Georeferencing Oblique Aerial Wildfire Photographs: An Untapped Source of Fire Behaviour Data

Abstract

In this study, we investigate a novel application of the photogrammetric monoplotting technique for assessing wildfires. We demonstrate the use of the software program WSL Monoplotting Tool (MPT) to georeference operational oblique aerial wildfire photographs taken during airtanker response in the early stages of fire growth. We located the position of the fire front in georeferenced pairs of photos from five fires taken 31–118 min apart, and calculated the head fire spread distance and head fire rate of spread (HROS). Our example photos were taken 0.7 to 4.7 km from fire fronts, with camera angles of incidence from −19 to −50° to image centre. Using high quality images with detailed landscape features, it is possible to identify fire front positions with high precision; in our example data, the mean 3D error was 0.533 m and the maximum 3D error for individual fire runs was less than 3 m. This resulted in a maximum HROS error due to monoplotting of only ~0.5%. We then compared HROS estimates with predictions from the Canadian Fire Behavior Prediction System, with differences mainly attributed to model error or uncertainty in weather and fuel inputs. This method can be used to obtain observations to validate fire spread models or create new empirical relationships where databases of such wildfire photos exist. Our initial work suggests that monophotogrammetry can provide reproducible estimates of fire front position, spread distance and rate of spread with high accuracy, and could potentially be used to characterize other fire features such as flame and smoke plume dimensions and spotting.

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

“…Although the CFFDRS was developed based on experimental crown fires, future data collection will likely need to rely on wild and prescribed fires due to the magnitude of data required to characterize modern-day shifting dynamics. To do so would require a coordinated effort in the systematic collection, storage, and integration of observational fuels and fire behavior data using field and remotely sensed approaches (e.g., Perrakis et al 2014 ; Hart et al 2021 ; Phelps et al 2022 ). Simply put, the relationship between fuels and fire behavior cannot be understood or predicted without data for the systems we seek to represent.…”

Improved logistic models of crown fire probability in Canadian conifer forests

“…WS 10 > 15 km h −1 , FFMC > 90), observations that will also be relevant to planning effective hazard reduction treatments (Agee and Skinner 2005;Beverly et al 2020). Fire behaviour observations in extreme conditions will likely only be obtained from detailed wildfire monitoring (Hart et al 2021), typically through collaboration with fire management organisations. Although there is potential to recover fuel structure for wildfires in areas with LIDAR coverage, obtaining highquality wind speed and fuel consumption data will continue to be problematic, suggesting that field-scale fire behaviour experiments will still be required.…”