EXPLORING THE EXPANSION OF GEOSPATIAL TECHNIQUES FOR MONITORING A LANDSLIDE-PRONE REGION IN THE PACIFIC NORTHWEST

Landslides put millions of people and billions of dollars of property at risk each year.

In this study the feasibility of monitoring landslide prone areas using, GIS, Remote Sensing, and LiDAR are examine. The area of focus is located in north Snohomish County, WA on highway 530, between the small communities of Oso and Darrington. Mass movement off, what is known as the Whitman Bench, has been recorded in this area since the late 30’s. The most recent of these slides was a fast moving catastrophic landslide that occurred on March 22, 2014, east of Oso, WA. [Township 32 Range7-8 Section 1, 7, 12]

The emphasis of the study was placed on the viability of using these tools to help enhance field-based techniques. A visual time line was established with aerial photography and Landsat TM, ETM, and OLI imagery from 1988 to present. The Landsat images were downloaded from Earth Explorer and processed in ERDAS. The images, along with DEM and LiDAR on the study area, were then transferred into a spatial database using a GIS. ArcMap10 and Arc Catalog was used to create a topographic visualization of slope, aspect, lithology, hydrology, and anthropogenic use, of the of landslide prone area in and around the Whitman Bench.

The focus was on integrating the qualitative examination of the area through geospatial analysis to determine if these technologies can help with preliminary evaluation and monitoring, before more extended and expensive methods are deployed. There are many variables that contribute to the events that lead to a landslide, such as slope, lithology, hydrology, anthropogenic use, and vegetation, each of which can be integrated into a GIS. The information from the spatial data obtained, regarding these variables, potentially may be used for further qualitative analysis of landslide prone areas. The purpose of this study was to use geospatial analysis to better understand the interdependence of these variables and potentially identify areas at high-risk for mass movement.