Date of Award

Summer 8-16-2024

Level of Access Assigned by Author

Open-Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Forest Resources

Advisor

Daniel J. Hayes

Second Committee Member

Aaron Weiskittel

Third Committee Member

Ivan Fernandez

Additional Committee Members

David Hiebeler

Joshua Fisher

Abstract

Arctic tundra landscapes are characterized by underlying permafrost sustained by extremely low average temperatures. These permafrost soils have been sequestering carbon for millennia, effectively locking it into the frozen ground. Currently, anthropogenic climate change, exacerbated by Arctic amplification, is driving rapid and unprecedented warming in the Arctic region putting the permafrost at risk of thaw. Thawing permafrost could release vast amounts of previously stored carbon as greenhouse gasses, driving the permafrost carbon feedback to accelerate warming. Unfortunately, the high spatial variability and complex feedback mechanisms limit our understanding of the connections and dynamics between above- and below-ground processes, and current models often fail to adequately capture permafrost C dynamics, a much-needed representation in climate predictions. First, we conducted a scaling exercise to evaluate the potential of novel remote sensing technologies to capture key tundra processes and reduce observational mismatches. Unoccupied aerial systems, airborne imaging spectroscopy, and satellite imagery were used to model the active layer and characterize key permafrost features. Medium spatial resolution image bands proved to be good predictors of average thaw depth, whereas high resolution imagery showed more contrast beneficial in complex landscapes like polygon tundra. And while average thaw depth predictions have proved valuable, when studying the resilience of the Arctic Boreal Region (ABR) it is important to observe local features at the matching scale. Second, airborne imaging spectroscopy allows for a region-wide mapping of spectral vegetation traits reflecting the variability in hydrology or nutrient availability. Key traits indicative of tundra functioning were selected and clustered to create a high-resolution spatial dataset reflecting above-ground tundra characteristics reflecting the below-ground permafrost conditions. Further analysis of the spectral traits revealed the local adaptation strategies to environmental conditions and disturbances. Lastly, based on the Landsat archive, yearly disturbances were mapped and disturbance trends by thermokarst zone were created. This study highlights the importance of landscape characteristics in analyzing and modeling disturbance trends. By leveraging each remote sensing data product, we enhanced the characterization of tundra landscapes. The scaling approach identified the benefits and pitfalls of each product for modeling, which is crucial for region-wide application. Remote sensing proved extremely valuable and provided insights into the historical and current state of the permafrost and allows for an improved prediction of future shifts in vegetation and ecosystem trajectories by improving the modeling of key vegetation parameters and understanding permafrost-vegetation interactions.

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