Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing
Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/14/7/373 |
| _version_ | 1827732932051075072 |
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| author | Saharsh Barve Jody M. Webster Rohitash Chandra |
| author_facet | Saharsh Barve Jody M. Webster Rohitash Chandra |
| author_sort | Saharsh Barve |
| collection | DOAJ |
| description | Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia’s Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. |
| first_indexed | 2024-03-11T00:59:57Z |
| format | Article |
| id | doaj.art-ac072015333e4c5bba18ddd60bf06494 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T00:59:57Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ac072015333e4c5bba18ddd60bf064942023-11-18T19:46:42ZengMDPI AGInformation2078-24892023-06-0114737310.3390/info14070373Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote SensingSaharsh Barve0Jody M. Webster1Rohitash Chandra2Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal 576104, IndiaGeocoastal Research Group, School of Geosciences, University of Sydney, Sydney 2006, AustraliaTransitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, Sydney 2052, AustraliaEnvironmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia’s Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects.https://www.mdpi.com/2078-2489/14/7/373clusteringreef habitat mappingGreat Barrier Reefmachine learning |
| spellingShingle | Saharsh Barve Jody M. Webster Rohitash Chandra Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing Information clustering reef habitat mapping Great Barrier Reef machine learning |
| title | Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing |
| title_full | Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing |
| title_fullStr | Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing |
| title_full_unstemmed | Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing |
| title_short | Reef-Insight: A Framework for Reef Habitat Mapping with Clustering Methods Using Remote Sensing |
| title_sort | reef insight a framework for reef habitat mapping with clustering methods using remote sensing |
| topic | clustering reef habitat mapping Great Barrier Reef machine learning |
| url | https://www.mdpi.com/2078-2489/14/7/373 |
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