NH033-0003
Playing the odds: using remotely sensed data to assess risk from wildfire and permafrost thaw in the Arctic

Tuesday, 15 December 2020
Poster
Jennifer Schmidt, University of Alaska Anchorage, Institute of Social and Economic Research, Anchorage, AK, United States, Robert Ziel, University of Alaska, Fairbanks, Fairbanks, United States, Monika P Calef, Soka University of America, Aliso Viejo, CA, United States and Anna Varvak, Soka University, Aliso Viejo, CA, United States
Abstract:
Currently, communities grapple with COVID-19 while already dealing with natural hazards, emphasizing the fact that communities and residents are forced to deal with multiple simultaneous hazards. This can create limitations in capacity; therefore, finding ways to maximize efficiency and utilize products that can provide insight into multiple hazards is urgently needed. Remotely sensed products provide vast amounts of information for little to no cost. Our research has worked with three communities over the last year to assess two hazards often faced by northern communities: wildfire and thawing permafrost. We used vegetation data produced by the ABoVE program to assess changes in these hazards from 1984 through 2014 in Anchorage and Fairbanks, Alaska and Whitehorse, Yukon. Risk assessment combines information about hazards and their potential to cause damage to highly valued assets or resources (HVRAs; i.e. infrastructure, human life, and other items society values). Finding historical records on HVRAs for our communities has been challenging, so we also used Trends.Earth which utilizes many remotely sensed products to assess urbanization since 2000. Overall, hazards have changed the largest around Fairbanks due to wildfire activity, but risk has changed nearly equally due to expansion into the urban wildland interface. While the use of remotely sensed data was helpful to show urbanization at a broad scale, it was of limited use at the community level in Arctic environments. This highlights some of the limitations of remotely sensed products to capture social attributes important for risk modeling. However, even when these data products lack the desired detail, they are useful for sparking conversations with communities and promoting co-produced knowledge to improve risk assessments.