Identification and feature analysis of regional loess landslides based on UAV tilt photogrammetry 3D modeling
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摘要:
研究目的 黄土滑坡是黄土地区人居与城镇建设安全的重大隐患。滑坡识别是滑坡灾害及其他研究工作的基础，因此基于无人机倾斜摄影测量三维建模从不同维度、不同视角直观快速地识别黄土滑坡并进行特征参数提取，能够为黄土滑坡风险识别及风险管理精细化研究提供技术支撑。
研究方法 以宁夏回族自治区固原市彭阳县红河镇西南部的黑牛沟村为研究区，采用无人机倾斜摄影测量数据获取、三维建模、现场验证结合地统计学分析，开展了区域黄土滑坡识别及其特征参数提取和分析。
研究结果 基于三维实景模型确定并分析研究区沟谷沿线地貌凹陷区是否存在陡壁及其周界形态，结合色调、纹理和微地貌等标志实现了黄土滑坡识别，共圈定了23个滑坡，结合现场验证移除2个非滑坡点，最终确定了21个滑坡；滑坡密集分布在主沟和支沟沟口，多呈对滑的形式出现在沟谷两侧且具有群发性；大型及特大型滑坡占比达到57.14%，滑坡的滑动方向主要以西南（阳坡）、东南（半阳坡）为主，相对高差集中在80~120 m，滑坡体坡形多呈凹形坡，滑坡体坡度主要集中在20°~30°；滑坡体土地利用类型主要为植被，其次为裸地，也有一部分为农田，道路和河流占比极少。
结论 基于无人机倾斜摄影测量构建的三维实景模型可从多维度、多视角精确快速地识别区域黄土滑坡，并分析其相关特征参数，能够弥补当前二维平面遥感影像存在的不足；还能够为滑坡易发性、危险性、易损性及风险评估等相关研究提供数据支撑。
Abstract:This paper is the result of geological hazard survey engineering.
Objective The loess landslide is a major hidden danger to the safety of human settlements and urban construction in the loess region. Landslide identification is the foundation of other research work on landslide disasters. By utilizing unmanned aerial vehicle oblique photogrammetry three-dimensional (3D) modeling, loess landslides can be intuitively and quickly identified from different dimensions and perspectives, enabling the extraction of feature parameters. This can provide technical support for risk identification and refined risk management research of loess landslides.
Methods While researching Heiniugou Village in the southwest of Honghe Town, Pengyang County, Guyuan City, and the Ningxia Hui Autonomous Region, regional loess landslide identification and feature parameter extraction and analysis were carried out using unmanned aerial vehicle oblique photogrammetry data acquisition, 3D modeling, on-site verification, and geostatistical analysis.
Results Based on a 3D−real−life model, we located steep walls and their surrounding shapes in the geomorphic depression areas along the valley in the study area. By combining color tone, texture, and micro-geomorphology indicators, we were able to identify all of the loess landslides in the specified region. A total of 23 landslides were delineated, and two non-landslide points were removed through on-site verification. The remaining 21 landslides were densely distributed at the mouth of the main and branch gullies, appearing to slide towards each other from opposite sides of the gullies and exhibiting a mass occurrence. The proportion of large and super−large landslides reached 57.14%. The landslides primarily slid to the southwest (sunny slope) and to the southeast (semi-sunny slope), with relative height differences between 80–120 m. The slopes of these landslides were mostly concave and measured between 20°–30°. The sites of these landslides were mainly sources of vegetation, bare land, or farmland, with a small percentage of the land made up of roads and rivers.
Conclusions A 3D-realistic model based on unmanned aerial vehicle oblique photogrammetry can accurately and quickly identify regional loess landslides from multiple dimensions and perspectives and analyze their related feature parameters, which can make up for the shortcomings of current two-dimensional planar remote sensing images. Moreover, this process can also provide data support for research on landslide susceptibility, danger, vulnerability, and risk assessment.
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表 1 研究区典型黄土滑坡三维模型实景图及现场验证
Table 1. Realistic map and field verification of a typical loess landslide 3D model in the study area
滑坡编号 三维模型实景图 现场验证 HN10 
HN12 
HN17 
HN04 
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表 2 研究区滑坡规模参数及分类统计
Table 2. Statistics of landslide scale parameters and classification in the study area
序号 面积/m2 体积/m3 规模分类 不同滑坡规模
数量占比/%HN10 200000.00 12874504.46 特大型滑坡 4.76 HN11 93109.29 3198563.09 大型滑坡 52.38 HN13 60692.50 2132063.60 大型滑坡 HN08 27962.60 1289852.99 大型滑坡 HN12 130000.00 4891470.36 大型滑坡 HN05 43478.31 3044124.96 大型滑坡 HN06 65486.60 9025757.73 大型滑坡 HN01 47871.17 2445661.20 大型滑坡 HN02 37306.10 2073038.93 大型滑坡 HN03 36606.17 1570681.05 大型滑坡 HN04 74556.36 3771416.47 大型滑坡 HN15 120000.00 8107899.04 大型滑坡 HN09 28601.31 874344.68 中型滑坡 38.10 HN19 6479.32 198073.47 中型滑坡 HN14 4091.98 116540.35 中型滑坡 HN20 25444.19 654278.30 中型滑坡 HN16 33164.47 699444.33 中型滑坡 HN17 13306.68 337214.37 中型滑坡 HN07 16863.67 653954.94 中型滑坡 HN18 13280.62 336740.34 中型滑坡 HN21 41114.39 1712871.97 小型滑坡 4.76 注：按照地质灾害风险调查评价规范（1:50000）（DZ/T 0439−2023）中滑坡体积进行滑坡规模分类。
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表 3 研究区黄土滑坡滑动方向统计
Table 3. Statistics of landslide aspect in the study area
滑动方向 滑坡面积
/m2滑坡面积
占比/%滑坡
数量/个滑坡数量
占比/%东北 130000.00 11.61 1 5 东 178123.67 15.91 5 24 东南 201035.65 17.96 3 14 西南 346635.98 30.97 4 19 西 60692.50 5.42 1 5 西北 202927.92 18.13 7 33
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表 4 研究区滑坡相对高差统计
Table 4. Statistics of relative height difference of landslides in the study area
滑坡相对
高差/m滑坡
面积/m2滑坡面积
占比/%滑坡
数量/个滑坡数量
占比/%0~40 0.00 0 0 0 40~80 95767.25 8.56 6 28.57 80~120 823648.46 73.58 14 66.67 120~160 200000.00 17.87 1 4.76
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表 5 研究区滑坡体坡形统计表
Table 5. Statistics of landslide slope shape in the study area
滑坡体坡形 滑坡面积
/m2滑坡面积
占比/%滑坡数量
/个滑坡数量
占比/%直形坡 0 0 0 0 凹形坡 959804.69 85.74 16 76.19 凸形坡 159611.02 14.26 5 23.81
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表 6 研究区滑坡体坡度统计表
Table 6. Statistics of landslide slope in the study area
滑坡体坡度
/°滑坡面积
/m2滑坡面积
占比/%滑坡数量
/个滑坡数量
占比/%0~10 0.00 0.00 0 0.00 10~20 130000.00 11.61 1 4.76 20~30 850800.76 76.00 13 61.90 30~40 138614.95 12.38 7 33.33
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表 7 研究区滑坡体土地利用面积统计表
Table 7. Statistics of land use types in the study area
土地利用 面积/m2 面积占比/% 植被 600917.17 53.68 农田 156146.31 13.95 裸地 355105.29 31.72 道路 4034.04 0.36 河流 3236.90 0.29
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