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

Author

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  • Quansheng Li

    (State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, CHN Energy Shendong Coal Group Co., Ltd., Ordos 017209, China
    Department of Ecological Restoration, National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Feiyue Li

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Junting Guo

    (State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, CHN Energy Shendong Coal Group Co., Ltd., Ordos 017209, China
    Department of Ecological Restoration, National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China)

  • Li Guo

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Shanshan Wang

    (Geological Hazard Investigation and Monitoring Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China)

  • Yaping Zhang

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Mengyuan Li

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Chengye Zhang

    (College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

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.

Suggested Citation

  • Quansheng Li & Feiyue Li & Junting Guo & Li Guo & Shanshan Wang & Yaping Zhang & Mengyuan Li & Chengye Zhang, 2023. "The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3759-:d:1075003
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    References listed on IDEAS

    as
    1. Jia Ke & Dandan Zhou & Chunxing Hai & Yanhua Yu & Hao Jun & Bingzi Li, 2022. "Temporal and Spatial Variation of Vegetation in Net Primary Productivity of the Shendong Coal Mining Area, Inner Mongolia Autonomous Region," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    2. Chenlu Huang & Qinke Yang & Weidong Huang, 2021. "Analysis of the Spatial and Temporal Changes of NDVI and Its Driving Factors in the Wei and Jing River Basins," IJERPH, MDPI, vol. 18(22), pages 1-16, November.
    3. Lubanzi Z. D. Dlamini & Sifiso Xulu, 2019. "Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    4. Hone-Jay Chu & Yi-Chin Chen & Muhammad Zeeshan Ali & Bernhard Höfle, 2019. "Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland," IJERPH, MDPI, vol. 16(7), pages 1-12, March.
    5. Chengye Zhang & Huiyu Zheng & Jun Li & Tingting Qin & Junting Guo & Menghao Du, 2022. "A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
    6. Asep Hidayatulloh & Anis Chaabani & Lifu Zhang & Mohamed Elhag, 2022. "DEM Study on Hydrological Response in Makkah City, Saudi Arabia," Sustainability, MDPI, vol. 14(20), pages 1-26, October.
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