Using 3D Data and Fire Modeling to Examine Future Wildfire Risk Following Understory Fuels Management in a Mixed Hardwood Forest
Dr. Lisa Bentley is the faculty-lead Principal Investigator (PI) alongside graduate student PI, Monica Delmartini. In the western U.S., more than a century of fire suppression, logging, and cessation of Indigenous burning has led to significantly altered forest stand conditions. Utilizing 3D data derived from terrestrial laser scanning and a physics-based fire model, this project will investigate if thinning prescriptions in mixed hardwood and hardwood/conifer vegetation communities lead to decreased future fire intensity.
toEvaluating Plot-level Remote Sensing Tools to Increase Accuracy and Efficiency of Fuels Management Approaches
Dr. Bentley and Dr. Clark will use new, emergent remote sensing technology (terrestrial laser scanners and unmanned aerial systems, i.e., drones) to acquire detailed measurements of 3-dimensional forest structure in coastal and southern Cascade forests of northern California. These measurements will be used to: 1) rapidly and more accurately estimate aboveground biomass for a range of tree species and 2) estimate crucial fuels parameters to help validate or refine fire probability and behavior models across these diverse forests.
toCAREER: 3DForests: Using Terrestrial Laser Scanning to Explore Forest Structure Changes Following Disturbance
This project will connect physiological and novel remote sensing tools to better understand the effects of wildfire and fuels management on forested ecosystems in California under a changing climate. This will integrate Dr. Bentley’s long-term research and education goals related to the responses of forests to environmental change using terrestrial laser scanning, metabolic scaling ecology, modeling and virtual reality based outreach.
toAssessing the utility of handheld LiDar to quantify forest understory structure and evaluate change following disturbance
This project will quantify forest structure changes in post-fire (Sonoma County) and managed stands (Mendocino County) via LiDAR voxel metrics. Data will be collected using a LiDAR handheld mobile laser scanner, and validated using limited destructive sampling. This study will add to the relatively new and growing body of work on LiDAR remote sensing to measure forest structure as a component of forest health.
toEvaluating the utilization of 3D physics-based fire models in conjunction with terrestrial remote sensing data
This project seeks to expand upon the research conducted by the Bentley Lab at Sonoma State University and the USFS Rocky Mountain Research Station Fire Science Lab by integrating terrestrial laser scanning data with a physics-based model framework to estimate fire effects on biomass, forest structure, and tree mortality in a wildfire affected oak-woodland and managed conifer forest in California.
toIdentification of effective and scalable forest health treatments for coastal California forest: pre and post fire approaches
This project will assess the effectiveness of forest health treatments for coastal California forests and identify agency characteristics that influence scaling to landscape levels. This work seeks to identify cost-benefit tradeoffs of competing forest health treatments, determine the threat posed by biologically-driven fuels accumulation, assess effectiveness of a set of contrasting forest treatments aimed at controlling fuels and mitigating disease impacts, and identify agency capacity to apply treatments at scale.
toUnderstanding the global 3D signature of tree biodiversity
This research seeks to revise fundamental scaling laws in Ecology by quantifying the broad controls on tree-level 3D structure. We will accomplish this through the development of a global-scale 3D trait databases based on terrestrial laser scanning data assembled from an international community of researchers. The project will result in an open access global 3D trait database that will support improved biodiversity characterization and mapping. Lisa Bentley will advise in all matters related to scaling theory in the proposed research and contribute to planned research manuscripts.
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