This is a review of recent efforts to develop and implement forest and wildland fire management decision support systems (FMDSS) that fire managers can use to enhance their management of their fire suppression activities. Fire managers need to resolve complex decisions associated with fuel management, fire prevention, detection, the suppression of potentially destructive wildfires and the use of prescribed fire to achieve an appropriate balance between the beneficial and detrimental social, economic and ecological impacts of fire on flammable landscapes, often under considerable uncertainty. This review focuses on the use of operational research and management science (OR/MS) methods to address their suppression resource management decision support needs.

This paper presents the results from employing survival analysis methods to model the probability distribution of the control time of forest fires. The Kaplan–Meier estimator, log–location–scale models, accelerated failure time models, and Cox proportional hazards (PH) models are described. Historical lightning and people-caused forest fire data from the Province of Ontario, Canada from 1989 through 2004 are employed to illustrate the use of the Cox PH model. We demonstrate how this methodology can be used to examine the association between the control time of a suppressed forest fire and local factors such as weather, vegetation and fuel moisture, as well as fire management variables including the response time between when a fire is reported and the initiation of suppression action. Significant covariates common to both the lightning and people-caused models were the size of the fire at the onset of initial attack, the Fine Fuel Moisture Code and the Initial Spread Index. The response time was also a significant predictor for the control time of lightning-caused fires, whereas the Drought Code and time of day of initial attack were significant for people-caused fires. Larger values of the covariates in these models were associated with larger survival probabilities.

Spring fire activity has increased in parts of Canada, particularly in the west, prompting fire managers to seek indicators of potential activity before the fire season starts. The overwintering adjustment of the Canadian Fire Weather Index System’s Drought Code (DC) is a method to adjust and carry-over the previous season’s drought conditions into the spring and potentially point to what lies ahead. The occurrence of spring fires is most strongly influenced by moisture in fine fuels. We used a zero-inflated Poisson regression model to examine the impact of the previous end of season Drought Code (DC_f) and overwinter precipitation (P_ow) while accounting for the day-to-day variation in fine fuel moisture that drives ignition potential. Impacts of DC_f and P_ow on area burned and fire suppression effectiveness were also explored using linear and logistic regression frameworks. Eight fire management regions across the boreal forests were analyzed using data from 1979 to 2018. For the majority of regions, drier fall conditions resulted in more human-caused spring fires, but not in greater area burned or reduced suppression effectiveness. The influence of P_ow was much more variable pointing to the conclusion that P_ow alone is not a good indicator of spring drought conditions.

Forest organic layers are important soil carbon pools that can, in the absence of disturbance, accumulate to great depths, especially in lowland areas. Across the Canadian boreal forest, fire is the primary disturbance agent, often limiting organic layer accumulation through the direct consumption of these fuels. Organic layer thickness (OLT) and fuel load (OLFL) are common physical attributes used to characterize these layers, especially for wildland fire science. Understanding the drivers and spatial distribution of these attributes is important to improve predictions of fire behaviour, emissions and effects models. We developed maps of OLT and OLFL using machine learning approaches (weighted K-nearest neighbour and random forests) for the forested region of the province of Alberta, Canada (538, 058 km²). The random forests approach was found to be the best approach to model the spatial distribution of these forest floor attributes. A databased of 3, 237 OLT and 594 OLFL plots were used to train the models. The error in our final model, particularly for OLT (5 cm), was relatively close to the variability we would expect to find naturally (3 cm). The dominant tree species was the most important covariate in the models. Age, solar radiation, spatial location, climate variables and surficial geology were also important drivers, although their level of importance varied between tree species and depended on the modelling method that was used.

We develop a stand-level fire threat index and incorporate it in a mixed integer programming model that can be used to help specify strategies that will maximize the expected volume harvested from a flammable forest over a finite planning horizon, subject to adjacency constraints. The inclusion of a threat index in our objective function implicitly identifies high volume harvest blocks that are most likely to burn over the planning horizon and accelerates the harvesting of those high risk stands to reduce the likelihood that they will burn before they are harvested. We illustrate the use of our model and evaluate its performance by simulating its application to two hypothetical flammable forest landscapes that are 16,000 hectares in size. Our results indicate that our inclusion of a threat index in our objective function produces timber harvest schedules that are better than those produced using an objective function that does not include potential fire loss measures, while satisfying harvest adjacency constraints.

Uncertainty concerning landscape burn fraction estimates is seldom considered by forest managers when they determine the forest age class structure for flammabl