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TitleUAV and High Resolution Satellite Mapping of Forage Lichen (Cladonia spp.) in a Rocky Canadian Shield Landscape / Cartographie par drones et par satellites haute résolution du lichen fourrage (Cladonia spp.) dans un paysage rocheux du Bouclier canadien
AuthorFraser, R HORCID logo; Pouliot, D; van der Sluijs, J
SourceCanadian Journal of Remote Sensing vol. 48, issue 1, 2021 p. 5-18,
Alt SeriesNatural Resources Canada, Contribution Series 20210073
PublisherBellwether Publishing, Ltd.
Mediapaper; on-line; digital
File formatpdf; html
ProvinceNorthwest Territories
NTS85I/12; 85J/09
AreaGreat Slave Lake; Yellowknife
Lat/Long WENS-114.0111 -113.9417 62.5639 62.5444
Lat/Long WENS-114.0167 -113.9333 62.5639 62.5417
Subjectsgeophysics; Science and Technology; Nature and Environment; remote sensing; satellite imagery; vegetation; mapping techniques; statistical analyses; regression analyses; models; Lichen; Cladonia spp.; Canadian Shield; WorldView-3; Planet CubeSat; drones; Classification; Methodology
Illustrationssatellite imagery; photographs; photomicrographs; tables; cross-plots
ProgramCanada Centre for Remote Sensing Remote Sensing Science Program - Optical methods and applications
Released2021 04 21
AbstractReindeer 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.
Summary(Plain Language Summary, not published)
Reindeer lichens (Cladonia) are an important source of food, especially during winter, for caribou populations. As such, large-area maps indicating the distribution of these lichens are needed for effective caribou management, and to understand the potential impact of natural disturbances and climate change on lichen abundance. Satellite remote sensing is being increasingly used to map the regional distribution and biomass of reindeer lichens to help characterize optimal caribou habitats and lichen changes. In our study, we tested the use of both Unmanned Aerial Vehicles (UAVs) and high-resolution satellite images to map the occurrence of Cladonia in a rocky, Canadian Shield environment near Yellowknife, Northwest Territories. We found that a UAV sensor containing a near-infrared band could map Cladonia with high accuracy (95%) over relatively small areas (3 ha). The UAV-based lichen maps were then scaled up to provide reference data for training machine learning classifiers applied to 2-3 m resolution Planet and WorldView satellite imagery. The WorldView satellite sensor yielded superior results, and predicted percent Cladonia cover at 2 m resolution with a 6.8 % error.

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