Traditional fire detection methods like visual observation and heat/smoke detectors are unfortunately limited by their localized detection and dependence on human observation.
With limitations in covering large areas or remote locations, they can be inferior to satellite monitoring, which can detect fires before they become visible, covering a large radius and being less dependent on weather conditions.
EOSDA Land Viewer aims to fill several critical gaps in fire detection using satellite monitoring and advanced analytics. The platform ensures early threat identification, accelerates response times and delivers detailed insights into fire size, intensity, direction of spread and affected areas.
Time series analysis, as well as images from different satellites, allow for a qualitative assessment of the threat and provide the necessary information to interested parties.
A comprehensive approach is key. Multispectral satellite sensors capture data across different parts of the electromagnetic spectrum, including ranges ideal for detecting early fires, hidden smoke, etc.
Kateryna Shevchenko
We then apply machine and deep learning models trained on vast archives of satellite images to detect fires, thermal anomalies, vegetation changes from burning and other indicators with high accuracy.
EOSDA LandViewer contains an automatically updated database of satellite images, which allows users to quickly find the data they need based on their criteria and assess the situation.
Tools like Change Detection or Time Series Analysis will also help to quickly determine the presence of a fire, its intensity, spread and potential damage and by calculating the importance of indices, immediately assess the consequences or predict fires, for example of dry vegetation.
That’s only one of its purposes. EOS SAT-1 was designed to provide reliable, high-resolution data for forest fire detection, early warning and environmental monitoring. It’s equipped with 11 multispectral bands and supports critical wildfire management tasks, like identifying thermal anomalies, assessing vegetation stress and mapping burned areas.
Unlike general-purpose satellites, EOS SAT-1 focuses on frequent, targeted monitoring of ecosystems. This makes it an effective tool for powering automated fire alert services, rapid disaster response and informed decision-making in wildfire prevention and forest management.
Free satellite imagery is excellent for retrospective analysis, but its revisit rate may be days, which isn’t fast enough when a wildfire can change direction within hours.
Tasking feature on EOSDA LandViewer solves that problem by allowing users to order high-resolution imagery exactly for their area of interest and for the needed period.
Kateryna Shevchenko
For wildfire response teams, that means they can see the fire’s exact perimeter, locate infrastructure under threat and even identify natural firebreaks.
Because the platform allows ordering images from multiple providers, users don’t need to negotiate with each operator separately. They can simply request and receive their data through one interface, which is a major time saver when every minute counts.
While EOSDA isn’t yet embedded in live field command for firefighting, we’ve supported post-event monitoring and assessment in several significant cases.
In 2024, for example, Portugal experienced more than 128 fires across its central and northern regions. Using EOSDA LandViewer, analysts could map burned areas, monitor smoke dispersion and assess impacts on towns like Albergaria-a-Velha, where multiple houses were destroyed.
We’ve done similar work in Canada, monitoring fires in Alberta and British Columbia, where the Normalized Burn Ratio (NBR) was applied to make burn scars highly visible.
Kateryna Shevchenko
Such fires occur annually in the region and we have been tracking them since 2024. These assessments, while not tactical in the moment, inform prevention strategies, resource planning and recovery funding applications.
To minimize errors, we offer several validation steps. First, EOSDA LandViewer users can combine satellite imagery with specific index calculations to get a clearer picture.
If needed, they also compare data from two different satellites to cross-check findings. Looking at historical trends is another key step as it helps to understand if a current change is really unusual or just part of normal seasonal variation.
Currently, the company has API Connect, which can be used by both government agencies and private customers. EOSDA API Connect integrates data from both EOSDA LandViewer and EOSDA Crop Monitoring, but with more complex and detailed parameters depending on the data you want to obtain.
In the future, for local authorities and forestry services, this means the ability not only to view information on the platform, but also to directly embed it into their own geographic information systems, dashboards or rapid response systems, create their own alerts and implement API data into potential risk analysis, if the client has one.
On the technical side, higher-resolution, more frequent imagery combined with AI-driven predictive models will be transformative. Imagine hourly updates at sub-meter resolution, processed instantly to pinpoint emerging threats.
On the policy side, integration is everything.
Kateryna Shevchenko
Governments need to embed satellite-derived alerts directly into emergency workflows and mandate their use in wildfire risk management.
