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Open AccessArticle

Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine

1
College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
2
Centre for Tropical Environmental and Sustainability Science, Cairns, QLD 4878, Australia
*
Author to whom correspondence should be addressed.
Drones 2020, 4(3), 50; https://doi.org/10.3390/drones4030050
Received: 18 July 2020 / Revised: 18 August 2020 / Accepted: 26 August 2020 / Published: 28 August 2020
(This article belongs to the Special Issue She Maps)
While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery. View Full-Text
Keywords: drone mapping; coral reefs; random forest; google earth engine; remote sensing; RPAS; heron reef; drone imagery drone mapping; coral reefs; random forest; google earth engine; remote sensing; RPAS; heron reef; drone imagery
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3986067
    Link: https://doi.org/10.5281/zenodo.3986067
    Description: Supplementary material contains the instructions and code (HTML file and Jupyter notebook file) needed to pre-process drone imagery in Python, QGIS, and ExifTool. It has instructions and code to use in Google Earth Engine to classify drone images and assess classification accuracy. We have included three drone image JPEGS of Heron Reef for users to use along with the instructions to test our workflow.
MDPI and ACS Style

Bennett, M.K.; Younes, N.; Joyce, K. Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine. Drones 2020, 4, 50.

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