Leveraging Multi-Source Remote Sensing Images and Deep Learning to Map Caribou Lichens

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Jozdani, Shahab
remote sensing , deep learning , lichen mapping
Given the importance of lichens for caribou during winters, disturbances to caribou lichens may affect caribou migration and distribution patterns, and possibly lead to their population declines. Using remote sensing (RS) data, it is possible to monitor forage lichen cover efficiently over large and inaccessible areas. In this thesis, three significantly challenging problems in lichen cover mapping (especially Cladonia spp.) in different regions of Canada (Newfoundland and Labrador, Quebec, and Northwest Territory) are addressed: 1) lichen mapping using limited ground-validation data; 2) deploying a single, universal model for lichen mapping in non-atmospherically corrected RS images; and 3) lichen fractional cover mapping over rocky landscapes where non-lichen features are spectrally similar to caribou lichens. To address these three challenges, we used a wide variety of RS images (micro-plot images, high-resolution aerial optical images, high-resolution satellite (HRS) images (WorldView-2 and -3), and airborne hyperspectral imagery (AVIRIS-NG)), and different advanced deep learning (DL) models. In the first phase of this research, we found that semi-supervised DL for lichen mapping can improve lichen mapping compared to fully supervised learning in the presence of limited training data. In the second phase, our experiments showed the high potential of Generative Adversarial Networks (GANs) for normalizing non-atmospherically corrected HRS images for lichen mapping using a single, universal lichen detector model under different atmospheric conditions. In the last phase of this research, we found that using AVIRIS-NG hyperspectral imagery, lichen fractional cover mapping was more accurate than using HRS imagery over a rocky landscape where non-lichen bright features resembled lichen species of interest (Cladonia spp.). The studies conducted in this thesis are significant as they open new insights into how the use of state-of-the-art DL solutions and multiple sources of RS data can improve the quality of lichen mapping under different challenging circumstances. One of the most important contributions of this study is that all the methods and results presented in this thesis can also be readily applied to other vegetation mapping applications using RS data as the methods were designed in such a way that they do not depend on a specific land cover type or application.
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