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    露天矿开发扰动识别的植被指数效果评价与优选方法

    Evaluation and optimization methods for vegetation index effects in identifying disturbances from open-pit mining development

    • 摘要: 露天矿开发作业是矿区周边植被和生态环境退化受损的直接影响因素之一,露天矿开发过程中对植被的扰动主要包括损毁和修复2个过程,利用植被指数的时序变化情况监测植被变化进而评价矿区生态修复状况是常用的监测方法。研究筛选了应用频次较高的7种植被指数:Normalized Differential Vegetation Index (NDVI)、Enhanced Vegetation Index (EVI)、Green Normalized Difference Vegetation Index (GNDVI)、Ratio Vegetation Index (RVI)、Difference Vegetation Index (DVI)、Modified Soil Adjustment Vegetation Index (MSAVI)、Redness Index (RI),对比分析了各植被指数对神东煤炭基地长时序(1990—2021年)植被扰动情况提取的效果。结果表明:在单个矿区尺度,植被覆盖度与各植被指数识别的准确度之间存在关联,在煤炭基地尺度NDVI、GNDVI、DVI均是适合神东煤炭基地植被扰动提取的指数。在损毁后修复、损毁未修复、未损毁3种扰动类型的识别方面,除RI外,其余指数均能有效提取矿区各扰动的类型,识别的总体精度均能达到80%以上,而在扰动时间的提取方面,NDVI、GNDVI、DVI 3种植被指数的识别效果更高,损毁时间提取的精度NDVI、GNDVI、DVI分别为82%、80%和76%,修复时间提取的精度NDVI、GNDVI、DVI分别为95%、91%、88%。神东煤炭基地大规模开采发生在2005年后,大规模修复发生在2010年以后,开采后各修复措施的加入使得矿区生态环境有所改善。为优选植被指数的研究及应用提供了数据支持和科学依据。合理的选择植被指数可以在总体上把握矿区的生态环境状况,对于监测露天煤矿开发过程中的生态影响、支持恢复措施的制定具有重要意义。

       

      Abstract: Open-pit mining is recognized as one of the direct factors affecting the degradation of vegetation and the ecological environment surrounding mining areas. The disturbance of vegetation during the process of open-pit mining is mainly characterized by two processes: damage and restoration. Monitoring vegetation changes through the temporal variation of vegetation indices is a commonly employed method to evaluate the ecological restoration status of mining areas.Seven frequently used vegetation indices were screened: Normalized Differential Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Modified Soil Adjustment Vegetation Index (MSAVI), Redness Index (RI) and a comparative analysis was then conducted to evaluate their effectiveness in extracting vegetation disturbance in the Shendong Coal Base over a long timespan (1990—2021). The findings reveal a correlation between vegetation coverage and the identification accuracy of various vegetation indices at the scale of individual mining areas. At the coal base scale, NDVI, GNDVI, and DVI are all suitable indices for extracting vegetation disturbances in the Shendong Coal Base. In identifying three types of disturbances—restored after damage, damaged without restoration, and undamaged—except for RI, all other indices effectively extracted the disturbance types within the mining area, achieving an overall identification accuracy exceeding 80%. Furthermore, for the extraction of disturbance timing, NDVI, GNDVI, and DVI demonstrated superior recognition effectiveness, with extraction accuracies for damage timing at 82%, 80%, and 76%, respectively, and for restoration timing at 95%, 91%, and 88%. Large-scale mining in the Shendong Coal Base began after 2005, followed by significant restoration efforts initiated after 2010. The integration of various restoration measures post-extraction has led to improvements in the ecological environment of the mining area. This research provides data support and scientific basis for the optimal selection and application of vegetation indices. A reasonable selection of vegetation indices is crucial for grasping the overall ecological status of mining areas, making it of great significance for monitoring the ecological impacts of open-pit coal mining and supporting the formulation of restoration measures.

       

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