Background: Crown fires are an ecologically necessary but hazardous process in conifer forests. Prediction of their behaviour in Canada has largely depended on the Canadian Forest Fire Behaviour Prediction System, in which fire weather indices drive primarily fixed fuel type models. The Crown Fire Initiation and Spread (CFIS) system presents a more flexible approach to predicting crown fire occurrence than fixed fuel type models. Aims: Using a multi-decadal database of experimental fires carried out in conifer plots (1960–2019, n = 113), our aim was to develop updated models based on the CFIS system approach, fitting crown fire occurrence models to fire environment variables using logistic regression. Methods: We tested alternative fuel moisture estimates and compared various model forms using repeated cross-validation. In two-storeyed stands, crown fire occurrence was defined as the involvement of lower canopy stratum fuels. Key results: Final models based on wind speed, fuel strata gap, litter moisture and surface fuel consumption predicted crowning events correctly in up to 92% of cases in training data (89% in cross-validation). Conclusions and implications: These new models offer improved accuracy and flexibility that will help users assess how competing environmental factors interact under different fuel treatments and wildfire scenarios.

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.

Surface fire frequently occurs beneath the canopy of North American forests under moderate wind speed and moisture-deficit conditions. Surface rate of spread (sROS) models can provide guidance for suppression operations and can be incorporated into fire growth modelling systems and other tools. We used a database of primarily Canadian experimental surface fires in conifer and deciduous stands from multiple sites to fit empirical sROS models for operational use and compare with pre-existing models. Various predictor combinations represented fires in boreal conifer (BOCON), deciduous, and Ponderosa pine-dominated stands, the latter analyzed to estimate grass-curing influence. The main predictors were wind speed (WS₁₀), estimated fuel moisture, and Canadian Fire Weather Index (FWI) System components (original and stand-adjusted). The ensuing fitted models (N = 51–93) were evaluated using standard metrics and tested using an independent conifer dataset (N = 26). The simplest model finds BOCON sROS to be equal to 1.2% of the WS₁₀, 1/7th the speed of crown fire spread under similar conditions; it is easily calculated as 20% of WS₁₀ using a common unit conversion (WS₁₀ in km h⁻¹, sROS in m min⁻¹). The best-performing sROS models displayed nonlinear-sigmoidal responses to wind and litter moisture variables, including the Initial Spread Index (ISI), and improved upon pre-existing models. Estimated accuracy was mostly +/− 2–4 m min⁻¹ within the range of the data in both training and validation datasets. These models reflect a dataset gathered from multiple sites using varying experimental methods. While imprecise, they are suitable for many applications, including operational forecasting and designing hazard reduction treatments.