UAV and High Resolution Satellite Mapping of Forage Lichen (Cladonia spp.) in a Rocky Canadian Shield Landscape

Abstract

Abstract Reindeer lichens (Cladonia spp.) are an important food source for woodland and barren ground caribou herds. In this study, we assessed Cladonia classification accuracy in a rocky, Canadian Shield landscape near Yellowknife, Northwest Territories using both Unmanned Aerial Vehicle (UAV) sensors and high-resolution satellite sensors. At the UAV scale, random forest classifications derived from a multispectral, visible-near infrared sensor (Micasense Altum) had an average 5% higher accuracy for mapping Cladonia (i.e., 95.5%) than when using a conventional color RGB camera (DJI Phantom 4 RTK). We aggregated Altum lichen classifications from three 5 ha study sites to train random forest regression models of fractional lichen cover using predictor features from WorldView-3 and Planet CubeSat satellite imagery. WorldView models at 6 m resolution had an average 6.8% RMSE (R 2 = 0.61) when tested at independent study sites and outperformed the 6 m Planet models, which had a 9.9% RMSE (R 2 = 0.34). These satellite results are comparable to previous lichen mapping studies focusing on woodlands, but the small cover of Cladonia in our study area (11.6% or 16.8% within the barren portions) results in a high relative RMSE (62.2%) expressed as a proportion of mean lichen cover.