California Wildfires: Rapid Monitoring of Flames and Damage from Space
California Wildfires: Rapid Monitoring of Flames and Damage from Space
PAYNE INSTITUTE COMMENTARY SERIES: COMMENTARY
The impact of wildfires in California has been both devastating and transformative. Recent reports from major media outlets highlight the escalating scale of destruction, with the Palisade Fire near Malibu and the Pasadena Fire serving as stark examples. These fires have not only claimed thousands of structures but also exposed the vulnerabilities in existing disaster response frameworks. The Palisade Fire, in particular, has drawn attention due to its rapid spread and the significant displacement of residents, while the Pasadena Fire has been noted for its widespread structural damage and challenging firefighting conditions. The Palisade Fire scorched over 2,000 acres, leading to the evacuation of approximately 37,000 residents and destroying over 5,300 structures (The Times, Wikipedia, AP News). The Pasadena Fire forced thousands of residents to evacuate and caused damage to approximately 7,081 structures (El Pais, Wikipedia, AP News). With rising global temperatures and prolonged droughts, the frequency and intensity of wildfires have increased dramatically, leaving profound effects on the environment, economy, and communities.
How the Maps Are Made
The wildfire maps are created using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire algorithm. VIIRS Nightfire detects combustion sources in multispectral images taken at night by identifying infrared radiation emitted during fires. This near real-time data enables localization and estimates of temperature, source area and radiative heat for active fire hotspots, which are then visualized on geographic information system (GIS) platforms. The accompanying maps offer a detailed visualization of wildfire patterns across California. These maps highlight key areas of impact, including:
Active Fire Zones: Current hotspots of wildfire activity are detected nightly. These fires can be observed multiple times per night using three satellites: Suomi NPP, NOAA-20, and NOAA-21. The nightly sequence of fire maps below is created from fire detections by Suomi NPP, offering a rapid and dynamic view of active fire zones.
Burned Areas: Areas that have suffered significant destruction due to wildfires. Satellites help measure the impact by identifying the various structures, such as homes, businesses, and government buildings within the burned regions. For the Palisade and Pasadena fires, satellite data reveals that 27,790 residential buildings are located within the affected areas—a number far higher than previously reported in the media. However, it is important to note that not all buildings within these zones are necessarily damaged. The map below highlights the burned area from the Pasadena fire, with residential buildings inside the affected zone shown in red.
Why This Matters
Wildfires are a growing challenge and understanding where and how they spread is crucial for protecting lives, homes, and communities. Satellites provide fast and reliable information, helping emergency teams see the big picture and act quickly. This real-time data is essential for first responders, allowing them to allocate resources effectively and stay safe while battling fires. It also helps homeowners and businesses recover by providing the scope of potential insurance claims. Finally, tracking wildfire patterns highlights the need to address climate change and improve infrastructure to make our communities more resilient.
ABOUT THE AUTHOR
Mikhail Zhizhin
Research Associate, Earth Observation Group
Mikhail Zhizhin, M.Science in mathematics from the Moscow State University in 1984, Ph.D. in computational seismology and pattern recognition from the Russian Acad. Sci. in 1992. Research positions from 1987 to 2012 in geophysics, space research and nuclear physics at Russian Acad. Sci., later at NOAA and CU Boulder. Currently he is a researcher at the Earth Observation Group at Colorado School of Mines. His applied research fields evolved from high performance computing in seismology, geodynamics, terrestrial and space weather to deep learning in remote sensing. He is developing new machine learning algorithms to better understand the Nature with Big Data.
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