Quantification of floating macroalgae blooms using the Scaled Algae Index
Quantifying the spatial coverage of floating macroalgae from satellite imagery, using methods such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI), requires the use of a scene-wide threshold to isolate and then compute the number of floating macroalgae pixels....
| Main Authors: | , , , |
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| Format: | Journal Article |
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Wiley-Blackwell Publishing Inc.
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/14819 |
| _version_ | 1848748725176893440 |
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| author | Garcia, Rodrigo Fearns, Peter Keesing, J. Liu, D. |
| author_facet | Garcia, Rodrigo Fearns, Peter Keesing, J. Liu, D. |
| author_sort | Garcia, Rodrigo |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Quantifying the spatial coverage of floating macroalgae from satellite imagery, using methods such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI), requires the use of a scene-wide threshold to isolate and then compute the number of floating macroalgae pixels. The problem faced is the sensitivity of the NDVI and, to a lesser extent, the FAI to radiance contributions from atmospheric aerosols and turbid water. Both these factors can vary significantly across a satellites' field-of-view generating irregular apparent reflectance of ocean and floating macroalgae pixels across an NDVI/FAI scene, leading to inaccuracies in spatial coverage estimates. We present a simple image processing algorithm, termed the scaled algae index (SAI) that removes any variability present in ocean and floating macroalgae pixels in NDVI or FAI imagery. The SAI does this by subtracting a given pixel's index by that of a local ocean pixel, effectively scaling ocean pixels to values near zero, and macroalgae pixels to positive values. The SAI algorithm has been tested on NDVI and FAI scenes of the 2008/2009 floating macroalgae blooms that occurred in the Yellow Sea, China. These SAI images show a major reduction in variability with scene-wide histograms being unimodal. Histogram analysis also indicates that sufficient contrast exists between ocean and floating macroalgae pixels to enable segmentation by a scene-wide threshold. A semiautomated threshold determination procedure is also presented, which together with the SAI algorithm can be used to compute accurate estimates of the spatial coverage of floating macroalgae. |
| first_indexed | 2025-11-14T07:09:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-14819 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:09:36Z |
| publishDate | 2013 |
| publisher | Wiley-Blackwell Publishing Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-148192017-09-19T05:46:30Z Quantification of floating macroalgae blooms using the Scaled Algae Index Garcia, Rodrigo Fearns, Peter Keesing, J. Liu, D. Yellow Sea algal bloom atmospheric correction turbid water Ulva prolifera Scaled algae index (SAI) green tide remote sensing NDVI Quantifying the spatial coverage of floating macroalgae from satellite imagery, using methods such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI), requires the use of a scene-wide threshold to isolate and then compute the number of floating macroalgae pixels. The problem faced is the sensitivity of the NDVI and, to a lesser extent, the FAI to radiance contributions from atmospheric aerosols and turbid water. Both these factors can vary significantly across a satellites' field-of-view generating irregular apparent reflectance of ocean and floating macroalgae pixels across an NDVI/FAI scene, leading to inaccuracies in spatial coverage estimates. We present a simple image processing algorithm, termed the scaled algae index (SAI) that removes any variability present in ocean and floating macroalgae pixels in NDVI or FAI imagery. The SAI does this by subtracting a given pixel's index by that of a local ocean pixel, effectively scaling ocean pixels to values near zero, and macroalgae pixels to positive values. The SAI algorithm has been tested on NDVI and FAI scenes of the 2008/2009 floating macroalgae blooms that occurred in the Yellow Sea, China. These SAI images show a major reduction in variability with scene-wide histograms being unimodal. Histogram analysis also indicates that sufficient contrast exists between ocean and floating macroalgae pixels to enable segmentation by a scene-wide threshold. A semiautomated threshold determination procedure is also presented, which together with the SAI algorithm can be used to compute accurate estimates of the spatial coverage of floating macroalgae. 2013 Journal Article http://hdl.handle.net/20.500.11937/14819 10.1029/2012JC008292 Wiley-Blackwell Publishing Inc. fulltext |
| spellingShingle | Yellow Sea algal bloom atmospheric correction turbid water Ulva prolifera Scaled algae index (SAI) green tide remote sensing NDVI Garcia, Rodrigo Fearns, Peter Keesing, J. Liu, D. Quantification of floating macroalgae blooms using the Scaled Algae Index |
| title | Quantification of floating macroalgae blooms using the Scaled Algae Index |
| title_full | Quantification of floating macroalgae blooms using the Scaled Algae Index |
| title_fullStr | Quantification of floating macroalgae blooms using the Scaled Algae Index |
| title_full_unstemmed | Quantification of floating macroalgae blooms using the Scaled Algae Index |
| title_short | Quantification of floating macroalgae blooms using the Scaled Algae Index |
| title_sort | quantification of floating macroalgae blooms using the scaled algae index |
| topic | Yellow Sea algal bloom atmospheric correction turbid water Ulva prolifera Scaled algae index (SAI) green tide remote sensing NDVI |
| url | http://hdl.handle.net/20.500.11937/14819 |