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

被引:3
|
作者
Zapata-Ramirez, Paula A. [1 ]
Hernandez-Hamon, Hernando [1 ]
Fitzsimmons, Clare [2 ]
Cano, Marcela [3 ]
Garcia, Julian [1 ]
Zuluaga, Carlos A. [1 ]
Vasquez, Rafael E. [1 ]
机构
[1] Univ Pontificia Bolivariana, Sch Engn, Medellin 050031, Colombia
[2] Newcastle Univ, Sch Nat & Environm Sci, Newcastle Upon Tyne NE1 7RU, England
[3] Parques Nacl Nat Colombia, Bogota 110221, Colombia
关键词
remote sensing; coral reefs; google earth engine; marine ecosystem management; drone imagery; machine learning; environmental monitoring; ECOSYSTEM-BASED MANAGEMENT; BIG DATA APPLICATIONS; GOVERNANCE;
D O I
10.3390/rs15143504
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] RiceMapEngine: A Google Earth Engine-Based Web Application for Fast Paddy Rice Mapping
    Yu, Zhiqi
    Di, Liping
    Shrestha, Sravan
    Zhang, Chen
    Guo, Liying
    Qamar, Faisal
    Mayer, Timothy J.
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 16 : 7264 - 7275
  • [2] Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine
    Bennett, Mary K.
    Younes, Nicolas
    Joyce, Karen
    DRONES, 2020, 4 (03) : 1 - 13
  • [3] Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery
    Ganjirad, Mohammad
    Bagheri, Hossein
    ECOLOGICAL INFORMATICS, 2024, 80
  • [4] Google Earth Engine-based detection of shoreline on mixed sand and gravel beaches
    Magana, Pedro
    Otinar, Pedro
    Silva, Marcus
    Cobos, Manuel
    Baquerizo, Asuncion
    PROCEEDINGS OF THE 39TH IAHR WORLD CONGRESS, 2022, : 5418 - 5423
  • [5] Three Years of Google Earth Engine-Based Archaeological Surveys in Iraqi Kurdistan: Results from the Ground
    Valente, Riccardo
    Maset, Eleonora
    Iamoni, Marco
    REMOTE SENSING, 2024, 16 (22)
  • [6] Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine
    Li, Jiwei
    Knapp, David E.
    Lyons, Mitchell
    Roelfsema, Chris
    Phinn, Stuart
    Schill, Steven R.
    Asner, Gregory P.
    REMOTE SENSING, 2021, 13 (08)
  • [7] WATER RESERVOIRS MONITORING THROUGH GOOGLE EARTH ENGINE: APPLICATION TO SENTINEL AND LANDSAT IMAGERY
    Bocchino, F.
    Ravanelli, R.
    Belloni, V.
    Mazzucchelli, P.
    Crespi, M.
    39TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT ISRSE-39 FROM HUMAN NEEDS TO SDGS, VOL. 48-M-1, 2023, : 41 - 47
  • [8] O-LCMapping: a Google Earth Engine-based web toolkit for supporting online land cover classification
    Huaqiao Xing
    Dongyang Hou
    Siyuan Wang
    Mingyang Yu
    Fei Meng
    Earth Science Informatics, 2021, 14 : 529 - 541
  • [9] A Google Earth Engine-Based Framework to Identify Patterns and Drivers of Mariculture Dynamics in an Intensive Aquaculture Bay in China
    Wang, Peng
    Wang, Jian
    Liu, Xiaoxiang
    Huang, Jinliang
    REMOTE SENSING, 2023, 15 (03)
  • [10] O-LCMapping: a Google Earth Engine-based web toolkit for supporting online land cover classification
    Xing, Huaqiao
    Hou, Dongyang
    Wang, Siyuan
    Yu, Mingyang
    Meng, Fei
    EARTH SCIENCE INFORMATICS, 2021, 14 (01) : 529 - 541