Quantifying Uncertainties in Snow Depth Mapping From Structure From Motion Photogrammetry in an Alpine Area

Goetz, Jason; Brenning, Alexander

doi:10.1029/2019wr025251

Cited by 30 publications

(23 citation statements)

References 58 publications

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“…When using an unpiloted aerial system (UAS), which deploys a camera on an unpiloted aerial vehicle (UAV), SfM is a low-cost method that has the capacity for routine snow depth monitoring (Adams et al, 2018;Bühler et al, 2016;De Michele et al, 2016;Harder et al, 2016;Vander Jagt et al, 2015). Reported accuracies range from 8 to 30 cm using UAS SfM (Adams et al, 2018;Bühler et al, 2016;Goetz and Brenning, 2019;Harder et al, 2016;Meyer and Skiles, 2019;Harder et al, 2020). The primary drawbacks of UAS SfM as compared to lidar for mapping snow depth are that the DSM needs to be georeferenced using ground control points (GCPs) with known coordinates and may require significant manual steps (Tonkin and Midgley, 2016;Meyer and Skiles, 2019), although new techniques are emerging that may reduce field data collection time (Gabrlik et al, 2019;Meyer and Skiles, 2019).…”

Section: Introductionmentioning

confidence: 99%

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

et al. 2021

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Abstract. Terrestrial and airborne laser scanning and structure from motion techniques have emerged as viable methods to map snow depths. While these systems have advanced snow hydrology, these techniques have noted limitations in either horizontal or vertical resolution. Lidar on an unpiloted aerial vehicle (UAV) is another potential method to observe field- and slope-scale variations at the vertical resolutions needed to resolve local variations in snowpack depth and to quantify snow depth when snowpacks are shallow. This paper provides some of the earliest snow depth mapping results on the landscape scale that were measured using lidar on a UAV. The system, which uses modest-cost, commercially available components, was assessed in a mixed deciduous and coniferous forest and open field for a thin snowpack (< 20 cm). The lidar-classified point clouds had an average of 90 and 364 points/m2 ground returns in the forest and field, respectively. In the field, in situ and lidar mean snow depths, at 0.4 m horizontal resolution, had a mean absolute difference of 0.96 cm and a root mean square error of 1.22 cm. At 1 m horizontal resolution, the field snow depth confidence intervals were consistently less than 1 cm. The forest areas had reduced performance with a mean absolute difference of 9.6 cm, a root mean square error of 10.5 cm, and an average one-sided confidence interval of 3.5 cm. Although the mean lidar snow depths were only 10.3 cm in the field and 6.0 cm in the forest, a pairwise Steel–Dwass test showed that snow depths were significantly different between the coniferous forest, the deciduous forest, and the field land covers (p < 0.0001). Snow depths were shallower, and snow depth confidence intervals were higher in areas with steep slopes. Results of this study suggest that performance depends on both the point cloud density, which can be increased or decreased by modifying the flight plan over different vegetation types, and the grid cell variability that depends on site surface conditions.

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Section: Introductionmentioning

confidence: 99%

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

et al. 2021

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“…Accordingly, quantifying SWE at the watershed scale for a single winter season can be inherently challenging and is often hindered by site accessibility (López‐Moreno & Nogués‐Bravo, 2006), the limited spatial extent of terrestrial survey techniques (Revuelto et al, 2014) or cost of airborne surveys (Painter et al, 2016). The development of low‐cost drone‐based stereo imaging has gained much popularity in monitoring snow depths recently (e.g., Avanzi et al, 2018; Bühler et al, 2016; Goetz & Brenning, 2019; Redpath et al, 2018), though it is still limited in terms of the spatial coverage it can provide. The recent development of low‐cost, high‐resolution techniques for deriving spatial snow depths from optical satellite imagery provides new opportunities to understand snow patterns at high elevations and produce snow initial conditions for seasonal hydrological simulations.…”

Section: Introductionmentioning

confidence: 99%

The Utility of Optical Satellite Winter Snow Depths for Initializing a Glacio‐Hydrological Model of a High‐Elevation, Andean Catchment

Shaw

Caro

Mendoza

et al. 2020

Water Resources Research

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Information about end‐of‐winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high‐mountain regions. Nevertheless, such information typically relies upon extrapolation from a sparse network of observations at low elevations. Here, we test the potential of high‐resolution snow depth data derived from optical stereophotogrammetry of Pléiades satellites for improving the representation of snow depth initial conditions (SDICs) in a glacio‐hydrological model and assess potential improvements in the skill of snowmelt and streamflow simulations in a high‐elevation Andean catchment. We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground‐based and satellite observations, and consider the relative importance of accurate estimates of SDICs compared to model parameters and forcings. We find that Pléiades SDICs improve the simulation of snow‐covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs based upon extrapolation of meteorological variables or statistical methods to estimate SDICs based upon topography. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months that control albedo and melt rates over a large elevation range (>2,400 m). As such, appropriately characterizing the distribution of total snow volume with elevation is important for reproducing total streamflow and the proportions of snowmelt. Therefore, optical stereo‐photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for large spatial domains.

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“…For example, at our montane plots, which have relatively gentle topography and sparse tree cover (average slope ~10% and average canopy cover~15%), the SfM-generated snow depth maps generally have high correspondence with the airborne lidar-generated snow depth maps (with R 2 ~0.75 to 0.85 between the two) and comparable agreement with ground observations (RMSE ~10 cm for both technologies). For comparison, most studies that evaluate the performance of SfM for snow depth mapping generally find RMSEs of ~5-15 cm in open settings [36,37,40,48,67,81]. SfM can even produce reasonable snowpack maps under very low snow conditions [37].…”

Section: Discussionmentioning

confidence: 99%

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

Broxton

Leeuwen

2020

Remote Sensing

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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.

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Quantifying Uncertainties in Snow Depth Mapping From Structure From Motion Photogrammetry in an Alpine Area

Cited by 30 publications

References 58 publications

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States

The Utility of Optical Satellite Winter Snow Depths for Initializing a Glacio‐Hydrological Model of a High‐Elevation, Andean Catchment

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

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