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: Optimizing care for patients with complex problems entails the integration of clinically appropriate problem-specific clinical protocols, and the optimization of service-system-encompassing clinical pathways. However, alignment of service system operations with Clinical Practice Guidelines (CPGs) is far more challenging than the time-bounded alignment of procedures with protocols. This is due to the challenge of identifying longitudinal patterns of service utilization in the cross-continuum data to assess adherence to the CPGs. Method: This paper proposes a new methodology for identifying patients’ patterns of service utilization (PSUs) within sparse high-dimensional cross-continuum health datasets using graph community detection. Result: The result has shown that by using iterative graph community detections, and graph metrics combined with input from clinical and operational subject matter experts, it is possible to extract meaningful functionally integrated PSUs. Conclusions: This introduces the possibility of influencing the reorganization of some services to provide better care for patients with complex problems. Additionally, this introduces a novel analytical framework relying on patients’ service pathways as a foundation to generate the basic entities required to evaluate conformance of interventions to cohort-specific clinical practice guidelines, which will be further explored in our future research.

Fuel moisture codes are numerical indices used to estimate the moisture content of different layers of forest fuels and are widely applied to assess the ignition potential and behavior of wildfires. Terrain-corrected L-band ALOS-2 PALSAR-2 synthetic aperture radar (SAR) imagery predicted the Drought Code (DC) and Duff Moisture Code (DMC) of the Canadian Forest Fire Danger Rating System in mountainous forests of British Columbia. Random forest regression models were developed using backscatter, decomposition parameters, and polarimetric discriminators obtained from forest (high-biomass, conifer-dominated), sparse (medium-biomass, open canopy), and unforested (low-biomass, herbaceous-dominated) fuel types near weather stations. Out-of-bag predicted values were compared against weather station-derived values. Models trained using SAR data predicted the DC with moderate accuracy in sparse (R² = 0.49, RMSE = 165.65) and forest sites (R² = 0.39, RMSE = 173.80), and with low accuracy in unforested sites (R² = 0.15, RMSE = 205.96). Models trained using SAR data predicted the DMC with moderate accuracy in sparse (R² = 0.64, RMSE = 26.07) and unforested sites (R² = 0.47, RMSE = 31.52), and with low accuracy in forest sites (R² = 0.12, RMSE = 40.71). Incorporating SAR data and day of year (DOY) improved the accuracy of DC models. Errors in both DC and DMC increased when models were evaluated with SAR data from weather stations not used in training, suggesting limited spatial transferability beyond training weather stations. Results suggest that terrain-corrected L-band polarimetric SAR has potential for predicting fuel moisture in mountainous and forested environments. Differences in surface and soil conditions across the fire environment must be accounted for to improve the spatial transferability of the models.