Using Remote Sensing and In Situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

Coral reefs are of undeniable importance to the environment, yet little is known of them on a global scale. Assessments rely on laborious, local in-water surveys. In recent years remote sensing has been useful on larger scales for certain aspects of reef science such as benthic functional type discrimination. However, remote sensing only gives indirect information about reef condition. Only through combination of remote sensing and in situ data can we achieve coverage to understand reef condition and monitor worldwide condition. This work presents an approach to global mapping of coral reef condition that intelligently selects local, in situ measurements that refine the accuracy and resolution of global remote sensing. To this end, we apply new techniques in remote sensing analysis, probabilistic modeling for coral reef mapping, and decision theory for sample selection. Our strategy represents a fundamental change in how we study coral reefs and assess their condition on a global scale. We demonstrate feasibility and performance of our approach in a case study using remote sensing together with high-quality airborne data from NASA’s CORAL mission. Results indicate that our method is capable of extrapolating in situ samples and refining information from remote sensing with great accuracy. Furthermore, the results confirm that decision theory is a powerful tool for sample selection.