Study on surface thermal environment differentiation effect in mining intensive area through developing remote sensing assessment model

The exploitation of mineral resources has promoted rapid economic growth, but it has also caused mining areas to have increased surface thermal flux, which has a negative impact on the ecological environment. In this study, using on Landsat satellite remote sensing images of the study area from 2000 to 2018, the radiative transfer equation method was used to invert Land Surface Temperature (LST). VFC in the study area was inverted based on the Normalized Difference Vegetation Index (NDVI) -Dry Fuel Index (DFI) three-component pixel model. Mixed pixels were decomposed into Photosynthetic Vegetation (PV). Non-Photosynthetic Vegetation (NPV) , and Bare Soil (BS). Based on the four ecological parameters, Factional Cover of Photosynthetic Vegetation (f(PV)), Normalized Difference Moisture Index (NDMI), Normalized Difference Built-up Index (NDBI), and Bare Soil Index (BSI), a remote sensing integrated ecological index (RSIEI) model which can comprehensively evaluate the differentiation effect of the surface thermal environment in mining intensive areas is proposed using Principal Component Analysis (PCA). The relationship between the differentiation effect of the surface thermal environment and the quality of the ecological environment was studied using the heat island variation index. The results showed that the NDVI-DFI feature space of the study area conforms to the basic assumption of the three-component pixel model. And the four ecological parameters are closely related to the differentiation effect of the surface thermal environment. From the regression equation of the four ecological parameters and LST in study area over three years, it can be seen that f(PV) and NDMI has a significant linear negative correlation with LST (p<0.01); NDBI and BSI have a significant linear positive correlation with LST (p<0.01). The spatial distribution of normalized RSIEI images and normalized LST images of study area showed an inverse spatial correlation, i. e. , the areas with high RSIEI (good ecological quality) in the study area correspond to the areas with low LST and vice versa. The quantitative regression analysis of RSIEI and LST in 3 years in 4 mining intensive areas shows that, when RSIEI is increased by 10%, LST was decreased by 0. 67 - 0. 77 degrees C. It is proved that the RSIEI model based on Principal Component Analysis (PCA) is suitable for the comprehensive evaluation of the surface thermal environment differentiation effect in mining intensive areas.