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Article

The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing

by
Quansheng Li
1,2,
Feiyue Li
3,*,
Junting Guo
1,2,
Li Guo
3,
Shanshan Wang
4,
Yaping Zhang
3,
Mengyuan Li
3 and
Chengye Zhang
3
1
State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, CHN Energy Shendong Coal Group Co., Ltd., Ordos 017209, China
2
Department of Ecological Restoration, National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China
3
College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
4
Geological Hazard Investigation and Monitoring Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(4), 3759; https://doi.org/10.3390/ijerph20043759
Submission received: 24 December 2022 / Revised: 15 February 2023 / Accepted: 16 February 2023 / Published: 20 February 2023
(This article belongs to the Special Issue Remote Sensing Application in Environmental Monitoring)
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Abstract

Understanding the synergistic effect between topography and vegetation in the underground coal mine is of great significance for the ecological restoration and sustainable development of mining areas. This paper took advantage of unmanned aerial vehicle (UAV) remote sensing to obtain high-precision topographic factors (i.e., digital elevation model (DEM), slope, and aspect) in the Shangwan Coal Mine. Then, a normalized difference vegetation index (NDVI) was calculated utilizing Landsat images from 2017 to 2021, and the NDVI with the same spatial resolution as the slope and aspect was acquired by down-sampling. Finally, the synergistic effect of topography and vegetation in the underground mining area was revealed by dividing the topography obtained using high-precision data into 21 types. The results show that: (1) the vegetation cover was dominated by “slightly low-VC”, “medium-VC”, and “slightly high-VC” in the study area, and there was a positive correlation between the slope and NDVI when the slope was more than 5°. (2) When the slope was slight, the aspect had less influence on the vegetation growth. When the slope was larger, the influence of the aspect increased in the study area. (3) “Rapidly steep–semi-sunny slope” was the most suitable combination for the vegetation growth in the study area. This paper revealed the relationship between the topography and vegetation. In addition, it provided a scientific and effective foundation for decision-making of ecological restoration in the underground coal mine.
Keywords: UAV remote sensing; underground coal mine; slope; aspect; normalized difference vegetation index UAV remote sensing; underground coal mine; slope; aspect; normalized difference vegetation index

Share and Cite

MDPI and ACS Style

Li, Q.; Li, F.; Guo, J.; Guo, L.; Wang, S.; Zhang, Y.; Li, M.; Zhang, C. The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing. Int. J. Environ. Res. Public Health 2023, 20, 3759. https://doi.org/10.3390/ijerph20043759

AMA Style

Li Q, Li F, Guo J, Guo L, Wang S, Zhang Y, Li M, Zhang C. The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing. International Journal of Environmental Research and Public Health. 2023; 20(4):3759. https://doi.org/10.3390/ijerph20043759

Chicago/Turabian Style

Li, Quansheng, Feiyue Li, Junting Guo, Li Guo, Shanshan Wang, Yaping Zhang, Mengyuan Li, and Chengye Zhang. 2023. "The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing" International Journal of Environmental Research and Public Health 20, no. 4: 3759. https://doi.org/10.3390/ijerph20043759

APA Style

Li, Q., Li, F., Guo, J., Guo, L., Wang, S., Zhang, Y., Li, M., & Zhang, C. (2023). The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing. International Journal of Environmental Research and Public Health, 20(4), 3759. https://doi.org/10.3390/ijerph20043759

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