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Multi-source data is used to evaluate the eco-environmental quality in the coal-mining regions.

Multi-source data is used to evaluate the eco-environmental quality in the coal-mining regions.

  • Gao, L., Ma, C., Wang, Q. & Zhou, A. Sustainable use of land resources in the Pearl River Delta Economic Zone, China. Sci. Rep. 9, 114. https://doi.org/10.1038/s41598-019-52355-7 (2019).

    CAS
    Article

    Google Scholar

  • Wang, Q. & Song, X. Why do China, India and the rest of the world consume 60% of the coal? A global decomposition analysis. Energy 227, 120389. https://doi.org/10.1016/j.energy.2021.120389 (2021).

    Article

    Google Scholar

  • Fan, G. Zhang S., Chen M., Zhang D., Ren S. The impact of underground coal mining upon the roots of xeromorphic plants. A case study. Environ. Eng. Sci. 38, 500512. https://doi.org/10.1089/ees.2020.0260 (2021).

    CAS
    Article

    Google Scholar

  • Jiang, S. Fan, G. Li, Q. Zhang, S. & Chen L. Effect of mining parameters upon surface deformation and coal column stability under customized shortwall mining deep extra-thick coal seams. Energy Rep. 7, 21382154. https://doi.org/10.1016/j.egyr.2021.04.008 (2021).

    Article

    Google Scholar

  • Zhu D. Chen T. Zhen N. and Niu R. Monitoring open-pit mining’s effects on the eco-environment with a moving-window-based remote sensing ecological indicator. Environ. Sci. Pollut. Res. Int. 27, 1571615728. https://doi.org/10.1007/s11356-020-08054-2 (2020).

    Article
    PubMed

    Google Scholar

  • Wang, Y. Peng, B. Peng, Wei and Elahi, E. A comprehensive evaluation and spatial difference analysis regional ecological carrying capacities: A case study of the Yangtze River Urban Agglomeration. Int. J. Environ. Res. Public Health 16, 3499. https://doi.org/10.3390/ijerph16183499 (2019).

    Article
    PubMed Central

    Google Scholar

  • Zhang, L. et al.Prioritizing rehabilitation of abandoned mine lands: Combining landscape connectivity with pattern indices with scenario analysis through land-use modeling. Isprs Int. J. Geo-Inf. 7, 305. https://doi.org/10.3390/ijgi7080305 (2018).

    Article

    Google Scholar

  • Boori, M. S., Choudhary, K., Paringer, R. & Kupriyanov, A. Eco-environmental quality assessment based on pressure-state-response framework by remote sensing and GIS. Remote Sens. Appl. Soc. Environ. 23, 100530. https://doi.org/10.1016/j.rsase.2021.100530 (2021).

    Article

    Google Scholar

  • Zhang, J. In 9th International Conference on Future Environment and Energy (2019).

  • Gao, S. et al.Dynamic state of ecosystem carrying ability under island urbanization: Case study of Pingtan Island, southeastern China. J. Environ. Eng. Landsc. Manag. 28, 18. https://doi.org/10.3846/jeelm.2020.9798 (2020).

    Article

    Google Scholar

  • Loiseau E., Junqua G., Roux P. & Bellon Mauel V. Environmental assessment of territory: An overview and use of tools and methods. J. Environ. Manag. 112, 213225. https://doi.org/10.1016/j.jenvman.2012.07.024 (2012).

    Article

    Google Scholar

  • Sun, X. et al.A combination method for eco-geological safety assessment in mining areas using PCA/catastrophe theory. Nat. Resour. Res. 29, 41334148. https://doi.org/10.1007/s11053-020-09682-8 (2020).

    Article

    Google Scholar

  • Xu H., Wang Y. Guan H. Shi T. & Hu X. Detecting ecological change with a remote sensing based eco index (RSEI). Time series and change vector analysis. Remote Sensing 11, 2345. https://doi.org/10.3390/rs11202345 (2019).

    ADS
    Article

    Google Scholar

  • Chen, Y. H., Li, X. B. Shi, P.J., Dou, W. & Li. X. Intraannual vegetation change characteristics within the NDVI–Ts space: Application for farming-pastoral zones in North China Acta Botanica Sinica 45, 11391145 (2003).


    Google Scholar

  • Yao, J., Li, X. & Zhang, J. Information entropy is used to evaluate and analyze the ecological environment quality. Fresenius Environ. Bull. 30, 91289134 (2021).

    CAS

    Google Scholar

  • Lu, L., Kuenzer, C., Wang, C., Guo, H. & Li, Q. Evaluation of three MODIS-derived vegetative index time series for dryland dynamics monitoring. Remote Sensing 7, 75977614. https://doi.org/10.3390/rs70607597 (2015).

    ADS
    Article

    Google Scholar

  • Wang, Y. Wu. X., He. S. & Niu. R. Ecoenvironmental assessment model for the mining area of Gongyi in China. Sci. Rep. 11, 17549. https://doi.org/10.1038/s41598-021-96625-9 (2021).

    ADS
    CAS
    Article
    PubMed
    PubMed Central

    Google Scholar

  • Saedpanah, S. & Amanollahi, J. Environmental pollution and geo-ecological risks assessment of Qhorveh’s mining area in western Iran. Environ. Pollut. 253, 811820. https://doi.org/10.1016/j.envpol.2019.07.049 (2019).

    CAS
    Article
    PubMed

    Google Scholar

  • Xiong, Y. et al.Combining AHP and GIS for synthetic evaluation of ecoenvironment qualityA case Study of Hunan Province in China. Ecol. Model. 209, 97109. https://doi.org/10.1016/j.ecolmodel.2007.06.007 (2007).

    Article

    Google Scholar

  • Zhang, J., Gong, X. Guo, L. International Conference on Materials Science and Information Technology 2011 14281432 (2012).

  • Zhai, H., Xie, W., Li, S. & Zhang, Q. Remote sensing-based ecological index model is used to evaluate urban ecological environment. Fresenius Environ. Bull. 30, 25272535 (2021).

    CAS

    Google Scholar

  • Wang, X. et al.In Fourth International Conference on Energy and Environmental Protection. 29602966 (2015).

  • Xu, X., Chen, W. & IEEE. In International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, (CyberC). 283289 (2017).

  • Shao, H. et al.A method for spatiotemporal process assessment of ecogeological environmental security within mining areas using catastrophe theory, and projection pursuit model. Prog. Phys. Geogr. Earth Environ. 45, 647668. https://doi.org/10.1177/0309133320982542 (2021).

    Article

    Google Scholar

  • Yu, X., Xie, J., Jiang, R., Zuo, G. & Liang, J. Assessment of water resource carrying capacity based on the chicken swarm optimization-projection pursuit model. Arab. J. Geosci. 13, 114. https://doi.org/10.1007/s12517-019-5010-z (2020).

    Article

    Google Scholar

  • Lu, X. Zhang Y., Lin C. & Wu F. Analysis of sustainable land usage in China: Based on the sustainable development goals framework. J. Clean. Prod. 310, 127205. https://doi.org/10.1016/j.jclepro.2021.127205 (2021).

    Article

    Google Scholar

  • Zhu, Z., Yang, Y., Cai, Y. & Yang, Z. Urban flood analysis in an ungauged drainage basin using remotely sensed rainfall records, both short-term and high resolution. Remote Sensing 13, 2204. https://doi.org/10.3390/rs13112204 (2021).

    ADS
    Article

    Google Scholar

  • Carlos Perez-Giron (J.), Alvarez Alvarez, P. Rafael Diaz-Varela E. & Mendes Lopes D. M. The influence of climate variations on primary productivity indicators and the resilience of forest ecosystems under a future scenario for climate change: Application of sweet chestnut agroforestry system in the Iberian Peninsula. Ecol. Indic. 113, 106199. https://doi.org/10.1016/j.ecolind.2020.106199 (2020).

    Article

    Google Scholar

  • Yan, Q. L., Zhu, J. J., Hu, Z. B. & Sun, O. J. The environmental impacts of the shelter forests in Horqin Sandy Land (Northern China) J. Environ. Qual. 40, 815824. https://doi.org/10.2134/jeq2010.0137 (2011).

    CAS
    Article
    PubMed

    Google Scholar

  • Lopez, N. Spizzico (V.) & Parise (M.) Geomorphological, hydrological, and pedological characteristics karst lake Conversano (Apulia. southern Italy). As a basis for environmental conservation. Environ. Geol. 58, 327337. https://doi.org/10.1007/s00254-008-1601-9 (2009).

    ADS
    CAS
    Article

    Google Scholar

  • Pan, F. Tian C., Shao F., Zhou W. & Chen F. Evaluations of ecological sensitivity in Karamay and Xinjiang, China. J. Geogr. Sci. 22, 329345. https://doi.org/10.1007/s11442-012-0930-5 (2012).

    Article

    Google Scholar

  • Lv. M. Xu. Z. Yang. Z. L. Lu. H. & Lv. M. Comprehensive review of specific yields in land surface and groundwater research. J. Adv. Model. Earth Syst. 13, e2020MS002270. https://doi.org/10.1029/2020ms002270 (2021).

    ADS
    Article

    Google Scholar

  • Lin, J., Chen, W., Qi, X. & Hou. Risk assessment of typical mountain environments and its influencing factors. J. Clean. Prod. 309, 127077. https://doi.org/10.1016/j.jclepro.2021.127077 (2021).

    Article

    Google Scholar

  • Ward, J. V., Tockner, K., Arscott, D. B. & Claret, C. Riverine landscape diversity. Freshw. Biol. 47, 517539. https://doi.org/10.1046/j.1365-2427.2002.00893.x (2002).

    Article

    Google Scholar

  • Mindje, R. et al.Flood susceptibility modeling in Rwanda and hazard perception Int. J. Disaster Risk Reduct. 38, 101211. https://doi.org/10.1016/j.ijdrr.2019.101211 (2019).

    Article

    Google Scholar

  • Jia, X. L., Wang, D., Liu, F. B. Q. M. Evaluation of highway construction’s impact on the ecological environment of Qinghai Tibet plateau. Environ. Eng. Manag. J. 19, 11571166 (2020).

    Article

    Google Scholar

  • Xiao, W., Zhang, W., Ye, Y., Lv, X. Yang, W. Underground coal mining is causing land degradation and significant damage to ecosystems in semi-arid regions. An Ecological Capital perspective is used to analyze the study. Degradation of Land Dev. 31, 19691989. https://doi.org/10.1002/ldr.3570 (2020).

    Article

    Google Scholar

  • Guo W., Zhao G., Lou G. & Wang S. A new method for predicting the heights of the fractured waters-conducting zones due to high-intensity coal mining in China. Rock Mech. Rock Eng. 52, 27892802. https://doi.org/10.1007/s00603-018-1567-1 (2018).

    ADS
    Article

    Google Scholar

  • da Silva, N., Cook D., & Lee E. K. A projection chase forest algorithm for supervised classification. J. Comput. Graph. Stat. https://doi.org/10.1080/10618600.2020.1870480 (2021).

    MathSciNet
    Article
    MATH

    Google Scholar

  • Espezua S. Villanueva E. and Maciel C. D. A genetic algorithm optimizer for sequential project pursuit. Neurocomputing 123, 4048. https://doi.org/10.1016/j.neucom.2012.09.045 (2014).

    Article

    Google Scholar

  • Hu, X. Ma, C. Huang, P. & Guo X. Ecological vulnerability assessment based upon AHP-PSR method and analysis its single parameter sensitivity & spatial autocorrelation for eco protectionA case from Weifang City in China. Ecol. Indic. 125, 107464. https://doi.org/10.1016/j.ecolind.2021.107464 (2021).

    Article

    Google Scholar

  • Xu, Q.-Y., Huang, M., Liu, H.-S. & Yan, H.-M. Based on RS/GIS, integrated assessment of ecoenvironmental vulnerability in Pearl River Delta Ying sheng taixue bao J. Appl. Ecol. 22, 29872995 (2011).

    CAS

    Google Scholar

  • Zhang, D., Yang, S., Wang, Z., Yang, C. & Chen, Y. Assessment of the ecological environment impact on highway construction activities using improved group AHP/FCE approach in China. Environ. Monit. Assess. 192, 451. https://doi.org/10.1007/s10661-020-08400-4 (2020).

    Article
    PubMed

    Google Scholar

  • Cao, W. Study on the Economic Impact of Flood Disasters. (Hunan University, 2013) (Chinese).


    Google Scholar

  • Dormann, C. F. et al.A review of methods to account for spatial correlation in the analysis species distributional data: Ecography 30, 609628. https://doi.org/10.1111/j.2007.0906-7590.05171.x (2007).

    Article

    Google Scholar

  • Shaikh, S. F. E. A. et al.To avoid misidentification of trade-offs or bundles among ecosystem services, it is important to account for spatial autocorrelation. Ecol. Indic. 129, 107992. https://doi.org/10.1016/j.ecolind.2021.107992 (2021).

    Article

    Google Scholar

  • Xiong, Y. et al.Assessment of spatialtemporal changes in ecological environment quality using RSEI/GEE: A case study from Erhai Lake Basin Yunnan province China. Ecol. Indic. 125, 107518. https://doi.org/10.1016/j.ecolind.2021.107518 (2021).

    Article

    Google Scholar

  • Chen, T. Feng, Z. Zhao, H. & Wu K. Identification of ecosystem services bundles and driving factors for Beijing and the surrounding areas. Sci. Total Environ. 711, 134687. https://doi.org/10.1016/j.scitotenv.2019.134687 (2020).

    ADS
    CAS
    Article
    PubMed

    Google Scholar

  • Zhu, L., Meng, J. Zhu, L., Meng J. Ecol. Indic. 117, 106545. https://doi.org/10.1016/j.ecolind.2020.106545 (2020).

    Article

    Google Scholar

  • Wu, X. Zhang, H. Evaluations of ecological environmental quality in western Chongqing, China. Ecol. Indic. 132, 108311. https://doi.org/10.1016/j.ecolind.2021.108311 (2021).

    Article

    Google Scholar

  • Chang, Y., Hou, K., Wu, Y., Li, X. & Zhang, J. A conceptual framework to establish the index system of ecological environmental evaluationA case study of China’s upper Hanjiang River. Ecol. Indic. 107, 105568. https://doi.org/10.1016/j.ecolind.2019.105568 (2019).

    Article

    Google Scholar

  • He, F., Gu, L., Wang, T. & Zhang, Z. A case study in Longkou (China): The synthetic geo-ecological assessment of a coastal city that is coal-mining using spatiotemporal large data J. Clean. Prod. 142, 854866. https://doi.org/10.1016/j.jclepro.2016.07.011 (2017).

    Article

    Google Scholar

  • Yang, Z., Li, W., Li, X., Wang, Q. & He, J. Assessment of ecogeo-environment quality with multivariate data: A case in Western China’s coal mining region. Ecol. Indic. 107, 105651. https://doi.org/10.1016/j.ecolind.2019.105651 (2019).

    Article

    Google Scholar

  • Cui E., Ren L. & Sun H. Evaluation of variations in eco-environmental quality and affecting factors during urbanization. Environ. Sci. Pollut. Res. Int. 22, 39583968. https://doi.org/10.1007/s11356-014-3779-6 (2015).

    Article
    PubMed

    Google Scholar

  • Xi, B. & Jing H. Research on horizontal compensation for ecological economic benefits under differential accountability. Environ. Sci. Pollut. Res. 28, 2987529889. https://doi.org/10.1007/s11356-021-12835-8 (2021).

    Article

    Google Scholar

  • Forkel, C., Hassel. S., Rinaldi. P. & Mueller. Future groundwater increase in the Rhenish lignite region and related tasks. Wasserwirtschaft 107, 1219. https://doi.org/10.1007/s35147-017-0027-2 (2017).

    Article

    Google Scholar

  • Ma, D. et al.Reutilization in underground backfilling mining of gangue wastes: Overburden Aquifer Protection Chemosphere 264, 128400. https://doi.org/10.1016/j.chemosphere.2020.128400 (2021).

    ADS
    CAS
    Article
    PubMed

    Google Scholar

  • Wang, X. F., Zhang, D. S., Sun, C. D. & Wang, Y. Surface subsidence control in bag filling mining super high-water content material within the Handan mining region. Int. J. Oil Gas Coal Technol. 13, 87102. https://doi.org/10.1504/ijogct.2016.078049 (2016).

    Article

    Google Scholar

  • Guo, W., Tan, Y. Bai, E. Top coal mining technique using the underground dam to extract thick coal seams. Int. J. Min. Sci. Technol. 27, 165170. https://doi.org/10.1016/j.ijmst.2016.11.005 (2017).

    CAS
    Article

    Google Scholar

  • Shao, H. et al.Methodology for assessing the impact of the project to return grazing land to pastureland on regional eco-environmental vulnerability. Environ. Assess. Rev. 56, 155167. https://doi.org/10.1016/j.eiar.2015.10.006 (2016).

    Article

    Google Scholar

  • Jones, M. M. et al.Explaining variation in the composition of tropical plant communities: Influence on environmental and spatial quality. Oecologia 155, 593604. https://doi.org/10.1007/s00442-007-0923-8 (2008).

    ADS
    Article
    PubMed

    Google Scholar

  • Wang, Y., Shao, M. A. & Liu, Z. Large-scale spatial variability in dried soil layers and related factors across China’s entire Loess Plateau. Geoderma 159, 99108. https://doi.org/10.1016/j.geoderma.2010.07.001 (2010).

    ADS
    Article

    Google Scholar

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