Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data
Satellite remote sensing is one of the most efficient techniques for marine habitat studies in shallow coastal waters, especially in clear waters where field observations can be easily carried out. However, such in situ observations have certain limitations: they are time consuming, have a limited a...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Coastal Education and Research Foundation
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/39734 |
| _version_ | 1848755670999891968 |
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| author | Hoang Cong, Tin O'Leary, Mick Fotedar, Ravi |
| author_facet | Hoang Cong, Tin O'Leary, Mick Fotedar, Ravi |
| author_sort | Hoang Cong, Tin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Satellite remote sensing is one of the most efficient techniques for marine habitat studies in shallow coastal waters, especially in clear waters where field observations can be easily carried out. However, such in situ observations have certain limitations: they are time consuming, have a limited ability to capture spatial variability, and require an interdisciplinary approach between marine biologists and remote-sensing specialists. The main objective of this study was to survey and map Sargassum beds around Rottnest Island, Western Australia, through a combination of high spatial resolution WorldView-2 imagery, using a validated depth invariant index model for water-column correction, and in-field observations. The combination of field survey data and four classification methods resulted in highly accurate classification outcomes that showed the distribution patterns of Sargassum spp. around Rottnest Island during the austral spring season (October 2013). Overall, the minimum distance and Mahalanobis classifiers yielded the highest overall accuracy rates of 98.32% (kappa coefficient, κ = 0.96) and 98.30% (κ = 0.96), respectively. The K-means classification method gave the lowest accuracy percentage of 42.50% (κ = 0.22). Thus, the primary results of this study provide useful baseline information that is necessary for marine-conservation strategic planning and the sustainable utilization of brown macroalgae resources around the Western Australian coast. |
| first_indexed | 2025-11-14T09:00:00Z |
| format | Journal Article |
| id | curtin-20.500.11937-39734 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:00:00Z |
| publishDate | 2016 |
| publisher | Coastal Education and Research Foundation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-397342017-09-13T15:05:25Z Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data Hoang Cong, Tin O'Leary, Mick Fotedar, Ravi Satellite remote sensing is one of the most efficient techniques for marine habitat studies in shallow coastal waters, especially in clear waters where field observations can be easily carried out. However, such in situ observations have certain limitations: they are time consuming, have a limited ability to capture spatial variability, and require an interdisciplinary approach between marine biologists and remote-sensing specialists. The main objective of this study was to survey and map Sargassum beds around Rottnest Island, Western Australia, through a combination of high spatial resolution WorldView-2 imagery, using a validated depth invariant index model for water-column correction, and in-field observations. The combination of field survey data and four classification methods resulted in highly accurate classification outcomes that showed the distribution patterns of Sargassum spp. around Rottnest Island during the austral spring season (October 2013). Overall, the minimum distance and Mahalanobis classifiers yielded the highest overall accuracy rates of 98.32% (kappa coefficient, κ = 0.96) and 98.30% (κ = 0.96), respectively. The K-means classification method gave the lowest accuracy percentage of 42.50% (κ = 0.22). Thus, the primary results of this study provide useful baseline information that is necessary for marine-conservation strategic planning and the sustainable utilization of brown macroalgae resources around the Western Australian coast. 2016 Journal Article http://hdl.handle.net/20.500.11937/39734 10.2112/JCOASTRES-D-15-00077.1 Coastal Education and Research Foundation restricted |
| spellingShingle | Hoang Cong, Tin O'Leary, Mick Fotedar, Ravi Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data |
| title | Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data |
| title_full | Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data |
| title_fullStr | Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data |
| title_full_unstemmed | Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data |
| title_short | Remote-Sensed Mapping of Sargassum spp. Distribution around Rottnest Island, Western Australia using High Spatial Resolution WorldView-2 Satellite Data |
| title_sort | remote-sensed mapping of sargassum spp. distribution around rottnest island, western australia using high spatial resolution worldview-2 satellite data |
| url | http://hdl.handle.net/20.500.11937/39734 |