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<0.05). Furthermore, the GW-RSEI was integrated each indicator to fully reflected the ecological environment of mining areas. The overall monitoring that obtained by GW-RSEI was similar to that by RSEI over the past two decades. However, the GW-RSEI was focused on the real ecological impacts at the surface, indicating the more reliable at local scales. Whether in areas with single or complex land use types, GW-RSEI exhibited the higher correlations with soil moisture (SM), net primary productivity (NPP) and particulate matter 2.5 (PM2.5) than RSEI. The GW-RSEI monitoring was more consistent with the actual surface, in order to effectively reflect the ecological environment of the mining areas. Therefore, the GW-RSEI was successfully used to monitor the severe pollution of coal dust in local coal mining activity areas. The grade of GW-RSEI was lower than that of RSEI in the areas with the severe pollution of coal dust. The trend was the gradually increasing from the area with the high level of coal dust pollution as the center to the surrounding areas, indicating the spatial pollution in mining areas. The GW-RSEI values were higher than RSEI ones in urban areas of the county with the medium to high vegetation cover, and in rural areas where the land type was mainly arable land and grassland. There were significantly more medium to low GW-RSEI value in the mining area and surroundings, compared with the RSEI. Furthermore, the gradual trend was highlighted the spatial continuity in ecological environment quality. GW-RSEI averages were 0.51, 0.48, 0.46, 0.59, and 0.56, indicating that the overall ecological environment experienced a process of first deterioration and then improvement. The trend in the southeastern region of Datong Coal field was consistent with the overall trend, while the northwestern region showed a trend of first improvement and then deterioration. The GW-RSEI can provide the more effective way to accurately monitor the ecology of the mining area.