A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images

Abstract

High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended time in the field to complete (i.e., point-intercept assessments). Digital photograph classification (DPC) may address these drawbacks. In this study, we first assess the performance of a DPC method developed through licensed software to estimate ground cover percentage (%) of bright lichens, a critical caribou forage in fall and winter when other food resources are scarce. We then evaluate the feasibility of replicating this workflow in an open-source environment with a modified U-net model to improve processing time and scalability. Our results indicate that DPC is appropriate for generating ground-truth data in support of large-scale EO-based lichen mapping within the boreal forests of eastern Canada. Our final open-sourced classification model, Lichen Convolutional Neural Network (LiCNN), is comparably accurate yet more efficient than the licensed workflow. Therefore, the LiCNN approach successfully addresses the mentioned shortcomings of conventional ground-truth data collection methods efficiently and without the need for specialized software.