Predicting the Unpredictable: Predicting Landcover in Boreal Alaska and the Yukon Including Succession and Wildfire Potential

• Published in: Forests
• Main Authors: Monika P. Calef, Jennifer I. Schmidt, Anna Varvak, Robert Ziel
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: boreal forest; wildfire; interior Alaska; Yukon; machine learning model; Plant ecology; QK900-989; Alaska
• Online Access: https://doi.org/10.3390/f14081577; https://doaj.org/article/b5401bcbf5f44742b174dd890b103021

The boreal forest of northwestern North America covers an extensive area, contains vast amounts of carbon in its vegetation and soil, and is characterized by extensive wildfires. Catastrophic crown fires in these forests are fueled predominantly by only two evergreen needle-leaf tree species, black spruce ( Picea mariana (Mill.) B.S.P.) and lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.). Identifying where these flammable species grow through time in the landscape is critical for understanding wildfire risk, damages, and human exposure. Because medium resolution landcover data that include species detail are lacking, we developed a compound modeling approach that enabled us to refine the available evergreen forest category into highly flammable species and less flammable species. We then expanded our refined landcover at decadal time steps from 1984 to 2014. With the aid of an existing burn model, FlamMap, and simple succession rules, we were able to predict future landcover at decadal steps until 2054. Our resulting land covers provide important information to communities in our study area on current and future wildfire risk and vegetation changes and could be developed in a similar fashion for other areas.