Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery
ABSTRACTSnow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial...
Published in: | Arctic, Antarctic, and Alpine Research |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Taylor & Francis Group
2024
|
Subjects: | |
Online Access: | https://doi.org/10.1080/15230430.2024.2310333 https://doaj.org/article/64113ab265f44d60b2b5bd84215395c1 |
id |
ftdoajarticles:oai:doaj.org/article:64113ab265f44d60b2b5bd84215395c1 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:64113ab265f44d60b2b5bd84215395c1 2024-09-15T17:49:03+00:00 Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery Erich H. Peitzsch Chelsea Martin-Mikle Jordy Hendrikx Karl Birkeland Daniel Fagre 2024-12-01T00:00:00Z https://doi.org/10.1080/15230430.2024.2310333 https://doaj.org/article/64113ab265f44d60b2b5bd84215395c1 EN eng Taylor & Francis Group https://www.tandfonline.com/doi/10.1080/15230430.2024.2310333 https://doaj.org/toc/1523-0430 https://doaj.org/toc/1938-4246 doi:10.1080/15230430.2024.2310333 1938-4246 1523-0430 https://doaj.org/article/64113ab265f44d60b2b5bd84215395c1 Arctic, Antarctic, and Alpine Research, Vol 56, Iss 1 (2024) Snow avalanche remote sensing dendrochronology Environmental sciences GE1-350 Ecology QH540-549.5 article 2024 ftdoajarticles https://doi.org/10.1080/15230430.2024.2310333 2024-08-05T17:49:56Z ABSTRACTSnow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths. Article in Journal/Newspaper Antarctic and Alpine Research Arctic Directory of Open Access Journals: DOAJ Articles Arctic, Antarctic, and Alpine Research 56 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Snow avalanche remote sensing dendrochronology Environmental sciences GE1-350 Ecology QH540-549.5 |
spellingShingle |
Snow avalanche remote sensing dendrochronology Environmental sciences GE1-350 Ecology QH540-549.5 Erich H. Peitzsch Chelsea Martin-Mikle Jordy Hendrikx Karl Birkeland Daniel Fagre Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
topic_facet |
Snow avalanche remote sensing dendrochronology Environmental sciences GE1-350 Ecology QH540-549.5 |
description |
ABSTRACTSnow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths. |
format |
Article in Journal/Newspaper |
author |
Erich H. Peitzsch Chelsea Martin-Mikle Jordy Hendrikx Karl Birkeland Daniel Fagre |
author_facet |
Erich H. Peitzsch Chelsea Martin-Mikle Jordy Hendrikx Karl Birkeland Daniel Fagre |
author_sort |
Erich H. Peitzsch |
title |
Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
title_short |
Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
title_full |
Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
title_fullStr |
Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
title_full_unstemmed |
Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
title_sort |
characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery |
publisher |
Taylor & Francis Group |
publishDate |
2024 |
url |
https://doi.org/10.1080/15230430.2024.2310333 https://doaj.org/article/64113ab265f44d60b2b5bd84215395c1 |
genre |
Antarctic and Alpine Research Arctic |
genre_facet |
Antarctic and Alpine Research Arctic |
op_source |
Arctic, Antarctic, and Alpine Research, Vol 56, Iss 1 (2024) |
op_relation |
https://www.tandfonline.com/doi/10.1080/15230430.2024.2310333 https://doaj.org/toc/1523-0430 https://doaj.org/toc/1938-4246 doi:10.1080/15230430.2024.2310333 1938-4246 1523-0430 https://doaj.org/article/64113ab265f44d60b2b5bd84215395c1 |
op_doi |
https://doi.org/10.1080/15230430.2024.2310333 |
container_title |
Arctic, Antarctic, and Alpine Research |
container_volume |
56 |
container_issue |
1 |
_version_ |
1810290746449723392 |