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Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China

Author

Listed:
  • Zhi Yang

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Wenping Li

    (School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Liangning Li

    (School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Shaogang Lei

    (School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

  • Jiawei Tian

    (School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China)

  • Gang Wang

    (Shaanxi 185 Coal Field Geology Co., Ltd., Yulin 719099, China)

  • Xuejia Sang

    (School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China)

Abstract

Based on MODIS EVI data of August collected from 2010 to 2021, and taking the Yingpanhao coal mine as an example, the spatiotemporal variation features of vegetation are analyzed using time series analysis, trend analysis and correlation analysis methods in the eco-geo-environment of the phreatic water desert shallows oasis. A significant increase trend is found for vegetation variation, and its development has improved generally in most areas. There is an obvious positive correlation between precipitation and vegetation growth, and a negative correlation between coal mining intensity and vegetation growth, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity in the eco-geo-environment. The research results effectively reflect that atmospheric precipitation is the primary factor advancing the vegetation growth status in the coal mining regions. Vegetation development response to coal mining would be degraded first, then improved, and finally restored in areas with a deeply buried phreatic water level; that would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants in areas with a shallowly buried phreatic water level. Therefore, it is necessary to carry out reasonable mine field planning according to the phreatic water level and the vegetation type distribution and to adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation to avoid damaging the original hydrogeological conditions as far as possible. This information is helpful for promoting the eco-geo-environmental protection and further establishing the need for the dynamic monitoring of the eco-environment in the coal mining regions in the arid and semi-arid ecologically vulnerable areas of Northern China, which play a significant role in the long-term protection and rehabilitation of the eco-geo-environment and in the promotion of sustainable development.

Suggested Citation

  • Zhi Yang & Wenping Li & Liangning Li & Shaogang Lei & Jiawei Tian & Gang Wang & Xuejia Sang, 2022. "Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China," Sustainability, MDPI, vol. 14(15), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9585-:d:880260
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    References listed on IDEAS

    as
    1. Jiaqi Jin & Chicheng Yan & Yixuan Tang & Yilong Yin & Zhihan Lv, 2021. "Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas," Complexity, Hindawi, vol. 2021, pages 1-10, June.
    2. Mohamed Elhag & Ioannis Gitas & Anas Othman & Jarbou Bahrawi & Aris Psilovikos & Nassir Al-Amri, 2021. "Time series analysis of remotely sensed water quality parameters in arid environments, Saudi Arabia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1392-1410, February.
    3. Xuejia Sang & Xiaopeng Leng & Linfu Xue & Xiangjin Ran, 2022. "Based on the Time-Spatial Power-Based Cryptocurrency Miner Driving Force Model, Establish a Global CO 2 Emission Prediction Framework after China Bans Cryptocurrency," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    4. Li, N. & Yan, C.Z. & Xie, J.L., 2015. "Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China, a case study of Mu Us Sandy Land," Resources, Conservation & Recycling, Elsevier, vol. 94(C), pages 129-135.
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