1. LANDSLIDE SUCCEPTABILITY MAPPING (Case study of SRILANKA)

2. Statistical Map Bivariate statistical method 1. Landslide rupture
2. Relevant factors (parameters) for the prediction of landslide :
Lithology Slope
Landuse Aspect
Soiltype - Curvature
Whenever we are performing some analysis we use some method to perform that analysis. In this exercise I will be using bivariate statistical method. Bivariate me we will be using two parameters at a time to perform analysis. I will explain this thing in detail as we will move further.

3. Statistical Analysis Map
3. Weight value for each factor : Landslide Index
- Dens. clas : Landslide density within parameter class
- Density map : Landslide density within entire map
- N pixel (Si) : Number of pixels, which contain landslides per parameter class
- N pixel (Ni): Total number of pixels in a parameter class

4. Statistical Analysis Map
Six weight value maps will be calculated:
1. lithology weight map
2. Soil type weight map
3. Landuse weight map
4. Slope weight map
5. Aspect weight map
6. Curvature weight map
Hazard succeptibility map

5. Data Available Soil map Contour lines(10m intervals) Land use map
Landslide rupture map
Reference coordinate system for Srilanka: Central Meridian, False Northing Latitude of origin, Scale factor, false northings and false eastings are 200,000meters,
Used Software : ArcGIS 9.3

6. Work Flow Created a file geodatabase (ArcGIS 9.3)
Imported our features( all shapefiles)
Rasterisation of our feature Landslide rupture (define the extent of our raster by using mask)
Generated DEM using contours: define cell size and mask
Reclassification : Aspect, slope and curvature
Rasterization : lithology, landuse and soil type features ( polygon to raster)
Zonal tables ( zonal statistics as table)
Join tables to corresponding classes
Calculate six weight value maps
Hazard susceptibility map (sum up all weight value maps) (Weighted Sum Operation)

7. 1.Rasterization:LandSide Rupture Feature
Cell size : 20m
Assign the extent of our raster
by using given mask feature
Total number of pixel (2752)

8. 2.Generation Aspect Slope and Curvature
1. DEM from Contour lines
(Spatial Analyst Tools TIN management)

9. Continue…. Spatial analyst tools (Aspect, slope and curvature) Aspect
All these rasters do not have values ( no Attribute tables, floating rasters)

10. 3.Rasterization:Lithology Landuse and Soil
Polygon Raster
Soiltype : 4 classes
Lithology : 3 classes
Landuse : 21 classes

11. 4.Reclassification Aspect 5 classes are made
Spatial analyst tools Reclass Reclassify
Aspect
5 classes are made

12. Continue…..
Slope
5 classes
5 classes
Curvature

13. Continue…….
Aspect
Curvature
Slope

14. Reclassification of LanduseRaster
4 classes

15. 5.Zonal Tables Spatial analyst tools Zonal Zonal statistics as table
We want to calculate the number of pixels of landslide that fall in each class of our raster slope,
Repeated the same process is done for 5 remaining Rasters

16. 6.Join Tables to Correspond Rasters
Join tables of each raster to its corresponding zonal statistic table,
Total number of pixels of that raster in each zonal statistic table,
Use “Field calculator”, added a new field of weight in our table

17. 7.Field Calculator Calculate the weight values
by introducing the given formula in field calculator

18. 8.Calculate Six Weight Value Maps
e.g. Aspect
-ve values mean Low Risk Area
+ve values mean High Risk Area

19. 9.Weight Maps
Aspect weight map
Slope weight map

20. Continue…..
Curvature weight map
Soiltype weight map

21. Continue….
Lithology weight map
Landuse weight map

22. 10.Final Landslide Susceptibility Map

23. Thank you for your attention.

24. QUESTIONS??? 24