A remote sensing index for assessing long-term ecological impact in arid mined land
Authors: Meng Dantong , Bao Nisha , Yang Tianhong , Li Qiyue 
Published: March 2024

Satellite remote sensing technology, with its ability to record spatial and temporal land surface conditions, has been extensively and effectively utilized in evaluating mining environments. Western China, characterized by arid ecosystems, is a significant mineral resource area, boasting at least ten super-large mineral bases, including coal, non-ferrous metal ores, and metal mines. Surface mining activities, marked by large spatial and temporal scales, can exacerbate the fragility and changes to the ecological environment in vulnerable areas. Consequently, it is crucial to assess and comprehend the spatial and temporal impacts of mining on arid ecological systems for green mine construction and mine reclamation. This study focuses on three typical open-pit mines in the arid regions of Xinjiang, China (site &#8544;: Jinbao Iron open-pit mine, site &#8545;: Heishan coal open-pit mine, site &#8546;: Wulagen Lead Zinc open-pit mine). The primary objective was to develop a remote sensing index (Mined Land Ecological Status Index, MLESI) that considers biological factors such as dryness, bare soil flatness, land surface temperature, and slope to assess the ecological status in arid mining areas. Subsequently, Principal Component Analysis was employed to couple these four factors to construct MLESI. The efficacy of MLESI was compared with the Remote Sensing Ecological Index (RSEI) and the Land Surface Ecological Status Composition Index (LSESCI) in different landforms in arid mining areas characterized by bare soil and rock. The spatial and temporal changes in mining effects from 2005 to 2020 were analyzed using the Sen+Mann-Kendall method based on Landsat time series images. The results indicated that the average Pearson correlation coefficient (r) between MLESI and each factor exceeded 0.65. The heat factor had the highest correlation coefficient of 0.8 with MLESI for mine site &#8544;, while the dryness factor had the highest correlation of 0.82 with MLESI for mine site &#8545;. The slope factor had the highest correlation coefficient of 0.82 for mine site &#8546;. For mine sites &#8544; and &#8545;, the LSESCI overestimated the areas of poor ecological status, identifying most of the natural land as poor. RSEI was unable to reveal the changes in ecological status correlating with landform variety. In general, MLESI was not only highly effective in characterizing the mining area from natural bare soil and rock lands but also in indicating ecological changes along the mining direction with landform changes. The ecological status in mine site I deteriorated by 54.84% since 2015 due to extensive surface mining activities. For mine site II, the ecological status gradually declined from an MLESI value of 0.68 in 2005 to 0.38 in 2020, with a total of 2.36 km2 area experiencing significant changes to poor ecological status over 15 years. For mine site &#8546;, the ecological status improved due to land reclamation, reaching the highest MLESI value of 0.77 in 2017. An area of 0.95 km2 experienced significant changes to good ecological status from 2008 to 2020. Therefore, the proposed MLESI outperformed RSEI and LSEISCI in monitoring different mine ecological statuses in a typical arid ecosystem.