Identification of surface mining and assessment of ecological restoration effects using GEE and Sentinel-2 image data - A case study on Yangtze River watershed, China

Abstract

Mineral resource development is essential for economic growth; however, its significant negative impacts on land, ecology, and the environment cannot be overlooked. This study aims to identify and assess the restoration status and ecological quality of large-scale surface mining areas, especially in the absence of specific mining location information. We propose a systematic workflow that utilizes open-source remote sensing data. The process includes: (1) extracting surface mining areas using masking, morphological operations, and visual interpretation techniques; (2) constructing time-series of Bare Surface Percentage (BSP) for each mining area on the Google Earth Engine platform to distinguish between abandoned and active mines and examine their restoration rates; (3) creating the Remote sensing Ecological indicator for Mining areas (REM) to quantify the ecological quality and analyze its temporal changes. A total of 1183 mine sites were identified in the study area, of which 381 abandoned mines showed a significant decreasing trend in BSP from 2016 to 2021, with a median decline from 98 % in 2016 to 81 % in 2022, indicating improved vegetation recovery during this period. Additionally, the REM of abandoned mines generally exhibited a stable upward trend from 2016 to 2022. This study provides a systematic solution for identifying surface mining areas and monitoring the restoration scope and ecological quality on a broader scale. The methodology is extendable to other regions and can support further ecological restoration decision-making.