Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

Alberto Candela,Michelle M Gierach,David R Thompson,David Wettergreen,Kevin Edelson,Gail Woodward

doi:10.3389/fmars.2021.689489

Alberto Candela, Michelle M Gierach + Show 4 more

Open Access

https://doi.org/10.3389/fmars.2021.689489

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Abstract

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 andin situdata 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 situmeasurements 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 proof of concept using spaceborne remote sensing together with high-quality airborne data from the NASA Earth Venture Suborbital-2 (EVS-2) Coral Reef Airborne Laboratory (CORAL) mission as a proxy forin situsamples. Results indicate that our method is capable of extrapolatingin situfeatures and refining information from remote sensing with increasing accuracy. Furthermore, the results confirm that decision theory is a powerful tool for sample selection.

Highlights

In addition to their significance in the marine biome, coral reefs are important to the cultural and economic lives of hundreds of millions of people around the world (Moberg and Folke, 1999; Costanza et al, 2014)
We apply and compare various optimization techniques designed for information gathering tasks; greedy heuristics for Bayesian experimental design, Monte Carlo tree search, and ergodic optimal control
We demonstrate that the combination of these methods, together with remote sensing, allows for the extrapolation of just a few in situ signatures to many locations in large areas

Summary

Introduction

In addition to their significance in the marine biome, coral reefs are important to the cultural and economic lives of hundreds of millions of people around the world (Moberg and Folke, 1999; Costanza et al, 2014). Our current understanding may not be representative of the reef under study, nor the regional and global reef ecosystem given existing data constraints (Hochberg and Gierach, 2021). Applications of remote sensing to coral reef environments focused on mapping reef geomorphology and ecological zonation (Kuchler et al, 1988; Green et al, 1996; Mumby et al, 2004). In the past few decades, much of remote sensing has focused on mapping habitats using qualitative descriptors comprising various combinations of substrate (e.g., sand, limestone, rubble), benthic functional type (e.g., coral, algae, seagrass), reef type (e.g., fringing, patch, barrier), and/or location within the reef system (e.g., slope, flat) (Hedley et al, 2016). Current remote sensing methods give only indirect information about reef condition, making in situ data critical

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