Big data modelling provides forest fuels mapping

Friday 25 Nov 2022

 
Modelling and mapping fire-vulnerable forest vegetation across millions of acres in California, scientists at the University of Nevada, Reno are using a variety of new technologies with massive amounts of data and computational power. This research will help optimize fuel management to reduce fire risk, support carbon sequestration and improve water quality.

The research team, led by Jonathan Greenberg and Erin Hanan in the University’s College of Agriculture, Biotechnology & Natural Resources, is working on a set of interrelated initiatives that are collectively called the "GigaFire Project." Their overarching goal is to understand, using remote sensing technology and process-based models, how vegetation and fuels are changing over large landscapes.

Greenberg and Hanan are researchers with the College’s Experiment Station and Department of Natural Resources & Environmental Science. Their research will produce statewide and localized fuel maps that will help identify where fire risk is the greatest. They will also inform modelling scenarios designed to predict how management can mitigate fire risk while also promoting carbon retention and water security.

With US$570,000 from the California Air Resources Board and nearly US$1.8 million from CAL FIRE, the researchers are mapping surface and canopy fuels across the state using:

• multi-sensor remote sensing data with Landsat and Airborne LiDAR (LiDAR stands for Light Detecting And Ranging, and is a remote sensing method used to examine the three dimensional structure of vegetation);
• field-based sampling with terrestrial laser scanning and ground based photogrammetry (the use of photography in surveying and mapping to measure distances between objects) to calibrate and validate changes over time;
• machine learning; and
• cloud and high-performance computing to map surface fuel model types, canopy base height, and canopy bulk density across the state.

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Source: unr.edu

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