Vegetation Disturbance and Recovery Dynamics of Different Surface Mining Sites via the LandTrendr Algorithm: Case Study in Inner Mongolia, China

Yunxuan Liu, Miaomiao Xie, Jinying Liu, Huihui Wang and Bin Chen
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Yunxuan Liu: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Miaomiao Xie: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Jinying Liu: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Huihui Wang: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
Bin Chen: School of Land Science and Technology, China University of Geosciences, Beijing 100083, China

Land, 2022, vol. 11, issue 6, 1-20

Abstract: Mining sites are areas where mining and restoration coexist and are constantly changing. The vegetation condition can reflect the process of surface mining and restoration, while quantifying the impacts of different mining patterns and surrounding environments on vegetation is the key to balancing mining activities and ecological restoration. In this study, long-term monitoring from 1986 to 2020 was implemented by the LandTrendr algorithm to reveal the ecological impacts of two concentrated and contiguous surface mining sites with different mining patterns (scattered and aggregated mining) and surrounding environments in Inner Mongolia, China. The results show that it is reasonable to use the LandTrendr algorithm for long-term monitoring of surface mining sites, and that the ecological impacts of different surface mining sites in ecologically fragile areas have the same regularity. As the duration increases, the magnitude of disturbance decreases, and the magnitude of recovery first decreases and then reaches a natural fluctuation state after 20 years of recovery. Different mining patterns and surrounding environments bring different ecological impacts. Scattered mining areas are more likely to produce natural recovery while the restored ecosystem is more stable. The performance of mining development disturbance is more obvious in places with better ecological environment, while the effect of ecological restoration is also more significant. This study can provide guidance for the rational planning of mining and restoration activities in ecologically fragile areas.

Keywords: LandTrendr algorithm; surface mining site; disturbance; recovery; time-series; Google Earth Engine (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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