TY - JOUR
T1 - High-resolution ground verification, cluster analysis and optical model of reef substate coverage on Landsat TM imagery (Red Sea, Egypt)
AU - Purkis, S.J.
AU - Kenter, J.A.M.
AU - Oikonomou, E.K.
AU - Robinson, I.S.
PY - 2002
Y1 - 2002
N2 - A combination of high-resolution ground verification, cluster analysis using Landsat Thematic Mapper (TM) data, and optical modelling, was applied to Red Sea reef substrate. Ground verification, in an area of 3 by 20 pixels (90 by 600 m) with one metre scale resolution, identified the presence of 30 different bottom types that were later reduced to twelve dominant bottom types. A combination of bispectral plots and principal component analysis using spectral bands 1, 2 and 3 confirmed the presence of nine bottom types. The identified clusters were separated and used as a training set to classify substrate. Optical modelling, using literature radiance values and coverage of the original twelve dominant bottom types and a simple model for atmospheric and water column absorption, revealed a difference of up to 60 W m
AB - A combination of high-resolution ground verification, cluster analysis using Landsat Thematic Mapper (TM) data, and optical modelling, was applied to Red Sea reef substrate. Ground verification, in an area of 3 by 20 pixels (90 by 600 m) with one metre scale resolution, identified the presence of 30 different bottom types that were later reduced to twelve dominant bottom types. A combination of bispectral plots and principal component analysis using spectral bands 1, 2 and 3 confirmed the presence of nine bottom types. The identified clusters were separated and used as a training set to classify substrate. Optical modelling, using literature radiance values and coverage of the original twelve dominant bottom types and a simple model for atmospheric and water column absorption, revealed a difference of up to 60 W m
U2 - 10.1080/01431160110047722
DO - 10.1080/01431160110047722
M3 - Article
SN - 0143-1161
VL - 23
SP - 1677
EP - 1698
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
ER -