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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001086 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1574-9541 |