基于多源数据融合的矿区生态修复路径优化研究

Optimization Research on Ecological Restoration Paths in Mining Areas Based on Multi-source Data Fusion

  • 摘要: 针对湖南郴州拱极社区历史遗留矿区生态破坏问题,构建融合激光点云、遥感影像与地面测量的多源数据建模体系,完成地形、水文、土壤与景观要素的空间识别与分类提取。基于DEM模型划分修复单元,优化施工路径与作业顺序,匹配边坡支护、土壤改良与植被重建方案,采用HRB400级螺纹钢锚杆与ZYG-450型液力喷播设备协同作业。经比对,坡度超限区减少71.6%,土壤pH升至6.5,有机质含量提升至2.45%,相较修复前增长163%,生态斑块连通性指数达0.61。结果验证了多源数据融合在提升修复精度与效率方面的有效性。

     

    Abstract: Aiming at the ecological damage problem in the historical legacy mining areas of Gongji Community in Chenzhou, Hunan, a multi-source data modeling system integrating laser point cloud, remote sensing image, and ground measurement is constructed to complete the spatial identification and classification extraction of terrain, hydrology, soil, and landscape elements. Based on the DEM model, restoration units are divided, construction paths and operation sequences are optimized, slope support, soil improvement, and vegetation reconstruction schemes are matched. HRB400 grade threaded steel anchor rod and ZYG-450 type hydraulic spraying equipment are adopted for collaborative operation. Through comparison, areas with excessive slope are decreased by 71.6%, the soil pH is increased to 6.5, and the organic matter content is increased to 2.45%, which is more than 163% as much as before restoration, and the ecological plaque connectivity index reaches 0.61. The results verify the effectiveness of multi-source data fusion in terms of improving restoration accuracy and efficiency.

     

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