Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery

Insect outbreaks cause significant tree mortality across western North America, including in high-elevation whitebark pine forests. These forests are under several threats, which include attack by insects and white pine blister rust, as well as conversion to other tree species as a result of fire suppression. Mapping tree mortality is critical to determining the status of whitebark pine as a species. Satellite remote sensing builds upon existing aerial surveys by using objective, repeatable methods that can result in high spatial resolution monitoring. Past studies concentrated on level terrain and only forest vegetation type. The objective of this study was to develop a means of classifying whitebark pine mortality caused by a mountain pine beetle infestation in rugged, remote terrain using high spatial resolution satellite imagery. We overcame three challenges of mapping mortality in this mountainous region: (1) separating non-vegetated cover types, green and brown herbaceous cover, green (live) tree cover, and red-attack (dead) tree cover; (2) variations in illumination as a result of variations in slope and aspect related to the mountainous terrain of the study site; and (3) the difficulty of georegistering the imagery for use in comparing field measurements. Quickbird multi-spectral imagery (2.4 m spatial resolution) was used, together with a maximum likelihood classification method, to classify vegetation cover types over a 6400 ha area. To train the classifier, we selected pixels in each cover class from the imagery guided by our knowledge of the study site. Variables used in the maximum likelihood classifier included the ratio of red reflectance to green reflectance as well as green reflectance. These variables were stratified by solar incidence angle to account for illumination variability. We evaluated the results of the classified image using a reserved set of image-derived class members and field measurements of live and dead trees. Classification results yielded high overall accuracy (86% and 91% using image-derived class members and field measurements respectively) and kappa statistics (0.82 and 0.82) and low commission (0.9% and 1.5%) and omission (6.5% and 15.9%) errors for the red-attack tree class. Across the scene, 700 ha or 31% of the forest was identified as in the red-attack stage. Severity (percent mortality by canopy cover) varied from nearly 100% for some areas to regions with little mortality. These results suggest that high spatial resolution satellite imagery can provide valuable information for mapping and monitoring tree mortality even in rugged, mountainous terrain.