What Can Artificial Intelligence Offer Coral Reef Managers?

Hamylton, Sarah; Zhou, Zhexuan; Wang, Lei

doi:10.3389/fmars.2020.603829

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…The new governance paradigm needs better understanding at largetime/space scales [20], to create pathways and achieve targets for recovery and climate adaptation, which require globally coordinated actions [21]. Remote-sensing techniques play a crucial role in the modernization of management processes since spatial resolution has increased significatively since the 1960s, and emerging tools such as artificial intelligence show promise for the coral reef remote-sensing specialists due to the emergence of machine-learning algorithms for mapping and feature detection from drone imagery of marine environments [22]. Recently, Cowburn et al [23] demonstrated how ecosystembased management (EBM) strategies can benefit from remote-sensing techniques; they addressed big-data sources, remote techniques to complement fieldwork, collaboration, and communication using virtual platforms, and toolboxes to be used by a modern coral reef scientist.…”

Section: Introductionmentioning

confidence: 99%

Development of a Google Earth Engine-Based Application for the Management of Shallow Coral Reefs Using Drone Imagery

et al. 2023

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The Caribbean is one of the world’s most vulnerable regions to the projected impacts of climate change, and changes in coral reef ecosystems have been studied over the last two decades. Lately, new technology-based methods using satellites and unmanned vehicles, among others have emerged as tools to aid the governance of these ecosystems by providing managers with high-quality data for decision-making processes. This paper addresses the development of a Google Earth Engine (GEE)-based application for use in the management processes of shallow coral reef ecosystems, using images acquired with Remotely Piloted Aircraft Systems (RPAS) known as drones, at the Old Providence McBean Lagoon National Natural Park; a Marine Protected Area (MPA) located northwest of Old Providence Island, Colombia. Image acquisition and processing, known as drone imagery, is first described for flights performed using an RTK multispectral drone at five different monitoring stations within the MPA. Then, the use of the GEE app is described and illustrated. The user executes four simple steps starting with the selection of the orthomosaics uploaded to GEE and obtaining the reef habitat classification for four categories: coral, macroalgae, sand, and rubble, at any of the five monitoring stations. Results show that these classes can be effectively mapped using different machine-learning (ML) algorithms available inside GEE, helping the manager obtain high-quality information about the reef. This remote-sensing application represents an easy-to-use tool for managers that can be integrated into modern ecosystem monitoring protocols, supporting effective reef governance within a digitized society with more demanding stakeholders.

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Section: Introductionmentioning

confidence: 99%

Development of a Google Earth Engine-Based Application for the Management of Shallow Coral Reefs Using Drone Imagery

et al. 2023

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“…RS offers a quick and synoptic overview of ecological features as well as providing repeatable, standardised, and verifiable information on long-term trends in ecosystem structure and processes [19,20]. Currently, RS is applied in various marine environments at different scales, including, but not limited to, marine vertebrate surveys, shoreline monitoring, coral bleaching events trajectory, coral reef bathymetry mapping, and marine habitat classification [21][22][23][24][25][26][27][28]. However, RS techniques rely on tremendous amounts of data, which would exceed conventional human power for direct visual inspection [29].…”

Section: Introductionmentioning

confidence: 99%

SeeCucumbers: Using Deep Learning and Drone Imagery to Detect Sea Cucumbers on Coral Reef Flats

Duce

Joyce

et al. 2021

Drones

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Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species.

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Coral Reefs and Blue Economy

Fairoz

2022

Blue Economy

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What Can Artificial Intelligence Offer Coral Reef Managers?

Cited by 9 publications

References 13 publications

Development of a Google Earth Engine-Based Application for the Management of Shallow Coral Reefs Using Drone Imagery

Development of a Google Earth Engine-Based Application for the Management of Shallow Coral Reefs Using Drone Imagery

SeeCucumbers: Using Deep Learning and Drone Imagery to Detect Sea Cucumbers on Coral Reef Flats

Coral Reefs and Blue Economy

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