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Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach
AbstractMultipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.
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Eilander, D.; Annor, F.O.; Iannini, L.; van de Giesen, N. Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach. Remote Sens. 2014, 6, 1191-1210.View more citation formats
Eilander D, Annor FO, Iannini L, van de Giesen N. Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach. Remote Sensing. 2014; 6(2):1191-1210.Chicago/Turabian Style
Eilander, Dirk; Annor, Frank O.; Iannini, Lorenzo; van de Giesen, Nick. 2014. "Remotely Sensed Monitoring of Small Reservoir Dynamics: A Bayesian Approach." Remote Sens. 6, no. 2: 1191-1210.
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