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Landslide is an extremely destructive natural disaster. Accurate assessment of the ecological restoration effect in mine landslide treatment areas is of great significance for ensuring the safety and ecological stability of the surrounding areas. Airborne Light Detection and Ranging (LiDAR) data were used to construct Digital Elevation Model, Digital Surface Model, and normalized Digital Surface Model for terrain feature extraction. Combined with visible difference vegetation index and texture information features, the Pianqiaogou landslide treatment area in Du'erping Coal Mine was selected as the study area to perform ground object information extraction and classification using support vector machine optimized by particle swarm optimization algorithm. The results show that this method has a good classification performance, with the overall classification accuracy reaching 94.12%, the Kappa coefficient 84.19% and the F₁-score 93.52%. It is superior to the other two traditional classification methods. In addition, the Shannon Diversity Index of the treatment area reaches 0.958 4, which verifies the effectiveness of the mine landslide control measures, and provides ideas for the application of LiDAR in mine landslide treatment areas.

, authors=Xuming BAI¹, Jin ZHANG¹, Junli HE², authorsList=Xuming BAI, Jin ZHANG, Junli HE, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322559879110316871, articleId=1322559878665720645, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=矿山滑坡治理区激光雷达数据特征提取与应用研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

滑坡是一种极具破坏力的自然灾害，准确评估矿山滑坡治理区的生态恢复效果对保障周边安全与生态稳定有重要意义。采用机载激光雷达数据构建数字高程模型、数字表面模型和归一化数字表面模型提取地形特征，结合可见差异植被指数与纹理信息特征，以杜儿坪煤矿偏桥沟滑坡治理区为研究对象，利用支持向量机结合粒子群优化算法进行地物信息提取与分类。结果表明，该方法分类效果良好，总体分类精度达94.12%，Kappa系数为84.19%，F₁指数达到93.52%，优于其他两种传统分类方法，且治理区香农多样性指数达0.958 4，验证了矿山滑坡治理措施的有效性，为激光雷达在矿山滑坡治理区的应用提供思路。

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白旭明（2000—），男，山西临汾人，硕士研究生，主要从事遥感技术与应用研究。E-mail:bxm0926@163.com

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白旭明（2000—），男，山西临汾人，硕士研究生，主要从事遥感技术与应用研究。E-mail:bxm0926@163.com

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