The 2024 wildfire over Jasper, Alberta devastated the town’s infrastructure and natural environment. Cloud coverage significantly hindered early detection efforts.
A wildfire is an uncontrolled combustion event that spreads rapidly through vegetation such as forests, grasslands, or shrublands. Ignition can result from natural causes, including lightning, or human activity, such as unattended campfires, discarded cigarettes, or sparks from electrical infrastructure, with human activity responsible for roughly 85% of U.S. wildfires. Fires propagate when fuel, oxygen, and heat—the “fire triangle”—are present. In Jasper, Alberta, wildfires in 2023 consumed over 50,000 hectares in under 72 hours. Flames exceeded 900°C, producing dense smoke that infiltrated communities, caused respiratory hospitalizations, and forced the evacuation of over 3,500 residents. Critical infrastructure, including highways and power lines, experienced partial or complete shutdowns, highlighting the rapid operational and human impact of wildfire events.
Wildfires impose multi-dimensional effects on communities, government resources, ecosystems, and regional economies. In Jasper, firefighting efforts cost over CAD $50 million, with additional recovery and rebuilding expenses. Ecologically, fires scorched lodgepole pine forests, killed wildlife, and released over 500,000 metric tons of CO₂. Economically, wildfire-related disruptions to tourism, forestry, and transportation caused an estimated CAD $25–30 million in losses. Smoke exposure led to over 200 hospitalizations, providing a quantifiable proxy for the societal and emotional burden on affected populations.
Real-time data collection is critical for preventing and mitigating wildfire damage. Satellites, drones, and ground sensors can detect ignition points, assess fuel moisture, and track fire spread in near real time. In Jasper, combining satellite and drone surveillance allowed authorities to detect early fire clusters within minutes, model high-risk corridors, and deploy resources efficiently. Predictive modelling using these data supports evacuation planning, firefighter safety, and fuel reduction strategies. As climate-driven droughts and elevated temperatures increase wildfire risk, continuous, high-resolution monitoring is essential for protecting lives, infrastructure, and ecosystems.
Jasper, Alberta is July 2024 (Parks Canada)
Jasper, Alberta is July 2024 (Parks Canada)
Modern wildfire monitoring systems rely mainly on thermal infrared (IR) cameras from satellites orbiting Earth. These satellites identify active fire pixels and estimate their energy output; wildfires emit abnormally high infrared radiation, typically in the range of 800–1200°C compared to their surroundings.
To detect these anomalies, satellites measure radiance at multiple IR wavelengths, including 4 µm (mid-infrared) and 11 µm (thermal infrared). A strong diagnostic signal for fire activity is when the brightness temperature difference between these bands exceeds ~10 K, indicating a localized high-temperature source consistent with combustion.
[Animation for Visible Infrared Imaging Radiometer Suite (VIIRS) (NASA).](attachment:ca10318f-d8ef-459c-98ca-af9dbb468bf1:2023-10_JPSS-2_VIIRs_2.mov)
Animation for Visible Infrared Imaging Radiometer Suite (VIIRS) (NASA).
Two of the most important satellite systems for wildfire detection are MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). MODIS, aboard NASA’s Terra and Aqua satellites, provides global coverage at 1 km spatial resolution and captures thermal anomalies using its 4 µm and 11 µm channels. VIIRS, which succeeded MODIS, offers finer 375 m resolution and improved radiometric sensitivity, enabling more precise detection of smaller and lower-intensity fires. Together, these instruments form the backbone of global fire monitoring, balancing spatial detail and temporal frequency to identify active burning regions across the planet.
Accurate cloud masking is a critical first step in satellite-based wildfire detection. Before a pixel can be analyzed for abnormal thermal emission, the algorithm must determine whether it is cloud-free. Modern systems, such as those using the Advanced Baseline Imager (ABI) on the GOES-R series, achieve this by comparing calibrated radiances across multiple infrared channels—primarily 3.9 µm and 11.2 µm, with auxiliary input from 12.3 µm and visible bands during daytime.
