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

Quansheng Li, Feiyue Li, Junting Guo, Li Guo, Shanshan Wang, Yaping Zhang, Mengyuan Li, Chengye Zhang

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Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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8 authors.

Quansheng LiState Key Laboratory of Water Resource Protection and Utilization in Coal Mining, CHN Energy Shendong Coal Group Co., Ltd., Ordos 017209, China.

Feiyue LiCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.

Junting GuoState Key Laboratory of Water Resource Protection and Utilization in Coal Mining, CHN Energy Shendong Coal Group Co., Ltd., Ordos 017209, China.

Li GuoCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.

Shanshan WangGeological Hazard Investigation and Monitoring Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China.

Yaping ZhangCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.

Mengyuan LiCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.

Chengye ZhangCollege of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.ORCID 0000-0002-4902-8704

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Remote Sensing TechnologyUnmanned Aerial DevicesCoalMiningCoalaspectnormalized difference vegetation indexslopeUAV remote sensingunderground coal mine

Identifiers

PMID36834465

DOI10.3390/ijerph20043759

PMCPMC9964143

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.