An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar

Abstract

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar.

Assessing UAV-based laser scanning for monitoring glacial processes and interactions at high spatial and temporal resolutions

“…Limitations arising from the terrain being surveyed must also be accounted for in mission planning. For example, reducing the number of laser returns that are acquired from high incidence angles will lead to more consistent point density in the final clouds (Deems et al, 2013;Dharmadasa et al, 2022), as demonstrated here when reconstructing the lower reaches of the deepest crevasses. Consistent point spacing across the scene is also required for deriving final surface models, as well as undertaking geometric analysis (Dharmadasa et al, 2022), with reductions in point density reducing confidence in results or even removing regions from the analysis entirely.…”

“…For example, reducing the number of laser returns that are acquired from high incidence angles will lead to more consistent point density in the final clouds (Deems et al, 2013;Dharmadasa et al, 2022), as demonstrated here when reconstructing the lower reaches of the deepest crevasses. Consistent point spacing across the scene is also required for deriving final surface models, as well as undertaking geometric analysis (Dharmadasa et al, 2022), with reductions in point density reducing confidence in results or even removing regions from the analysis entirely. This was observed here in the frontal position change analysis where both the sub-daily and daily comparisons had regions of the glacier front removed due to insufficient point densities.…”

“…A significant limitation of UAV-LS is the reliance on direct georeferencing capabilities in order to obtain a fully processed point cloud, with errors in the positional and orientation data directly affecting model outputs (Pilarska et al, 2016;Dharmadasa et al, 2022). If no GNSS or orientation data is received, and the point density is not great enough for SLAM (Simultaneous Location and Mapping) algorithms to geolocate incoming laser scan data (Del Perugia et al, 2019), then no final point cloud is created.…”

“…As demonstrated in this study, although UAV-SfM can also be impacted by the conditions described above, there are alternatives for point cloud generation should GNSS data be unavailable, with the method able to reconstruct scenes without any prior knowledge of camera locations and requiring only a few GCPs to coarsely align the model to real world coordinates (e.g., Westoby et al, 2012). Whilst GCPs can be utilised in UAV-LS surveys to enhance model accuracy and correct for any systematic bias (e.g., Harder et al, 2020;Dharmadasa et al, 2022), their inclusion will not resolve the need for high quality GNSS data as outlined above.…”

“…This was observed here in the frontal position change analysis where both the sub-daily and daily comparisons had regions of the glacier front removed due to insufficient point densities. However, these can be overcome through detailed flight planning to ensure scan angles and overlap are sufficient in order to allow high-quality point clouds to be obtained (Deems et al, 2013;Dharmadasa et al, 2022).…”