Marine & Environmental Sciences Faculty Articles
High Resolution Ground Verification, Cluster Analysis and Optical Model of Reef Substrate Coverage on Landsat TM Imagery (Red Sea, Egypt)
ResearcherID
B-8552-2013
Document Type
Article
Publication Title
International Journal of Remote Sensing
ISSN
0143-1161
Publication Date
2002
Abstract
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-2 between predicted substrate radiance and the satellite sensor values in the reef top area. Considering the simple atmospheric correction model, the lack of in situ radiance measurements and the uncertainties with respect to possible changes in bottom type distribution since the acquisition of the 14 year old image, the results show the potential use of satellite imagery for reef research in both biological and geological analysis through very precise and semi-quantitative ground verification, including in situ reflectance measurements.
DOI
10.1080/01431160110047722
Volume
23
Issue
8
First Page
1677
Last Page
1698
NSUWorks Citation
Samuel J. Purkis, J. A. M. Kenter, E. K. Oikonomou, and I. S. Robinson. 2002. High Resolution Ground Verification, Cluster Analysis and Optical Model of Reef Substrate Coverage on Landsat TM Imagery (Red Sea, Egypt) .International Journal of Remote Sensing , (8) : 1677 -1698. https://nsuworks.nova.edu/occ_facarticles/270.
Comments
©2002 Taylor & Francis Ltd