基于无人机倾斜摄影测量三维建模的区域黄土滑坡识别及特征分析
Identification and feature analysis of regional loess landslides based on UAV tilt photogrammetry 3D modeling
毛正君 1于海泳 1梁伟 2马旭 3仲佳鑫 4高广胜 5石硕杰 6田彦山3
作者信息
- 1. 西安科技大学地质与环境学院,陕西 西安 710054
- 2. 宁夏回族自治区遥感调查院,宁夏 银川 750021
- 3. 宁夏师范学院数学与计算机科学学院,宁夏 固原 756000
- 4. 宁夏回族自治区国土资源调查监测院,宁夏 银川 750021
- 5. 宁夏师范学院创新创业学院,宁夏 固原 756000
- 6. 内蒙古煤炭地质勘查(集团)一五一有限公司,内蒙古 包头 014010
- 折叠
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.
关键词
无人机倾斜摄影测量/三维建模/黄土滑坡/滑坡识别/特征参数提取/地质灾害风险调查评价/地质灾害调查工程Key words
UAV tilt photogrammetry/3D modeling/loess landslide/landslide identification/characteristic parameter extraction/geological hazard risk investigation and evaluation/geological hazard survey engineering引用本文复制引用
基金项目
宁夏回族自治区重点研发计划(2023BEG02072)
宁夏回族自治区重点研发计划(2020BEG03023)
宁夏回族自治区自然科学基金(2022AAC03700)
出版年
2024
国家科技期刊平台
NSTL
万方数据