Extraction of eucalyptus trees along railway lines based on decision tree classification and identification of potential landslides: A case study along Guangxi section of the Guizhou—Guangxi Railway
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摘要:
桉树因生长习性特殊，会导致土壤的肥料和养分流失，引发土地退化，造成滑坡等地质灾害。广西地区气候条件适宜，区域内铁路沿线种植有大量桉树，致使铁路沿线存在滑坡隐患。为了对铁路沿线的滑坡隐患进行超前识别，文章以贵南高铁广西段为例，基于Landsat9 OLI、GF-7影像和DEM数据，使用决策树分类算法提取铁路沿线1 km缓冲区内桉树种植范围，再综合地形地貌等因素进行分析，识别出滑坡隐患。研究结果表明：（1）文章构建的决策树分类算法较其他方法来说，分类精度有所提升，总体分类精度平均值达到87.19%，Kappa系数平均值达到0.80，表明该方法在研究区内能有效提取桉树的范围；（2）贵南高铁广西段沿线大量种植桉树，桉树林呈片状分布在山丘地区，铁路沿线1 km缓冲区内桉树种植面积约为14.48 km2；（3）研究区内桉树的种植对铁路的桥梁、路基和隧道口存在一定影响，文章识别出的滑坡隐患共33处，经过现场核查准确率达到86.84%。通过以上方法可有效地对铁路沿线桉树种植范围进行提取，综合地质因素后可做到滑坡隐患的超前识别，提高了铁路的行车安全。
Abstract:The unique growth characteristics of eucalyptus can result in the depletion of soil nutrient and fertilizers, leading to land degradation and geological hazards like landslides. The Guangxi region in China has suitable climatic conditions, and numerous eucalyptus are strategically planted along railway lines, posing the risk of potential landslides. To proactively identify potential landslides along railway lines, this study takes the Guangxi section of the Guizhou-Guangxi high-speed railway as a case study. Based on Landsat 9 OLI, GF-7 imagery, and DEM data, a decision tree classification algorithm is employed to extract the planting areas of eucalyptus within a 1 km buffer zone along the railway. Then, comprehensive analysis incorporating terrain and landforms factors is conducted to identify potential landslides hazards. The research findings show that: 1) compared to other methods, the decision tree classification algorithm conducted in this study improves the classification accuracy, with an overall average classification accuracy of 87.19% and an average Kappa coefficient of 0.80, indicating that this method can effectively extract the range of eucalyptus in the study area; 2) A large number of eucalyptus are planted along the Guangxi section of the Guinan high-speed railway, with eucalyptus distributed in patches in hilly areas. The eucalyptus planting area within the 1 km buffer zone along the railway is approximately 14.48 km2; 3) The planting of eucalyptus in the study area has a certain impact on the bridges, emban kments, and tunnel entrances of the railway. A total of 33 potential landslides were identified in this study, with an accuracy rate of 86.84% through field verification. The above method can effectively extract the planting range of eucalyptus along the railway line, and combined with geological factors, it can proactively identify potential landslides, enhancing the safety of railway operation.
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•特别致谢•
2020年，54位审稿专家付出很多时间和精力为本刊审读稿件，为提高期刊学术质量做出了重要贡献。编辑部全体成员对所有参与审稿的专家表示最衷心的感谢！
表 1 2020年度《中国地质灾害与防治学报》审稿专家（按姓氏笔画为序） 姓名 单位 姓名 单位 马凤山 中国科学院地质与地球物理研究所 张永双 中国地质科学院水文地质环境地质研究所 王 清 吉林大学 张永波 太原理工大学 王文沛 中国地质环境监测院 张志龙 中国矿业大学（北京） 王贤能 深圳市工勘岩土集团有限公司 张鸣之 中国地质环境监测院 王树仁 河南理工大学 陈红旗 中国地质环境监测院 王桂杰 中国地质环境监测院 陈昌彦 北京市勘察设计研究院有限公司 王雁林 陕西省国土资源厅地质环境处 陈洪凯 枣庄学院 牛瑞卿 中国地质大学（武汉） 佴 磊 吉林大学 齐 干 中国地质环境监测院 赵文祎 中国地质环境监测院 邢爱国 上海交通大学 赵鲁强 中国气象局公共气象服务中心 朱赛楠 中国地质环境监测院 胡凯衡 中国科学院水利部成都山地灾害与环境研究所 乔建平 中国科学院水利部成都山地灾害与环境研究所 胡卸文 西南交通大学 向喜琼 贵州大学 胡新丽 中国地质大学（武汉） 刘东升 重庆市地质矿产勘查开发局 姜月华 中国地质调查局南京地质调查中心 刘希林 中山大学 夏元友 武汉理工大学 闫金凯 中国地质科学院 钱江澎 四川省地质工程勘察院 江 耀 中国科学院水利部成都山地灾害与环境研究所 徐则民 昆明理工大学 许国辉 中国海洋大学 唐辉明 中国地质大学（武汉） 孙文洁 中国矿业大学（北京） 黄波林 三峡大学 孙亚军 中国矿业大学 曹修定 中国地质调查局水文地质环境地质调查中心 孙红福 中国矿业大学（北京） 龚士良 中国地质调查局地面沉降研究中心 李 忠 防灾科技学院 韩 冰 中国地质环境监测院 李树志 中煤科工集团唐山研究院有限公司 喻孟良 中国地质环境监测院 肖锐华 中国地质环境监测院 程国明 中国地质环境监测院 何发亮 中铁西南科学研究院有限公司 温铭生 中华人民共和国应急管理部 余志山 甘肃省地质环境监测院 谭维贤 内蒙古工业大学 邹正盛 河南理工大学 魏云杰 中国地质环境监测院 第八届《中国地质灾害与防治学报》优秀论文评选结果
为了鼓励广大作者撰写高质量的科技论文, 推动我国地质灾害防治工程行业的学术交流, 提升和扩大《中国地质灾害与防治学报》的办刊质量与影响力, 《中国地质灾害与防治学报》编辑部从2013年起开展优秀论文评选活动。
评选活动遵守客观公正, 严格筛选, 优中选优的原则。 由责任编辑提出推荐名单, 并参考一年中论文的被引频次、下载次数和影响程度, 经编辑部认真初评和复评, 按得票高低评选出优秀论文。
2019年《中国地质灾害与防治学报》发表论文112篇, 从中评选出第八届《中国地质灾害与防治学报》优秀论文20篇。希望获奖作者再接再厉, 开拓创新, 为推动我国地质灾害防治工程学科的发展继续努力！
表 2 第八届《中国地质灾害与防治学报》优秀论文名单（2019年发表）题目 作者 期刊 中国西藏金沙江白格滑坡灾害研究 王立朝，温铭生，冯振，孙炜锋，魏云杰，
李俊峰，王文沛第一期 库水位波动及降雨作用下巫峡干井子滑坡流-固耦合特征及稳定性分析 梁鑫，殷坤龙，陈丽霞，康璇，杨永刚，张亮 第一期 汉江孤山航电枢纽工程区近坝滑坡稳定性分析及防治工程建议 王启国 第一期 基于不同因子分级法的滑坡易发性评价——以湖北远安县为例 闫举生，谭建民 第一期 基于函数赋值模型与模糊综合评判法的单沟泥石流危险性评价 尚慧，王明轩，罗东海，冯皎，王爱军，薛跃明 第一期 地质灾害无人机调查数据管理云平台建设 马娟，张鸣之，韩冰，黄喆，石爱军 第一期 高分二号卫星数据在地质灾害调查中的应用——以重庆万州区为例 董文，潘建平，阳振宇，夏鑫，张定凯，
向淇文，曹建虎，廖振环第一期 邻近输电塔路堑边坡失稳风险定量评估及加固工程设计优化 林阿娜，王浩，颜斌，戴旭明，胡燮，赵小盘，王晨 第二期 单体危岩崩塌灾害危险性评价——以贵州威宁县新发乡樊家岩为例 武中鹏，刘宏，董秀群，邓凯伦 第二期 降雨和库水联合作用下边坡稳定性变化规律 徐翔，王义兴，方正 第二期 秦望山隧道南口高陡岩质边坡稳定性分析及治理效果评价 郝社锋，蒋波，喻永祥，宋京雷，徐昊，孙少锐 第二期 黄土湿陷系数影响因素的相关性分析 朱凤基，南静静，魏颖琪，白兰 第二期 地质灾害防治标准化建设的思考 刘传正 第三期 重庆甑子岩崩塌落石动力学特征及危险性分区 孙敬辉，石豫川 第三期 基于离散元的含软弱夹层岩质边坡滑移机理分析 范昊天，孙少锐，王亚山，张纪星，刘宝生 第三期 广西桂林市规划中心城区岩溶发育特征及分布规律 江思义，吴福，刘庆超，李海良，吴莹莹 第三期 降雨引发的兰州黄土滑坡时空规律分析和临界降雨量预测 朱晓霞，张力，杨树文 第四期 湖北武汉典型地区岩溶发育特征分析 李慧娟，金小刚，涂婧，魏瑞均，李海涛，杨涛 第四期 广西桂林市规划中心城区岩溶塌陷易发性评价 吴福，江思义，刘庆超，何源，李海良 第五期 湖北武汉岩溶塌陷时空分布规律及其影响因素分析 涂婧，魏瑞均，杨戈欣，刘长宪，金小刚，李海涛 第六期 -
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图 3 阈值优化过程中局部范围分类结果
Figure 3. Local area classification results during threshold optimization process
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图 6 3种方法在2022年4月9日Landsat9 OLI影像中的分类结果
Figure 6. Classification results of three methods in Landsat9 OLI image on April 9, 2022
表 1 决策树各类指数信息表
Table 1 Information table of various indices in the decision tree
指数类型 计算公式 NDVI EVI DVI RVI PVI SAVI OSAVI WET 注：BLUE为蓝光波段，GREEN为绿光波段，RED为红光波段，NIR为近红外波段，SWIR1为短波近红1，SWIR2为短波近红2。 
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表 2 决策树各类指数阈值
Table 2 Threshold values of various indices in the decision tree
指数类型 初始阈值 中间阈值 最终阈值 NDVI [0.71, 0.84] [0.52, 0.85] [0.64, 0.85] EVI [0.42, 0.61] [0.31, 0.73] [0.38, 0.65] DVI [0.21, 0.34] [0.14, 0.41] [0.19, 0.36] RVI [6.06, 11.33] [3.21, 12.69] [4.58, 12.08] PVI [0.31, 0.43] [0.25, 0.51] [0.29, 0.47] SAVI [0.41, 0.56] [0.28, 0.62] [0.36, 0.58] OSAVI [0.47, 0.59] [0.33, 0.62] [0.43, 0.61] WET [−0.027, 0.015] [−0.072, 0.023] [−0.045, 0.019]
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表 3 混淆矩阵
Table 3 Summary table of the confusion matrix
分类模型 评价指标 验证区 平均值 1 2 3 CART 总体分类精度/% 87.12 87.27 87.17 87.19 Kappa 0.80 0.80 0.80 0.80 SVM 总体分类精度/% 84.90 85.77 83.52 84.73 Kappa 0.76 0.77 0.74 0.76 NN 总体分类精度/% 84.36 83.15 81.24 82.92 Kappa 0.75 0.73 0.71 0.73
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表 4 坡度等级表
Table 4 The grade table of slope
等级 类型 坡度/(°) 特性 1 较缓坡面 10～15 较稳定，极端天气下可能形成滑坡 2 中等陡峭坡面 15～25 一般稳定，极端天气下易形成滑坡 3 陡峭坡面 25～45 不稳定，易形成滑坡
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表 5 滑坡隐患详情表
Table 5 The detailed table of potential landslides
序号 类型 等级 是否正确 说明 序号 类型 等级 是否正确 说明 序号 类型 等级 是否正确 说明 1 桥梁 1 是 14 隧道 1 是 27 隧道 2 否 零星桉树干扰 2 桥梁 1 是 15 隧道 2 是 28 隧道 2 否 零星桉树干扰 3 隧道 3 是 16 桥梁 2 是 29 桥梁 1 是 4 隧道 2 是 17 路基 2 是 30 隧道 1 是 5 桥梁 1 是 18 路基 1 是 31 隧道 1 是 6 隧道 2 是 19 隧道 2 是 32 路基 2 是 7 隧道 2 是 20 隧道 2 是 33 隧道 2 是 8 隧道 2 否 零星桉树干扰 21 路基 2 是 34 隧道 2 是 9 隧道 2 否 零星桉树干扰 22 路基 1 是 35 路基 3 是 10 隧道 2 是 23 路基 1 是 36 路基 3 是 11 桥梁 2 是 24 路基 1 是 37 隧道 2 是 12 隧道 3 否 地形改变 25 桥梁 1 是 38 隧道 2 是 13 路基 2 是 26 路基 1 是
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