Using Geoinformatics to Monitor Forest Fires and Climate Change Impacts

Forest fires are among the most powerful natural disturbances on Earth, reshaping ecosystems and releasing massive amounts of carbon into the atmosphere. As climate change intensifies fire risks worldwide, the ability to detect, monitor, and analyze these events has become critical for environmental management and climate science. Geoinformatics-the science of collecting, analyzing, and interpreting geographic data using technologies like remote sensing and GIS-provides powerful tools for tracking forest fires and understanding their far-reaching impacts on our climate system.

Forest fires as major disturbance factors 🔗

Forest fires are dominant disturbance factors that affect physical, biological, and environmental systems at both global and regional scales. The Intergovernmental Panel on Climate Change projects that rising global temperatures and altered rainfall patterns will lead to increased fire risks across many regions of the world. Nearly half of all forested areas globally are now more vulnerable to drought than they were before the turn of the century, creating conditions where fires ignite more easily and spread more rapidly.

The scale of destruction has grown substantially in recent decades. Forest fires now burn more than twice as much tree cover each year as they did at the start of the century. Canada experienced its worst fire season on record in 2023, losing over 7.7 million hectares of tree cover-an area larger than Panama. South America saw record-breaking fires across Brazil, Bolivia, and Peru in 2024, with large portions of the Amazon rainforest engulfed in flames during periods of extreme heat and drought.

These fires don’t just destroy forests-they fundamentally alter landscapes, affect air quality across continents, displace communities, and release enormous quantities of stored carbon back into the atmosphere. Understanding fire patterns through geoinformatics helps scientists, policymakers, and resource managers develop strategies to mitigate these impacts.

The climate-fire feedback loop 🔗

One of the most concerning aspects of forest fires is their relationship with climate change. Scientists have documented a climate-fire feedback loop where rising temperatures and forest fires reinforce each other in a dangerous cycle. Rising greenhouse gas emissions lead to hotter, drier conditions, which makes it easier for fires to ignite and spread. As fires burn through vegetation, they release stored carbon into the atmosphere, further accelerating climate change and perpetuating the cycle.

This feedback mechanism has measurable consequences. Forest fires released more than 4 billion tons of greenhouse gases in both 2023 and 2024-approximately 2.5 times more than average years and exceeding the annual emissions of India, the world’s third-largest climate polluter. Research from the University of California, Merced and other institutions has shown that carbon dioxide emissions from forest fires have surged by 60% globally since 2001, with emissions nearly tripling in climate-sensitive boreal forests of North America and Eurasia.

Global inter-annual variability in terrestrial ecosystem carbon fluxes and atmospheric CO2 concentrations are strongly influenced by forest fire emissions. This creates a situation where studying fires becomes essential for understanding climate dynamics, and where monitoring fire activity through satellite-based geoinformatics provides critical data for climate models and projections.

Factors amplifying fire severity 🔗

Beyond direct climate effects, several other factors contribute to worsening fire conditions. Forest degradation and deforestation expose more of a forest’s interior to high temperatures that dry it out. Human activities-from discarded cigarettes to land-clearing fires that escape control-create additional ignition opportunities. However, climate change exacerbates nearly all these fires by creating the hot, dry conditions that allow them to grow from small ignitions into destructive conflagrations.

The western United States has seen fire seasons lengthen by about three days per year since the 1970s due to rising temperatures and earlier snowmelt. Weather data indicates that fire seasons in parts of the western U.S., Mexico, Brazil, and East Africa are now more than a month longer than they were 35 years ago. These extended seasons, combined with the greater intensity of individual fires, make comprehensive monitoring through geoinformatics increasingly vital.

Remote sensing for fire monitoring 🔗

Early detection and real-time monitoring of forest fires are essential for effective management and planning. Remote sensing technology provides the capability to observe fires across vast areas, often in remote regions where ground-based monitoring would be impractical or impossible. Modern satellite sensors capture data across multiple spatial, temporal, and spectral resolutions, enabling detection and monitoring of fires through various portions of the electromagnetic spectrum.

Different spectral bands provide different types of information about fires and burned areas. Near-infrared light (700-1,100 nanometers) is absorbed by water, making it useful for distinguishing between vegetated and burned areas. Healthy vegetation reflects near-infrared light strongly, while burned areas absorb it. Shortwave infrared (SWIR) bands, spanning roughly 1,100-3,000 nanometers, are particularly valuable for fire applications because newly burned land reflects strongly in these wavelengths, making fire damage easy to map.

Understanding spectral signatures in fire detection 🔗

SWIR imagery is especially valuable during active wildfire events because it can penetrate smoke and thin cloud cover that would obstruct visible-spectrum observations. This capability allows emergency responders to see the location of active fire fronts obscured by thick smoke, distinguish between hot ash and open flames, and identify smoldering zones that may reignite. During California’s devastating Camp Fire and Dixie Fire events, SWIR imagery allowed analysts to track fire movement even when smoke made visible-light observations impossible.

Mid-wave infrared (MWIR) wavelengths between 3,000-5,000 nanometers are particularly effective for detecting thermal radiation from active fires at night. Fire detection algorithms use multiple infrared bands to compare pixel temperatures, identifying areas that are significantly hotter than their surroundings. The thermal infrared (TIR) band, spanning 8,000-15,000 nanometers, captures emitted heat radiation from the Earth’s surface and is used to gauge surface temperatures and detect heat sources like active fires day or night.

MODIS fire detection capabilities 🔗

NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, carried aboard the Terra and Aqua satellites, were specifically designed to measure reflected and emitted radiation across multiple spectral channels. The MODIS fire detection strategy is based on absolute detection when fire strength is sufficient, combined with detection relative to background temperatures to account for variability in surface conditions and reflection by sunlight.

The MODIS system provides several key advantages for fire monitoring. With four daily overpasses (two from Terra, two from Aqua), the sensors can track fire evolution throughout the day and night. Each active fire location represents the center of a 1-kilometer pixel that the algorithm flags as containing one or more fires. The products include fire occurrence (distinguishing between day and night), precise fire location, detection confidence levels, Fire Radiative Power measurements, and numerous other attributes describing fire pixel characteristics.

Technical basis of fire detection 🔗

The MODIS thermal anomalies and fire products are derived primarily from 4-micrometer and 11-micrometer radiance measurements. The detection algorithms compare the temperature of a potential fire with the temperature of surrounding land cover. If the temperature difference exceeds established thresholds, the potential fire is confirmed as an active fire or “hot spot.”

Under typical conditions, MODIS can routinely detect both flaming and smoldering fires approximately 1,000 square meters in size. Under very good observing conditions-such as near-nadir viewing angles, minimal smoke, and relatively homogeneous land surfaces-flaming fires one-tenth this size can be detected. In exceptionally clear conditions, even smaller fires around 50 square meters may be identifiable, though such pristine observing conditions are rare.

Active fire algorithms and data products 🔗

The MODIS active fire algorithm has undergone continuous refinement since its initial deployment. The Collection 6 update, implemented in 2015, addressed limitations observed in earlier versions, particularly false alarms caused by small forest clearings and omission errors for large fires obscured by thick smoke. The updated algorithm achieved significant improvements, with the daytime global commission error (false alarm rate) dropping to 1.2% compared to 2.4% in the previous version.

Regional performance also improved substantially. The probability of detection increased by approximately 3% in boreal forests of North America and Asia compared to earlier algorithm versions, with smaller but meaningful improvements in Equatorial Asia and Central Asia. These improvements were validated using more than 2,500 high-resolution reference images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

Fire Information for Resource Management System 🔗

NASA’s Fire Information for Resource Management System (FIRMS) provides access to satellite imagery, active fire and hotspot data, and related products with minimal delay. The system delivers near real-time data to help identify the location, extent, and intensity of wildfire activity globally. Information on active fires is typically updated six times daily and made available within two to three hours of satellite image acquisition.

FIRMS supports the U.S. Forest Service, partner agencies, and the general public by providing tools and data for visualizing and monitoring wildfire activity. The system incorporates data from both MODIS and the newer Visible Infrared Imaging Radiometer Suite (VIIRS), which provides improved spatial resolution at 375 meters compared to MODIS’s 1-kilometer pixels. This enhanced resolution enables more precise fire mapping and earlier detection of smaller fires.

Applications of GIS in fire analysis 🔗

While satellite remote sensing provides the raw data for fire detection, Geographic Information Systems (GIS) enable scientists and resource managers to analyze, visualize, and interpret this information in meaningful ways. GIS technology allows fire data to be combined with other spatial information-such as vegetation maps, topography, weather patterns, population centers, and infrastructure-to create comprehensive assessments of fire risk, behavior, and impacts.

Burned area mapping represents one critical application. The MODIS burned area algorithm detects the approximate date of burning at 500-meter resolution by identifying rapid changes in daily surface reflectance data. Burned areas are characterized by deposits of charcoal and ash, removal of vegetation, and alteration of vegetation structure-all of which produce distinctive spectral signatures. This mapping provides essential information for assessing fire damage, planning restoration efforts, and understanding fire behavior patterns.

Integration with climate studies 🔗

The combination of remote sensing and GIS technologies provides critical data for climate change research. By tracking fire occurrence, extent, and intensity over time, scientists can analyze trends in fire activity and correlate them with climate variables like temperature, precipitation, and drought indices. Machine learning algorithms are increasingly being integrated with satellite data to enhance detection efficiency and accuracy, analyzing patterns to provide near real-time alerts and improve performance over time.

These integrated systems help researchers understand how fire regimes are changing in response to climate change and how fire emissions contribute to atmospheric carbon concentrations. The data informs climate models and helps project future fire risks under different warming scenarios, supporting both scientific understanding and policy decisions.

Emerging technologies and future directions 🔗

The field of fire monitoring through geoinformatics continues to evolve rapidly. Commercial satellites like WorldView-3 now offer SWIR imaging at 3.7-meter resolution-a dramatic improvement over earlier systems that enables precise fire detection even in complex terrain. The Copernicus Sentinel-2 satellites provide SWIR data at 20-meter resolution with revisit times as short as five days, making this capability freely available to researchers worldwide.

Advanced algorithms incorporating machine learning are improving fire detection capabilities. Support Vector Machines and Convolutional Neural Networks can analyze satellite imagery to classify heat sources and distinguish active fires from other thermal anomalies with increasing accuracy. Novel indices like the Normalized Difference Fire Index are being developed to differentiate burned and unburned areas more precisely, particularly in areas with varying vegetation and topography.

As fire seasons lengthen and fire intensity increases, these technological advances become increasingly important. The integration of multiple satellite data sources, improved algorithms, and sophisticated GIS analysis provides the foundation for better understanding fire-climate relationships and developing more effective strategies for fire management and climate change mitigation.

What do you think? As forest fires become more frequent and intense, how might improved satellite monitoring change the way communities prepare for and respond to fire threats? And given the strong connection between fires and climate change, what role should fire emission data play in international climate agreements and carbon accounting?

References

1. https://www.wri.org/insights/climate-fire-feedback-loop-explained
2. https://www.wri.org/insights/global-trends-forest-fires
3. https://news.ucmerced.edu/news/2024/global-co2-emissions-forest-fires-increase-60
4. https://science.nasa.gov/earth/earth-observatory/how-to-interpret-a-false-color-satellite-image
5. https://ongeo-intelligence.com/blog/seeing-through-the-smoke-how-swir-satellite-imagery-detects-hidden-wildfires-from-orbit
6. https://pmc.ncbi.nlm.nih.gov/articles/PMC5017475/
7. https://modis.gsfc.nasa.gov/data/dataprod/mod14.php
8. https://pmc.ncbi.nlm.nih.gov/articles/PMC6110110/
9. https://forest-fire.emergency.copernicus.eu/about-effis/technical-background/active-fire-detection
10. https://www.usfa.fema.gov/wui/data/wildfire-report-series/modis-detected-fires-report/modis.html
11. https://www.earthdata.nasa.gov/data/tools/firms
12. https://link.springer.com/article/10.1007/s11069-025-07163-w