Using 3D Terrestrial Laser to Estimate Forest Fire Fuel Loads

Research Issue

Terrestrial laser scanning 3D point clouds for estimating fuel loads at the plot scaleWildland fire managers are very interested in accurately measuring the 3-D structural characteristics of forest canopies because they are directly related to fire behavior. For example, ladder fuels can facilitate transition of surface fires to the canopy, where they are much more difficult and expensive to suppress. Maximizing the accuracy of canopy structure measurements is vital to guiding the effective allocation of resources by fire management agencies and evaluating the effectiveness of fuel treatments.

Estimates of fuel load are traditionally made using time-consuming field sampling protocols that can be difficult to implement consistently over large spatial extents or for many plots. However, recent advances in terrestrial laser scanning, reduction in cost of equipment and improvements in modeling techniques now offer a wealth of site-specific information to characterize a forest stand or plot and the amount of combustible materials present without significant cost or field time.

Laser scanning allows scientists to accurately measure and collect data from objects, surfaces, buildings, and landscapes. Laser scanners collect information in the form of point cloud data, consisting of millions of 3D coordinates. However, there is still a need to generate methods and protocols that allow for consistent and accurate results to be obtained from the 3D point cloud data.

Our Research

We are exploring the estimation of forest fuel load variables using methods that are reproduceable and well documented so that they can be applied by practicing foresters, managers, and fire mitigation professionals. Specifically, we are investigating the use of single-return, single-wavelength terrestrial laser scanning data, which are generally cheaper than multi-return, multispectral, and/or full waveform units. This reduced cost will enhance the practical application of the technique.

Our goal is to determine an optimal subset of variables for making accurate predictions of fuel loads using a parsimonious model that can be easily applied to new data. Once a final set of models are generated, we will produce R functions that allow the trained models to be applied to new point cloud data. We will then assist U. S. Forest Service staff in the documentation of required field protocols.

Expected Outcomes

The results of the research will have practical applications for estimating fuel loads using a predictive tool and associated documentation and field protocols. Ultimately, practicing foresters, managers and fire mitigation professionals will be able to follow our protocols and use our open-source tools to estimate fuel load metrics consistently and accurately from their own single-return point cloud data.

Research Results

Skowronski, Nicholas S.; Clark, Kenneth L.; Duveneck, Matthew; Hom, John. 2011. Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sensing of Environment. 115: 703-714.

Skowronski, Nicholas; Clark, Kenneth; Nelson, Ross; Hom, John; Patterson, Matt. 2007. Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey. Remote Sensing of Environment. 108: 123-129.

Research Participants

Principal Investigators


  • Alexis Everland, Tall Timbers Research Station, Lead Fire Ecology Research Technician
  • Luis Andres Guillen, West Virginia University, PhD Candidate

Research Partner

  • Last modified: February 22, 2021