Using POI and time series Landsat data to identify and rebuilt surface mining, vegetation disturbance and land reclamation process based on Google Earth Engine. (1st February 2023)

• Record Type: Journal Article
• Authors: Xiao, Wu; Deng, Xinyu; He, Tingting; Guo, Jiwang
• Is Part Of: Journal of environmental management. Volume 327(2023)
• Publication Date: 2023-02-01
• Subjects: POI -- Surface coal mining -- Land disturbance -- Google Earth Engine -- Change detection -- LandTrendr; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2022.116920
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: The development of coal resources is necessary, but it has a huge negative impact on land, ecology, and the environment. With the increasing awareness of environmental protection and the requirements of related regulations, the design and practice of reclamation projects run through the mining life cycle and continue for a long time after the coal production. High-precision monitoring of mining disturbance and reclamation, quantifying the degree and time of vegetation disturbance and restoration, is of great significance to minimize the environmental effect of mining. Remote sensing, widely used as efficient monitoring tool, but there is not enough research on disturbance and reclamation monitoring taking into account large-scale areas and high temporal and spatial accuracy. Especially when mining sites remain unknown, how to distinguish the disturbance of coal mining and other human activities affecting the surface land cover has become a challenge. Therefore, this paper proposed a method to reconstruct the time series of mining disturbance and reclamation in a large area by using the POI (point of interest) and Landsat time series images using multiple buffer analysis methods. The process includes: (1) Retrieval of POI in the study area based on the public mining list using Python crawler, and buffering 100 km for preliminary extraction of potential mining areas; (2) Using spectral index mask and random forest algorithm to accurately extract the exposed coal on the Google Earth Engine (GEE) platform; (3) Buffering 10 km to identify the occurrence of disturbance and reclamation, using pixel-based temporal trajectory identification of LandTrendr algorithm under GEE. The method successful detect the change points of surface coal mining disturbance and reclamation in eastern Inner Mongolia of China. The results show that: (1) The method can effectively identify the extent of surface coal mining disturbance and reclamation, and the overall extraction accuracy is 81%. (2) Surface coal mining disturbance in eastern Inner Mongolia was concentrated in 2006–2011. By 2020, the total disturbed area is 627.8 km 2, with an average annual disturbance of 18.5 km 2, and the annual maximum disturbance to the ground reached 64.6 km 2 in 2008. With the total reclaimed area being 236.3 km 2, the reclamation rate is about 37.6%. This study provides a systematic solution and process for monitoring the disturbance and reclamation of surface coal mining in a large range with little known about the mines' location. It can effectively identify the mining disturbance and reclamation process which can also be extended to other areas, providing a quantitative assessment of mining disturbance and reclamation, which can support further ecological restoration decision-making. Graphical abstract: Image 1 Highlights: A remote sensing procedure of surface coal mine classification, disturbance and reclamation monitoring is developed. Landsat, random forest, web scraping, and Google Earth Engine has been used to detect 73 surface coal mining sites. 0.94% of the total study area has been disturbed by surface mining. The developed procedure is able to reveal the surface coal mine reclamation pattern.