Using remote sensing imagery to map and quantify aspen mortality in NW Alberta

Abstract

Land managers and researchers have documented a rapid decline in aspen health in western North America since the early 2000s, which has been linked to drought episodes combined with caterpillar defoliation outbreaks. As the most abundant commercial deciduous tree species in Alberta, aspen serves many functions in Alberta forests, including providing forage and habitat for many wildlife species, water cycling and conservation, carbon sequestration, and wood fibre. Aspen mortality first became apparent in northwest Alberta in the late 2000s, and aerial surveyors began mapping it in 2011. Because of its clumpy, dispersed distribution within stands, this is a difficult forest health disturbance to accurately map. The Grande Prairie Forest area in northwest Alberta was chosen as the study area for this research project because it has the highest aspen mortality rate in the province. The procedures developed for this area are intended to be used in other Alberta Forest areas that have only recently begun to see and map aspen mortality. The goal of this research project was to map and categorise the current amount of aspen mortality in the Grande Prairie Forest area into the three mortality classes currently used by aerial surveyors in Alberta using remote sensing imagery. Using Landsat 8 OLI imagery, this project evaluated automated image classification methods to delineate and quantify mortality. Classification algorithms used in this project were Random Forest (RF), Support Vector Machine (SVM), Maximum Likelihood (ML), and ISO Data. A confusion matrix was used to compare overall accuracies as well as misclassifications. Both SVM and RF had similar acceptable accuracies with F1-scores of 79.4% and 78.4 respectively. The resulting categorized mortality map is to be used by land managers to focus detailed ground surveys and aid in forest management planning to ensure sufficient regeneration of stands experiencing high mortality.