Background Satellite-based remote sensing is important for managing wildfires. However, cloud cover could hinder its effectiveness.Aims We examine potential cloud cover impacts on satellite-based wildfire detection and monitoring in Canada, analysing spatio-temporal patterns and relationships with local fire conditions.Methods Geostationary Operational Environmental Satellite 16 (GOES-16) Clear Sky Mask (CSM) data at relevant satellite overpass times in Ontario and Alberta are used to characterise cloud cover at a variety of spatial and temporal scales. Two potential fire behaviour indicators are compared under cloudy and clear conditions.Key results Cloud cover can vary by time of day, seasonally and by sub-region. In general, cloud cover was lowest overnight, during July-August and typically greatest at the overpass time of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments (13:30). Initial Spread Index (ISI) and Head Fire Intensity (HFI) distributions were similar under clear and cloudy conditions.Conclusions Satellite-derived active fire products will experience omission error rates varying in space-time and this could occur under potentially dangerous fire behaviour conditions. Care may need to be taken when relying on these products for fire monitoring and detection.Implications Wildfire-detection and monitoring strategies should leverage satellite, airborne and ground-based methods.

Wildfire management entities (FME) across the globe operate under diverse situations, all while facing escalating challenges from climate change. Earth observation (EO) satellites offer unique value for active fire monitoring, however, the extent to which EO-derived active fire data (EO-AFD) is adopted remains unclear. To better understand adoption by FMEs, we analyzed responses from a global survey with representation from 56 countries, conducted under the Committee on Earth Observation Satellites Working Group on Disasters, Wildfire Pilot. We applied a mixed-method approach using hierarchical agglomerative clustering and inductive reasoning to identify common FME functional ‘archetypes’ and provide a notional archetype development pathway relative to operational dependence on EO-AFD. Six end-user archetypes were identified: Curious, Nascent, Pragmatic Generalist, Specialist, and Leader. While 75% of all end-users had at least some degree of trust in EO-AFD and trust was generally high across all archetypes, integration levels varied, often constrained by technical, cost, resource, and expertise related barriers. The spatial distribution of archetypes and the barriers to EO-AFD use varied by region. Considered together, these may indicate where barriers play a stronger role in shaping archetypes. The archetypes can serve as a baseline for developing EO-AFD adoption engagements.

Research and development for a real-time wildfire decision support system was undertaken to support appropriate response decision-making in Ontario, Canada. We describe the context, history, requirements, research and development process, and components of FireGUARD (Fire Growth under Uncertainty for Appropriate Response Decision Support) and show examples of its prototype products. The work was a collaborative effort between researchers, specialists, and fire management experts. FireGUARD prototype outputs include a weather forecast and high-resolution maps of burn probability out to 14 days, fuel type, impact, and risk. Additional uses of include triaging multiple new fires, prioritizing scarce suppression resources, and large fire management. FireGUARD was very useful and remains in demand; its success led to further decision-support initiatives.

Globally, managing wildland fire is increasing in complexity. Satellite Earth Observation (EO) data, specifically active fire ‘hotspot’ data, is often used to inform wildland fire management. This study explores hotspot data usage via web traffic data (‘user counts’) for the FIRMS, GWIS and EFFIS web portals between September 2019 and April 2023. Global active fire data use is characterized by multi-month periods of relatively low, stable user counts, interspersed with periodic spikes (4.1x median monthly activity) of activity broadly aligned with the North American / European fire season (late summer-fall). Users from the Americas (45%) and Europe (36%) dominate web traffic. We also examined correlative relationships between web page user counts and environmental and social variables at multiple spatial scales. Globally, the strongest relationships were found between user counts and the total number of fires, total burned area, number of fire disaster events; country population levels and internet proliferation. Notably, at a country level, the strongest (and weakest) relationships between weekly user counts and fire activity were found in a diverse range of countries suggesting that a variety of hotspot data use situations exist and further investigation is needed to better understand EO active fire data usage.

The level of implementation and uptake of specific tools used to support wildland fire management decision-making has received little attention in Canada. Our aim is to aid the fire research-to-practice discourse in Canada by describing key terms and concepts for characterizing implementation, uptake, and capacity. We also designed and conducted a survey to assess the implementation and uptake of some of the available tools used by Canadian provincial and territorial fire management agencies. We assessed nine tools and found distinct differences in their implementation and uptake, with differing results at national versus provincial and territorial scale. The Canadian Fire Weather Index and Fire Behaviour Prediction Systems had the highest level of both implementation and uptake nationally. The other tools have substantially lower but varying degrees of implementation and uptake across the country. The results encourage further investigation into the factors affecting implementation and uptake of fire management tools, both nationally and in provinces and territories.

Communities interspersed throughout the Canadian wildland are threatened by fires that have become bigger and more frequent in some parts of the country in recent decades. Identifying the fireshed (source area) and pathways from which wildland fire may ignite and spread from the landscape to a community is crucial for risk-reduction strategy and planning. We used outputs from a fire simulation model, including fire polygons and rate of spread, to map firesheds, fire pathways and corridors and spread distances for 1980 communities in the forested areas of Canada. We found fireshed sizes are larger in the north, where the mean distances between ecumene and fireshed perimeters were greater than 10 km. The Rayleigh Z test indicated that simulated fires around a large proportion of communities show significant directional trends, and these trends are stronger in the Boreal Plains and Shields than in the Rocky Mountain area. The average distance from which fire, when spreading at the maximum simulated rate, could reach the community perimeter was approximately 5, 12 and 18 km in 1, 2 and 3 days, respectively. The average daily spread distances increased latitudinally, from south to north. Spread distances were the shortest in the Pacific Maritime, Atlantic Maritime and Boreal Plains Ecozones, implying lower rates of spread compared to the rest of the country. The fire corridors generated from random ignitions and from ignitions predicted from local fire history differ, indicating that factors other than fuel (e.g. fire weather, ignition pattern) play a significant role in determining the direction that fires burn into a community.

Governmental legislation, regulations, and policies are used to prevent and mitigate the negative impact of human-caused wildland fires. In Ontario, Canada, the Modifying Industrial Operations Protocol (MIOP) aims to manage and limit the risk associated with fires ignited because of industrial forestry operations while maintaining flexibility in terms of daily restrictions. The MIOP was enacted in Ontario in 2008, when it replaced the Woods Modifications Guidelines, which had been in effect since 1989. We use quantile regression to quantify how the distribution of incremental growth has changed when contrasting three prevention time periods (MIOP, Woods Guidelines, Pre-Woods) while controlling for several possible confounding variables that drive fire growth. We analyze data of industrial forestry-caused wildland fires ignited on Crown forest land in Ontario from 1976 to 2019. This type of retrospective analysis is important for monitoring the performance of Ontario’s prevention and mitigation efforts and providing insight for the future, especially in a changing environment. Our findings provide evidence of MIOP succeeding at its goal of mitigating the negative impact of ignited industrial forestry fires when compared against previous regulations.

Wildfires are worsening in Canada and globally, partly due to climate change. The government of Canada is designing and building WildFireSat, the world’s first purpose-built operational satellite system for wildfire monitoring. It will provide new fire intelligence to support decision-making. It takes time for fire management agencies to use new information: to understand it and its implications, change processes, develop training, and modify computer systems. Preparing for the system’s prelaunch will allow agencies to benefit more rapidly from the new information. We present (1) an assessment of the readiness of 12 Canadian fire management agencies to integrate WildFireSat information and (2) guidance for reducing readiness gaps. We used survey and other data to score readiness indicators for three readiness components: understanding, organization, and information technology. We weighted the influence of each indicator score on each component. We modelled scoring and weighting uncertainties and used Monte Carlo simulation to generate distributions of aggregated agency readiness. The results indicated that most agencies have a moderate level of readiness while others have a higher level of readiness. Cluster analysis was used to group agencies by similarity in multiple dimensions. Strategies for increasing readiness are highlighted. This identifies opportunities for agencies and the WildFireSat team to collaborate on enhancing readiness for the forthcoming WildFireSat data products.

Background There has been little quantification of the extent and duration of micrometeorological changes within a forest after airtanker drops of water-based suppressant. It has been speculated that a period of prolonged relative humidity – referred to as a ‘relative humidity (RH) bubble’ – temporarily exists in the canopy understorey post-drop. Aims We quantify the RH bubble from the drops of five airtankers commonly used by wildland fire management organisations in Canada. Methods We measured airtankers dropping water, foam concentrates, and gel enhancers in a mature jack pine stand. We examined the duration of change in RH and temperature using Generalised Additive Models, and the consequence of these changes on fine fuel moisture. Key results The average maximum RH increased and temperature decreased, indicating that the effects of the ‘RH bubble’ in-stand lasted from 25 to 76 min, depending upon the airtanker type and load configuration. Conclusion Airtanker drops cause an in-stand increase in RH and decrease in temperature, but this ambient change has a limited effect on potential fire behaviour. Implications The direct effect of water wetting the fuel is the most impactful effect of an airtanker drop. The ‘RH bubble’ created, though observable, does not change fine fuel moisture enough to impact fire behaviour.

Wildland fire is an important natural disturbance in many vegetated areas of the world. However, fire management actions are critical not only to prevent and suppress unwanted fires, but also mitigate and recover from the negative impacts of fire on people and communities. Advancements in wildland fire science can help inform these necessary actions in wildland fire management. How science is created and integrated into these fire management decision-making processes, whether through collaborations with external researchers and/or with scientists within a wildland fire management agency itself, requires a conscious understanding of how the science is useful and goes beyond the simple existence of knowledge. This chapter outlines the goal of integrating fire science and management using a conceptual knowledge exchange (KE) framework, informed from existing work on KE. We provide a review of the KE literature relevant to wildland fire management and develop a KE framework for the fire management context. In this context, we address the potential barriers and facilitators throughout this process followed by a discussion of an active learning approach aimed at developing effective data translation skills amongst students in a data analytics consulting course.

Evidence‐based studies in the environmental sciences frequently rely on the presence of spatially dense climatological data. However, such data are often available only at a fixed set of locations that may be regularly or irregularly arranged across a region. Spatial interpolation enables the approximation of variables of interest at locations between those sites. When conducting interpolation in collaboration with an end user or in interdisciplinary research, mutual knowledge exchange allows for greater insight on what is required of an interpolation method since each may have different pros and cons. We outline and discuss several key considerations one should make in an interpolation study, such as the purpose of the variable and the goals of the end user, including how the variable is used to inform decisions. This process is then illustrated via case study within a wildland fire weather context. For the province of Ontario, Canada, we contrast several methods for interpolating the Fire Weather Index (FWI), comparing them quantitatively via metrics and qualitatively using a proposed categorical gradients visualization scheme. Conditional simulations and a spatial ensemble are also investigated. This work is in collaboration with the Ontario Ministry of Natural Resources and Forestry.

Wildland fire prevention and mitigation is of mutual interest to both government and the forest industry. In 1989, the Ontario Ministry of Natural Resources and Forestry introduced the Woods Modification Guidelines that provided rules on how forestry operations should be modified based on local fire danger conditions. Those guidelines were replaced by the Modifying Industrial Operations Protocol (MIOP) in 2008. One objective of MIOP is to allow forestry operations to be done safely for as long as possible as the fire danger increases. We investigate the impacts of these sets of regulations on the frequency of industrial forestry-caused (IDF) wildland fires in the province of Ontario, Canada. Data from 1976 to 2019 are analyzed. A case-crossover study finds no evidence to suggest that MIOP's greater flexibility in operating hours has increased the probability of IDF fire occurrences. This result indicates that MIOP's regulations have had the desired effect of allowing longer working hours on days with heightened fire risk without adding to the seasonal wildland fire load.

Background Industrial forestry operations in Ontario, Canada, may be restricted to reduce the risk of wildland fires. This is currently done according to the Modifying Industrial Operations Protocol (MIOP), which was implemented in 2008 as a replacement for the Woods Modification Guidelines that had been in place since 1989. One of MIOP’s objectives is to limit the negative impact or damage caused by fires ignited by industrial forestry operations. Aims Treating the incremental growth between discovery and final sizes as a measure of suppression effectiveness, we aimed to characterise and contrast growth distributions for three successive time periods using data spanning 1976–2019 on Crown forest areas of Ontario. Methods Stratifying by first responding group (Ontario Ministry vs forestry personnel), we tested for evidence of changes in the growth distribution using the Kruskal–Wallis and Mann–Whitney U tests. Key results We found iterative improvements between successive time periods (Pre-Woods, then Woods Guidelines, then MIOP) in the growth distribution of fires first responded to by forestry personnel. Conclusions MIOP appears to be successfully limiting the negative impact of industrial forestry fires while increasing operational flexibility relative to the Woods Modification Guidelines. Implications MIOP has been implemented in a manner that still encourages safe operations while not contradicting this objective.

Background Among the most successful examples of Knowledge Exchange (KE) between researchers and practitioners in Canadian wildland fire management is the development and integration of the Canadian Forest Fire Danger Rating System (CFFDRS) into operational use. Aims Our aim was to identify key factors for this success. Methods Through a case study, we investigated historical KE of two CFFDRS components in Ontario, Canada. We held semi-structured interviews with principal Canadian Forest Service researchers and Ontario fire management practitioners active in development and implementation of CFFDRS from the late 1960s to 2010s. Key results The importance of both formal and informal facilitators to support KE was emphasised. Conclusion Participants were most likely to associate successful implementation with informal facilitators such as personal relationships, shared field-based experiences, and opportunities for dialogue between researchers and practitioners. Critical to success were the credibility and soft skills of the knowledge brokers, early engagement, and consideration of training needs for end users in the design of products. Implications This identification of factors that facilitated or hindered the development and implementation of CFFDRS can enhance the impact of research that will help wildland fire management deal with its present and future challenges.

We describe the development and implementation of an operational human-caused wildland fire occurrence prediction (FOP) system in the province of Ontario, Canada. A suite of supervised statistical learning models was developed using more than 50 years of high-resolution data over a 73.8 million ha study area, partitioned into Ontario’s Northwest and Northeast Fire Management Regions. A stratified modelling approach accounts for different seasonal baselines regionally and for a set of communities in the Far North. Response-dependent sampling and modelling techniques using logistic generalized additive models are used to develop a fine-scale, spatiotemporal FOP system with models that include nonlinear relationships with key predictors. These predictors include inter- and intra-annual temporal trends, spatial trends, ecological variables, fuel moisture measures, human land-use characteristics, and a novel measure of human activity. The system produces fine-scale, spatially explicit maps of daily probabilistic human-caused FOP based on locally observed conditions along with point and interval predictions for the expected number of fires in each region. A simulation-based approach for generating the prediction intervals is described. Daily predictions were made available to fire management practitioners through a custom dashboard and integrated into daily regional planning to support detection and fire suppression preparedness needs.

Wildland fire management decision-makers need to quickly understand large amounts of quantitative information under stressful conditions. Categorization and visualization “schemes” have long been used to help, but how they are done affects the speed and accuracy of interpretation. Using traditional fire management schemes can unduly restrict the design of new products. Our design process for Ontario’s fine-scale, spatially explicit, daily fire occurrence prediction (FOP) models led us to develop guidance for designing new schemes. We show selected historical fire management schemes and describe our method. It includes specifying goals and requirements, exploring design options and making trade-offs. The design options include gradient continuity, hue selection, range completeness and scale linearity. We apply our method to a case study on designing the scheme for Ontario’s FOP models. We arrived at a smooth, nonlinear scale that accommodates data spanning many orders of magnitude. The colouring draws attention according to levels of concern, reveals meaningful spatial patterns and accommodates some colour vision deficiencies. Our method seems simple now but reconciles complex considerations and is useful for mapping many other datasets. Our method improved the clarity and ease of interpretation of several information products used by fire management decision-makers.

Weather forecasts are needed in fire management to support risk-based decision-making that considers both the probability of an outcome and its potential impact. These decisions are complicated by the large amount of uncertainty surrounding many aspects of the decision, such as weather forecasts. Wildland fires in Ontario, Canada can burn and actively spread for days, weeks, or even months, or be naturally limited or extinguished by rain. Conventional fire weather forecasts have typically been a single scenario for a period of one to five days. These forecasts have two limitations: they are not long enough to inform some fire management decisions, and they do not convey any uncertainty to inform risk-based decision-making. We present an overview of a method for the assembly and customization of forecasts that (1) combines short-, medium-, and long-term forecasts of different types, (2) calculates Fire Weather Indices and Fire Behaviour Predictions, including modelling seasonal weather station start-up and shutdown, (3) resolves differing spatial resolutions, and (4) communicates forecasts. It is used for burn probability modelling and other fire management applications.

This study presents a model developed using a risk-based framework that is calibrated by experts, and provides a spatially explicit measure of need for aerial detection daily in Ontario, Canada. This framework accounts for potential fire occurrence, behaviour and impact as well as the likelihood of detection by the public. A three-step assessment process of risk, opportunity and tolerance is employed, and the results represent the risk of not searching a specified area for the detection of wildland fires. Subjective assessment of the relative importance of these factors was elicited from Ontario Ministry of Natural Resources and Forestry experts to develop an index that captures their behaviour when they plan aerial detection patrol routes. The model is implemented to automatically produce a province-wide, fine-scale risk index map each day. A retrospective analysis found a statistically significant association between points that aerial detection patrols passed over and their aerial detection demand index values: detection patrols were more likely to pass over areas where the index was higher.

Despite increasing concern about wildland fire risk in Canada, there is little synthesis of knowledge that could contribute to the development of a comprehensive risk framework for a wide range of values, which is an essential need for the country. With dramatic variability in costs and losses from this natural hazard, there must be more support for complex decision-making under the uncertainty of how to assess and manage risk to coexist with wildland fire. A long history of Canadian wildland fire research offers solid foundational knowledge related to risk, but the key knowledge gaps must be addressed to fully consider risk in a comprehensive manner. We provide a review of the current context in which risk is variably defined, and recommend use of the general paradigm where risk is the product of both the likelihood and the potential impacts of wildland fire. Wethen synthesize research related to wildland fire risk from the Canadian scientific literature. With this review, weaim to provide a better understanding of research challenges, limitations, and opportunities for future work on fire risk within the country.

In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design. In this study we emphasize the first objective by documenting the results of wildfire management end-user engagement activities which were used to identify the key Fire Management Functionalities (FMFs) required for an Earth Observation wildfire monitoring system. These FMFs are then used to define the User Requirements for the Canadian Wildland Fire Monitoring System (CWFMS) which are refined here for the WildFireSat mission. The User Requirements are divided into Observational, Measurement, and Precision requirements and form the foundation for the design of the WildFireSat mission (currently in Phase-A, summer 2020).