UAV-Based Estimation of Fuel Structure and Dynamics in a California Canyon Fire Experiment

Abstract

Wildfires in California increasingly threaten communities and ecosystems. However, comprehensive estimation of fire dynamics and fuel structure remains limited. Recent advances in Uncrewed Aerial Vehicle (UAV) technology and high-spatial-resolution mapping have provided increasingly important tools for estimating wildfire fuel-height loss across fuel types. This study used a one-year Uncrewed Aerial Vehicle (UAV) time series to quantify fuel-height loss and vegetation regrowth associated with a prescribed upslope canyon fire near Salinas, California, USA. Multispectral, infrared, and visible UAV imagery collected before, during, and after burning was used to generate orthomosaic, digital surface models (DSMs), fuel-type classifications, and surface-volume estimates. To enable reliable pre- and post-fire comparison, ground control points and tie points were used to train linear regression calibrations that corrected angular discrepancies and elevation offsets among time-series DSMs. Calibrated DSMs were then integrated with ecological field measurements to map fuel-height consumption and post-fire recovery at the individual-plant scale. UAV-derived fuel-height change was associated with in situ twig-diameter measurements, which provide field-based indicators of fire effects in chaparral vegetation, while the maximum recorded temperature explained only a small proportion of variation in fuel-height loss. This workflow can support integrated fire ecology and remote-sensing studies by providing repeatable measurements of post-fire changes in vegetation structure.