Integration of IKONOS imagery for geostatistical mapping of sediment grain size at Baramarae beach, Korea

Abstract

We describe the potential of high-resolution remote sensing imagery in the geostatistical mapping of sediment grain size distribution in order to supplement sparsely sampled ground observations. Within a multi-Gaussian framework, the IKONOS imagery is used as local means both to estimate the grain size values and to model local uncertainty at unsampled locations. Multiple regression and generalized additive models are applied to compute local mean values. From a case study of Baramarae beach, Korea, all imagery bands showed a reasonable linear relationship with grain size values in phi units, having a correlation coefficient of more than –0.80. Accounting for the IKONOS imagery via simple kriging with local means could reflect detailed surface characteristics with less smoothing effects. Cross validation results showed that the mean square errors from simple kriging with local means via the generalized additive model provided a relative improvement of about 60% over univariate multi-Gaussian kriging and a superior predictive capability when compared with simple kriging with local means via the traditional multiple regression model.