Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery

Snow 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 com-plex 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 catego-rical 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

Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery

Snow 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.</p

Using tree rings to compare Colorado's 2019 avalanche cycle to previous large avalanche cycles

Large magnitude avalanches (size ≥D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a historically large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a 2-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. The extensive number of downed trees from this avalanche cycle allowed us to collect 1188 cross-sections and cores from 1023 unique trees within 24 avalanche paths across the state. We recorded 4135 growth disturbances in these samples. These data comprise the largest known avalanche tree-ring dataset in the world. We employed a strategic nested sampling design to account for scale by including several individual avalanche paths within a given drainage to create sub-regions and then sampled six major sub-regions (counties) throughout the greater region (state). We identified 76 avalanche years within 24 individual avalanche paths from 1698 to 2020. Large magnitude empirical avalanche event frequency varied across paths and sub-regions. Our results indicate the most widespread avalanche cycle in our study area prior to 2019 occurred in 1899, where 12 avalanche paths show evidence of large magnitude avalanche activity. Historical records also highlight 1899 as a year with widespread and large magnitude avalanche activity. These results indicate the avalanche cycle of March 2019 was of similar magnitude. Understanding the spatial extent and return frequency of large magnitude avalanche cycles across multiple spatial scales, from individual paths to an entire state, helps avalanche forecasters improve their products and mitigation strategies and assists infrastructure planners when designing and planning in avalanche terrain. </p