242 / 2023-09-29 21:27:36

Multiscale Geographically Weighted Regression-based Analysis of Vegetation Driving Factors and Mining-induced Quantification in the Fengfeng District, China.

mining cities, driving factors, mining impact extraction, Multiscale Geographic Weighted Regression (MGWR), normalized difference vegetation index (NDVI)

Mining cities in eastern China represent unique ecosystems influenced by the combined impact of mineral extraction, urban development, and climate change. Conducting scientific assessments of ecological and environmental quality plays a crucial role in facilitating their sustainable transformation and development towards green practices. In this study, the Theil-Sen Median trend analysis and the Mann-Kendall test were used to evaluate the spatio-temporal patterns of Landsat-NDVI in the Fengfeng District from 2000 to 2022. The Multiscale Geographic Weighted Regression (MGWR) model was constructed using natural, socio-economic and accessibility factors to extract the effects of mining on vegetation and analyze the impact mechanisms of each driver. The main results were as follows: (1) In the two phases of 2000 – 2014 and 2014 – 2022, the Fengfeng District initially experienced degradation at a rate of 0.0015 /yr and subsequently displayed a gradual recovery rate of 0.0058 /yr. Overall, the degraded area only accounted for 2.07%; (2) The impact of mining on NDVI in the Fengfeng District exhibited distance decay characteristics and time cumulative effects, with the turning point of mining impact degree and boundary point 2.0 km and 3.0 km away from the mine, respectively; (3) The MGWR model achieved a maximum Adj. R² of 0.90, with the smallest spatial heterogeneity in the effects of the distance to the road (DRoad) on NDVI and the largest heterogeneity in terms of elevation; (4) The order of impact strength of NDVI drivers was as follows: population density (POP) >GDP density (GDP) > precipitation (P)> temperature (T) > elevation (DEM) > distance to the road (DRoad), with less positive impact intensity of natural factors and more extensive negative impact of socio-economic factors. This study can provide scientific reference for the green transformation development of the mining cities in eastern China.