Spectral Unmixing and Mapping of Coral Reef Benthic Cover

Coral reefs are an important ecosystem to the local communities and indigenous wildlife that rely on them. However, reefs have greatly degraded in recent decades with the remaining at increasing risk of loss. Quantitatively mapping these reefs would provide a resource for us to monitor changes and understand their health. We explore methods leveraging limited spectral data and resources for efficient global scale modeling of coral reefs. We then evaluate performance on a Deep Neural Network and our previously developed Deep Conditional Dirichlet Model. Regions of high uncertainty based on the model output prediction are used to determine informative in situ sampling. An ergodic planner is implemented to generate a path through these regions to acquire samples that best improve the coral map. The result is a resource efficient learning based pipeline that augments existing spectral data and maps coral reefs globally to improve our understanding of their condition.