A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping
Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth,...
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ftmdpi:oai:mdpi.com:/2072-4292/7/10/13157/ 2023-08-20T04:02:38+02:00 A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping Rodrigo Garcia John Hedley Hoang Tin Peter Fearns agris 2015-10-02 application/pdf https://doi.org/10.3390/rs71013157 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs71013157 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 7; Issue 10; Pages: 13157-13189 shallow water benthic classification hierarchical clustering linear discriminant analysis uncertainty propagation hyperspectral multispectral Text 2015 ftmdpi https://doi.org/10.3390/rs71013157 2023-07-31T20:46:58Z Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae. Text antartic* MDPI Open Access Publishing Remote Sensing 7 10 13157 13189 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
shallow water benthic classification hierarchical clustering linear discriminant analysis uncertainty propagation hyperspectral multispectral |
spellingShingle |
shallow water benthic classification hierarchical clustering linear discriminant analysis uncertainty propagation hyperspectral multispectral Rodrigo Garcia John Hedley Hoang Tin Peter Fearns A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping |
topic_facet |
shallow water benthic classification hierarchical clustering linear discriminant analysis uncertainty propagation hyperspectral multispectral |
description |
Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae. |
format |
Text |
author |
Rodrigo Garcia John Hedley Hoang Tin Peter Fearns |
author_facet |
Rodrigo Garcia John Hedley Hoang Tin Peter Fearns |
author_sort |
Rodrigo Garcia |
title |
A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping |
title_short |
A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping |
title_full |
A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping |
title_fullStr |
A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping |
title_full_unstemmed |
A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping |
title_sort |
method to analyze the potential of optical remote sensing for benthic habitat mapping |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2015 |
url |
https://doi.org/10.3390/rs71013157 |
op_coverage |
agris |
genre |
antartic* |
genre_facet |
antartic* |
op_source |
Remote Sensing; Volume 7; Issue 10; Pages: 13157-13189 |
op_relation |
https://dx.doi.org/10.3390/rs71013157 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs71013157 |
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Remote Sensing |
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7 |
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10 |
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13157 |
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13189 |
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