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

Paula A. Zapata-Ramírez, Hernando Hernández-Hamón, Clare Fitzsimmons, Marcela Cano, Julián García, Carlos A. Zuluaga, Rafael E. Vásquez

Producción científica: Contribución a una revistaArtículo en revista científica indexadarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.
Idioma originalInglés
Número de artículo3504
PublicaciónRemote Sensing
Volumen15
N.º14
DOI
EstadoPublicada - 12 jul. 2023

Nota bibliográfica

Publisher Copyright:
© 2023 by the authors.

Palabras clave

  • Remote sensing
  • Coral reefs
  • Google Earth Engine
  • Marine ecosystem management
  • Drone imagery
  • Machine learning
  • Environmental monitoring

Tipos de Productos Minciencias

  • Artículos de investigación con calidad A1 / Q1

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