A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil

Statistically based landslide susceptibility mapping has become an important research area in the last decades, and several bivariate and multivariate statistical approaches to landslide susceptibility assessments have been applied and compared in all regions of the world. The aim of this study was to compare different statistical approaches and to analyse the degree of spatial agreement between the landslide susceptibility maps produced. To this end, we selected seven statistical methods for comparison, namely, landslide density, likelihood ratio, information value, Bayesian model, weights of evidence, logistic regression and discriminant analysis, and then applied these to an inventory comprising 940 translational landslides, in the southeast region of Minas Gerais state in Brazil, at the western edge of the Quadrilátero Ferrífero (642.13 km2). In some statistical approaches, modifications were made to the input dependent variables. The landslides registered in the inventory map have been used in punctual and polygonal form. Six factors were considered as input landslide predisposing factors: slope angle, geomorphological units, slope curvature, lithological units, slope aspect and inverse wetness index. The combination order of the landslide predisposing factors was established based on a sensitivity analysis, which gave rise to five different cartographic combinations. In total, 58 statistical models of landslide susceptibility were produced, and the results were validated using success and prediction rate curves. The spatial agreement evaluation between the model results was carried out with kappa statistics. There were 214 comparisons of spatial agreement involving classified models at three relative degrees of susceptibility (high, medium and low landslide susceptibility classes). The results showed that all of the models so produced had satisfactory validation rates. The best landslide susceptibility models obtained areas under the curve of > 0.80 in the success and prediction rate curves, with emphasis on the weights of evidence, the information value and the likelihood ratio statistical methods. These statistical approaches were performed with the landslides mapped in the form of points. The landslide susceptibility classes of these models visually demonstrated a slightly more irregular spatial distribution when compared to the models performed with landslide polygons. The likelihood ratio model performed with landslide points presented one of the smallest areas for the high susceptibility class and the largest area for the low susceptibility class. The analysis of the spatial agreement showed that the models produced with a polygonal dependent variable tend to be more concordant, regardless of the statistical technique used. Moreover, we verified that spatial agreement tends to increase with increasing accuracy of the models. Despite the discrepancies found, most of the models compared showed a substantial or almost perfect degree of agreement.