Towards an automated acquisition and parametrization of debris‐flow prone torrent channel properties based on photogrammetric‐derived uncrewed aerial vehicle data

Author:

Schmucki Gregor123ORCID,Bartelt Perry12,Bühler Yves12,Caviezel Andrin12,Graf Christoph4,Marty Mauro4,Stoffel Andreas12,Huggel Christian3

Affiliation:

1. WSL Institute for Snow and Avalanche Research SLF Davos Switzerland

2. Climate Change, Extremes, and Natural Hazards in Alpine Regions Research Center CERC Davos Switzerland

3. Department of Geography University of Zurich Zurich Switzerland

4. Swiss Federal Institute for Forest, Snow and Landscape Research WSL Birmensdorf Switzerland

Abstract

AbstractDebris flows are a hazard in mountainous regions. Cost‐effective, long‐term studies of debris‐flow torrents, however, are rare, leading to uncertainties in hazard assessment and hazard prevention. Here, we address the question of whether cost‐effective remote sensing techniques can be applied for assessment of mountain torrents and possibly further gather accurate, long‐term information on the evolution of the catchment. Torrents prone to debris flows are often devoid of vegetation in the near channel area and hence can be well captured with photogrammetrically derived methods using uncrewed aerial vehicle (UAV) surveys. The possibility of automatically extracting specific torrent parameters from high‐resolution terrain models, such as cross‐section area or gradient, is investigated. The presented methodology yields continuous and automatically derived geometrical parameters such as torrent bed width, inclination and cross‐section area, which is a major advantage compared with point‐based, often dangerous field surveys. Their cross‐validation with field measurements shows strong agreement. Those parameters are accurate along sharply incised sections with strong limitations along sections with steep adjacent slopes and/or dense vegetation. The information along the torrent allows fast identification of key sections and weak spots which can be precisely evaluated in the field. The study highlights that proper classification of real ground points poses the key challenge. We show that photogrammetric routines to derive a high‐resolution digital terrain model (DTM) are limited in the case of dense vegetation coverage. In such cases, LiDAR surveys have clear advantages even though they are also limited by very dense vegetation. We find that UAV data can be used for an objective method of estimating debris‐flow torrent geometric properties. And the introduced approaches therefore build a stepping stone towards a more comprehensive, reproducible and objective assessment of torrent processes and predispositions. However, ground‐referencing fieldwork remains essential, and further research on remote sensing supported hazard assessment of debris‐flow‐prone torrents is indispensable.

Publisher

Wiley

Subject

Earth and Planetary Sciences (miscellaneous),Earth-Surface Processes,Geography, Planning and Development

Reference55 articles.

1. AGNES. (2022)AGNES—Automated GNSS Network for Switzerland. Available from:https://pnac.swisstopo.admin.ch/pages/en/agnes.html

2. Abele G.(1974)Bergstürze in den Alpen: ihre Verbreitung Morphologie und Folgeerscheinungen Bände 23–25 Bergstürze in den Alpen: ihre Verbreitung Morphologie und Folgeerscheinungen Gerhard Abele Ausgabe 25 von Wissenschaftliche Alpenvereinshefte Deutscher Alpenverein ISSN 0084‐0912. Wissenschaftliche Alpenvereinshefte: München.

3. HIGH-RESOLUTION DEBRIS FLOW VOLUME MAPPING WITH UNMANNED AERIAL SYSTEMS (UAS) AND PHOTOGRAMMETRIC TECHNIQUES

4. Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3