C013-0011
Shrub-associated thermokarst detection using high density UAV-based LiDAR

Tuesday, 8 December 2020
Poster
Shannon Dillard, University of Wisconsin Madison, Geography, Madison, WI, United States; Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, NM, United States, Christian G Andresen, Los Alamos National Laboratory, Los Alamos, NM, United States, Adam Collins, Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States, Julian B Dann, Los Alamos National Laboratory, Space Science and Applications, Los Alamos, NM, United States and Cathy Jean Wilson, Los Alamos National Laboratory, Earth and Environmental Science Division, Los Alamos, NM, United States
Abstract:
Light detection and ranging (LiDAR) technologies are changing the ways in which scientists research the Arctic. Unmanned aerial vehicle (UAV)-based LiDAR collects detailed structural landscape data by returning high density point clouds. LiDAR systems are improving the quality and accuracy of data collection compared to field surveys and help to remove some of the logistical barriers of research in remote and complicated terrain.

Our study mapped thermokarst depressions in a 3 km2 watershed on the Seward Peninsula near Nome, Alaska in 2017 and 2018. The watershed is characterized as tussock permafrost landscape consisting of grasses and mosses interspersed with patches of dense shrubs. By configuring the UAV with a 32 laser swath and flying slowly at low altitude, we collected high density point clouds of about 4,000 points m-2, including high density terrain surface points underneath dense shrubby vegetation. We then modeled the sub-vegetation terrain surface at very fine detail to detect thermokarst depressions. Combining these high resolution data with vegetation surveys and topographic properties, we tested the relationship between permafrost subsidence, thermokarst depressions and vegetation type, specifically the relationships in shrub-associated thermokarst features.

By coupling our LiDAR data and analysis with hydrologic models, climate variables (e.g., snow depth, soil moisture), and vegetation surveys, we can infer geospatial relationships between thermokarst development, vegetation, and landscape position throughout the watershed. The technologies used in our study have implications for predicting the development of future thermokarst features and permafrost thaw sites across the Arctic.

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