Home Unmanned aerial vehicle-based aerial survey of mines in Shanxi Province based on image data
Article
Licensed
Unlicensed Requires Authentication

Unmanned aerial vehicle-based aerial survey of mines in Shanxi Province based on image data

  • Xuanjie Wang ORCID logo EMAIL logo
Published/Copyright: October 2, 2024
Become an author with De Gruyter Brill

Abstract

Accurately monitoring the change of mine area in the mining process is beneficial to mine safety management. This paper briefly introduces the collection of remote sensing images by unmanned aerial vehicles (UAVs) and its application in measuring surface mining subsidence and surrounding vegetation in the mining area. A case study was carried out in some mining areas of Nanshan Mountain, Yuncheng City, Shanxi Province. The surface mining subsidence value and vegetation-related parameters were measured by comparing the digital elevation model and multi-spectral images collected on May 12 and June 12, 2023. The validity experiment verified that the UAV image data could be used to measure the mining subsidence and vegetation parameters. Moreover, it was found that mining underground coal could lead to significant ground subsidence and pollute the surrounding environment, reducing vegetation. The innovation of this article lies in using UAV-collected remote sensing images instead of manually collecting ground elevation data and vegetation distribution data, providing effective references for safe mining in mining areas.


Corresponding author: Xuanjie Wang, Shanxi Conservancy Technical Institute, No. 34, Miaofeng West Road, Anyi Town, Yanhu District, Yuncheng, Shanxi 044000, China, E-mail: 

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  4. Competing interests: The author declares no conflict of interests.

  5. Research funding: None declared.

  6. Data availability: The raw data can be obtained on request from the corresponding author.

References

1. Gao, G, Hou, E, Xie, X, Xu, Y, Wei, Q, Liu, J. The monitoring of ground surface subsidence related to coal seams mining in Yangchangwan coal mine by means of unmanned aerial vehicle with quad-rotors. Geol Bull China 2018;37:2264–9.Search in Google Scholar

2. Zhang, C, Li, T, Han, X. Slope failure mechanism affected by mining subsidence: a case study of highway slopes in Yangquan, Shanxi Province, China. IOP Conf Ser Earth Environ Sci 2020;570:1–12.10.1088/1755-1315/570/2/022067Search in Google Scholar

3. Yang, D, Qiu, H, Ma, S, Liu, Z, Du, C, Zhu, Y, et al.. Slow surface subsidence and its impact on shallow loess landslides in a coal mining area. Catena Interdiscipl J Soil Sci Hydrol Geomorphol Focus Geoecol Landsc Evol 2022;209:1–15. https://doi.org/10.1016/j.catena.2021.105830.Search in Google Scholar

4. Zheng, L, Liu, X, Tang, Q, Ou, J. Lead pollution and isotope tracing of surface sediments in the huainan panji coal mining subsidence area, Anhui, China. Bull Environ Contam Toxicol 2019;103:10–15. https://doi.org/10.1007/s00128-019-02558-5.Search in Google Scholar PubMed

5. Suh, J, Choi, Y. Mapping hazardous mining-induced sinkhole subsidence using unmanned aerial vehicle (drone) photogrammetry. Environ Earth Sci 2017;76:1–12. https://doi.org/10.1007/s12665-017-6458-3.Search in Google Scholar

6. Zhou, D, Qi, L, Zhang, D, Zhou, B, Guo, L. Unmanned aerial vehicle (UAV) photogrammetry technology for dynamic mining subsidence monitoring and parameter inversion: a case study in China. IEEE Access 2020;8:16372–86. https://doi.org/10.1109/access.2020.2967410.Search in Google Scholar

7. Lian, X, Liu, X, Ge, L, Hu, H, Du, Z, Wu, Y. Time-series unmanned aerial vehicle photogrammetry monitoring method without ground control points to measure mining subsidence. J Appl Remote Sens 2021;15:1–15. https://doi.org/10.1117/1.jrs.15.024505.Search in Google Scholar

8. Li, L, Wu, Z, Wan, M, Zhang, Z, Li, J, Jin, Y. Accurate identification and continuous extraction of fissures in loess areas based on unmanned aerial vehicle visible light images. Environ Earth Sci 2023;82:205.1–9. https://doi.org/10.1007/s12665-023-10888-1.Search in Google Scholar

9. Chen, Z, Hou, H, Zhang, S, Campbell, T, Yang, Y, Tu, M, et al.. Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas. Land Degrad Dev 2024;35:1599–613. https://doi.org/10.1002/ldr.5010.Search in Google Scholar

10. He, YR, Lu, H, Xu, JD, Song, YF, Chen, CC, Wang, ZY, et al.. Application of unmanned aerial vehicle 3D model to comprehensive supervision of mining and virtual simulation training and teaching. Sensor Mater Int J Sens Technol 2023;35:745–61. https://doi.org/10.18494/sam4112.Search in Google Scholar

11. Zhao, Y, Zhao, X, Dai, J, Yu, W. Analysis of the surface subsidence induced by mining near-surface thick lead-zinc deposit based on numerical simulation. Processes 2021;9:1–22. https://doi.org/10.3390/pr9040717.Search in Google Scholar

12. Zhong, S, Wei, C, Liu, B, Zhang, W, Du, J, Zhang, S. Restoration technologies of damaged paddy in hilly post-mining and subsidence-stable area of Southwest China. Int J Agric Bioeng 2015;8:46–57.Search in Google Scholar

13. Zhou, D, Wang, L, An, S, Wang, X, An, Y. Integration of unmanned aerial vehicle (UAV)-based photogrammetry and InSAR for mining subsidence and parameters inversion: a case study of the Wangjiata Mine, China. Bull Eng Geol Environ 2022;81:1–23. https://doi.org/10.1007/s10064-022-02845-2.Search in Google Scholar

14. Hu, X, Niu, B, Li, X, Min, X. Unmanned aerial vehicle (UAV) remote sensing estimation of wheat chlorophyll in subsidence area of coal mine with high phreatic level. Earth Sci Inform 2021;14:2171–81. https://doi.org/10.1007/s12145-021-00676-5.Search in Google Scholar

15. Xiao, W, Chen, J, Zhao, Y, Hu, Z, Lü, X, Zhang, S. Identify maize chlorophyll impacted by coal mining subsidence in high groundwater table area based on UAV remote sensing. Meitan Xuebao/J China Coal Soc 2019;44:295–306.Search in Google Scholar

16. Stupar, DI, Roer, J, Vuli, M. Investigation of unmanned aerial vehicles-based photogrammetry for large mine subsidence monitoring. Minerals 2020;10:1–14.10.3390/min10020196Search in Google Scholar

17. Liu, Z, Mei, G, Sun, Y. Investigating deformation patterns of a mining-induced landslide using multisource remote sensing: the songmugou landslide in Shanxi Province, China. Bull Eng Geol Environ 2022;81:1–16. https://doi.org/10.1007/s10064-022-02699-8.Search in Google Scholar

18. Motyka, Z, Jelle, BP. System model for spatial mapping of anthropogenic sinkholes and subsidence basins in mining areas applying 2D laser scanner technique. E3S Web Conf 2019;106:1–7. https://doi.org/10.1051/e3sconf/201910601007.Search in Google Scholar

Received: 2024-08-20
Accepted: 2024-09-14
Published Online: 2024-10-02
Published in Print: 2025-04-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Original Research Articles
  3. Locally robust Msplit estimation
  4. Extending geodetic networks for geo-monitoring by supervised point cloud matching
  5. Evaluation and homogenization of a marine gravity database from shipborne and satellite altimetry-derived gravity data over the coastal region of Nigeria
  6. Modelling geoid height errors for local areas based on data of global models
  7. Unmanned aerial vehicle-based aerial survey of mines in Shanxi Province based on image data
  8. Ionospheric TEC and its irregularities over Egypt: a comprehensive study of spatial and temporal variations using GOCE satellite data
  9. Monitoring of volcanic precursors using satellite data: the case of Taftan volcano in Iran
  10. Modeling of temperature deformations on the Dnister HPP dam (Ukraine)
  11. Impact of temporal resolution in global ionospheric models on satellite positioning during low and high solar activity years of solar cycle 24
  12. Comparative performance of PPP software packages in atmospheric delay estimation using GNSS data
  13. Assessment and fitting of high/ultra resolution global geopotential models using GNSS/levelling over Egypt
  14. An efficient ‘P1’ algorithm for dual mixed-integer least-squares problems with scalar real-valued parameters
  15. Spatio-temporal trajectory alignment for trajectory evaluation
  16. Monitoring of networked RTK reference stations for coordinate reference system realization and maintenance – case study of the Czech Republic
  17. Crustal deformation in East of Cairo, Egypt, induced by rapid urbanization, as seen from remote sensing and GNSS data
Downloaded on 18.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/jag-2024-0072/html
Scroll to top button