Kaytan Kelkar – Texas A&M University

Statement of Purpose:

The propagation of mass movement is a natural phenomenon observed in rugged topography of the western San Juan Mountains, Colorado. Diverse mass movement, including debris flows, landslides and snow avalanches in the area, pose a significant threat to human life and infrastructure. Climate change and increased human development is modifying the equilibrium conditions of remnant ice contact slope deposits which potentially will lead to greater frequencies of mass movement in the area. Although, landslide studies in the area have focused on the documentation of triggers for slope instability and field mapping, inadequate investigation of components responsible for susceptibility to mass movement still exists. After extensive hazard mapping in area during the 1970s, since then landslide studies in the region have not been undertaken. Therefore, a comprehensive up-to-date mass movement susceptibility study, addressing the risk from mass movement in the western San Juan Mountains, is required. The objective of this project is to identify components associated with slope instability and map areas susceptible to mass movement in the Western San Juan Mountains. A growing number of people choose to inhabit alpine regions even with the risk from mass movement. Thus, the prediction of mass movement is crucial to protecting lives and preventing damage to critical infrastructure. This study has developed a cost effective; accurate method to evaluate potential for mass movement in mountain terrain. The use of emerging geospatial methods utilizing GIS and 3-dimensional modelling will help improve prediction of mass movement. A GIS module integrating slope angle and length, aspect, geology, vegetation cover, and soil drainage served as the framework to develop this dynamic immersive 3-D model. Geospatial analysis in ArcGIS® of six spatial layers was conducted using a weighted overlay approach. Each spatial layer was assigned a weight as a percentage for relative influence to cause slope failure. This study has implemented cost effective emerging geospatial method to improve accurate prediction of landslides. The proposed model has numerous practical applications and will contribute to the growing need to quantify landslide risk in alpine terrain.

 

Description of Data Sets:

The following are data sets were used:
Colorado Digital Elevation Model (DEM): Coloradoview.org The digital elevation model was manipulated to procure: slope, aspect, surface roughness and slope length. Landsat Imagery USGS Earthexplorer: Landsat data was manipulated to produce Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) respectively.
Natural Resources Conservation Service (NRCS): Digital soil map
United States Geological Survey (USGS): Digital geologic map of Colorado.
All stated data sources are readily accessible to the public.