Expanding narratives of governance constraints to improve coral reef conservation

Turner, Rachel; Forster, Johanna; Fitzsimmons, Clare; Mahon, Robin

doi:10.1111/cobi.13933

Cited by 3 publications

(1 citation statement)

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“…Such new technologies can make data collection quicker, cheaper, and more extensive, helping to provide high-quality data to expand our understanding of coral reefs. This will help address the multi-dimensional interactions between reefs and humans [18], facilitating management processes in a digitized society with more demanding stakeholders, and building appropriate capacities to steer reef governance by improving monitoring and assessment [19]. 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].…”

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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