Integration of remote sensing and morphometric data for geomorphologic mapping

The evaluation of the efficiency of multisource remote sensing and morphometric data for geomorphologic mapping has been made. The authors have developed a method based on the synergistic relation between surface objects mapable with remote sensing data, morphometric information and mapping of surficial deposits. Maximum likelihood classification along with various vegetation indices (VI) including NDVI and TSAVI, RI and SI were used to identify and discriminate the geomorphological surface units using Landsat 5 and SPOT 4 images. The elevation, slope and aspect data were derived from a DEM. Linear discriminant analysis models were used to determine the level of synergy between these variables and their effectiveness as geomorphological mapping tools. Average classification accuracies ranges between 85 and 95 percent. Correlation between the classification results and the geological maps units was 87% and a combination of morphometric variables, principally elevation, and VI variables, principally TSAVI, were dominant in the numerical models.