Spectral modeling for the identification and quantification of algal blooms: A test of approach
- Univ. of Edinburgh (United Kingdom)
- Univ. of Wolverhampton (United Kingdom)
The aim of this paper is to develop and test a Monte Carlo modelling approach for the characterization of reflectance for different bloom-forming marine phytoplankton species. The model was tested on optical data for four species (Dunaliella salina, Pavlova pinguis, Emiliania huxleyi and Synechocystes spp.) and simulations performed over a range of chlorophyll concentrations. Discriminant analysis identified 10 key wavelengths which could be used to maximize the separation between the four species. The resulting wavelengths were combined in a neural network to show 100% accuracy in classifying species type. Further simulations were undertaken to investigate the effect of aquatic humus on reflectance characteristics and the change in wavelengths for algal discrimination. The implications for the development of algorithms for the identification of algal bloom species type by remote sensing are briefly discussed.
- OSTI ID:
- 478134
- Report Number(s):
- CONF-970319-; CNN: Grant GR/9; TRN: 97:002802-0027
- Resource Relation:
- Conference: 4. thematic international conference on remote sensing for marine and coastal environments: technology and applications, Orlando, FL (United States), 17-19 Mar 1997; Other Information: PBD: 1997; Related Information: Is Part Of Proceedings of the fourth international conference on remote sensing for marine and coastal environments. Technology and applications: Volume I; PB: 741 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
Genome Sequence and Transcriptome Analyses of Chrysochromulina tobin: Metabolic Tools for Enhanced Algal Fitness in the Prominent Order Prymnesiales (Haptophyceae)
System development for linked-fermentation production of solvents from algal biomass. [Dunaliella tertiolecta, D. primolecta, D. parva, D. bardawil, D. salina]