The goal of this project was to map landslide susceptibility throughout the state of Minnesota using multiple variables. The research question pursued was "Which regions within the state of Minnesota are most prone to landslide hazards?' In the end, the hope would be that a correlation would be revealed between landslide prone areas which had association with previous landslide occurrences.
Data & Methods
Data was acquired from Minnesota GIS Clearinghouse and Multi-Resolution Land Characteristics Consortium. The DEM and soil data were acquired from the clearing house, and land cover was acquired from MRLC. Slope, aspect, land cover and soil were all found to have significant roles in landslide occurrences. Therefore, out of the DEM, slope and aspect maps were created using the slope and aspect spatial analyst tools. Figure 1 below shows a slope map of the state of Minnesota.
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| Figure 1. A slope map of Minnesota |
Slope was reclassified into five categories. The lowest slope represented lowest landslide risk, highest represented highest landslide risk (very low, low, moderate, high, very high).
Aspect was reclassified, having highest landslide densities in the W, then N, E, S. South was ranked lowest risk (south facing slopes typically have the lowest landslide densities). East was moderate, north was high, and west was very high. A flat value was assigned as very low risk.
Land cover was reclassified and ranked from very low to very high as well. Open water being very low, woody wetlands low risk, forest and shrub lands moderate risk, urban high risk, barren lands very high risk.
Soil erodibility, represented in K values, was reclassified and broken up into 5 classes from .02 to .55. Figure 2 below shows the K Factor scale. .02 was considered very low, being composed of fine textures with high clay content, and .55 being ranked high. This consisted of silt sized particles which were prone to crusting and were easily detachable.
After all the values were reclassified, they were inputted into the weighted overlay tool and a map was produced out of the combined variables. Figure 3 below shows the weighted overlay inputs, and figure 4 shows the workflow.
Results
Figure 5 below shows the resulting susceptibility map compared alongside a historical map of landslides in the state of Minnesota. The results revealed that there is a fair amount of relevance among the four weighted factors and previous landslide occurrences. It's most prominent in the southwestern part of the state, particularly in Winona county. The Hennepin County area appears rather moderate for the susceptibility map compared with the high concentration of landslide occurrences in the historic map . A recommendation for future mapping could consider putting more weight on urbanization or population density as a landslide risk.
Conclusions
The motive of this project was to model how GIS can be a useful tool in delineating areas of high or low risk for landslide hazards. The results revealed a moderate correlation between the four chosen factors and historic landslides. Future work could involve more a detailed analysis of landslide prone neighborhoods for urban planning.
Sources
Chalkias, Christos, Maria Ferentinou, and Christos Polykretis. "GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece." Geosciences. August 20, 2014. www.mdpi.com/journal/geosciences.
Jennnings, C.E., M. Presnail, E. Kurak, R. Meier, C. Schmidt, J. Palazzolo, S. Jiwani, E. Waage, and J.M. Feinberg. "Historical Landslide Inventory for the Twin Cities Metropolitan Area." Minnesota Department of Natural Resources, 2016, 1-34.
https://files.dnr.state.mn.us/waters/watermgmt_section/shoreland/landslide-inventory.pdf.
Keller, Edward A. “Introduction to Environmental Geology.” 3rd ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2005.
Lee, Saro , and Touch Sambath. "Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models." Korea Institute of Geoscience & Mineral Resources 50 (April 2006): 847-55. Accessed March 3, 2018. https://geo.mnsu.edu/geo/Landslides-LCCMR/Susceptibility%20Mapping/Lee%20and%20Sambath%202006.pdf.
DEM and Geology data: http://www.mngeo.state.mn.us/chouse/metalong.html#elevation
Land cover data: https://www.mrlc.gov/nlcd11_data.php
Soil erodibility, represented in K values, was reclassified and broken up into 5 classes from .02 to .55. Figure 2 below shows the K Factor scale. .02 was considered very low, being composed of fine textures with high clay content, and .55 being ranked high. This consisted of silt sized particles which were prone to crusting and were easily detachable.
 |
| Figure 2. A K Factor soil scale |
After all the values were reclassified, they were inputted into the weighted overlay tool and a map was produced out of the combined variables. Figure 3 below shows the weighted overlay inputs, and figure 4 shows the workflow.
 |
| Figure 3. All four variables inputted into weighted overlay tool |
 |
| Figure 4. A workflow of the data processing |
Results
Figure 5 below shows the resulting susceptibility map compared alongside a historical map of landslides in the state of Minnesota. The results revealed that there is a fair amount of relevance among the four weighted factors and previous landslide occurrences. It's most prominent in the southwestern part of the state, particularly in Winona county. The Hennepin County area appears rather moderate for the susceptibility map compared with the high concentration of landslide occurrences in the historic map . A recommendation for future mapping could consider putting more weight on urbanization or population density as a landslide risk.
 |
| Figure 5. the resulting susceptibility map (left) compared alongside to a historic landslide map of Minnesota |
Conclusions
The motive of this project was to model how GIS can be a useful tool in delineating areas of high or low risk for landslide hazards. The results revealed a moderate correlation between the four chosen factors and historic landslides. Future work could involve more a detailed analysis of landslide prone neighborhoods for urban planning.
Sources
Chalkias, Christos, Maria Ferentinou, and Christos Polykretis. "GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece." Geosciences. August 20, 2014. www.mdpi.com/journal/geosciences.
Jennnings, C.E., M. Presnail, E. Kurak, R. Meier, C. Schmidt, J. Palazzolo, S. Jiwani, E. Waage, and J.M. Feinberg. "Historical Landslide Inventory for the Twin Cities Metropolitan Area." Minnesota Department of Natural Resources, 2016, 1-34.
https://files.dnr.state.mn.us/waters/watermgmt_section/shoreland/landslide-inventory.pdf.
Keller, Edward A. “Introduction to Environmental Geology.” 3rd ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2005.
Lee, Saro , and Touch Sambath. "Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models." Korea Institute of Geoscience & Mineral Resources 50 (April 2006): 847-55. Accessed March 3, 2018. https://geo.mnsu.edu/geo/Landslides-LCCMR/Susceptibility%20Mapping/Lee%20and%20Sambath%202006.pdf.
DEM and Geology data: http://www.mngeo.state.mn.us/chouse/metalong.html#elevation
Land cover data: https://www.mrlc.gov/nlcd11_data.php
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