A remote sensing index for assessing long-term ecological impact in arid mined land

• DOI: 10.5194/egusphere-egu24-319
• Publication Date: 2024-03-08T09:25:42Z
• Authors: Dantong Meng; Nisha Bao; Tianhong Yang; Qiyue Li

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