Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine
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 satellite...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/4/3/50 |
| _version_ | 1797555272361705472 |
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| author | Mary K. Bennett Nicolas Younes Karen Joyce |
| author_facet | Mary K. Bennett Nicolas Younes Karen Joyce |
| author_sort | Mary K. Bennett |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:44:02Z |
| format | Article |
| id | doaj.art-9dfc4376ad4b4f95a45e864d9febc799 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T16:44:02Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-9dfc4376ad4b4f95a45e864d9febc7992023-11-20T11:45:09ZengMDPI AGDrones2504-446X2020-08-01435010.3390/drones4030050Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth EngineMary K. Bennett0Nicolas Younes1Karen Joyce2College of Science and Engineering, James Cook University, Townsville, QLD 4811, AustraliaCollege of Science and Engineering, James Cook University, Townsville, QLD 4811, AustraliaCollege of Science and Engineering, James Cook University, Townsville, QLD 4811, AustraliaWhile 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.https://www.mdpi.com/2504-446X/4/3/50drone mappingcoral reefsrandom forestgoogle earth engineremote sensingRPAS |
| spellingShingle | Mary K. Bennett Nicolas Younes Karen Joyce Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine Drones drone mapping coral reefs random forest google earth engine remote sensing RPAS |
| title | Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine |
| title_full | Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine |
| title_fullStr | Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine |
| title_full_unstemmed | Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine |
| title_short | Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine |
| title_sort | automating drone image processing to map coral reef substrates using google earth engine |
| topic | drone mapping coral reefs random forest google earth engine remote sensing RPAS |
| url | https://www.mdpi.com/2504-446X/4/3/50 |
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