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Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China

Author

Listed:
  • Xue Wang

    (Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China
    K.T. and X.W. contributed equally to this work.)

  • Kun Tan

    (Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China
    Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
    K.T. and X.W. contributed equally to this work.)

  • Kailei Xu

    (Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China
    MEIHANG Remote Sensing Information Co., Ltd, Xi’an 710199, China)

  • Yu Chen

    (Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China)

  • Jianwei Ding

    (The Second Surveying and Mapping Institute of Hebei, Shijiazhuang 050037, China)

Abstract

With the exploitation of coalfields, the eco-environment around the coalfields can become badly damaged. To address this issue, “mine greening” has been proposed by the Ministry of Land and Resources of China. The sustainable development of mine environments has now become one of the most prominent issues in China. In this study, we aimed to make use of Landsat 7 ETM+ and Landsat 8 OLI images obtained between 2005 and 2016 to analyze the eco-environment in a coalfield. Land cover was implemented as the basic evaluation factor to establish the evaluation model for the eco-environment. Analysis and investigation of the eco-environment in the Yuxian coalfield was conducted using a novel evaluation model, based on the biological abundance index, vegetation coverage index, water density index, and natural geographical factors. The weight of each indicator was determined by an analytic hierarchy process. Meanwhile, we also used the classic ecological footprint to calculate the ecological carrying capacity in order to verify the effectiveness of the evaluation model. Results showed that the eco-environment index illustrated a slowly increasing tendency over the study period, and the ecological quality could be considered as “good”. The results of the evaluation model showed a strong correlation with the ecological carrying capacity with a correlation coefficient of 0.9734. In conclusion, the evaluation method is a supplement to the time-series quantitative evaluation of the eco-environment, and also helps us to explore the eco-environment in the mining area.

Suggested Citation

  • Xue Wang & Kun Tan & Kailei Xu & Yu Chen & Jianwei Ding, 2019. "Quantitative Evaluation of the Eco-Environment in a Coalfield Based on Multi-Temporal Remote Sensing Imagery: A Case Study of Yuxian, China," IJERPH, MDPI, vol. 16(3), pages 1-18, February.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:511-:d:205052
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    References listed on IDEAS

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    1. Rajat Agarwal & P. Garg, 2016. "Remote Sensing and GIS Based Groundwater Potential & Recharge Zones Mapping Using Multi-Criteria Decision Making Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 243-260, January.
    2. Ferng, Jiun-Jiun, 2014. "Nested open systems: An important concept for applying ecological footprint analysis to sustainable development assessment," Ecological Economics, Elsevier, vol. 106(C), pages 105-111.
    3. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    4. Ximin Cui & Yongge Gao & Debao Yuan, 2014. "Sudden surface collapse disasters caused by shallow partial mining in Datong coalfield, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 911-929, November.
    5. Rajat Agarwal & P. K. Garg, 2016. "Remote Sensing and GIS Based Groundwater Potential & Recharge Zones Mapping Using Multi-Criteria Decision Making Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 243-260, January.
    6. Chen Zeng & Yaolin Liu & Yanfang Liu & Jiameng Hu & Xiaogang Bai & Xiaoyu Yang, 2011. "An Integrated Approach for Assessing Aquatic Ecological Carrying Capacity: A Case Study of Wujin District in the Tai Lake Basin, China," IJERPH, MDPI, vol. 8(1), pages 1-17, January.
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