T7 A Bird’s Eye View: Remote Sensing Applications for Geohazards

Sunday, 9 October 2022
8:00 AM - 12:00 PM
405

LEVERAGING MACHINE LEARNING FOR REMOTE CLAST SIZE IDENTIFICATION IN POST-WILDFIRE DEBRIS-FLOW DEPOSIT MATERIALS

Paper number: T7-13
Program: Technical Sessions
Time: 10:45 AM – 11:00 AM

Authors

Abstract

Post-wildfire debris flows are a growing hazard in the Western U.S. due to the increased annual areal extent of wildfire linked to climate change. There are relatively few observations of debris-flow grain-size distributions. This limits modeling efforts and hazard assessments that rely on properties related to the grain-size distribution (such as friction angle). Additionally, these properties are difficult to measure in a lab environment due to the large fraction of cobble-sized (6.4 cm) and larger particles. To improve our understanding, we collected a sub-centimeter resolution terrestrial lidar scan of a uniquely well-preserved debris flow deposit associated with the Grizzly Creek Fire in Glenwood Canyon, Colorado. These remotely sensed topographic data are leveraged to develop a semi-automated workflow for clast identification and grain-size distribution calculation. First, a training and testing dataset is created by performing point-level classification of matrix material or clasts larger than cobble size in the lidar point cloud. This analysis is a preliminary step towards automatically delineating the grain size distribution of a deposit.

Geological Society of America Abstracts with Programs. Vol. 54, No. 6

doi: 10.1130/abs/2022AM-380186

© Copyright 2022 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.