Using Unpiloted Aerial Vehicle Structure from Motion and SnowModel to Map Spatial Distribution of Wind Deposited Snow in Mount Washington, NH Avalanche Terrain. Wagner, C. Ph.D. Thesis, University of New Hampshire, 2023. Book Title: Using Unpiloted Aerial Vehicle Structure from Motion and SnowModel to Map Spatial Distribution of Wind Deposited Snow in Mount Washington, NH Avalanche Terrain ISBN: 9798379712600![Using Unpiloted Aerial Vehicle Structure from Motion and SnowModel to Map Spatial Distribution of Wind Deposited Snow in Mount Washington, NH Avalanche Terrain [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex East of the Rocky Mountains, United States avalanche terrain is almost exclusive to Mount Washington, New Hampshire. Mount Washington’s east-aspect glacial cirques are subject to frequent wind slab avalanche problems due to high winds and ample snowfall in fetch areas above the cirques. Quantification of these slabs’ location, extent, and depth is an integral part of avalanche forecasting and risk assessment. This research used SnowModel, a spatially distributed snow-evolution model, to simulate wind slab depth maps using Mount Washington Observatory meteorologic station data on a 1 m grid. SnowModel’s SnowTran-3D, a snow redistribution by wind algorithm, is tested for one of the first times in the Eastern United States. Snowpack seasonal evolution and accumulation event-based model performance is calibrated and validated using 15 snow depth maps collected throughout the winter of 2021-2022. Snow depth maps were constructed via Structure from Motion (SfM) analysis photogrammetry. SfM maps were derived from optical imagery collected using an Unpiloted Aerial Vehicle (UAV) and were able to quantify wind slab depth with a 5 cm spatial resolution. Limited ground validation showed UAV SfM values are accurate with a 30 cm RMSE on the 2/01/2022 sample date. Total snow depth and snow depth change map time series of each study location consistently show wind-transported snow accumulation and erosion patterns on Mount Washington. SnowModel can capture Mount Washington’s widespread snow redistribution trends but fails to quantify the magnitude and distribution of wind slabs as the UAV SfM can. SnowModel-derived snow depth was compared to Landsat 8’s Normalized Difference Snow Index (NDSI) and shows a significant signal in snow depth increase when NDSI exceeds 0.4. This study provides the first of its kind approach for capturing Mount Washington’s winter snowpack evolution using UAV SfM and a physically based snow evolution model.
@phdthesis{wagner_using_2023,
type = {Master of {Science}, {Civil} and {Environmental} {Engineering}},
title = {Using {Unpiloted} {Aerial} {Vehicle} {Structure} from {Motion} and {SnowModel} to {Map} {Spatial} {Distribution} of {Wind} {Deposited} {Snow} in {Mount} {Washington}, {NH} {Avalanche} {Terrain}},
url = {https://unh.primo.exlibrisgroup.com/discovery/fulldisplay?context=PC&vid=01USNH_UNH:MAIN&search_scope=MyInst_and_CI&tab=Everything&docid=cdi_proquest_journals_2825660613},
abstract = {East of the Rocky Mountains, United States avalanche terrain is almost exclusive to Mount Washington, New Hampshire. Mount Washington’s east-aspect glacial cirques are subject to frequent wind slab avalanche problems due to high winds and ample snowfall in fetch areas above the cirques. Quantification of these slabs’ location, extent, and depth is an integral part of avalanche forecasting and risk assessment. This research used SnowModel, a spatially distributed snow-evolution model, to simulate wind slab depth maps using Mount Washington Observatory meteorologic station data on a 1 m grid. SnowModel’s SnowTran-3D, a snow redistribution by wind algorithm, is tested for one of the first times in the Eastern United States. Snowpack seasonal evolution and accumulation event-based model performance is calibrated and validated using 15 snow depth maps collected throughout the winter of 2021-2022. Snow depth maps were constructed via Structure from Motion (SfM) analysis photogrammetry. SfM maps were derived from optical imagery collected using an Unpiloted Aerial Vehicle (UAV) and were able to quantify wind slab depth with a 5 cm spatial resolution. Limited ground validation showed UAV SfM values are accurate with a 30 cm RMSE on the 2/01/2022 sample date. Total snow depth and snow depth change map time series of each study location consistently show wind-transported snow accumulation and erosion patterns on Mount Washington. SnowModel can capture Mount Washington’s widespread snow redistribution trends but fails to quantify the magnitude and distribution of wind slabs as the UAV SfM can. SnowModel-derived snow depth was compared to Landsat 8’s Normalized Difference Snow Index (NDSI) and shows a significant signal in snow depth increase when NDSI exceeds 0.4. This study provides the first of its kind approach for capturing Mount Washington’s winter snowpack evolution using UAV SfM and a physically based snow evolution model.},
language = {eng},
urldate = {2023-08-11},
school = {University of New Hampshire},
author = {Wagner, Cameron},
year = {2023},
note = {Book Title: Using Unpiloted Aerial Vehicle Structure from Motion and SnowModel to Map Spatial Distribution of Wind Deposited Snow in Mount Washington, NH Avalanche Terrain
ISBN: 9798379712600},
keywords = {NALCMS},
}
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Quantification of these slabs’ location, extent, and depth is an integral part of avalanche forecasting and risk assessment. This research used SnowModel, a spatially distributed snow-evolution model, to simulate wind slab depth maps using Mount Washington Observatory meteorologic station data on a 1 m grid. SnowModel’s SnowTran-3D, a snow redistribution by wind algorithm, is tested for one of the first times in the Eastern United States. Snowpack seasonal evolution and accumulation event-based model performance is calibrated and validated using 15 snow depth maps collected throughout the winter of 2021-2022. Snow depth maps were constructed via Structure from Motion (SfM) analysis photogrammetry. SfM maps were derived from optical imagery collected using an Unpiloted Aerial Vehicle (UAV) and were able to quantify wind slab depth with a 5 cm spatial resolution. Limited ground validation showed UAV SfM values are accurate with a 30 cm RMSE on the 2/01/2022 sample date. 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