@Article{ChoOMWKMCSB:2014:EvHyIm,
author = "Cho, Hyun Jung and Ogashawara, Igor and Mishra, Deepak and White,
Joseph and Kamerosky, Andrew and Morris, Lori and Clarke,
Christopher and Simpson, Ali and Banisakher, Deya",
affiliation = "{Bethune-Cookman University} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {University of Georgia} and {Bethune-Cookman
University} and {Bethune-Cookman University} and {St. Johns River
Water Management District} and {Bethune-Cookman University} and
{Bethune-Cookman University} and {Bethune-Cookman University}",
title = "Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data
for seagrass mapping in Indian River Lagoon, FL",
journal = "GIScience and Remote Sensing",
year = "2014",
volume = "51",
number = "2",
pages = "120--138",
keywords = "coastal lagoon, image analysis, image classification, macroalga,
mapping, remote sensing, seagrass, Florida [United States], Indian
River [Florida], United States, algae.",
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.",
doi = "10.1080/15481603.2014.895577",
url = "http://dx.doi.org/10.1080/15481603.2014.895577",
issn = "1548-1603",
label = "scopus 2014-05 ChoOMWKMCSB:2014:EvHyIm",
language = "en",
urlaccessdate = "28 abr. 2024"
}