Remote sensing diagnosis of ecological health in typical coal mining areas

doi:10.13474/j.cnki.11-2246.2023.0012

Abstract

Abstract: Coal resource is an important disposable energy source in China, and it is crucial to scientifically monitor and assess the ecological health of mining areas so as to maintain a balanced relationship between economic development and ecological health. In this study, three typical coal mining areas located in Shanxi province are selected. Landsat remote sensing data from 2001—2021 are used to analyze the multi-year land type evolution pattern of the open pit mining areas by visual interpretation. The greenness, humidity, dryness, and heat of the three typical mining areas are calculated, respectively. Then we construct a remote sensing ecological index(RSEI) of the mining areas based on the weight calculation method of knowledge granularity entropy, so as to complete the multi-year remote sensing diagnosis of mine ecological health. The results show that the two open-pit mines maintain reclamation while mining, and during half of the studying years their mean RSEI reach to 0.5. The underground mine area basically does not cause above-ground disturbance, and its ecological health is stable for many years. Its mean RSEI is around 0.7. In 2021, the mean RSEI values of the three mining areas are 0.53,0.48 and 0.70, respectively. This study improves the traditional remote sensing ecological index construction method, and provides scientific guidance for long time series ecological monitoring and remote sensing diagnosis of coal mining areas.

Key words: typical coal mining area, ecology, index, knowledge granularity entropy, remote sensing diagnosis

CLC Number:

P237

CHEN Yiyu, CAO Chunxiang, XU Min, XIE Bo, ZHANG Jiutang. Remote sensing diagnosis of ecological health in typical coal mining areas[J]. Bulletin of Surveying and Mapping, 2023, 0(1): 71-76,94.

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