Coral reef detection using ICESat-2 and machine learning
As anthropogenic impacts threaten natural habitats, effective monitoring strategies are crucial. Coral reefs, among the most vulnerable ecosystems, traditionally employ monitoring techniques that are labor-intensive and costly, prompting the exploration of remote sensing as a cost-effective alternat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001086 |
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| author | Gabrielle A. Trudeau Kim Lowell Jennifer A. Dijkstra |
| author_facet | Gabrielle A. Trudeau Kim Lowell Jennifer A. Dijkstra |
| author_sort | Gabrielle A. Trudeau |
| collection | DOAJ |
| description | As anthropogenic impacts threaten natural habitats, effective monitoring strategies are crucial. Coral reefs, among the most vulnerable ecosystems, traditionally employ monitoring techniques that are labor-intensive and costly, prompting the exploration of remote sensing as a cost-effective alternative. Launched in October 2018, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides high-resolution, high-frequency data, with its green laser offering unprecedented opportunities for bathymetric and coral reef applications. This study investigates the use of ICESat-2 data for atoll coral reef detection, utilizing Heron Island in the Great Barrier Reef, AU, and employing machine learning models. A binary logistic regression (BLR) model and convolutional neural network (CNN) were tested for determining coral reef presence, with the CNN outperforming the BLR in accuracy (85.4%), F1 score (43%), and false positive rate (13.1%). A challenge of the study included the difficulty of balancing false positive rates in predictive models to avoid over- or underestimations of reef extent. These obstacles were mitigated through the integration of algorithmically derived pseudo-rugosity and slope metrics as innovative proxies for seafloor complexity, significantly improving predictive performance. Feature importance analysis identified satellite-derived bathymetry (SDB) depth as the most critical predictor of coral presence, followed by pseudo-rugosity, slope, and various other depth measurements. This research establishes a new application of ICESat-2 data combined with advanced machine learning techniques as a promising method for efficient and cost-effective coral reef monitoring. Future work should refine algorithms and incorporate additional environmental variables to improve model performance across various reef types. |
| format | Article |
| id | doaj-art-d037a2d769af49f2a3dbbf09ca7af193 |
| institution | OA Journals |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-d037a2d769af49f2a3dbbf09ca7af1932025-08-20T02:27:35ZengElsevierEcological Informatics1574-95412025-07-018710309910.1016/j.ecoinf.2025.103099Coral reef detection using ICESat-2 and machine learningGabrielle A. Trudeau0Kim Lowell1Jennifer A. Dijkstra2Center for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, University of New Hampshire, Durham, NH 03824, USA; Integrated Applied Mathematics Program, Department of Mathematics and Statistics, University of New Hampshire, Durham, NH 03824, USA; Corresponding author at: Center for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, University of New Hampshire, Durham, NH 03824, USA.Center for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, University of New Hampshire, Durham, NH 03824, USACenter for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, University of New Hampshire, Durham, NH 03824, USAAs anthropogenic impacts threaten natural habitats, effective monitoring strategies are crucial. Coral reefs, among the most vulnerable ecosystems, traditionally employ monitoring techniques that are labor-intensive and costly, prompting the exploration of remote sensing as a cost-effective alternative. Launched in October 2018, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides high-resolution, high-frequency data, with its green laser offering unprecedented opportunities for bathymetric and coral reef applications. This study investigates the use of ICESat-2 data for atoll coral reef detection, utilizing Heron Island in the Great Barrier Reef, AU, and employing machine learning models. A binary logistic regression (BLR) model and convolutional neural network (CNN) were tested for determining coral reef presence, with the CNN outperforming the BLR in accuracy (85.4%), F1 score (43%), and false positive rate (13.1%). A challenge of the study included the difficulty of balancing false positive rates in predictive models to avoid over- or underestimations of reef extent. These obstacles were mitigated through the integration of algorithmically derived pseudo-rugosity and slope metrics as innovative proxies for seafloor complexity, significantly improving predictive performance. Feature importance analysis identified satellite-derived bathymetry (SDB) depth as the most critical predictor of coral presence, followed by pseudo-rugosity, slope, and various other depth measurements. This research establishes a new application of ICESat-2 data combined with advanced machine learning techniques as a promising method for efficient and cost-effective coral reef monitoring. Future work should refine algorithms and incorporate additional environmental variables to improve model performance across various reef types.http://www.sciencedirect.com/science/article/pii/S1574954125001086ICESat-2Coral reefsMachine learningRugosityHeron island |
| spellingShingle | Gabrielle A. Trudeau Kim Lowell Jennifer A. Dijkstra Coral reef detection using ICESat-2 and machine learning Ecological Informatics ICESat-2 Coral reefs Machine learning Rugosity Heron island |
| title | Coral reef detection using ICESat-2 and machine learning |
| title_full | Coral reef detection using ICESat-2 and machine learning |
| title_fullStr | Coral reef detection using ICESat-2 and machine learning |
| title_full_unstemmed | Coral reef detection using ICESat-2 and machine learning |
| title_short | Coral reef detection using ICESat-2 and machine learning |
| title_sort | coral reef detection using icesat 2 and machine learning |
| topic | ICESat-2 Coral reefs Machine learning Rugosity Heron island |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125001086 |
| work_keys_str_mv | AT gabrielleatrudeau coralreefdetectionusingicesat2andmachinelearning AT kimlowell coralreefdetectionusingicesat2andmachinelearning AT jenniferadijkstra coralreefdetectionusingicesat2andmachinelearning |