Consumer-grade UAV solid-state LiDAR accurately quantifies topography in a vegetated fluvial environment

MacDonell, C. J. , Williams, R. D. , Maniatis, G., Roberts, K. and Naylor, M. (2023) Consumer-grade UAV solid-state LiDAR accurately quantifies topography in a vegetated fluvial environment. Earth Surface Processes and Landforms, 48(11), pp. 2211-2229. (doi: 10.1002/esp.5608)

Abstract

Unoccupied aerial vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion (SfM) photogrammetry. Advances in UAV payloads and the advent of solid-state LiDAR have enabled consumer-grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV-acquired images. We evaluate the application of a DJI Zenmuse L1 solid-state LiDAR sensor on a Matrice 300 RTK UAV to generate digital elevation models (DEMs). To assess flying height (60–80 m) and speed parameters (5–10 ms−1) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter and classify the point clouds to produce a raster DEM product. A dense control network showed that there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multiscale Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of −0.015 m on unvegetated gravel bars. Multiscale Model to Model Cloud Comparison (M3C2) residuals compared UAV LiDAR and Terrestrial Laser Scanner point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid-state LiDAR was effective at penetrating sparse canopy-type vegetation but was less penetrable through dense ground-hugging vegetation (e.g. heather, thick grass). Whilst UAV solid-state LiDAR needs to be supplemented with bathymetric mapping to produce wet–dry DEMs, by itself, it offers advantages to comparable geomatics technologies for kilometre-scale surveys. Ten best practice recommendations will assist users of UAV solid-state LiDAR to produce bare earth DEMs.

• Item Type: Articles
• Additional Information: Naylor, Williams and MacDonell were funded through NERC grants NE/S003312/1 and NE/T005920/1. Williams was also funded by Carnegie Research Incentive Grant RIG007856. RTK-GNSS equipment was provided through NERC Geophysical Equipment Facility Loan 1118.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Williams, Professor Richard and MacDonell, Mr Craig and Roberts, Mr Kenny
• Authors: MacDonell, C. J., Williams, R. D., Maniatis, G., Roberts, K., and Naylor, M.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Earth Surface Processes and Landforms
• Publisher: Wiley
• ISSN: 0197-9337
• ISSN (Online): 1096-9837
• Published Online: 20 April 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Earth Surface Processes and Landforms 48(11):2211-2229
• Publisher Policy: Reproduced under a Creative Commons license
• Data DOI: 10.5525/gla.researchdata.1433

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
303063		Catchment susceptibility to hydrometeorological events: sediment flux and geomorphic change as drivers of flood risk in the Philippines	Richard Williams	Natural Environment Research Council (NERC)	NE/S003312/1	GES - Geography
303762		Investigating similarity between gravel-bed, sand-bed and laboratory braided rivers	Richard Williams	The Carnegie Trust for the Universities of Scotland (CARNEGTR)	RIG007856	GES - Geography