The document discusses an automated method for classifying earth surfaces to assess landslide susceptibility using topographic data. It examines different geomorphometric classification approaches and parameters to distinguish landscape types related to landslides. The study tests supervised and unsupervised classification methods, compares results, and develops an integrated method using slope gradient, convexity and texture that identifies terrain features correlated with past landslide events. The automated integrated classification approach provides a useful tool for landslide susceptibility analysis at a territorial scale.

1. “ Automated Unsupervised Geomorphometric Classification of Earth Surface for Landslide Susceptibility Assessment ” Alessandro Paregiani and Maria Ioannilli International Conference on Computational Science and Its Applications ICCSA 2008 June 30th - July 3rd, 2008 Perugia, Italy "Geographical Analysis, Urban Modeling, Spatial Statistics" University of Rome “Tor Vergata”

2. Outline 4. Experimented Classification Methods 1. Landslide Hazard vs. Landslide Susceptibility 2. Purpose of the Work 3. Approaches to Landslide Susceptibility Analysis 6. Integrated Classification Method 5. Comparison of Intermediate Results 7. Correlation Analysis between Geomorphometric Classes and Types of Landslide 8. Conclusions

3. Landslides constitute one of the major hazards that cause losses in lives and property. To assess landslide occurrences is a complex analysis, involving multitude of factors and need to be studied systematically in order to evaluate the hazard. There are no universally accepted forecasting methods of "natural hazard" and in particular of landslide hazard. Landslide Hazard R = P x ( V x E ) (UNESCO)

5. Approaches to Landslide Susceptibility Analysis Indirect methodologies Geomorphometric Approach high degree of subjectivity this method doesn’t consider the relationships between instability factors Limits Algorithm Heuristic Approach Statistical Approach Method

7. Experimented Classification Methods Applying State-of-the-Art Classification Methods Supervised Classification: types of topography are recognized starting from selected “training samples” Unsupervised Classification: unconstrained by pre-set conditions, and allow the input data to determine “optimal” categories Parameters Authors Parameters - Single – Cell Topological Parameters “ Context” Parameters (extended neighborhood) Evans (1981) Pike – Iwahashi (2006) Pike (1971)   Nested - Means Divided Parameters Clustering  • Mean • S.D. • Variation coefficient • Symmetry • Slope gradient • Texture • Convexity - -  • Slope gradient • Aspect • Plan curvature • Profile curvature - Method   • Mean • S.D. • Variation Coefficient • Symmetry • Slope Gradient • Texture • Convexity  • Slope Gradient • Aspect • Plan Curvature • Profile Curvature Types of

11. Nested-Means Multivariate Analysis (Pike - Iwahashi) The classification underline a remarkable distinction among mountainside surfaces in four different classes characterized by increasing values of elevation and slope gradient

16. Comparison of Intermediate Results

18. Integrated Classification Method A particular of the classification discriminating mountainside surfaces Overlapping of the three classes representing ridges, fluvial and torrential riverbeds A particular of the new integrated classification that considers both mountainside surfaces and hydrological factors

19. Correlation Analysis Geomorphometric Classes/Landslides Integrated Classification Method K: type of landslide J: geomorphometric class M: number of k-type landslides in class j N: total number of k-type landslides

21. Thanks for your attention