Wildfire management can be improved through geodesy and geospatial science, offering proactive, evidence-based strategies and advanced technologies that enhance risk assessment, resource allocation, and response efforts.
Geodesy and geospatial science are fundamental to wildfire management and can move the involved processes from reactive response to anticipatory, evidence-based action. Conventional measures such as fuel reduction, patrols, aircraft reconnaissance, emergency calls and firefighting crews remain essential, but geospatial systems add the spatial intelligence needed to act earlier and target resources more precisely. By integrating satellite, aircraft, drone and ground-based observations with other data on terrain, vegetation, and weather through classical and artificial intelligence (AI) methods, we can identify where risk is building, how a fire may evolve and which communities, infrastructure and ecosystems are most exposed.
The geospatial data sets involved are vast. On the fundamental level, geodetic techniques provide all the reference systems for mapping, models and methods to operate the different platforms such as satellites, aircraft, and drones, as well as enable accurate positioning, navigation and timing. On the applied level, the geospatial technologies and sensors provide the needed optical and multi- or hyperspectral imagery, Synthetic Aperture Radar (SAR) imagery, Light Detection and Ranging (LiDAR) 3D point clouds or even GNSS-derived atmospheric products.
What potential does Geodesy and Geospatial Science have for the management of wildfires, compared to alternative measures?
The major potential is to make wildfire management more proactive, broader and more precise. Physical measures such as fuel management, prescribed burning of firebreaks, firefighting crews and aircraft remain essential, but they are often constrained by cost, safety, access, visibility and timing. Geospatial science provides the continuous, area-wide intelligence that helps managers understand risk before ignition, track events during a fire, and quantify impacts afterwards. In all these stages, the employed technologies provide accurate data and allow for wildfire management and response teams to communicate precise information to those concerned.
The advantage is not simply seeing a fire from space. It is seeing the conditions that make fire more likely: vegetation stress, fuel structure, terrain, drought, wind exposure, land use and proximity to settlements or critical infrastructure. This allows authorities to target prevention work, deploy crews or aircraft where they can have the greatest effect and plan evacuations or asset protection using evidence rather than incomplete local reports.
Geodesy is also the foundation for reliability. It provides the precise coordinate reference frameworks that allow satellite, aircraft, drone and terrestrial data (imagery, weather, etc), field teams and emergency-service reports to be aligned in space and time. This common operating picture is vital in fast-moving emergencies.
The key point is that geodesy and geospatial science do not replace physical wildfire measures. They make those measures more targeted, timely and efficient.
What are some of the key systems that can transform wildfire management?
The most transformative systems are those that connect observations, models and decisions. They include:
Operational satellite fire services: NASA FIRMS¹ (Fire Information for Resource Management System), which provides near-real-time active-fire and thermal-anomaly information from MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), and European/global services such as EFFIS (European Forest Fire Information System)² and GWIS (Global Wildfire Information System)³ for forest-fire monitoring and risk information.
Emergency mapping and decision platforms: Copernicus Emergency Management Service mapping, national emergency GIS platforms and common operating pictures that combine satellite imagery with in-situ, open and operational data.
High-resolution local sensing: drones equipped with optical, thermal, LiDAR and hyperspectral sensors for fire perimeter mapping, residual hot-spot detection, fuel assessment and post-fire damage mapping.
Fire-weather and environmental monitoring: GNSS-derived atmospheric products, weather-station networks, wind and humidity forecasts, soil-moisture products, drought indicators and hydrological information that place fire risk in the environmental context.
AI and data-fusion pipelines: Machine-learning systems that combine imagery, terrain, weather, vegetation, infrastructure and field reports into risk maps, alerts, spread forecasts and recovery assessments.
The real transformation comes when these systems are not used separately but integrated into an operational workflow that emergency managers can trust and use quickly.
What types of geospatial data are most valuable for wildfire detection and monitoring?
The most valuable data are those that connect fire activity, fuels, weather, terrain and exposure. No single dataset is sufficient; the value comes from combining data layers into a coherent fire-risk and fire-monitoring system.
Active-fire data: Thermal infrared and mid-wave infrared observations from satellites, aircraft and drones are central for identifying hot spots, fire fronts and residual heat. These data support early detection, perimeter mapping and identification of reignition risk.
Vegetation and fuel data: Optical, multispectral and hyperspectral imagery can show vegetation cover, plant water content, stress, species composition and burn scars. This is crucial before a fire because vegetation condition is a major indicator of susceptibility and likely spread.
Structure and all-weather data: LiDAR describes canopy height, tree density, biomass, ladder fuels and terrain beneath the canopy. SAR is valuable because it can operate through cloud, smoke and darkness, supporting burned-area mapping, soil-moisture estimation, surface change and post-event damage assessment.
Weather, drought and hydrological context: Wind, temperature, humidity, precipitation, soil moisture, drought indices and GNSS-derived atmospheric water-vapour products help explain ignition risk and likely fire behaviour.
Terrain and exposure layers: Digital elevation models, slope, aspect, land cover, road networks, settlements, hospitals, evacuation routes, power lines, water sources and other critical infrastructure layers help emergency services prioritise response and protect people and assets.
In practice, the strongest systems combine these data streams so decision-makers can see where fires may start, how they are spreading, what is at risk and how recovery should be managed.
Which geospatial technologies are most effective in wildfire scenarios?
There is no single best technology. Effectiveness depends on the stage of the wildfire cycle: prevention, detection, active response or recovery. The strongest approach is a layered, multi-sensor system.
VIIRS and MODIS: These are effective for broad, frequent active-fire monitoring and regional to global situational awareness. Their limitation is that coarse spatial resolution can miss very small fires or fine fire-perimeter detail (375m and 750m resolution).NASA’s MODIS specification page lists these as 250m, 500m and 1,000m spatial resolution, with 36 spectral bands.
Geostationary sensors: For rapid temporal updates, geostationary sensors are important because they observe the same region continuously. For Europe and Africa, Meteosat and Spinning Enhanced Visible and Infrared Imager (SEVIRI)-type services are more directly relevant; GOES ABI is a useful example for the Americas.
Sentinel, Landsat and high-resolution optical imagery: These are valuable for burned-area mapping, vegetation condition, land-cover change, infrastructure exposure and post-fire recovery, although they can be limited by cloud, smoke and revisit time.
Unmanned Aerial Vehicles (UAVs) and drones: These are extremely valuable for detailed local assessment when conditions and airspace rules allow. With thermal, optical, LiDAR or hyperspectral sensors, they can map perimeters, detect residual hot spots, assess fuel structure and document damage at very high resolution. Emergency response managers can have real-time information on many aspects, such as the location of their firefighting units and of the fires.
LiDAR and hyperspectral imaging: LiDAR provides three-dimensional information on vegetation structure and terrain. Hyperspectral imagery provides detailed information on vegetation stress, moisture and tree health. Together, they are highly relevant for fuel-condition and forest-health assessment.SAR: Synthetic Aperture Radar is valuable because it can operate in cloud, smoke and darkness better than optical systems. It supports all-weather monitoring, burned-area mapping, soil-moisture analysis and damage assessment.
AI-based data fusion: AI helps convert large, diverse and fast-moving datasets into usable products such as risk maps, automated alerts, spread forecasts and damage assessments. Its value is not simply automation; it turns complex geospatial data into operational information.
Overall, the most effective wildfire systems combine coarse but frequent sensors for early warning, high-resolution optical and UAV data for local detail, SAR for difficult observing conditions, LiDAR and hyperspectral data for fuel and vegetation assessment, and AI-driven platforms to integrate the information into clear risk and response products.
Can you share some examples of the projects/technologies that the University of Luxembourg is pioneering in this space?
The strongest way to frame the University of Luxembourg’s contribution is as contributing to enabling technology for wildfire intelligence. The Geodesy and Geospatial Engineering team works across many of the measurement and data-analysis capabilities needed for this: GNSS, LiDAR, SAR, digital photogrammetry, hyperspectral imaging, laser scanning and AI-based feature extraction from geospatial big data. On a more fundamental level, some of the scientific model developments also led to improvements in global terrestrial coordinate reference systems and in positioning and navigation.
A particularly relevant example is project ForestPIONEER, which combines UAV-based LiDAR, hyperspectral sensing, machine learning and citizen science to improve forest inventory and health monitoring. For wildfire prevention, this is highly relevant because fuel condition, forest structure, tree health, species composition and biomass are all important indicators of susceptibility and possible spread.
Other projects, such as VAPOUR, GNSS-METEO and NWPLUX, are also relevant, but in a different way. They support GNSS/SAR atmospheric products and near-real-time GNSS-derived water-vapour information for weather forecasting. These are not direct fire-detection systems; their value is in improving the fire-weather context, including atmospheric moisture, humidity and severe-weather indicators that influence fire risk and behaviour.
Finally, project GlobalCDA adds the hydrological dimension by combining geodetic and remote-sensing information with hydrological modelling. Its relevance to wildfire is drought, terrestrial water storage and vegetation-water stress, all of which shape the environmental conditions in which fire risk develops.
Put together, these projects show how forest condition, atmospheric conditions, hydrological stress and spatial risk can be connected into decision-ready intelligence for wildfire prevention, response and recovery.
What are the main challenges in collecting and using geospatial data during wildfires? How are you working to overcome these?
The first challenge is the trade-off between spatial and temporal resolution. Some satellite systems provide frequent (daily to weekly) observations over large areas, but at relatively coarse spatial (100s of metres) resolution. Others provide much finer spatial detail (several metres to tens of metres), but less often (weekly to monthly). We address this through layered monitoring: broad, frequent systems show where activity is developing, while higher-resolution satellite, UAV and field data provide detail where decisions are most urgent.
A second challenge is obscuration and shadowing. Smoke, cloud, rugged terrain and forest canopy can limit what optical sensors can see. The response is multi-sensor fusion. Thermal sensors detect heat, SAR can observe through cloud, smoke and darkness, LiDAR captures vegetation and terrain structure, and ground or citizen observations can add local confirmation.
A third challenge is latency. Wildfires can evolve in minutes, so data must move quickly from observation to usable information. This requires near-real-time processing chains, automated detection algorithms, cloud-based data handling and alert systems that deliver information directly to emergency managers while it is still operationally useful.
A fourth challenge is trust. Automated detections, fuel maps and spread models must be reliable enough for operational decisions. False alarms waste resources; missed detections can be dangerous. We address this through validation with field measurements, ground sensors, emergency-service reports and carefully designed citizen-science observations, supported by robust geodetic reference systems so datasets align accurately in space and time.
Finally, there are practical challenges around UAV deployment, data access, interoperability and coordination. Drones may be constrained by wind, smoke, battery life, terrain and emergency airspace rules. Satellite data may come from different providers in different formats. Emergency services, scientists and public authorities may use different systems. Overcoming this requires standardised workflows, interoperable data formats, pre-agreed emergency protocols and close collaboration with civil protection agencies. While all of this requires a cohort of experts from many different disciplines, remote sensing and geospatial scientists are almost always part of this. In the end, all need to pull along with the overall aim of moving from fragmented observations to trusted, timely and decision-ready geospatial intelligence for wildfire prevention, response and recovery.
NASA FIRMS is a NASA platform that provides near-real-time satellite-based fire and thermal anomaly information. In simple terms, it shows where satellites have detected unusually hot spots on Earth, which may indicate wildfires, agricultural burning, industrial heat sources, volcanic activity, or other fires. It uses satellite observations from instruments such as MODIS and VIIRS to detect active fires and thermal anomalies, and NASA says global data are generally available within about 3 hours of satellite observation.
EFFIS stands for European Forest Fire Information System. It is part of the EU’s Copernicus Emergency Management Service and is implemented by the European Commission’s Joint Research Centre. It provides near-real-time and historical information on forest fires in Europe, neighbouring countries, the Middle East and North Africa. It covers the full fire cycle: fire danger, active-fire detection, rapid damage assessment, fire severity, emissions, potential soil loss and vegetation regeneration.
GWIS stands for Global Wildfire Information System. It is the global-level equivalent/complement, developed as a joint initiative of the Group on Earth Observations and Copernicus. It brings together national and regional wildfire information sources to provide a global view of fire regimes, fire effects and operational wildfire-management information. Its tools include fire-danger forecasts, active-fire information, burnt-area information, fire emissions and global fuel mapping.
Acknowledgments
ForestPIONEER (2023-2026) is funded through the University of Luxembourg Institute for Advanced Studies (IAS), VAPOUR (2019-2022), NWPLUX (2022-2026) and GlobalCDA (2022-2025) are funded through various calls of the Luxembourg National Research Fund (FNR), and GNSS-METEO (2023-2027) is funded through the UK Met Office.
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This article will feature in our upcoming Wildfires Special Focus Publication.
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