A Cost-Effective Laser Scanning Method for Mapping Stream Channel Geometry and Roughness
Number of Authors: 5
2015 (English)In: Journal of the American Water Resources Association, ISSN 1093-474X, E-ISSN 1752-1688, Vol. 51, no 5, 1211-1220 p.Article in journal (Refereed) Published
This brief pilot study implements a camera-based laser scanning system that potentially offers a viable, cost-effective alternative to traditional terrestrial laser scanning (TLS) and LiDAR equipment. We adapted a low-cost laser ranging system (SICK LSM111) to acquire area scans of the channel and bed for a temporarily diverted stream. The 5mx2m study area was scanned at a 4mm point spacing which resulted in a point cloud density of 5,600 points/m(2). A local maxima search algorithm was applied to the point cloud and a grain size distribution of the stream bed was extracted. The 84th and 90th percentiles of this distribution, which are commonly used to characterize channel roughness, were 90mm and 109mm, respectively. Our example shows the system can resolve both large-scale geometry (e.g., bed slope and channel width) and small-scale roughness elements (e.g., grain sizes between about 30 and 255mm) in an exposed stream channel thereby providing a resolution adequate for the estimation of ecohydraulic roughness parameters such as Manning's n. While more work is necessary to refine our specific field-deployable system's design, these initial results are promising in particular for those working on a limited or fixed budget. This opens up a realm of laser scanning applications and monitoring strategies for water resources that may not have been possible previously due to cost limitations associated with traditional TLS systems.
Place, publisher, year, edition, pages
2015. Vol. 51, no 5, 1211-1220 p.
laser scanning, stream channels, river bed roughness, ecohydraulics, Manning's n
Environmental Engineering Earth and Related Environmental Sciences
IdentifiersURN: urn:nbn:se:su:diva-122756DOI: 10.1111/1752-1688.12299ISI: 000362369900005OAI: oai:DiVA.org:su-122756DiVA: diva2:871705