Shallow water substrate mapping using hyperspectral remote sensing
During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastalregion of Western Australia. These data were processed by inversion of a semi-analytical shallow wateroptical model to classify the substrate. Inputs to the optical model include water column constituent...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier Ltd
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/49219 |
| _version_ | 1848758190479507456 |
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| author | Fearns, Peter Klonowski, Wojciech Babcock, R. England, P. Phillips, J. |
| author_facet | Fearns, Peter Klonowski, Wojciech Babcock, R. England, P. Phillips, J. |
| author_sort | Fearns, Peter |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastalregion of Western Australia. These data were processed by inversion of a semi-analytical shallow wateroptical model to classify the substrate. Inputs to the optical model include water column constituentspecific inherent optical properties (SIOPs), view and illumination geometry, surface condition (basedon wind speed) and normalised reflectance spectra of substrate types. A sub-scene of the HyMap datacovering approximately 4 km2 was processed such that each 3!3 m2 pixel was classed as sand,seagrass, brown algae or various mixtures of these three components. Coincident video data werecollected and used to estimate substrate types. We present comparisons of the habitat classificationsdetermined by these two methods and show that the percentage validation of the remotely sensedhabitat map may be optimised by selection of appropriate optical model parameters. The optical modelwas able to retrieve classes for approximately 80% of all pixels in the scene, with validation percentagesof approximately 50% for sand and seagrass classification, and 90% for brown algae classification. Thesemi-analytical model inversion approach to classification can be expected to be applied to any shallowwater region where substrate reflectance spectra and SIOPs are known or can be inferred. |
| first_indexed | 2025-11-14T09:40:03Z |
| format | Journal Article |
| id | curtin-20.500.11937-49219 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:40:03Z |
| publishDate | 2011 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-492192018-04-19T01:30:15Z Shallow water substrate mapping using hyperspectral remote sensing Fearns, Peter Klonowski, Wojciech Babcock, R. England, P. Phillips, J. Shallow water habitat mapping HyMap Remote sensing Hyperspectral During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastalregion of Western Australia. These data were processed by inversion of a semi-analytical shallow wateroptical model to classify the substrate. Inputs to the optical model include water column constituentspecific inherent optical properties (SIOPs), view and illumination geometry, surface condition (basedon wind speed) and normalised reflectance spectra of substrate types. A sub-scene of the HyMap datacovering approximately 4 km2 was processed such that each 3!3 m2 pixel was classed as sand,seagrass, brown algae or various mixtures of these three components. Coincident video data werecollected and used to estimate substrate types. We present comparisons of the habitat classificationsdetermined by these two methods and show that the percentage validation of the remotely sensedhabitat map may be optimised by selection of appropriate optical model parameters. The optical modelwas able to retrieve classes for approximately 80% of all pixels in the scene, with validation percentagesof approximately 50% for sand and seagrass classification, and 90% for brown algae classification. Thesemi-analytical model inversion approach to classification can be expected to be applied to any shallowwater region where substrate reflectance spectra and SIOPs are known or can be inferred. 2011 Journal Article http://hdl.handle.net/20.500.11937/49219 10.1016/j.csr.2011.04.005 Elsevier Ltd restricted |
| spellingShingle | Shallow water habitat mapping HyMap Remote sensing Hyperspectral Fearns, Peter Klonowski, Wojciech Babcock, R. England, P. Phillips, J. Shallow water substrate mapping using hyperspectral remote sensing |
| title | Shallow water substrate mapping using hyperspectral remote sensing |
| title_full | Shallow water substrate mapping using hyperspectral remote sensing |
| title_fullStr | Shallow water substrate mapping using hyperspectral remote sensing |
| title_full_unstemmed | Shallow water substrate mapping using hyperspectral remote sensing |
| title_short | Shallow water substrate mapping using hyperspectral remote sensing |
| title_sort | shallow water substrate mapping using hyperspectral remote sensing |
| topic | Shallow water habitat mapping HyMap Remote sensing Hyperspectral |
| url | http://hdl.handle.net/20.500.11937/49219 |