AUTHORS: Nicholas Kruskamp*, Joshua Gray, Ross Meentemeyer – North Carolina State University
ABSTRACT: Accurate maps of host plant species are essential for predicting disease spread in forest environments, but species-level vegetation data rarely exist at sufficiently high accuracies or spatial resolutions to model epidemiological processes. We examine the manner in which host map accuracy and resolution affect spatio-temporal models of disease spread. Sudden oak death is used as a case study of an emerging infectious disease requiring predictive models with reliable host maps for disease management and quarantine policy. We used field measurements, muli-temporal moderate resolution Sentinel-2 imagery, and a random forest algorithm to map the density and biomass of tanoak (Lithocarpus densiflorus) - the reservoir host species undergoing substantial tree mortality at a new outbreak of international concern in southwest Oregon. Moderate-resolution remotely sensed images from Sentinel-2 provided spectrally-derived environmental covariates as predictor variables in the random forest model. Seventy-two field sites were surveyed for species density and composition. Elevation, G(reen)NDVI, solar illumination, and the first image principal component were the most important variables in the random forest model. Our mapping approach resulted in significantly less area of the landscape occupied by tanoak compared to existing vegetation datasets (CALVEG, LEMMA). Preliminary results suggest that the more accurate, highly resolved maps of tanoak density have a major impact on epidemiological modeling results, with slower rates of disease spread caused by the reduced extent of the host species. Our results have substantial implications for disease management. Results from this study suggest that remote sensing with machine learning techniques should be more commonly used to improve landscape epidemiological modeling inputs, and more critically examine the impact of input host species maps on model outputs.
