lanslide succeptability mapping a case study of srilanka

8/11/2019 Lanslide Succeptability Mapping a Case Study of Srilanka

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Bivariate statistical method

1. Landslide rupture

2. Relevant factors (parameters) for the prediction oflandslide :

- Lithology - Slope

- Landuse - Aspect

- Soiltype - Curvature

Statistical Map


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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 perparameter class

- N pixel (Ni): Total number of pixels in a parameter class


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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


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Data Available

Soil map

Contour lines(10m intervals)

Land use map

Landslide rupture map

Reference coordinate system for Srilanka: Central Meridian, FalseNorthing Latitude of origin, Scale factor, false northings and falseeastings are 200,000meters,

Used Software : ArcGIS 9.3


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Work Flow

1. Created a file geodatabase (ArcGIS 9.3)

2. Imported our features( all shapefiles)

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

4. Generated DEM using contours: define cell size and mask

5. Reclassification : Aspect, slope and curvature

6. Rasterization : lithology, landuse and soil type features ( polygon

to raster)

7. Zonal tables ( zonal statistics as table)

8. Join tables to corresponding classes

9. Calculate six weight value maps

10. Hazard susceptibility map (sum up all weight value maps)

(Weighted Sum Operation)


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1.Rasterization:LandSide Rupture Feature

Cell size : 20m

Assign the extent of our raster

by using given mask feature

Total number of pixel

(2752)


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2.Generation Aspect Slope and Curvature

1. DEM from Contour lines

(Spatial Analyst Tools TIN management)


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Continue.

Spatial analyst tools (Aspect, slope and curvature)

Aspect Slope Curvature

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

rasters)


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3.Rasterization:Lithology Landuse and Soil

Polygon Raster

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


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4.Reclassification

Spatial analyst tools Reclass Reclassify

Aspect 5 classes are made


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Continue..

Slope 5 classes

Curvature 5 classes


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Continue.

SlopeCurvatureAspect


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Reclassification of LanduseRaster

Landuse 4 classes


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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


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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


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7.Field Calculator

Calculate the weight values

by introducing the given

formula in field calculator


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8.Calculate Six Weight Value Maps

e.g. Aspect

-ve values mean Low Risk Area

+ve values mean High Risk Area


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9.Weight Maps

Aspect weight map Slope weight map


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Continue..

Curvature weight map Soiltype weight map


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Continue.

Lithology weight map Landuse weight map


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10.Final Landslide Susceptibility Map


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Thank you for your attention.


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QUESTIONS???

lanslide succeptability mapping a case study of srilanka

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