Small and medium reservoirs are of vital importance to the local communities in Ghana. Since these smaller reservoirs are especially sensitive to problems such as evaporation and sedimentation, and these reservoirs react strongly to seasonal fluctuations in temperature and precip
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Small and medium reservoirs are of vital importance to the local communities in Ghana. Since these smaller reservoirs are especially sensitive to problems such as evaporation and sedimentation, and these reservoirs react strongly to seasonal fluctuations in temperature and precipitation patterns, continuous monitoring is of great importance. Sentinel-1 SAR imagery offers a reliable manner in which these reservoirs can be delineated and monitored, but the wind-induced phenomenon of Bragg scattering poses problems when we want to further increase automation of the monitoring process. In this thesis, a classifier has been developed that uses Sentinel-1 imagery, Gray Level Co-Occurance Matrix (GLCM) features and a Simple Non-Iterative Clustering (SNIC) to differentiate between surrounding shore, unaffected water and Bragg scattering patches. The combination of these features and methods, together with a Random Forest classifier showed to be accurate in differentiating between the different classes and delineating the final reservoirs, even when Bragg scattering is present on the SAR imagery.
