Evaluating shrub expansion in a subarctic mountain basin using multi-temporal LiDAR data

High-latitude ecosystems have experienced substantial warming over the past 40 years, which is expected to continue into the foreseeable future. Consequently, an increase in vegetation growth has occurred throughout the circumpolar North as documented through remote sensing and plot-level studies. A...

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Bibliographic Details
Main Author: Leipe, Sean
Other Authors: Carey, Sean, Earth and Environmental Sciences
Format: Thesis
Language:English
Published: 2020
Subjects:
GIS
Online Access:http://hdl.handle.net/11375/25757
Description
Summary:High-latitude ecosystems have experienced substantial warming over the past 40 years, which is expected to continue into the foreseeable future. Consequently, an increase in vegetation growth has occurred throughout the circumpolar North as documented through remote sensing and plot-level studies. A major component of this change is shrub expansion (shrubbing) in arctic and subarctic ecotones. However, these changes are highly variable depending on plant species, topographic position, hydrology, soils and other ecosystem properties. Changes in shrub and other vegetation properties are critical to document due to their first-order control on water, energy and carbon balances. This study uses a combination of multi-temporal LiDAR (Light Detection and Ranging) and field surveys to measure temporal changes in shrub vegetation cover over the Wolf Creek Research Basin (WCRB), a 180 km2 long-term watershed research facility located ~15 km south of Whitehorse, Yukon Territory. This work focuses on the smaller Granger Basin, a 7.6 km2 subarctic headwater catchment that straddles WCRB’s subalpine and alpine tundra ecozones with a wide range of elevation, landscape topography, and vegetation. Airborne LiDAR surveys of WCRB were conducted in August 2007 and 2018, providing an ideal opportunity to explore vegetation changes between survey years. Vegetation surveys were conducted throughout Granger Basin in summer 2019 to evaluate shrub properties for comparisons to the LiDAR. Machine learning classification algorithms were used to predict shrub presence/absence in 2018 based on rasterized LiDAR metrics with up to 97% overall independent accuracy compared to field validation points, with the best-performing model applied to the 2007 LiDAR to create binary shrub cover layers to compare between survey years. Results show a 63.3% total increase in detectable shrub cover > 0.45 m in height throughout Granger Basin between 2007 and 2018, with an average yearly expansion of 5.8%. These changes in detectable shrub cover were ...