Modeling and mapping forest diversity in the boreal forest of interior Alaska

Abstract

Patterns of forest diversity are less well known in the boreal forest of interior Alaska than in most ecosystems of North America. Proactive forest planning requires spatially accurate information about forest diversity. Modeling is a cost-efficient way of predicting key forest diversity measures as a function of human and environmental factors. Investigate and predict the patterns and processes in tree species and tree size-class diversity within the boreal forest of Alaska for a first mapped quantitative baseline. For the boreal forest of Alaska, USA, we employed Random Forest Analysis (machine learning) and the Boruta algorithm in R to predict tree species and tree size-class diversity for the entire region using a combination of forest inventory data and a suite of 30 predictors from public open-access data archives that included climatic, distance, and topographic variables. We developed prediction maps in a GIS for the current levels (Year 2012) of tree size-class and species diversity. The method employed here yielded good accuracy for the huge Alaskan landscape despite the exclusion of spectral reflectance data. It’s the first quantified GIS prediction baseline. The results indicate that the geographic pattern of tree species diversity differs from the pattern of tree size-class diversity across this forest type. The results suggest that human factors combined with topographical factors had a large impact on predicting the patterns of diversity in the boreal forest of interior Alaska.