A cost-benefit analysis of WildFireSat, a wildfire monitoring satellite mission for Canada.

In anticipation of growing wildfire management challenges, the Canadian government is investing in WildFireSat, an Earth observation satellite mission designed to collect data in support of Canadian wildfire management. Although costs of the mission can be reasonably estimated, the benefits of such an investment are unknown. Here we forecast the possible benefits of WildFireSat via an avoided cost approach. We consider five socio-economic components: suppression costs (fixed and variable), timber losses, property, asset and infrastructure losses, evacuation costs, and smoke related health costs. Using a Monte Carlo analysis, we evaluated a range of possible changes to these components based on expert opinions. The resulting Net Present Value (NPV) estimates depend on the presumed impact of using WildFireSat decision support data products, with pessimistic and conservative assumptions generating mission costs that typically exceed potential benefits by 1.16 to 1.59 times, while more optimistic assumptions generate benefits in excess of costs by 8.72 to 10.48 times. The analysis here excludes some possibly significant market and non-market impacts expected from WildFireSat due to data limitations; accounting for these additional impacts would likely generate positive NPVs under even cautious impact assumptions.

Towards a whole-system framework for wildfire monitoring using Earth observations.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole-system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre-fire vegetation inventories, (2) active-fire monitoring, (3) post-fire assessment, and (4) multi-scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co-development of information products using cloud computing that benefits from open-access Earth observation and other geospatial data layers.

ReSVA: A MATLAB method to co-register and mosaic airborne video-based remotely sensed data.

Airborne remotely sensed data (e.g. hyperspectral imagery, thermal videography, full frame RGB photography) often requires post-processing to be combined into a series of images or a mosaic for analysis. This is generally accomplished through the use of position and attitude hardware (i.e. Global Navigation Satellite System - GNSS / Inertial Measurement Unit - IMU) in combination with specialized software. Occasionally, hardware failure in the GNSS/IMU instrumentation occurs, however the data are still recoverable through a correction process, which allows image registration to mosaic the data. Here we present a simple and flexible MATLAB® code package that has been developed to combine video-based remotely sensed data. It first applies an iterative image registration process to align all frames, using pre-existing GPS information if supplied by the user, and then grids the frame data together to develop a final, single mosaic dataset that can be used for analysis. An example of this method using airborne infrared video data of a wildfire is shown as a demonstration.•MATLAB functions are easily adaptable to specific user needs and datasets.•The method outputs the combined data and positional information in three separate MATLAB variables that can be readily used for analysis in MATLAB or exported for use in other software.

Sensors for Fire and Smoke Monitoring.

Mastery of fire is intimately linked to advances in human civilization, culture and technology [...].

A Bi-Spectral Microbolometer Sensor for Wildfire Measurement.

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

Development of the User Requirements for the Canadian WildFireSat Satellite Mission.

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).

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

Severe bleeding in a woman heterozygous for the fibrinogen gammaR275C mutation.

The dysfibrinogen gammaR275C can be a clinically silent mutation, with only two out of 17 cases in the literature reporting a hemorrhagic presentation and four cases reporting a thrombotic presentation. We describe here a particularly severe presentation in 54-year-old female patient who required a hysterectomy at 47 years of age due to heavy menstrual bleeding. Coagulation studies revealed a prolonged prothrombin time and thrombin time, a normal fibrinogen antigen level, and a low fibrinogen activity level. Molecular analysis of the patient's DNA revealed a gamma chain gene mutation resulting in an amino acid substitution at residue 275 (gammaR275C). Protein sequencing of the fibrinogen gamma chain confirmed this mutation, which was named Fibrinogen Portland I. This case demonstrates that the gammaR275C mutation can lead to a severe hemorrhagic phenotype.