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Quantifying streambank movement and topography using unmanned aircraft system photogrammetry with comparison to terrestrial laser scanning
Author(s) -
Hamshaw Scott D.,
Bryce Thomas,
Rizzo Donna M.,
O'NeilDunne Jarlath,
Frolik Jeff,
Dewoolkar Mandar M.
Publication year - 2017
Publication title -
river research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.679
H-Index - 94
eISSN - 1535-1467
pISSN - 1535-1459
DOI - 10.1002/rra.3183
Subject(s) - photogrammetry , environmental science , vegetation (pathology) , bank erosion , erosion , hydrology (agriculture) , remote sensing , global positioning system , lidar , physical geography , geology , geography , geomorphology , computer science , geotechnical engineering , medicine , telecommunications , pathology
Streambank movement is an integral part of geomorphic changes along river corridors and affects a range of physical, ecological, and socio‐economic systems including aquatic habitat, water quality, and infrastructure. Various methods have been used to quantify streambank erosion, including bank pins, ground surveys, lidar, and analytical models, however, due to high‐cost or labour intensive fieldwork these are typically feasible or appropriate only for site‐specific studies. Advancements in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for more rapid and economical quantification of streambank erosion and deposition at variable scales. This work assesses the performance of UAS‐based photogrammetry for capturing topography of streambank surfaces and quantifying bank movement. UAS data are compared to terrestrial laser scanner (TLS) and GPS surveying from streambank sites located in Vermont that featured a variety of bank conditions and vegetation. Cross‐sectional analysis of data from UAS and TLS revealed that the UAS reliably captured the bank surface within 0.2 m of TLS and GPS surveys across all sites during leaf‐off conditions. Mean error between UAS and TLS was only 0.11 m in early spring conditions. Dense summer vegetation resulted in decreased accuracy and was a limiting factor in the ability of the UAS to capture the ground surface. At areas with observed bank movement, the change in cross‐sectional area estimated using UAS data compared reliably to TLS survey for net cross‐sectional changes greater than 3.5 m 2 , given a 10% error tolerance. At locations with smaller changes, error increased due to the effect of vegetation, georeferencing, and overhanging bank profiles. UAS‐based photogrammetry shows significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner.