Geostatistical Integration of Field Measurements and Multi-Sensor Remote Sensing Images for Spatial Prediction of Grain Size of Intertidal Surface Sediments

Abstract

Park, N.-W., 2019. Geostatistical integration of field measurements and multi-sensor remote sensing images for spatial prediction of grain size of intertidal surface sediments. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 190-196. Coconut Creek (Florida), ISSN 0749-0208.The objective of this paper is to demonstrate the potential benefit of using high-resolution optical and SAR images for the geostatistical mapping of grain size of intertidal surface sediments. The grain size values from field measurements are integrated with reflectance and backscattering coefficients from multi-sensor images via regression kriging. The trends of grain size variations are estimated using support vector regression (SVR) modeling to account for a nonlinear relationship, and rank transformation is applied to original input variables to highlight the relative differences in input values from multi-sensor images. Unlike the conventional regression-based mapping approach, the residual component that cannot be explained by the multi-sensor remote sensing images is considered and predicted via kriging. The final grain size values are then obtained by adding these two components. From a case study on the Baramarae tidal flats in Korea with KOMPSAT-2 and COSMO-SkyMed images, the integration of multi-sensor images with field measurements via SVR and rank transformation could explain 58 % of grain size variance, leading to a significant improvement in predictive performance (approximately 29 %) over ordinary kriging based on field measurements only. Furthermore, using reflectance and scattering information from multi-sensor images generated the grain size distribution with more detailed variations in the study area. Therefore, the synergistic use of multi-sensor images within an advanced geostatistical integration framework is expected to be very effective for the reliable mapping of the grain size of intertidal surface sediments when only a limited number of field measurements are available.