Introduction
The Safford Lab is proud to partner with the University of California on the Climate Action Initiative project, “Scaling Science-Driven Vegetation Treatments for a Wildfire Resilient California.” With a budget of $1,997,050, this project aims to:
- Develop 3D fuel models for California by integrating multimodal vegetation data.
- Create scenario-specific 3D models for prescribed burns and monitor their ecological effects.
These objectives will be implemented through the BurnPro3D platform, a web-based tool designed for planning and executing prescribed burns using high-resolution 3D vegetation and fire models. In collaboration with the California Department of Forestry and Fire Protection (CAL FIRE), we support the agency’s mission to enhance prescribed burning activities.
Our Contribution to the Project
At Safford Lab, our responsibilities include:
- Designing and implementing field and laboratory protocols.
- Selecting burn sites and designing the inventory.
- Field work with a dedicated three-person field crew.
- Collecting, processing, and distributing all data.
Additionally, we are researching how terrestrial lidar data can enhance the understanding of prescribed fires' effects on forest ecosystems.
How We Operate
Our field protocols incorporate and merge three methodologies:
California Prescribed Fire Monitoring Program
We follow the established field protocol for the CPFMP.
In this protocol the following parameters are measured:
- Plot Description: Gather descriptive data about the plot and estimate ground cover.
- Regeneration: Collect quantitative data on regeneration, categorized into seedlings, saplings, and resprouts.
- Vegetation Cover: Estimate percentage cover to the nearest 1% (less than 5%) and to the nearest 5% (greater than 5%).
- Trees: Collect quantitative and descriptive data about trees (DBH cutoff of 7.6 cm and height cutoff of 1.37 m).
- Basal Area: Estimate stand basal area by species and status using “Cruzall” or prism methods.
- Species Cover: Estimate percentage cover to the nearest 1% and 5% for species, status, and layer.
- Woody Fuels: Collect fuels data from four Brown’s Transects laid out in cardinal directions, extending 11.3 m from the plot center.
- Burn Piles: Describe and count each burn pile within the entire 1/10th acre plot
Terrestrial Lidar Scanning
Using a simple scanner with our Leica BLK G1 that captures approximately 300,000 points per scan, with an accuracy of 6 millimeters at a distance of 10 meters.
Clip Plots
Classifying, collecting, drying, and weighing forest fuels across different vertical strata (0 to 30 cm, 30 cm to 1 meter, and above 1 meter) to gather data on the dry weight of fuels in each stratum.
We conduct two inventories in each plot—one before and one after the prescribed burning.
Some Results
While this is a two-year project, we already have some preliminary results and products:
Field data
Through this application you can check the location and type of field plots that we are inventorying in real time
Scans in IntELiMon
One of our partners in the project is IntELiMon: Interagency LiDAR Monitoring and Research Applications. Their mission is to develop efficient methods for monitoring our natural and cultural resources and provide effective decision support tools for land managers. This project is a collaborative effort developed with partners from the USDA Forest Service, U.S. Fish and Wildlife Service, Bureau of Indian Affairs, National Park Service, U.S. Geological Survey, universities, and the New Mexico Consortium. This multidisciplinary team seeks to integrate modeling and LiDAR techniques to monitor fuels and ecosystems and link fire modeling for decision support in land management. The ultimate goal is to standardize monitoring across agencies with uniform language, methods, and data analysis.
All our scans have been processed and uploaded to the Intelimon platform, where you can consult and download various metrics and results from the Lidar data.
Visualization of Plots
We utilize the Point2pano platform for visualizing Lidar data, developed by UC San Diego. You can watch the following video to learn more about data visualization.
