胡胜^1,2,3,,
邱海军^1,2,3,,,
王新刚^2,4,
谢婉丽^2,4,
龙永清^1,2,3,
土祥^1,2,3,
杨冬冬^1,2,3,
马舒悦^1,2,3,
张焱^1,2,3,
曹明明¹
1. 西北大学城市与环境学院, 陕西 西安 710127
2. 西北大学地表系统与灾害研究院, 陕西 西安 710127
3. 陕西省地表系统与环境承载力重点实验室, 陕西 西安 710127
4. 西北大学地质学系, 大陆动力学国家重点实验室, 陕西 西安 710069

基金项目: 国家自然科学基金项目（批准号：41771539）和中国科学院国际合作局对外合作重点项目（批准号：131551KYSB20160002）共同资助


详细信息

作者简介: 胡胜, 男, 30岁, 博士研究生, 自然地理学专业, E-mail:husheng198800@126.com

通讯作者: 邱海军, E-mail:rgbitxpl@163.com

中图分类号: P954;P931

收稿日期:2017-11-06
修回日期:2018-01-16
刊出日期:2018-03-30

Extracting characteristic parameters of loess landslides based on high-resolution topography and its application prospect

Hu Sheng^1,2,3,,
Qiu Haijun^1,2,3,,,
Wang Xingang^2,4,
Xie Wanli^2,4,
Long Yongqing^1,2,3,
Tu Xiang^1,2,3,
Yang Dongdong^1,2,3,
Ma Shuyue^1,2,3,
Zhang Yan^1,2,3,
Cao Mingming¹
1. College of Urban and Environmental Science, Northwest University, Xi'an 710127, Shaanxi
2. Institute of Earth Surface System and Hazards, Northwest University, Xi'an 710127, Shaanxi
3. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi'an 710127, Shaanxi
4. State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi'an 710069, Shaanxi


More Information

Corresponding author: Qiu Haijun,E-mail:rgbitxpl@163.com

MSC: P954;P931

--> Received Date: 06 November 2017
Revised Date: 16 January 2018
Publish Date: 30 March 2018


摘要
摘要:黄土高原是地质灾害的易发区和频发区，传统的野外调查方法费时费力，且难以满足地质灾害精细化制图要求。而近些年来兴起的无人机（UAVs）摄影测量技术和SfM（Structure from Motion）三维建模技术已成为获取野外高分辨率地形数据的新技术。在无人机精度初步验证和野外调查的基础上，建立了11个黄土滑坡的三维数字模型，生成了高分辨率的数字正射影像（DOM）和数字高程模型（DEM）产品。在Agisoft PhotoScan、ArcGIS 10.2、Global Mapper 17、Origin Pro 9.0等平台下，完成了黄土滑坡特征参数提取和分析。研究结果表明：不同飞行高度下，无人机获取的DOM存在0.5 m左右的水平偏移，获取的DEM高程与飞行高度呈正相关，但剖面线趋势高度吻合，无人机DEM高程校准后的垂直精度可达±3 cm；与传统野外调查相比，无人机摄影测量技术和SfM建模技术能够快速、准确地获取黄土滑坡几何特征、地形特征、剖面结构等基本特征参数；低成本无人机系统在坡面尺度下非常适合黄土高原地区的滑坡调查与分析，这个新方法具有巨大的潜在应用价值。
关键词: 无人机/
黄土滑坡/
特征参数提取/
黄土高原

Abstract:In China, loess covers an area of approximately 6.31×10⁵ km², accounting for about 6.6% of the country's total land area, and is mainly distributed in the Chinese Loess Plateau(33°~41°N, 100°~114°E). Loess landslide, one of typical geo-hazards types in the Chinese Loess Plateau, is occurring at a significantly increasing frequency, posing a serious threat to people's lives and property. Conventional field surveys require tremendous human labor, material, and financial resources, and the acquired characteristic parameters of loess landslides are only at the macroscopic level and not sufficiently accurate. Nowadays, the UAVs(Unmanned Aerial Vehicles) photogrammetry technique for capturing photos and Structure from Motion(SfM)for 3D modeling from 2D photos have advantages of fast, high efficiency, low-cost, security and high resolution, which has become a new powerful technology to obtain high-resolution terrain data. The main purpose of this article is to prove that low-cost UAVs has high accuracy of measurement and can be well used in landslide field investigation. This research focuses on how to use low-cost UAVs to extract characteristic parameters of 11 loess landslides surveyed in Shaanxi Loess Plateau. We got eleven loess landslides data including high-resolution DEMs and DOMs in this field investigation. Among these landslides, five are located in the Loess Hilly Region such as Zhidan County(Yan'an City)and Jingbian County(Yulin City). Other landslides are located in the Loess Tableland Region such the Bailu Tableland(Baqiao District, Xi'an City)and the South Jingyang Tableland(Jingyang County, Xianyang City). Most of them occurred mainly in Loess Tableland slope and Loess Hilly Region's steep slope were triggered by rain, engineering excavation and agricultural irrigation. The results show that UAVs and SfM technique can quickly and accurately obtain high-resolution characteristic parameters including loess landslides' geometric feature parameters, topographic feature parameters and section structure. Low-cost UAVs is very suitable for investigation and analysis of landslides in the Loess Plateau covered with sparse vegetation. This new technique has a tremendous application value in geosciences field investigation.
Key words:UAVs/
loess landslides/
characteristic parameters extraction/
Chinese Loess Plateau



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