BATHYMETRIC FORECASTING USING MULTILAYER SPATIAL IMAGES

Abstract

Earth-observing satellite, such Landsat, provide many multitemporal images of earth surface, either water body or land. By using spectral water body characteristics and field measurement, the bathymetry (water depth) of the study area can be derived from images recorded/acquired at different times. In this study, four multitemporal spatial images were used for generating bathymetry images which were arranged as multilayer images in cubic raster database format. Bathymetric forecasting is needed for a dynamic (rapidly change) area, such as an estuary of a river transporting a lot of sediments. Bathymetric forecasting in this study area utilized linear and quadratic regressions techniques. The spatial image layers representing standard errors values, constants of linear and quadratic equations were generated from the cubic raster database containing multitemporal images. These layers were also arranged in a cubic raster database format. By visual observation on image of standard error, a user may analyse which part of the study area that close to the field reality and which part that does not. The values of each layers were then classified into several classes and were displayed using distinctive colors to ease the user in visual observation. The bathymetric forecasting (either forward or backward) can be calculated from the spatial linear and quadratic equations. In order to provide more impressive visualization, the time series of bathymetric images generated were compiled into one file in animated gif format.