Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation

McKenzie, Len J., Langlois, Lucas A., and Roelfsema, Chris M. (2022) Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation. Remote Sensing, 14 (11). 2604.

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Abstract

Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of seagrass meadows in the optically complex waters of the Great Barrier Reef and explored enhanced mapping approaches with a focus on emerging technologies, and key considerations for future mapping. Our review showed that field-based mapping of seagrass has traditionally been the most common approach in the GBR-WHA, with few attempts to adopt remote sensing approaches and emerging technologies. Using a series of case studies to harness the power of machine-and deep-learning, we mapped seagrass cover with PlanetScope and UAV-captured imagery in a variety of settings. Using a machine-learn-ing pixel-based classification coupled with a bootstrapping process, we were able to significantly improve maps of seagrass, particularly in low cover, fragmented and complex habitats. We also used deep-learning models to derive enhanced maps from UAV imagery. Combined, these lessons and emerging technologies show that more accurate and efficient seagrass mapping approaches are possible, producing maps of higher confidence for users and enabling the upscaling of seagrass mapping into the future.

Item ID: 76515
Item Type: Article (Research - C1)
ISSN: 2072-4292
Keywords: deep-learning, earth observing, Great Barrier Reef, machine-learning, map confidence, mapping, seagrass, spaceborne, UAV
Copyright Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited: 23 Mar 2023 01:25
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410401 Conservation and biodiversity @ 50%
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management @ 50%
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