T252 Landslide Inventories, Databases, Hazard Maps, Risk Analysis, and Beyond (Posters)

Tuesday, 24 October 2017
9:00 AM - 6:30 PM
Halls 4EF

INCORPORATING SEMI-AUTOMATED LANDSLIDE MAPPING WITHIN AN EXISTING MANUAL, EXPERT-BASED FRAMEWORK

Paper number: T252-11
Program: Technical Sessions
Time: 9:00 AM – 6:30 PM

Authors

Abstract

Landslides are a persistent and pervasive natural hazard in Oregon. The Oregon Department of Geology and Mineral Industries (DOGAMI) have been using a lidar-based landslide inventory mapping method (Special Paper 42, Burns and Madin, 2009) to better understand landslide hazards and convey them to the public. While very effective toward accomplishing this task, inventory mapping is time intensive and requires experience, and thus, is expensive to implement, especially across large areas of concern. Researchers from Oregon State University (OSU) have created a DEM-based landslide mapping program, building upon the Contour Connection Method (CCM, Leshchinsky et al., 2015) with the addition of a semi-automated scarp identification procedure. Together, the DOGAMI and OSU research team have collaborated to implement the semi-automatic mapping method for use in a landslide inventory, prepared to DOGAMI Special Paper 42 standards. Mapping was performed on a watershed in the central Coast Range of western Oregon with available high-resolution lidar data, and without an existing landslide inventory. In this work, we highlight the workflow of running the scarp identification tool and the Contour Connection Method, review the landslide deposit polygon outputs, and incorporate these semi-automated methods into the Special Paper 42 methodology. The outcome of this collaboration is a method for landslide inventory mapping that may be used by practitioners such as state geological surveys, or other experienced geologists to conduct increasingly efficient landslide inventory mapping. The aim is to add consistency to mapping results, increase speed and efficiency of the mapping process, decrease costs for the entire effort, and enable large areas to be mapped for overview analyses. Results of this study, presented here, include spatial statistics of agreement between three variants of landslide inventory mapping: 1) semi-automatically generated scarp lines and associated CCM output polygons, 2) manually mapped scarp lines and associated CCM output polygons, and 3) manually mapped landslide deposits, using Special Paper 42 criteria for interpretation.

Geological Society of America Abstracts with Programs. Vol. 49, No. 6

doi: 10.1130/abs/2017AM-304217

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