Abstract:
Periodic surveys of reservoirs are essential to evaluate the decrease in storage
capacity due to inflow and trapping of sediment. Conventional hydro-graphic surveys are
time consuming, labour intensive and expensive. Satellite remote sensing techniques
provide time and cost effective approach for the periodical capacity surveys. The
traditional approaches of image classification such as maximum likelihood and the band
thresholding method, involve the per-pixel approach to delineate the water spread area of a
reservoir. One of the limitations of these approaches is that the pixels representing the
reservoir border, containing a mixture of water, soil and vegetation, are classified entirely as
water, thereby resulting in, inaccurate estimate of the water spread area. To compute the
water spread area accurately, the sub-pixel approach has been used in this study. The
water spread areas extracted using per-pixel and sub-pixel approaches from IRS-1C and IRS-
1D satellite image data were in turn used to quantify the capacity of the Somasila
Reservoir, Andhra Pradesh, India. The estimated capacity of the reservoir using the perpixel
and sub-pixel approaches was 1134.16 Mm3 and 1123.59 Mm3, respectively. The
robustness of sub-pixel approach in classifying the water spread has been evaluated by
comparing it with the high resolution data. The validation shows that the sub-pixel approach
produced much less error (1.08%) than the per-pixel based approach (3.14%).
