Research Updates
Year One
猎奇重口's Fire Science Team conducted successful prescribed burns at two different University properties. Data was collected using a new experimental design to field test fire modeling results that indicate fire-atmospheric interactions occur on firelines with specific geometric properties.
Year Two
UM’s fire management team’s research progressed with three major field experiments and one completed journal publication. A second manuscript has been submitted for review and a third is in development. The use of UAS was incorporated for data collection and machine learning researchers developed a scheme for classification of the probable burn severity for landscapes.
Year Three
Fire science research progressed with the publication of three journal articles and a conference proceeding. Graduate students were heavily involved in analysis of data collected from previous years’ experiments. Machine learning techniques developed in year two were applied to the problem of fuel changes following wildfires. A Gradient Boosted Regression Tree model was developed and employed to model a time series of canopy fuels burned sites. Fire Radiative Flux Density data from experimental burns in year two are being used to compare three different analysis areas and will likely result in a publication in year four. A third field experiment from year two produced an insufficient data set which will be combined with data collected from a proposed (and complementary) experiment in 2025. This new field experiment intends to compare fuel consumption in the fireline interaction zone to consumption outside of this zone in order to characterize differences in fire energy independently from remote sensing measurements. Finally, an effort to calibrate and reduce complexity in fire behavior models pivoted to focus on field experiments rather than LiDAR observations with the goal of determining the dominant mechanism that allows fire to spread across a region with lower fuel loading. Work here will have practical applications inclusive of fireline creation and prescribed burning. This research area continues to rely on close collaborative work and support from machine learning and UAS teams.
Prescribed Burns at Bandy Ranch and Lubrecht
This material is based upon work supported in part by the . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.