Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests

Abstract

Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

Increasing the Physical Representation of Forest‐Snow Processes in Coarse‐Resolution Models: Lessons Learned From Upscaling Hyper‐Resolution Simulations

“…However, diverging results at the experimental plots in Laret and Sodanklyä imply that such parametrizations would need to account for climatic conditions as well. Future studies could combine spatially distributed forest snow datasets from repeated lidar flights (Broxton & van Leeuwen, 2020; Painter et al., 2016; Pflug & Lundquist, 2020) and hyper‐resolution models to develop dedicated snow‐covered fraction parametrizations for forested areas.…”

Concept for a novel airborne LiDAR system combining high-resolution snow height mapping with co-registered spatial information on the water content of the snowpack

“…Broxton and van Leeuwen quite recently reported an approach combining airborne LiDAR data from a similar platform as Painter et al with structure from motion (SfM) data measured from a UAV over the same plot. 11 They could demonstrate that airborne LiDAR outperforms SfM in densely forested regions, whereas in sparsely forested areas both methods give comparable results. Since this is no real surprise, the more important finding is rather that they found a relationship between a small scale SfM map and a previous LiDAR data set over a larger area.…”

“…This is already a huge advantage of LiDAR itself over other techniques such as SfM because it allows for precise snow height mapping even under conditions like a canopy covering the ground. 8,11,12 This advantage of full-waveform recording is illustrated in the panel on the right. The full-waveform signal provides additional information on parameters like canopy height or structure.…”

Editorial for Special Issue “UAV Photogrammetry and Remote Sensing”

“…In the paper [1], the authors carried out an interesting combination of UAV Photogrammetry and Large-Scale Airborne Lidar Data to monitor snow masses in a forested region in central Arizona, United States. They observed that in low dense forest conditions, both sources of data deliver similar snow depth maps while in high dense forest, lidar maps are more accurate.…”