LANDSLIDE SUCCEPTIBILITY MAP(Case of study SRILANKA)

Presented by

Rashid JavedMeenat Allah Saleem

February 24, 2012

Bivariate statistical method

1. Landslide rupture

2. Relevant factors (parameters) for the prediction of landslide :

- Lithology - Slope- Landuse - Aspect- Soiltype - Curvature

Statistical Map

Statistical Analysis Map3. Weight value for each factor :

- 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 MapSix 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 10

Work Flow

1.Created a file geodatabase(arcGIS 10)

2.Assigned the given coordinate system to our geodatabase,

3.Imported our features( all shapefiles)

4. Rasterisation of our feature Landslide rupture (define the extent of our raster by using mask)

5. Generated TIN from contour lines

6.TIN to Ratser(GRID): define cell size and mask

Continue…..7.Reclassification : Aspect, slope and curvature

8. Rasterization : lithology, landuse and soil type features ( polygon to raster)

9. Zonal tables ( zonal statistics as table)

10.Join tables to corresponding classes

11. Calculate six weight value maps

12. Hazard susceptibility map(sum up all weight value maps) (Map algebra i.e. Raster Calculator)

1.Creataion File Geodatabase• Assigned the given coordinate system and projection• Imported all our features

2.Rasterization:LandSide Rupture Feature

•Cell size : 20m • Assign the extent of our raster •by using given mask feature

•Total number of pixel (2752)

3.Generation Aspect Slope and Curvature

1. TIN from Contour lines(3D Analyst tools TIN management)

2. TIN to Raster(GRID)(3D Analyst tools from TIN TIN to Raster)

Continue….Spatial analyst tools (Aspect, slope and curvature)

Aspect Slope Curvature

All these rasters do not have values ( no Attribute tables, floating rasters)

4.Rasterization:Lithology Landuse and Soil Polygon Raster

Soiltype : 4 classes Lithology : 3 classes Landuse : 21 classes

5.Reclassification Spatial analyst tools Reclass Reclassify

Aspect 5 classes are made

Continue…..

Slope 5 classes

Curvature 5 classes

Continue…….

Slope CurvatureAspect

Reclassification of LanduseRaster

Landuse 4 classes

6.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

7.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

8.Field Calculator

Calculate the weight valuesby introducing the given formula in field calculator

9.Calculate Six Weight Value Maps

e.g. Aspect-ve values mean Low Risk Area+ve values mean High Risk Area

10.Weight Maps

Aspect weight map Slope weight map

Continue…..

Curvature weight map Soiltype weight map

Continue….

Lithology weight map Landuse weight map

11.Final Landslide Susceptibility Map

Thank you for your attention.

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QUESTIONS???