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Extensive mortality of susceptible spruce can be caused by spruce beetles at epidemic population levels, as in the ongoing outbreak in Southcentral Alaska. Although information on outbreak extent and severity underpins forest management and research, the data products available in Alaska have substantial gaps. Widely available high-resolution satellite imagery are a promising data source for detecting beetle kill because it is possible, though challenging, to identify individual trees. However, the applicability of automated deep-learning approaches for regional-scale mapping has not been evaluated. Here, we assess a deep convolutional network for mapping dead spruce in high-resolution (∼2 m) satellite imagery of Southcentral Alaska. The network identified dead spruce pixels across stand characteristics, achieving an average accuracy of 95%. To upscale to the stand scale, we mitigated overestimation of dead tree pixels at elevated severity by calibration. Stand-scale areal severity, the fraction of dead spruce pixels within a stand, was mapped with an RMSE of 0.02 at 90 m scale. The estimated severity exceeded 0.05 in fewer than 4% of the landscape, and approximately 90% of dead trees pixels were found in low-severity stands. Severity was weakly associated with stand-scale Landsat reflectance changes, a clear relation between SWIR reflectance change and severity only emerging above 0.1 severity. In conclusion, high-resolution satellite imagery are suited to automated mapping of beetle-associated kill at tree and stand scale across the severity spectrum. Such data products support forest and fire management and further understanding of the dynamics and consequences of beetle outbreaks.
Zwieback et al. (Sat,) studied this question.