Evaluating Fire Fuels in Northern California Oak Woodlands using Unmanned Aerial Systems

Thesis
Year: 
2022
Abstract: 

This study will test the benefit and feasibility of using Unmanned Aerial Systems (UAS) Structure for Motion (SfM) 3D point cloud data to estimate fire fuels and produce accurate fire model predictions in Northern California oak woodland forests. A goal of this study Is to assess methods that aim to provide fire fuels data at a finer spatial resolution to enhance or substitute traditional estimates.

Multispectral image data, acquired using Sensefly eBee fixed-wing UAS platfonn (equipped with a muttispectral sensor), will be used in concert with UAS SfM point cloud data (generated using Pix40 software). The UAS data will be used to estimate fire fuels parameters induding: canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates will be validated with field-measured data and ALS imagery. Following validation, UAS-derived data will be used in fire behavior modeling to evaluate the inclusion of UAS-derived parameters. 

Matthew Clark
Posted PDF: 
Status: 
In Progress