Constructing and evaluating geographically weighted-remote sensing ecological index for ecological monitoring in mining areas

The ecology of mining areas has been one of the most crucial components in terrestrial ecosystems. However, the over-exploitation of mineral resources has posed a great threaten to the ecosystem in recent years, leading to frequent environmental issues, such as land degradation, vegetation loss, and water scarcity. Therefore, accurate monitoring of mining ecology is of great importance to protect the ecological environment and balance. Remote sensing technology can be expected to provide an effective means for the ecological monitoring in mining areas. In this study, Remote Sensing Ecological Index (RSEI) was improved to realize the overall spatial average on the indicator weights for the ecological monitoring of mining sites. The factor of coal dust pollution was also added into the conventional greenness, wetness, dryness and heatness. The indicator weights were then determined using Geographically Weighted Principal Component Analysis (GWPCA). The indicator was finally selected to construct Geographically Weighted-Remote Sensing Ecological Index (GW-RSEI). Taking the Datong coal field in Shanxi Province as an example, the validity and applicability of GW-RSEI were verified for monitoring mining area ecology using multi-phase remote sensing images from 2000 to 2020. The results showed that the indicator weights of GW-RSEI were varied continuously over the space, indicating the spatial heterogeneity within different local surface areas. GW-RSEI shared the average correlation coefficients with universal normalized vegetation index (UNVI), normalized difference moisture index (NDMI), soil index (SI), temperature vegetation dryness index (TVDI) and index-based coal dust index (ICDI) of 0.76, 0.78, -0.77, -0.82 and -0.41, respectively, (P © 2024 Chinese Society of Agricultural Engineering. All rights reserved.