We’ve reached a point where managing wildfires requires more than just a quick response but a deeper look at the landscape itself. A forest healthy to the naked eye might actually be a tinderbox of dry, stressed vegetation when seen through a satellite’s lens. Modern Earth observation is moving us away from a purely reactive “wait and see” approach toward a smarter, more predictive strategy. By analyzing the full cycle of a forest — from its health in the spring to its recovery after a burn — we can turn raw imagery into a practical tool for local communities.
Wildfire Intelligence: From Raw Imagery to Field Decisions
A wildfire satellite product that only shows fire location isn’t very useful. The questions that matter operationally come next: What burned? How badly? What infrastructure or communities are in the affected zone? Where should rescue teams go first?
Answering those questions requires a structured approach. Not a single layer, but a sequence of analyses that builds from detection to severity to impact. For example, EOSDA treats wildfire intelligence as a pipeline — one built onhigh-resolution images and on-top analytics:
- Optical and SAR satellite data help identify fire activity and track it over time. While optical imagery is great for clear days, SAR (radar) is a critical backup because it penetrates through the thick smoke and heavy cloud cover that usually mask a fire from above.
- Once the fire is contained, pre- and post-fire imagery are compared to draw burned-area boundaries. By generating precise vector polygons of the footprint, we can calculate exactly how many hectares were lost with much higher accuracy than ground estimates.
- Burn severity classification then breaks that map based on the fire impact intensity. This is vital because a forest with light surface damage and the one that lost the entire canopy need completely different recovery efforts.
- Finally, burned-area and severity layers are placed over infrastructure and settlement data — roads, power lines, pipelines, and buildings — to turn a fire map into an actionable impact map. This helps teams prioritize which power grids need emergency shutdowns or which transport corridors are likely blocked by fallen trees.
EOSDA’s pipeline applies AI and ML at several points, including burn scar segmentation, smoke and cloud filtering, severity classification, and infrastructure exposure scoring, to accelerate delivery. These models automate the heavy lifting, like filtering out clouds to get a clear view of the ground, while expert validation remains a core part of the process.
As Oleksii Neskorovnyi, Head of Head of Сustom Solutions and Intelligence (CSI), a multi-industry satellite intelligence product by EOSDA, puts it,“The real value here isn’t just seeing where the fire was. It’s turning those pixels into a sequence: detection, severity, damage, and recovery priorities.”
Delivering that sequence reliably requires more than satellite imagery. There should be purpose-built solutions for each stage of fire risk analysis. EOSDA has developed custom forestry solutions that cover the cycle from early forest vulnerability through active fire damage and into recovery assessment. Used together, they feed directly into the kind of decision-support pipeline Oleksii Neskorovnyi describes — each solution contributing a different layer to the overall picture.
Forest burned areas detectionidentifies and classifies fire-damaged zones using multi-burned-area statistics and a detailed analytical report — inputs that go directly into damage assessment and recovery planning
A map of fire-affected forest areas, with burn severity classified using Normalized Burn Ratio (NBR). Image: EOS Data Analytics
Forest cover, deforestation, and reforestation detectiontracks how forest structure changes over time. The solution mainly uses Sentinel-1 and Sentinel-2 data, comparing two-date imagery to generate deforestation and reforestation masksatters because both dry and exposed deforested areas and young regrowth with high fuel load and vertical continuity are more vulnerable to fire ignition and spread
An easy-to-read satellite map of forest cover changes with deforestated and reforestated areas marked in contrasting colors. Image: EOS Data Analytics
Forest health analysisassesses vegetation condition using indices derived from Sentinel-2 or other satellite imagery across current snapshots or multi-year time series. Forest stands under stress, from drought, pest damage, or disease, carry a higher fire risk. Identifying them early allows for targeted intervention before conditions worsen.
An NDVI-based vegetation index map showing long-term forest health trends by area. Intuitive color coding represents forest conditions from very severe degradation to moderate improvement.Image: EOS Data Analytics
Used before fire season, these solutions help identify the highest-risk zones. Used after a fire, they feed directly into damage assessment, response prioritization, and recovery tracking.
“The future of wildfire analytics,”Oleksii Neskorovnyi says,“lies beyond standalone models and satellite imagery. It is a robust pipeline where multi-sensor data, automated processing, and expert insights turn into immediate, life-saving decisions for local communities.”
EOSDA’s Wildfire Monitoring Project in Portugal’s Coimbra Region
Portugal’s 2025 wildfire season was severe by any measure. The Coimbra Region alonelost over 64,000 hectares. Across the country, 270,000 hectares burned. Numbers like these turn abstract talk about satellite wildfire management into something practical.
Starting March 2026, EOSDA will put its wildfire analytics pipeline to work across the entire Coimbra Region under a contract with ESA’sInCubed programme— an Earth Observation initiative run byESA Φ-labthat funds the development of commercial EO products with
real-world applications. Partnering withCIM Coimbra, EOSDA will monitor 19 municipalities and 4,336 km² of territory over the next 12 months.
The geography of the Coimbra Region explains why satellite monitoring is the right tool here. More than 45% of the region is forested. Conducting meaningful risk assessments across this area through ground-based methods alone would take too long, cost too much, and miss early warning signals that satellite imagery can detect on the fly.
EOSDA’s solution is built around EOS SAT-1, its own satellite providing 3-meter resolution imagery, combined with automated processing and expert validation. Four times a year, CIM Coimbra will receive updated maps, covering forest cover changes, burned areas, and restoration progress — the full monitoring cycle in a format municipal authorities can act on directly.
Each deliverable draws on the same analytical pipeline described earlier:
- Pre- season analysis of fire-prone areas highlights early warning signs and patterns.
- Deforestation and reforestation layers track structural changes that affect fire behavior.
- Post-fire burned area mapping documents damage and supports recovery planning.
In Coimbra, these tools are applied across an entire administrative region, with outputs integrated into the platform the municipalities already use. This makes the project operationally relevant rather than just technically interesting.
Portugal is just one part of a much bigger picture. From Southern Europe to Australia, wildfire seasons are becoming longer and more aggressive than they were 20 years ago. The problem is that most land managers still rely on tools like field surveys and local sensors that simply weren’t built for this scale. Satellite analytics changes the starting point. It offers a bird’s-eye view that stays on 24/7, catching waring signs before the first spark and tracking damage long after the smoke clears.
But information is only useful if it’s actionable. We want to make sure that when a municipality receives our analysis, they can make a decision right then and there. That is exactly what the Coimbra project is here to prove. If the approach works in Coimbra — and the data suggests the conditions for testing it are unfortunately well established — the same methodology can be applied to other fire-prone regions.
