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

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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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. 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 km-scale surveys. Ten best practice recommendations will assist users of UAV solid-state LiDAR to produce bare earth DEMs
Original languageEnglish
Pages (from-to)2211-2229
JournalEarth Surface Processes and Landforms
Issue number11
Early online date20 Apr 2023
Publication statusPublished - 15 Sept 2023

Keywords / Materials (for Non-textual outputs)

  • accuracy
  • fluvial remote sensing
  • ground classification
  • solid-state LiDAR
  • topography
  • vegetation


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