Disturbance Study and Analysis of Vegetation Cover Dynamics in Jiaozuo Mining Area Based on Remote Sensing Monitoring
Article Sidebar
Main Article Content
Abstract
Vegetation disturbance induced by coal resource development represents a critical issue in ecological management and restoration in mining areas. This study has chosen the buffer zone surrounding the Jiaozuo mining area as the research region. Based on Landsat imagery, an improved modified three-band gradient difference model was employed to extract fractional vegetation cover (FVC). Spatiotemporal vegetation trends were revealed using Mann-Kendall and Theil-Sen slope analyses, while disturbance extent was identified by integrating the correlation coefficient method with the t-tests. Disturbance intensity was assessed using the interannual standard deviation of FVC. The results showed that: (1) During the study period, FVC exhibited significant fluctuations, with annual mean values ranging between 46.36% and 73.03%, displaying a slight overall degradation trend. A notable decline occurred between 2009 and 2010. (2) The identified high-intensity disturbance areas showed strong spatial correspondence with active mining sites, exhibiting outward expansion correlating with mining intensity. (3) Regarding the disturbance intensity, high and extremely high disturbance levels covered 35.82% of the mining area, significantly exceeding 19.90% observed in the buffer zone. Spatially, the disturbance displayed a "strong-weak" gradient distribution radiating southeastward from the mining cores. This study developed an integrated remote sensing framework for FVC extraction and disturbance assessment in mining areas, enhancing the precision and spatial explicitness of ecological monitoring. The findings provided a quantitative basis for zonal ecological restoration management in mining areas, carrying significant theoretical and practical implications for promoting sustainable development in resource-based regions.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
By submitting a manuscript to Evidence in Earth Science, the author(s) agree to the following terms:
-
Copyright Ownership:
Authors retain copyright to their submitted work. Upon acceptance for publication, the authors grant Evidence in Earth Science a license to publish the work under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). -
Open Access License (CC-BY 4.0):
The articles published in Evidence in Earth Science are open access and distributed under the Creative Commons Attribution 4.0 International License (CC-BY 4.0). This means that:- Users are free to share, copy, modify, distribute, and display the work, including for commercial purposes, as long as they provide appropriate attribution to the original author(s).
- The work can be adapted or used in derivative works, but proper credit must be given.
For more detailed information on the licensing terms, please refer to the Creative Commons Attribution 4.0 International License (CC-BY 4.0).
References
Bai, S.Y., Zhu, Q.W., Shen, W.S., et al., 2016. Study on Ecological Degradation in the Bayan Obo Mining Area. Journal of Ecology and Rural Environment, 32(03): 367-373.
Bai, X., 2022. Analysis of Spatiotemporal Changes and Influencing Factors of Vegetation Coverage in Yulin City. Chang'an University, https://doi.org/10.26976/d.cnki.gchau.2022.000450.
Bi, R., Bai Zhongke, 2007. Study on Land Characteristic Information and Classification in Opencast Coal Mine Area Based on Remote Sensing Image. Transactions of the Chinese Society of Agricultural Engineering, (02): 77-82+291.
Bi, Y.L., Liu, T., 2022. Multi-source Data Time Series Analysis of Vegetation Co-evolution in Open-pit Mining Area——Taking the Zhungeer Mining Area as an Example. Coal Science and Technology, 50(01): 293-302.
Chen, J., Gao, Z.H., Wang, S.S., et al., 2020. Review on the Development of Aerial Remote Sensing Geological Survey Technology in the Three Gorges Reservoir Area. Remote Sensing for Land & Resources, 32(02): 1-10.
de Castro, Pena, J.C. et al., 2017. Impacts of mining activities on the potential geographic distribution of eastern Brazil mountaintop endemic species. Perspectives in Ecology and Conservation, 15(3): 172-178, https://doi.org/10.1016/j.pecon.2017.07.005.
Gao, J.X., Zhao, S.H., 2020. Forty Years of Remote Sensing of Ecological Environment in China. Journal of Geo-information Science, 22(04): 705-719.
Guli, J., Yi, Q.X., Yao, F., et al., 2017. Comparison of non-destructive LAI determination methods and optimization of sampling schemes in an open Populus euphratica ecosystem. Urban Forestry & Urban Greening, 26: 114-123, https://doi.org/10.1016/j.ufug.2017.06.010.
Hejmanowski, R., Malinowska, A.A., 2016. Significance of the uncertainty level for the modeling of ground deformation ranges. International Journal of Rock Mechanics and Mining Sciences, 83: 140-148, https://doi.org/10.1016/j.ijrmms.2015.12.019.
Hou, J., Hou, P., Gao, H.F., et al., 2024. Spatiotemporal Changes of Vegetation in Chinese Forest Nature Reserves and Their Response to Climate Change. Chinese Journal of Ecology: 1-9.
Lechner, A.M., Kassulke, O., Unger, C., 2016. Spatial assessment of open cut coal mining progressive rehabilitation to support the monitoring of rehabilitation liabilities. Resources Policy, 50: 234-243, https://doi.org/10.1016/j.resourpol.2016.10.009.
Lei, S.G., 2010. Monitoring of Key Environmental Elements and Study on Mining-induced Influence Patterns in Desert Mining Areas. Journal of China Coal Society, 35(09): 1587-1588, https://doi.org/10.1016/j.resourpol.2016.10.009.
Li, H.C., Liu, L., X, Z.L., et al., 2022. Analysis of Spatiotemporal Changes in Ecological Environment Quality in the Zhongliangshan Mining Area, Chongqing. Environmental Science & Technology, 45(S1): 220-226, https://doi.org/10.19672/j.cnki.10036504.2375.21.338.
Li, J., Han, Y., Yang, Z., Miao, H., Yin, S.Q., 2018. Remote Sensing Extraction of Coal Mining Impact Boundary in Yanzhou Coalfield Based on Temperature Vegetation Dryness Index. Transactions of the Chinese Society of Agricultural Engineering, 34(19): 258-265.
Li, J., Peng, S.P., Zhang, C.Y., et al., 2022. Technical Framework and Application of Quantitative Remote Sensing Monitoring and Evaluation for Ecological Environment in Mining Areas. Journal of Mining Science and Technology, 7(01): 9-25+88, https://doi.org/10.19606/j.cnki.jmst.2022.01.002.
Li, J.W., Li, X.T., Liu, C.Y., et al., 2020. Dynamic changes in surface damage induced by high-intensity mining of shallow, thick coal seams in gully areas. Advances in Civil Engineering, 2020: 1-16, https://doi.org/10.1155/2020/5151246.
Li, J., Zipper, C.E., Donovan, P.F., et al., 2015. Reconstructing disturbance history for an intensively mined region by time-series analysis of Landsat imagery. Environmental monitoring and assessment, 187: 1-17.
Li, Q.S., Xu, Y.L., Li, J., et al., 2022. Extraction of Mining Impacts on Vegetation Change and Quantitative Analysis of Ecological Cumulative Effects. Journal of China Coal Society, 47(06): 2420-2434, https://doi.org/10.13225/j.cnki.jccs.2021.1296.
Li, R.J., Yang, Z.W., Wu, S.W., et al., 2020. Study on the Impact of Coal Mining on Spatiotemporal Variation of Surface Vegetation Coverage Based on Landsat Images. Journal of North China University of Water Resources and Electric Power (Natural Science Edition), 41(04): 52-60, https://doi.org/10.19760/j.ncwu.zk.20200049.
Li, X.J., Zhou, J.J., 2020. Research on Extraction Method of Surface Subsidence Information in Coal Mining Areas with High Groundwater Level. Coal Science and Technology, 48(04): 105-112, https://doi.org/10.13199/j.cnki.cst.2020.04.010.
Liao, C.H., Liu, X.H., 2010. 3S-based Identification of the Influence Scope of Coal Mining on Regional Vegetation in Yangquan. Journal of Natural Resources, 25(02): 185-191.
Liu, Y., Lei, S.G., Chen, X.Y., et al., 2021. Analysis of Temporal Variation of Vegetation Coverage and Driving Factors in Shendong Mining Area and Guided Restoration Strategies. Journal of China Coal Society, 46(10): 3319-3331, https://doi.org/10.13225/j.cnki.jccs.2020.1387.
Liu, Y., Lei, S.G., Gong, C.G., et al., 2019. Response of Chlorophyll Content in Caragana Leaves to Soil Water Content Changes in Coal Mining Subsidence Fissure Areas. Acta Ecologica Sinica, 39(09): 3267-3276.
Lu, F., Wang, X., Zhang, H., et al., 2018. Analysis on landscape pattern evolution and driving forces in Jiaozuo mining area from 1980 to 2015. Research of Soil and Water Conservation, 25(4), 237-243, https://doi.org/10.13869/j.cnki.rswc.2018.04.035.
Lü, G.P., Liao, C.R., Gao, Y.Y., et al., 2017. Application of LiDAR Technology in Mnitoring Mine Ecological Environment. Journal of Ecology and Rural Environment, 33(07): 577-585.
Qiao, G., Xü, Y.N., Chen, H.Q., et al., 2018. Impact of Ground Fissures on Vegetation Ecology in the Ningdong Coal Mining Area. Geological Bulletin of China, 37(12): 2176-2183.
Reinhaeckel, G., Zhukov, B., Oertel, D., et al., 1998. Unmixing of simulated ASTER data with applications for the assessment of mining impacts in central Germany, Imaging Spectrometry IV. SPIE, pp. 345-354, https://doi.org/10.1117/12.328115.
Sahu, S.P., Yadav, M., Pradhan, D.S., et al., 2018. Spatio-temporal variations of respirable particles at residential areas located in the vicinity of opencast coal projects, India: a case study. Arabian Journal of Geosciences, 11: 1-15, https://doi.org/10.1007/s12517-018-3551-1.
Si, J., Wang, S., 2021. Ecological security evaluation and spatiotemporal differentiation of Jiaozuo mining area based on combination weighting method. Research of Soil and Water Conservation, 28(3), 348-354, https://doi.org/10.13869/j.cnki.rswc.2021.03.038.
Sun, Q. Bai, Z.K., Cao, Y.G.,et al., 2015. Ecological Risk Assessment of Land Damage in Extra-large Open-pit Coal Mine. Transactions of the Chinese Society of Agricultural Engineering, 31(17): 278-288.
Wang, R., Ma, S.C., Zhang, H.B., et al., 2016. Effects of Surface Fissures Induced by High-intensity Mining in Arid Areas on Soil Microbiological Properties and Plant Communities. Research of Environmental Sciences, 29(09): 1249-1255, https://doi.org/10.13198/j.issn.1001-6929.2016.09.01.
Wang, Y.J., 2017. Progress and Prospects of Ecological Disturbance Monitoring in Mining Areas. Acta Geodaetica et Cartographica Sinica, 46(10): 1705-1716.
Weir, J.N., Mahoney, S.P., McLaren, B., et al., 2007. Effects of mine development on woodland caribou Rangifer tarandus distribution. Wildlife Biology, 13(1): 66-74.
Wu, G., Wei, D., Zhou, Z.D., et al., 2014. A Review of Ecological Restoration Technologies for Large Coal Base Construction in China. Acta Ecologica Sinica, 34(11): 2812-2820.
Wu, J.S., Zhu, Q.L., Qiao, N., et al., 2021. Ecological risk assessment of coal mine area based on "source-sink" landscape theory – A case study of Pingshuo mining area. Journal of Cleaner Production, 295: 126371, https://doi.org/10.1016/J.JCLEPRO.2021.126371.
Yao, F., Guli, J., Bao, A.M., et al., 2013. Assessment of Vegetation Community Damage in Open-pit Coal Mines in Arid Desert Areas Based on Remote Sensing Technology. China Environmental Science, 33(04): 707-713.
Yuan, L.H., Jiang, W.G., Shen, W.M., et al., 2013. Spatiotemporal Variation of Vegetation Coverage over the Yellow River Basin during 2000–2010. Acta Ecologica Sinica, 33(24): 7798-7806.
Zhang, F., Wang, Q., Li, Y., 2010. A Quantitative Method for Monitoring Spatiotemporal Dynamic Changes of Vegetation Coverage in Hulun Buir Grassland. Journal of Natural Resources, 25(10): 1698-1708.
Zhang, H., Zhang, K., Liu, P., et al., 2020. Extraction of ecological indicators and security evaluation in mining area based on RS and GIS: A case study of Jiaozuo mining area. Coal Science and Technology, 48(4), 80-88, https://doi.org/10.13199/j.cnki.cst.2020.04.007.
Zhang, M., 2022. Study on Spatiotemporal Evolution Characteristics of Land Use and Ecological Impacts in Large Open-pit Coal Mines. PhD Thesis, China University of Geosciences (Beijing), https://doi.org/10.27493/d.cnki.gzdzy.2021.000031.
Zhang, X.H., Zhu, B., Wang, W., et al., 2019. Research and Application of Stepwise Extraction Method for Ground Fissures Based on Objects. Remote Sensing for Land & Resources, 31(01): 87-94.
Zhang, X.W., Wu, B.F., 2015. A Method for Temporal Phase Transformation of Fractional Vegetation Cover Based on Medium and High Resolution Remote Sensing. Acta Ecologica Sinica, 35(04): 1155-1164.