ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 765–772, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-765-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 765–772, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-765-2020

  03 Aug 2020

03 Aug 2020

3D POINT ERRORS AND CHANGE DETECTION ACCURACY OF UNMANNED AERIAL VEHICLE LASER SCANNING DATA

A. Mayr1, M. Bremer1,2, and M. Rutzinger1,2 A. Mayr et al.
  • 1University of Innsbruck, Institute of Geography, Innsbruck, Austria
  • 2Austrian Academy of Sciences, Institute for Interdisciplinary Mountain Research, Innsbruck, Austria

Keywords: Topographic LiDAR, UAV, Point Cloud Quality, Level of Detection, Geomorphology, Erosion, Deformation Monitoring

Abstract. Unmanned aerial vehicle laser scanning (ULS) has recently become available for operational mapping and monitoring (e.g. for forestry applications or erosion studies). It combines advantages of terrestrial and airborne laser scanning, but there is still little proof of ULS accuracy. For the detection and monitoring of small-magnitude surfaces changes with multitemporal point clouds, an estimate of the level of detection (LOD) is required. The LOD is a threshold applied on distance measurements to separate real surface change (e.g. due to erosion or deposition by geomorphic processes) from errors. This paper investigates key components of the error budget for two ULS point clouds acquired for erosion monitoring at a grassland site in the Alps. In addition to the registration error and effects of the local surface roughness, we assess the positional uncertainties of each point that result from laser footprint effects, which are a function of the scanning geometry (including range, incidence angle and beam divergence). By removing erroneous points with an increasingly stricter point error criterion, we illustrate that the positional point errors strongly affect the LOD and discuss how this type of error can be mitigated. Moreover, our experimental results with three different surface classes (bare earth and rock, buildings and grassland) show that the level of detection tends to be slightly better for areas with bare earth and rock than for grass-covered areas (due to their roughness). For all these surface types reliable distance measurements are possible with sub-decimetre levels of detection.