LANDSLIDE SUCCEPTABILITY MAPPING (Case study of SRILANKA)

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.

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

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

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

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)

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

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

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

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

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

Continue….. Slope 5 classes 5 classes Curvature

Continue……. Aspect Curvature Slope

Reclassification of LanduseRaster 4 classes

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

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

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

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

9.Weight Maps Aspect weight map Slope weight map

Continue….. Curvature weight map Soiltype weight map

Continue…. Lithology weight map Landuse weight map

10.Final Landslide Susceptibility Map

Thank you for your attention.

QUESTIONS??? 24