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.

摘要

加拿大林省条件和死亡率和死亡率的空中素描图（ASM）汇编对加拿大卫星电视台1984-2017年期间Landsat卫星时间序列年度最佳象素（BAP）复合材料进行了抽样和分层。落叶。利用逻辑回归决策规则对云杉芽虫（Choristoneura fumiferana），松果虫（Choristoneura pinus pinus）和森林帐篷毛虫（Malacosoma disstria）的干扰前后的多光谱图像和纹理数据进行分类。在120个林分的998公顷样本中，总体分类准确度达到79.6％。