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

• Published in: Canadian Journal of Remote Sensing
• Main Authors: Robert H. Fraser, Darren Pouliot, Jurjen van der Sluijs
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: Taylor & Francis Group 2022
• Subjects: Environmental sciences; GE1-350; Technology; T; Northwest Territories; Yellowknife
• Online Access: https://doi.org/10.1080/07038992.2021.1908118; https://doaj.org/article/0c6e31516e514774b60d3cde3b9214be

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 (R2 = 0.61) when tested at independent study sites and outperformed the 6 m Planet models, which had a 9.9% RMSE (R2 = 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.