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corresponding spatial objects (Peng et al, 2010). When fusing 
the two selected images, pairs of objects will be identified (one 
object from each image), and objects in each pair will be fused 
into a single object (Peng et al, 2010). This single fused object 
will have pair of objects that represent the same entity and same 
location (Peng et al, 2010). The spatial attributes of these 
objects will describe the location, appearance characteristic, 
shape and topology of an entity (Peng et al, 2010). The large 
number of image objects will help to accurately classify the 
image data. 
3.6 Image Classification (Fused Images) 
The next step will be classifying the fused images into different 
classes using the same previous classification methods; object- 
oriented classification (OOC), maximum likelihood classifier 
(MLC) and migrating Means Clustering classifier (MMC). The 
classes of the fused images will be superimposed in a GIS 
layers into different maps for further analysis. 
3.7 Accuracy Assessment 
Accuracy assessment will be performed for the selected 
algorithms at selected sites (Kubbar and Umm Al-Maradem 
CRE) based on field work data. It will help determining the 
quality of the information derived from image classification and 
it will be used as a comparative tool between various algorithms 
and techniques to test which is the best. Accuracy control points 
(ACPs) will be distributed utilizing a stratified random 
sampling methodology around the two southern coral reefs 
(Kubbar and Umm Al-Maradem). The ACPs will be similar to 
those ACPs of pervious study done by Al-Hazeem (2007). 
However, this study may require additional ACPs in order to 
obtain a more reliable result. 
REFERENCES 
Al-Hazeem, S 2007, An ecological study of the coral reefs of 
Kuwait islands, School of Ocean Sciences, University of 
Wales, Bangor, (Thesis for degree of Doctor of Philosophy) 
Andrefouet, S, Kramer P, Torres-Pulliza, D, Joyce, K E, 
Hochberg, E J, Garza-Pérez, R, et al 2003, Multi-site 
evaluation of IKONOS data for classification of tropical coral 
reef environments, Remote Sensing of Environment, vol. 88, 
pp. 128-143 
Andrefouet, S, Muller-Karger, F, Hochberg, E, Hu, C & Carder, 
K 2001, Change detection in shallow coral reef environments 
using Landsat 7 ETM+ data, Remote Sensing of Environment, 
vol. 79, pp. 150-162 
Bryant, D, Burke, L, Mcmanus, J& Spalding, M 1998, Reef At 
Risk: A Map-Based Indicator of Threats to the World's Coral 
Reefs, World Resources Institute, Washington 
Cetin, H 2004, Comparison of spaceborne and airborne 
hyperspectral imaging systems for environmental mapping, 
Proceedings of Commission VII, XXthISPRS Congress 
Istanbul, Turkey 
Chang, C 2003, Hyperspectral Imaging: Techniques for Spectral 
Detection and Classification, Kluwer Academic/Plenum 
Publishers, New York 
Clarke, C., Ripley, H., Green, E., Edwards, A. & Mumby, P, 
1997, Mapping and measurement of tropical coastal 
environments with hyperspectral and high spatial resolution 
data, International Journal of Remote Sensing, 18, 237- 42. 
Connell, J H 1978, Diversity in tropical rain forests and coral 
reefs, Science (199) 1302-1310 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
3.8 Coral Reef Monitoring 
Additionally, time and resources permitting, two sets of images 
of the test site with different dates will be compared to 
determine the quality of the information derived from image 
classification and to detect the changes in coral reef ecosystem 
over time 
The same processes of image calibration and classification 
established previously in the image processing analysis will be 
applied to the second image of the test site. GIS analysis of the 
change between the two dates will reveal if significant change 
has occurred. However, this process will be only undertaken if 
the developed fusion method was successful and sufficient time 
and funds persist. 
4. CONCLUSION 
This research study will potentially contribute to the body of 
knowledge by providing researchers and decision makers with a 
tool to identify and map coral reef features in more detail. The 
resolution limitations of current satellite sensors in mapping 
coral reef ecosystem can be overcome by using improved image 
fusion techniques. This will potentially provide more accurate 
information on the current condition and community 
assemblage of CRE to marine decision makers, leading to better 
management, conservation and sustainable utilization of these 
marine resources. In addition, if the developed methods are 
successful, they can be applied to other CREs around the World 
due to the operational nature and near global coverage available 
from Low Earth Orbit (LEO) remote sensing satellites. 
Dekker, G A, Brando, E V, Anstee, M J, Pinnel, N, Kutser, T, 
Hoogenboom, J E, Peters, S, Pasterkamp, R, VOS, R, Olbert, 
C& Malthus, J M T 2001, Imaging Spectrometry: Basic 
Principles Applications: Imaging Spectrometry of Water, 
Kluwer Academic Publisher, Netherlands 
DigitalGlobe 2009, WorldView-2, viewed 10 July 2011, < 
2http://worldview2.digitalglobe.com/about/> 
Dustan, P, Dobson, E, Nelson, G 2001, Landsat thematic mapper: 
Detection of shifts in community composition of coral reefs, 
Conserv Biol 15: 892-902 
Eakin, CM, Nim, C, Brainard, RE, Aubrecht, C, Elvidge, C, 
Gledhill, DK, Muller-Karger, F, Mumby, PJ, Skirving, W J, 
Strong, A E, Wang, M, Weeks, S & Wentz, F 2010, 
Monitoring coral reefs from space. Oceanography (in press) 
Eghtesadi-Araghi, P 2011, Coral reefs in the Persian Gulf and 
Oman Sea: An integrated perspective on some important 
stressors, Journal of Fishiers and Aquatic Science, vol. 6 no. 
1, pp. 48-56 
ESRI 2011, ArcGIS Online: Bing Maps, viewed 16 July 2011, 
<http://www.esri.com/software/arcgis/arcgisonline/bing- 
maps.html> 
Fonseca, A, Guzmán, H, Cortés, J & Soto, C 2010, Marine 
habitats map of “Isla del Caño”, Costa Rica,comparing 
Quickbird and Hymap images classification results, Revista 
de Biologia Tropical, vol. 58, no. 1, pp. 373 
GeoEye, 2011, /magery Sources: IKONOS Setting the Standard, 
viewed 9 July 2011, 
<http://www.geoeye.com/CorpSite/products-and- 
services/imagery-sources/>