciences, Vol. 13, Pages 6359: Analyzing Spatial-Temporal Change of Vegetation Ecological Quality and Its Influencing Factors in Anhui Province, Eastern China Using Multiscale Geographically Weighted Regression

Applied Sciences doi: 10.3390/app13116359

Authors: Tao Wang Mingsong Zhao Yingfeng Gao Zhilin Yu Zhidong Zhao

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.