UAV-based LiDAR Bathymetry at an Alpine Mountain Lake

Katja Richter, D. Mader, H. Sardemann, Hans-Gerd Maas
{"title":"UAV-based LiDAR Bathymetry at an Alpine Mountain Lake","authors":"Katja Richter, D. Mader, H. Sardemann, Hans-Gerd Maas","doi":"10.5194/isprs-archives-xlviii-2-2024-341-2024","DOIUrl":null,"url":null,"abstract":"Abstract. LiDAR bathymetry provides an efficient and comprehensive way to capture the topography of water bodies in shallow water areas. However, the penetration depth of this measurement method into the water column is limited by the medium water and water turbidity, resulting in a limited detectability of the bottom topography in deeper waters. An increase of the analyzable water depth is possible by the use of extended evaluation methods, in detail full-waveform stacking methods. So far, however, this has only been investigated for water depths of up to 3.50 m due to water turbidity. In this article, the potential of these extended data processing methods is investigated on an alpine mountain lake with low water turbidity and thus high analyzable water depth. Compared to the standard data processing, the penetration depth could be significantly increased by 58%. In addition, methods for depth-resolved water turbidity parameter determination on the basis of LiDAR bathymetry data were successfully tested.\n","PeriodicalId":505918,"journal":{"name":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","volume":"16 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/isprs-archives-xlviii-2-2024-341-2024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract. LiDAR bathymetry provides an efficient and comprehensive way to capture the topography of water bodies in shallow water areas. However, the penetration depth of this measurement method into the water column is limited by the medium water and water turbidity, resulting in a limited detectability of the bottom topography in deeper waters. An increase of the analyzable water depth is possible by the use of extended evaluation methods, in detail full-waveform stacking methods. So far, however, this has only been investigated for water depths of up to 3.50 m due to water turbidity. In this article, the potential of these extended data processing methods is investigated on an alpine mountain lake with low water turbidity and thus high analyzable water depth. Compared to the standard data processing, the penetration depth could be significantly increased by 58%. In addition, methods for depth-resolved water turbidity parameter determination on the basis of LiDAR bathymetry data were successfully tested.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于无人机的高山湖泊激光雷达测深技术
摘要激光雷达测深为捕捉浅水区水体地形提供了一种高效而全面的方法。然而,这种测量方法对水体的穿透深度受到介质水和水体浊度的限制,导致对较深水域底部地形的探测能力有限。通过使用扩展评估方法,特别是全波形叠加方法,可以增加可分析的水深。然而,由于水体浑浊度的原因,迄今为止,只对水深不超过 3.50 米的水域进行过研究。本文研究了这些扩展数据处理方法在高山湖泊中的应用潜力,该湖泊水体浑浊度低,因此可分析水深较高。与标准数据处理相比,穿透深度可显著增加 58%。此外,还成功测试了基于激光雷达测深数据的深度分辨水浊度参数测定方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The 19th 3D GeoInfo Conference: Preface Archives Monitoring Time-Varying Changes of Historic Structures Through Photogrammetry-Driven Digital Twinning Multimedia Photogrammetry for Automated 3D Monitoring in Archaeological Waterlogged Wood Conservation Efficient Calculation of Multi-Scale Features for MMS Point Clouds Concepts for compensation of wave effects when measuring through water surfaces in photogrammetric applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1