Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon,FL |
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| Authors: | Hyun Jung Cho Igor Ogashawara Deepak Mishra Joseph White Andrew Kamerosky Lori Morris |
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| Institution: | 1. Department of Integrated Environmental Science, Bethune-Cookman University, 640 Dr. Mary McLeod Bethune Blvd., Daytona Beach FL 32114, USAchoh@cookman.edu;3. Remote Sensing Division, National Institute for Space Research, Avenida dos Astronautas, S?o José dos Campos-SP 1758, Brazil;4. Department of Geography, University of Georgia, Athens, GA 30602 USA;5. Department of Integrated Environmental Science, Bethune-Cookman University, 640 Dr. Mary McLeod Bethune Blvd., Daytona Beach FL 32114, USA;6. St. Johns River Water Management District, PO Box 1429, Palatka, FL 32178 USA |
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| Abstract: | Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, SlopeRED and SlopeNIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods. |
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| Keywords: | HICO Indian River Lagoon seagrass macroalgae SlopeRED SlopeNIR |
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