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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 3 of this Preprint.


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Craig John MacDonell , Richard David Williams, Georgios Maniatis, Kenny Roberts, Mark Naylor


Unoccupied Aerial Vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion photogrammetry (SfM). 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 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 Multi-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. M3C2 residuals compared UAV LiDAR and TLS 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 such as heather or thick grass. Whilst UAV solid-state LiDAR needs to be supplemented with bathymetric mapping to produce spatially continuous wet-dry DEMs, by itself it offers several advantages to comparable geomatics technologies for km-scale surveys. Ten best practice recommendations will assist users of UAV solid-state LiDAR to produce bare earth DEMs in geomorphic environments.



Physical Sciences and Mathematics


solid-state LiDAR, accuracy, Topography, Vegetation, ground classification, Fluvial Remote Sensing


Published: 2023-01-13 07:05

Last Updated: 2023-04-21 08:15

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CC BY Attribution 4.0 International

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Data Availability (Reason not available):
Following peer review, the UAV and TLS georeferenced point clouds, GNSS check points and final DEM will be made available from the digital depository at the lead author’s institution, with an associated DOI. Prior to completion of peer review, data can be requested by contacting the corresponding author.

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