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DC Field | Value | Language |
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dc.contributor.author | Jeyakanthan, V. S. | - |
dc.contributor.author | Sanjeevi, Shanmugam | - |
dc.date.accessioned | 2019-12-06T05:13:10Z | - |
dc.date.available | 2019-12-06T05:13:10Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | Geocarto International Vol. 26, No. 4, July 2011, 305–320 | en_US |
dc.identifier.uri | http://117.252.14.250:8080/jspui/handle/123456789/3993 | - |
dc.description.abstract | 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 estimates 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-1D and P6 satellite image data were in turn used to quantify the capacity of the Singoor reservoir, Andhra Pradesh, India. The estimated capacity of the reservoir using the per-pixel and sub-pixel approaches was 727.75 Mm3 and 716.11 Mm3, respectively. The validation shows that the sub-pixel approach produced much less error (1.08%) than the per-pixel based approach (3.14%). | en_US |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.subject | Reservoir | en_US |
dc.subject | Water spread area | en_US |
dc.subject | Capacity estimation | en_US |
dc.subject | Sub pixel approach | en_US |
dc.title | Reservoir capacity estimation of Singoor reservoir, India, by per-pixel and sub-pixel processing of multi-date satellite images Veeraraghu S. | en_US |
dc.type | Article | en_US |
Appears in Collections: | Research papers in International Journals |
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