Bogdan Roşca

Landslide Susceptibility Assessment: GIS Application to a Complex Mountainous Environment

This study attempts to quantify landslide susceptibility in the upper Putna River basin in the Romanian Carpathians Bend using GIS techniques and logistic regression. First, a detailed landslide inventory was carried out and a GIS database was built, comprising potential predictors of landslide occurrence. The GIS database included 11 quantitative predictors, mostly geomorphometric parameters, and 4 qualitative predictors which were transformed into quantitative variables using landslide density approach. The logistic regression analysis, combined with a stepwise selection of the predictors, showed that landslide occurrence is best explained by slope inclination class, altitude, soil class, distance to drainage network and surface geology. The results show that the potentially unstable terrains, displaying high and very high landslide susceptibility values, cover an area about 3 times greater than the mapped landslide area.

Statistical Spatial Models for Soil Parameters: A Comparative Analysis of Several Methods Applied to Dobrovăţ Basin (Eastern Romania)

This study compares the performance of several statistical methods (multiple linear regression, analysis of covariance, geographically weighted regression, regression kriging, and ordinary kriging) for deriving spatial models of soil parameters. The applications were carried out within a 186-km2 hydrographic basin situated in eastern Romania. Statistical models were computed from a sample of approximately 180 soil profiles, scattered in the eastern half of the basin. Two independent samples, each of 50 soil profiles, were used for validation inside (interpolation) and outside (extrapolation) the main sampling area. The predictors included X and Y coordinates of soil profiles, geomorphometrical parameters (altitude, slope, aspect, wetness index, terrain curvature), climate parameters (mean annual temperatures, precipitation, global radiation), the normalized difference vegetation index, the main soil types, land use, and surface lithology. For only three soil variables the geostatistical approach proved to be useful: occurrence depth of calcium carbonates, pH, and base saturation. The best spatial models were achieved using analysis of covariance, geographically weighted regression, and ordinary kriging. The most relevant continuous predictor is the mean annual precipitation, whereas the most relevant qualitative factor is the soil type.