| Summary: | Thesis (M.S.) University of Alaska Fairbanks, 2017 The Yukon-Kuskokwim Delta (YKD) is one of the largest and most ecologically productive coastal wetland regions in the pan-Arctic. Formed by the Yukon and Kuskokwim Rivers flowing into the Bering Sea, nearly 130,000 square kilometers of delta support 23,000 Alaskan Natives living subsistence lifestyles. Permafrost on the outer delta commonly occurs on the abandoned floodplain deposits. Ground ice in the soil raises surface elevations on the order of 1-2 meters, creating plateaus on the landscape. Better drainage on the plateaus supports distinct Sphagnum-rich vegetation, which in turn protects the permafrost from rising air temperatures with low thermal conductivity during the summer. This ecosystem-protected permafrost is thus vulnerable to disturbances from rising air temperatures, vegetation mortality, and inland storm surges, which have been known to flood up to 37 km inland. This thesis assesses several novel techniques to map permafrost distribution at high-resolution on the YKD. Accurate baseline maps of permafrost extent are critical for a variety of applications, including long-term monitoring. As air and ground temperatures rise across the Arctic, monitoring landscape change is important for understanding permafrost degradation processes (e.g. thermokarst) and greenhouse gas dynamics from the local to global scales. This thesis separately explored the value of Light Detection And Ranging (LiDAR) and spectral datasets as tools to map permafrost at a high spatial resolution. Furthermore, this thesis sought to automate these processes, with the vision of high-resolution mapping over large spatial extents. Fieldwork was conducted in July 2016 to both parameterize and then validate the mapping efforts. The LiDAR mapping extent assessed a 135 km² area (~15% permafrost cover), and the spectral mapping extent assessed an 8 km² area (~20% permafrost cover). For the LiDAR dataset, the use of a simple elevation threshold informed by field ground truth values ...
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