Assessing beech bark-diseased forest canopies over landscapes using high resolution open-source imagery in an ecological framework

Locating and tracking outbreaks of pests and pathogens enables forest ecologists and managers to assess impact or develop interventions. Beech bark disease (BBD) is actively affecting eastern US forests, including within the upper Great Lakes region. Our goal was to develop and apply an open-source framework for identification of BBD-affected tree crowns and for assessment of their spatial-temporal distribution over heterogeneous forested landscapes. We used the freely available USDA NAIP high spatial resolution aerial imagery, developed the identification algorithm in the open-source R environment, and integrated these within several open-data ecological classifications. The study landscape was the uplands of the similar to 4200-ha University of Michigan Biological Station (UMBS) in northern Lower Michigan. Our objectives were to: 1) collect field data to characterize BBD on the landscape and to train and test remote sensing methods; 2) compile mapped ecological classifications; 3) explore NAIP spectral separability of BBD-affected crowns from other types of interest; 4) develop and apply a mostly-automated segmentation algorithm for identification of BBD-affected tree crowns; 5) assess mapped results over the entire landscape and within ecological classification strata; and 6) suggest an ecologically based framework for extensibility beyond the study landscape. Of a sample of visually interpreted yellowed tree crowns on NAIP at Training Sites, 99% were field-verified as beech with Neonectria. Spectral explorations confirmed affected (yellow) beech crowns had consistently higher reflection in NAIP red and green bands and were distinct from healthy beech and aspen undergoing age-related mortality; there was some spectral confusion with yellowed herbaceous areas. The segmentation algorithm was accurate to 82 %, and over the 2012-2018 biennial time series mapped > 9,700 BBD-affected canopies (single or interspersed beech tree crowns). Optimized Getis-Ord Gi* showed significant clustered distributions of BBD hotspots and coldspots on the landscape at each date. Ecological classifications presented strata statistically associated with the greatest density of mapped BBD-affected canopies and provide a framework for extensibility. This integrated approach may provide an accessible option for forest managers to map and assess hotspots of canopy-level BBD symptoms over heterogeneous landscapes and over time.