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Water is a very important
natural resource and it supports all life forms on earth. It is used by humans
in various ways including drinking, agriculture and for scientific research.
The aim of this research was to develop a routine to automatically extract
water masks from RapidEye images, which could be used for further investigation
such as water quality monitoring and change detection. A Python-based algorithm
was therefore developed for this particular purpose. The developed routine
combines three spectral indices namely Simple Ratios (SRs), Normalized Green
Index (NGI) and Normalized Difference Water Index (NDWI). The two SRs are
calculated between the NIR and green band, and between the NIR and red band.
The NGI is calculated by rationing the green band to the sum of all bands in
each image. The NDWI is calculated by differencing the green to the NIR and
dividing by the sum of the green and NIR bands. The routine generates five
intermediate water masks, which are spatially intersected to create a single
intermediate water mask. In order to remove very small waterbodies and any
remaining gaps in the intermediate water mask, morphological opening and
closing were performed to generate the final water mask. This proposed
algorithm was used to extract water masks from some RapidEye images. It yielded
an Overall Accuracy of 95% and a mean Kappa Statistic of 0.889 using the
confusion matrix approach.