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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Key words:
- high-speed railway /
- potential landslides /
- decision tree classification /
- eucalyptus /
- confusion matrix
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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 $ NDVI = \dfrac{{NIR - RED}}{{NIR + RED}} $ EVI $ EVI = \dfrac{{2.5(NIR - RED)}}{{NIR + 6RED - 7.5BLUE + 1}} $ DVI $ DVI = NIR - RED $ RVI $ RVI = \dfrac{{NIR}}{{RED}} $ PVI $ PVI = 0.939NIR - 0.344RED + 0.09 $ SAVI $ SAVI = \dfrac{{(1 + 0.5)(NIR - RED)}}{{NIR + RED + 0.5}} $ OSAVI $ OSAVI = \dfrac{{NIR - RED}}{{NIR + RED + 0.16}} $ WET $ \begin{aligned} WET = & 0.151\;1BLUE + 0.197\;3GREEN + 0.328\;3RED + 0.340\;7NIR- \\ & 0.711\;7SWIR1 - 0.455\;9SWIR2 \\ \end{aligned} $ 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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