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A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

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  • Chengye Zhang

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

  • Huiyu Zheng

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

  • Jun Li

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

  • Tingting Qin

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

  • Junting Guo

    (State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102209, China)

  • Menghao Du

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

Abstract

Identifying the spatial range of mining disturbance on vegetation is of significant importance for the plan of environmental rehabilitation in mining areas. This paper proposes a method to identify the spatial range of mining disturbance (SRMD). First, a non-linear and quantitative relationship between driving factors and fractional vegetation cover (FVC) was constructed by geographically weighted artificial neural network (GWANN). The driving factors include precipitation, temperature, topography, urban activities, and mining activities. Second, the contribution of mining activities ( W mine ) to FVC was quantified using the differential method. Third, the virtual contribution of mining activities ( V-W mine ) to FVC during the period without mining activity was calculated, which was taken as the noise in the contribution of mining activities. Finally, the SRMD in 2020 was identified by the significance test based on the W mine and noise. The results show that: (1) the mean RMSE and MRE for the 11 years of the GWANN in the whole study area are 0.0526 and 0.1029, which illustrates the successful construction of the relationship between driving factors and FVC; (2) the noise in the contribution of mining activities obeys normal distribution, and the critical value is 0.085 for the significance test; (3) most of the SRMD are inside the 3 km buffer with an average disturbance distance of 2.25 km for the whole SRMD, and significant directional heterogeneity is possessed by the SRMD. In conclusion, the usability of the proposed method for identifying SRMD has been demonstrated, with the advantages of elimination of coupling impact, spatial continuity, and threshold stability. This study can serve as an early environmental warning by identifying SRMD and also provide scientific data for developing plans of environmental rehabilitation in mining areas.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5176-:d:801215
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    References listed on IDEAS

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    1. Satomi Kimijima & Masayuki Sakakibara & Masahiko Nagai & Nurfitri Abdul Gafur, 2021. "Time-Series Assessment of Camp-Type Artisanal and Small-Scale Gold Mining Sectors with Large Influxes of Miners Using LANDSAT Imagery," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    2. Dawuda Usman Kaku & Yonghong Cao & Yousef Ahmed Al-Masnay & Jean Claude Nizeyimana, 2021. "An Integrated Approach to Assess the Environmental Impacts of Large-Scale Gold Mining: The Nzema-Gold Mines in the Ellembelle District of Ghana as a Case Study," IJERPH, MDPI, vol. 18(13), pages 1-20, July.
    3. Qianhan Wu & Kai Liu & Chunqiao Song & Jida Wang & Linghong Ke & Ronghua Ma & Wensong Zhang & Hang Pan & Xinyuan Deng, 2018. "Remote Sensing Detection of Vegetation and Landform Damages by Coal Mining on the Tibetan Plateau," Sustainability, MDPI, vol. 10(11), pages 1-17, October.
    4. Zhenhua Wu & Shaogang Lei & Bao-Jie He & Zhengfu Bian & Yinghong Wang & Qingqing Lu & Shangui Peng & Linghua Duo, 2019. "Assessment of Landscape Ecological Health: A Case Study of a Mining City in a Semi-Arid Steppe," IJERPH, MDPI, vol. 16(5), pages 1-21, March.
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    Cited by:

    1. Gholami, Alireza & Tokac, Batur & Zhang, Qian, 2024. "Knowledge synthesis on the mine life cycle and the mining value chain to address climate change," Resources Policy, Elsevier, vol. 95(C).
    2. 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.
    3. Junting Guo & Quansheng Li & Huizhen Xie & Jun Li & Linwei Qiao & Chengye Zhang & Guozhu Yang & Fei Wang, 2022. "Monitoring of Vegetation Disturbance and Restoration at the Dumping Sites of the Baorixile Open-Pit Mine Based on the LandTrendr Algorithm," IJERPH, MDPI, vol. 19(15), pages 1-15, July.

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