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LANDSLIDE SUCCEPTABILITY MAPPING (Case study of SRILANKA)

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.

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LANDSLIDE SUCCEPTABILITY MAPPING (Case study of SRILANKA)

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  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

  3. 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

  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 Slope Curvature 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 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 Landuse 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

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