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Methodology for detecting firebrand generation and the analysis of influential variables in quantification

Year: 2025
Type: article
Abstract: Firebrands play a dominant role in wildland fire propagation, especially in spotting, where embers are carried away from the main fire to ignite new areas. Three mechanisms govern their dynamics: generation, transport, and landing. While transport and landing have received more attention in the literature, generation remains poorly understood due to challenges in real-time quantification and detection. This shortcoming undermines fire spread models that require accurate estimation of particle properties and numbers. To address this, a UNet-based convolutional neural network (CNN) image processing method was developed to detect and segment firebrands from images and video. The model demonstrated over 80 % accuracy with limited training data, suggesting potential for field use with low-cost, heterogeneous imaging systems. Experiments were conducted in large wind tunnel (1.5m × 2.1m × 6 m) at uniform wind speeds up to 6.2 m/s and test velocities between 0.5 to 1.5 m/s. Douglas Fir samples were burned, and mass loss was measured using balances. Fuel positioning affected firebrand production: free samples (1.5 kg) exhibited multiple intense burning stages due to deformation, whereas restrained samples (0.76 kg) burned more uniformly. The proposed CNN approach offers a promising tool for supporting firebrand detection.
DOI: 10.1016/j.firesaf.2025.104549
Language: English
Open Access status: hybrid
Estimated APC paid: $3,770
Award: PURPOSE: THIS PROJECT WILL IDENTIFY RELATIONSHIPS BETWEEN THE GENERATION OF FIREBRANDS AND FIRE INTENSITY FOR DIFFERENT VEGETATIVE FUELS. ACTIVITIES TO BE PERFORMED: THIS PROJECT WILL DEVELOP THE RELATIONSHIP BETWEEN THE CHARACTERISTICS OF THE FIREBRANDS GENERATED AND FIRE INTENSITY, AND DURATION FOR DIFFERENT FUELS AND IDENTIFYING FUEL CHARACTERISTICS WHICH MAY BE PREDICTORS OF FIREBRAND GENERATION PROPENSITY (E.G. FUEL SHAPE, BARK STRUCTURE). ADDITIONALLY, THE DATA OBTAINED FROM THIS RESEARCH PROJECT WILL BE UTILIZED TO PARAMETERIZE THE NUMERICAL MODELS OF FIREBRAND GENERATION, TRANSPORT, AND DEPOSITION.EXPECTED OUTCOMES: FIREBRANDS ARE A SIGNIFICANT SOURCE OF IGNITION FOR HOMES NEAR THE WILDLAND-URBAN INTERFACE (WUI). THE PROJECT WILL GENERATE FIREBRAND GENERATION PARAMETERS, WHICH ARE CRITICAL INPUTS TO WUI FIRE MODELS. DATA PUBLISHED FROM THIS WORK WILL ALSO SERVE AS A FOUNDATION FOR FUTURE FIREBRAND TRANSPORT AND MODELING RESEARCH. THIS PROJECT WILL MAKE TOOLS AVAILABLE TO PRACTITIONERS, ENGINEERS, AND INVESTIGATORS TO PREDICT THE POTENTIAL IGNITION OF A STRUCTURE BY FIREBRANDS POTENTIAL OUTCOMES OF THIS PROJECT TO THE FIRE COMMUNITY WILL BE IMPROVING THE RESILIENCE OF THE US COMMUNITIES AT RISK OF WUI FIRES.INTENDED BENEFICIARIES: US COMMUNITIES AT RISK OF WUI FIRES WILL HAVE IMPROVED TOOLS FOR PLANNING RESILIENCE EFFORTS. THIS PROJECT IS ALIGNED WITH THE STATED DRRG GOALS OF THE NIST WUI FIRE PROGRAM TO SUPPORT THE DEVELOPMENT OF COMPUTATIONAL TOOLS DIRECTLY RELATED THE PREDICTION OF IGNITION BY FIREBRANDS BY COLLECTION OF NECESSARY DATA FOR UNDERSTANDING FIREBRAND TRANSPORT CONDITIONS AND VALIDATION OF COMPUTER MODELS.SUBRECIPIENT ACTIVITIES: THE UNIVERSITY OF EDINBURGH, SCOTLAND, UK AND CALIFORNIA POLYTECHNIC STATE UNIVERSITY, CA, USA WILL BE SUBAWARDEES ON THIS PROPOSAL. THE UNIVERSITY OF EDINBURGH HAS A COMPLEMENTARY SETUP TO WPI'S WIND-TUNNEL THAT HAS ALREADY BEEN USED FOR WILDLAND FIRE RESEARCH AND A STRONG TRACK-RECORD OF FIREBRAND RESEARCH (INCLUDING NIST-FUNDED PROJECTS).
OpenAlex ID: W4414946452
FWCI: 0.596
Cites: 19
Cited by: 1
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