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Geo 327G Semester Project

Landslide Suitability Assessment of Olympic National Park, WA

Fall 2011

Shane Lewis

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I. Problem

Landslides cause millions of dollars of damage nationally every year, and are very prevalent in

Washington. There are several causes for landslides, but they are often triggered by seismic

activity. Nearby earthquakes or volcanic eruptions can cause unstable soils to shake, thus

reducing pore space and increasing pressure. The material then acts as a fluid and

consequently gives way, causing a landslide. Geologic hazards such as these have become a

very real concern for residents and those looking to build in the state. Liquefaction

susceptibility is now an important part of surveys in order to produce an assessment that will

help to locate these hazards. The use of GIS software to process data can be very helpful in

creating an accurate assessment for areas of concern. This project will focus on the areas of

Olympic National Park that appear to be most prone to landslides based on slope, geology, and

soil liquefaction. This will involve the collection of DEM raster data, vector data such as

geologic contacts, units, faulting, and liquefaction susceptibility.

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I. Data Collection

The data used in this project came from a few different sources for GIS data. The digital

elevation model (DEM) for the project, which covers most of the Olympic peninsula and is the 1

arc second NED shaded relief , was collected using tools from The National Map Seamless

Server at http://seamless.usgs.gov/Website/Seamless/viewer.htm. Several datasets including

geology, and previously landslide information was gathered from the Washington State

Department of Natural Resources site at

http://www.dnr.wa.gov/ResearchScience/Topics/GeosciencesData/Pages/gis_data.aspx. These

files came as their own geodatabase sets containing related information, for example, the

surface_geology geodatabase contains the contacts, dikes, faults, folds, and unit polygon

feature classes. One of the other datasets containing several different formats of features was

http://seamless.usgs.gov/Website/Seamless/viewer.htmhttp://www.dnr.wa.gov/ResearchScience/Topics/GeosciencesData/Pages/gis_data.aspx

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taken from the Integrated Resource Management Applications (IRMA) Portal at

https://irma.nps.gov/. Many of the datasets collected from these sources came with metadata

that include definitions, ages, and spatial data. One example of this can be seen in Figure 1

below.

Figure 1: Metadata information for part of the DEM

II. Data Preprocessing

All of the GIS data collected from these online sources were saved into compressed (zip) files.

Before I could work with any of them, I had to extract the files from the compressed folder and

save them in the formats that would be readable in ArcGIS (shapefiles, geodatabase feature

classes). Since they were still saved in readable formats, I did not have to do any conversions to

view most of them. Some of the data, for example, the files that came from the IRMA Portal

https://irma.nps.gov/

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were not spatially defined, so I had to make a guess about what coordinate system to put them

in so that the data would still fit correctly with the other layers in the data frame. A couple of

the shapefiles seemed to work best in NAD83 UTM Zone 10.

III. ArcGIS Processing

After all of the data was converted into readable formats and defined properly, I began

processing by adding the four DEM rasters that I obtained from the Seamless Server. Because

they are technically four different rasters at this point, they assign elevation values to each cell

differently based on the elevations present in each one. This is visibly noticeable and the seams

between them are quite obvious as shown in Figure 2 below.

Figure 2: The raster elevation values do not match up with each other

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To fix this problem, I had to combine the rasters into a single one, so that the values of the cells

are on the same scale. First, I had to navigate to the mosaic tool by going through ArcToolbox >

“Data Management Tools” > “Raster” > “Raster Dataset” > “Mosaic To New Raster”. Once the

tool opened up, I selected all four existing rasters for the input rasters, and my data folder as

the output location. The pixel type was 32_BIT_FLOAT because that was the type of the original

rasters. The number of bands was set to 1, and the new raster was created, showing no seams.

With the new raster in the table of contents, I was able to delete the original rasters and start

adding the other data. From the surface_geology_250k geodatabase, I added the

geologic_unit_poly_250k shapefile. At first, it appeared as a single color for all units, so to fix

this problem; I went into the layer properties symbology tab and displayed the categories by

unique values. Once in this menu, I set the value field to “GEOLOGIC_UNIT_LABEL” and clicked

on the “Add All Values” button to get a display like the Figure 3.

Figure 3: Categorizing the layer values by the geologic unit label given to each unit

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Now we have a map showing the digital elevation model, and a matching layer of geologic unit

polygons. The next step was to add a polygon of Olympic National Park in order to define an

area of interest. The file I added was the park_polygon shapefile from my olym_wqgis folder

downloaded from the IRMA Portal. Now the map contains the park boundaries, the geologic

units, and a visible DEM raster underneath after setting the transparency of the unit layer to

40%. The resulting map display is shown in Figure 4 below.

Figure 4: Map showing the boundaries of Olympic National Park, geologic units, and the digital elevation model beneath

Since we are only interested in the area within the park boundaries, we will want to clip the

geologic units layer to the park_polygon layer. Since we are trying to clip vector data rather

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than a raster, we will use a clipping tool. To do this, I navigated to the “Clip” tool in ArcToolbox

by going through “Analysis Tools” > “Extract” > “Clip”. Once the menu box opens for the tool, I

selected the geologic units layer as the input feature and the park_polygon layer as the clip

feature. The resulting output was my geounit_clip layer. After setting the symbology to show

the values from the lithology field, the map now displays the areas of the park with different

types of lithology, rather than individual units. This makes it easier to distinguish areas that are

more susceptible to landslides.

Figure 5: Areas of different lithology within Olympic National Park

It is possible to see the areas of different elevation with the DEM and the corresponding

lithology; however it is much easier to analyze slopes with the addition of a “hillshade”. To do

this, I went through ArcToolbox to “Spatial Analyst Tools” > “Surface” > “Hillshade” to bring up

the menu box for the tool. From there all I had to do was to set the input to the DEM raster

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and name the output file and location to generate a hillshade image that acts as a shaded relief

raster. It can then be placed underneath the DEM raster already present, and will be most

useful when the DEM transparency is set to 40%. The result is displayed in Figure 6.

Figure 6: Map generated after the addition of a hillshade raster

Now that the hillshade layer is in place it is easier to visualize the conditions where different

lithologies are likely to be found. For example, the dark areas on the map are valleys or

depressed areas, some of them containing rivers and streams. From the lithology display that is

clipped to the park boundary, we can see that the lithology commonly found in these valleys is

unconsolidated sediments (dark pink). It can be assumed that areas with unconsolidated

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sediments will be more prone to landslides that areas with harder rock due to the lack of

stability, but the slope angle must be taken into consideration as well. I was able to find data

on ground response at the Washington State Department of Natural Resources site, and added

that to the map next. The liquefaction_susceptibility shapefile gives values based on the

degree of susceptibility. It ranks areas from very low to high susceptibility and also displays

those areas that are not susceptible (bedrock, ice, peat, water). As expected, the areas I

identified as unconsolidated sediments earlier are now appearing as areas of “moderate to

high” susceptibility (Orange below).

Figure 7: Liquefaction Susceptibility

Now that I have several pieces of data to use, I can start to form my own suitability raster. First,

I needed to make a raster based on slope values alone. I did this by going through “Spatial

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Analyst Tools” > “Surface” > “Slope” in ArcToolbox. For the input raster, I selected the DEM,

and the output raster was named “slope”. I selected the output measurement as degrees

because the slope measurements are degress. The conversion factor is 0.000009. The resulting

slope raster shows several different divisions of values, but the objective is to create a raster

that has suitability rankings of 1 to 5. To fix this, I went into the property settings of the new

slope raster, and under “Classification” I changed the number of classes to 5. Next I clicked the

“Classify” button, and changed the new break values to 10, 20, 40, 60, and 90 degrees. This

allowed me to separate the slope values into varying degrees of landslide susceptibility.

Figure 8: Classifying the break values so that there are five divisions

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Next, I needed to make each of the degree increments correspond to a rank of 1 – 5. I used the

reclassify tool by going through “Spatial Analyst Tools” > “Reclass” > “Reclassify” in ArcToolbox.

This tool allowed me to assign new values to represent the ranges of degrees I classified before.

Figure 9: Reclassification of degree values to represent rank

Figure 10: Reclassified slope raster with green representing the shallowest slopes and red showing the steepest

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Now the slope raster portion of the suitability assessment is complete. Next, I had to make a

raster of the geology to be able to add to the slope raster. The lithology in the geounit_clip

layer was not ranked for suitability, so I went into the attribute table and added a new field

named “rank”. I then assigned each type of lithology a number based on how landslide prone it

is. The ranking system I used is as follows:

1 – Glaciers and Snowfields, Water

2 – Intrusive Rocks, Volcanic Deposits and Rocks

3 – Mixed Volcanic and Sedimentary Rocks

4 – Sedimentary Deposits and Rocks

5 – Unconsolidated Sediments

At this point, the geology is in the form of vector data. To convert the geology polygons into a

raster, I went through “Conversion Tools” > “To Raster” > “Polygon to Raster” in ArcToolbox. I

then filled in the box to make it look like the one in Figure 11. Note that the cell size must be

the same as the slope raster. This will make it possible to add the rasters together and make

the cells match up when I try to form the suitability raster. Once the tool has finished, the

geology is displayed in the five ranks that I assigned in the previous step. A picture of the result

is shown in Figure 12 on the next page. It is important that the rasters and the park boundary

polygon are in the same coordinate system before we add everything together. To convert the

slope raster from GCS NAD83 into UTM Zone 10, I used the “Project Raster” tool found in the

“Projections and Transformations” section of ArcToolbox. I then set the output coordinate

system to UTM Zone 10, the resampling technique to bilinear, and the output cell size to 30

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meters. Now the slope raster is in UTM coordinates and I just need to clip it to the park

boundary.

Figure 11: Polygon to Raster Tool

Figure 12: Map of lithology ranked from high susceptibility (red-orange) to low susceptibility (dark blue)

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To clip the new slope raster to the park boundary, I used the “Extract by Mask” tool in the

“Spatial Analyst Tools” section, and set the input raster to the “slopeUTM” raster while the

feature mask was the park_polygon. Now both the slope and the geology rasters are clipped to

the park boundary, and I need to convert the geology raster to UTM coordinates. I followed the

same procedure as the slopeUTM conversion to do this. In order to make the assessment as

accurate as possible, I made a third raster with rankings from the liquefaction_susceptibility

layer. I started by clipping the liquefaction polygons to the park boundary using the “clip” tool

again. I then used the same procedure to add an attribute field and assign ranks. The ranking

system used for liquefaction is as follows:

1 – Bedrock, Ice, Water

2 – Very Low, Very Low to Low

3 – Low

4 – Low to Moderate

5 – Moderate to High

Once the ranks had been assigned I proceeded to convert the polygon to a raster using the

same tool used to convert the geology layer. With the new liquefaction raster in a different

coordinate system as the other rasters, it was again necessary to use the “Project Raster” tool

to convert it to UTM Zone 10. An image of the Project Raster menu is shown in Figure 13. Now

that all of the rasters have the same coordinate systems and are ranked 1-5 for landslide

susceptibility, they are ready to add together. To add them, I went through “Spatial Analysis

Tools” > “Map Algebra” > “Raster Calculator” and made the map algebra expression shown in

the Figure 14 on the next page.

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Figure 13: Project Raster Tool

Figure 14: Raster Calculator tool used to perform map algebra

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The Raster Calculator tool took the rank values from each of the three rasters and added them

together to get a new value. For example, a cell with a value of 3 in the liquUTM raster, a value

of 4 in the geoUTM raster, and a value of 3 in the slopeUTM raster, would now have the value

of 10 in the new landslide susceptibility raster. The new raster ranking system goes from a

minimum of 3 to a maximum of 15. The result of the raster calculator after changing the

coordinate system of the whole data frame to UTM Zone 10 is shown in Figures 15 and 16.

Figure 15: Landslide susceptibility based on slope, geology, and liquefaction data. Red represents highest susceptibility and blue represents lowest

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IV. Conclusion

With the final landslide suitability raster displayed, I can start to analyze the data and draw

conclusions about the areas which appear to have the most concern. As we can see from the

map, areas that display in blue colors are those that are least prone. The most obvious of these

are bodies of water, ice, and snowfields which show in the darkest shades of blue. The least

prone cells have the lowest rank in each set of conditions (geology, liquefaction, < 10° slope).

At the other extreme, we can see some areas where a darker orange and even red color is

displayed, which represent the conditions that landslides are most likely to occur. The areas

that appear in red have the set of conditions in which they are unconsolidated sediments, > 60°

slope, and have a moderate to high liquefaction rating.

Figure 16: Blue represents lowest landslide susceptibility, red represents highest

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Most of the area within the park falls within an intermediate value on the scale because a lot of

it is bedrock, but is exposed on slopes at higher angles. This accounts for much of the light

green and yellow colored areas on the map. Another factor we can look at in deciding areas of

concern, are previous landslides and other geologic hazards that have been recorded in the

area already. The figure below shows mapped landslides and earthquakes above magnitude 3

that have occurred in the past. The gray spots within the park are small landslide polygons, and

the yellow dots represent earthquakes.

Figure 17: Past Landslides and Earthquakes

Other factors other than seismic triggers are erosion, rainfall and human activity. In the case of rainfall,

the water gets into the soil and rock which drives up the fluid pressure and makes it less stable. These

may also have an impact on the susceptibility of the park.

Landslide Susceptibility Assessment of Olympic National Park, WashingtonSuitability Assessment Based On Slope, Geology, and Liquefaction

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LegendPark Boundary

Landslide Susceptibility3 Very Low4567891011121314 Very High

0 10 20 30 40Kilometers

GCS North American 1983NAD 1983 UTM Zone 10N1:600,000