Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 10; p. 1612
• Main Authors: Xiao, Wu, Deng, Xinyu, He, Tingting, Chen, Wenqi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2020
• Subjects: Algorithms; Archives & records; Barren lands; case studies; China; Classification; Cloud computing; coal; Coal mining; Data analysis; Disturbances; Earth; Economic conditions; Economic development; Ecosystems; Environmental economics; Environmental impact; Environmental protection; Google Earth Engine; Grasslands; Internet; land disturbance trajectory; Land reclamation; land restoration; Land use; Landforms; Landsat; Landsat data; Landsat satellites; LandTrendr algorithm; Mapping; Mine reclamation; Mines; monitoring; Open pit mining; Remote sensing; Satellite imagery; Steppes; Strip mining; Studies; Surface mining; Time series; time series analysis; Trends; Underground mining; Vegetation; United States > US; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12101612

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km2 of land from grassland, water, etc., to barren earth, and only 23.54 km2 was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas.