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ABSTRACT
Landsat satellite time series annual Best-Available-Pixel (BAP) composites for the period 1984–2017 of the Kenora Forest Management Unit in northwestern Ontario, Canada were sampled and stratified by forest stand conditions and aerial sketch map (ASM) compilations of mortality and defoliation. Pre- and post-disturbance multispectral image and textural data were classified using a logistic regression decision rule for spruce budworm (Choristoneura fumiferana), jackpine budworm (Choristoneura pinus pinus), and forest tent caterpillar (Malacosoma disstria). Overall classification accuracy of 79.6% was obtained in a 998 ha sample of 120 forest stands.
Acknowledgments
Thank you to the editor and two anonymous reviewers for helpful comments that improved the manuscript.