ASSESSING SURFICIAL LANDSLIDE VOLUME: A CASE STUDY IN WHATCOM COUNTY, WASHINGTON, USA

Worldwide, gravitational processes represent a major natural hazard for human settlements and their economic activities. To counter the effects of landslides, scientists determine landslide distribution, type, abundance, and landslide sediment production at multiple spatial and time scales. The characterization and analysis of landslide sediment volume is crucial in gauging the potential effect in an area. In this research, the partial mobilized surface volume or accumulated volume is determined by analyzing topographic changes. In the case of surficial volume calculation, the advent of new technologies such as airborne LiDAR and UAVs to produce high-resolution DEMs has opened up new opportunities to calculate volumes. To calculate surficial landslide volume, studies focus on two generic landslide volume models: 1) an overlay model, based on the use of existing pre- and post-landslide DEMs, or 2) an interpolation model, based on the reconstruction of pre-landslide topography through interpolation. In either case, landslide volumes remain poorly quantified, especially in terms of their incidence at the level of the entire country or of an individual state, where the models need to be implemented for large datasets. The aim of this poster is to present the ongoing research project from the Institute of Geography at the National Autonomous University of Mexico and Washington Geological Survey, Department of Natural Resources to estimate surficial landslide volume and distribution by taking full advantage of LiDAR and standardized landslide volume calculation in a Geographic Information System. We implemented the two generic landslide volume models by using Python scripts. The models were tested in real and theoretical conditions to highlight advantages and limitations. At the same time, we explored how the interpolation model is affected by local altimetric variation. The results show that one of the models can be used to make first-order interpretations regarding volume of eroded sediment for landslide deposits at a local or national scale, while the other can help to assess the sequence of landslide activity. Theoretical evaluations show that local altimetric variation of < 1 m could lead to errors of almost 17%. The approach is explored with examples from Sumas Mountain in Whatcom County, Washington, USA.