In This Space
Forest fire & risk mapping
Forest fire mapping, Braganca, Portugal 2013 (Source: GIO EMS)
The product provides maps of actual forest fires and the risk potential for fire in other areas. Several satellites with different capabilities in terms of spatial resolution, sensitivity, spectral bands, and times and frequencies of overpasses provide observation and measurement capabilities for monitoring different fire characteristics including areas that are dry and susceptible to wildfire outbreak, actively burning fires and their extent and burnt area.
Forest fire risk:
Based on the combination of different forest-fire-causing factors, a forest fire risk zone map can be derived. For instant, elevation, slope, aspect, biomass/fuel loading (based on NDVI, LAI, etc.), soil moisture and land cover and land use data are classified and various degrees of fire sensitivity are calculated. A risk level can be defined (high, moderate, low) to indicate areas which are under high, moderate or low risk with regard to forest fires.
Fires and their extent can be detected in near-real time with satellite instruments that sense heat. Changes can be monitored over short periods of time and fire maps can be generated within a few hours to provide an overview of affected areas and aid more effective firefighting. In recording the frequency it can be identified which different vegetation types/zones are affected.
Burnt scars in the land can also be clearly identified by satellites to identify vegetation damage and to develop recovery plans after a fire occurred.
Known restrictions / limitations
Dependent on area and sensors required. A lack of adequate re-visit frequency can be a challenge for active fire monitoring.
Earth observing systems operating in the visible and infrared spectral region are sensitive to atmospheric conditions. The channels used for the detection of forest fires as described, are sensitive in so-called window regions. Under cloud-free conditions, the radiance reaching the sensors has been emitted or reflected from the Earth's surface. But sensors in the VIS and IR region are very sensitive to the presence of clouds in the field of view. Thermal IR can be used for wildfire monitoring. Thermal IR from EO data has relatively coarse resolution, this leads to some restrictions if using to assess risk to infrastructure.
Land cover information from optical satellites
In tropical rain forest areas frequent cloud cover can be an issue for the production of the maps but may be mitigated by combing radar and optical satellite images.
Slope, Elevation, Curvature
High latitudes coverage is restricted. When generating DEM from stereo pairs, good quality imagery needs to be available with 2 or more images showing the same area from different directions. This can be a time consuming process. Lower resolution DEM data is available off the shelf.
Lifecycle stage and demand
Exploration, Development & Production:
Geographic coverage and demand
Globally in wooded or vegetated areas.
OTM:057 Fire mapping
Input data sources
Optical: HR1, HR2, MR1, MR2
Radar: HR1, HR2, MR1, MR2
Spatial resolution and coverage
Spatial resolution: 4 m – 1 km (due to the great variety of the used input data there is a wide span of input data resolution)
Minimum Mapping Unit (MMU)
The MMU is dependent on the input data resolution, the mapped objects and the accuracy to be achieved. For monitoring forest stands, typically hectares to km² at a time.
For optical satellite data with 4 m spatial resolution a MMU of 256 m² can be achieved (for example).
Accuracy / constraints
Thematic accuracy: 70-90%
Spatial accuracy: The goal would be one pixel, but depends on reference data
Accuracy assessment approach & quality control measures
Stratified random points sampling approach utilizing VHR reference or other geospatial in-situ data. Statistical confusion matrix with user’s, producer’s accuracy and kappa statistics for forest fire and risk mapping.
Frequency / timeliness
Observation frequency: The frequency is constrained by satellite revisit and acquisition timeframes, but also processing requirements. Depending on the requirements of the customer the most suitable satellite sensor has to be chosen considering spatial / spectral resolution as well as revisit frequency. There may be a need for better temporal frequency from thermal imagery for wildfire monitoring.
Timeliness of deliverable: For fire extent and burnt scars, the processing can be completed in near real time (< 24 hours) depending on set up of the processing system and availability of base images. The processing of fire risk is dependent on the size of the mapped area, resolution, MMU and number of mapped classes.
Freely available or commercially acquired is depending on the sensor selected. For components of this product (biomass, elevation etc.) free sensors would be adequate.
Delivery / output format
|Peer Reviewer:||Hatfield Consultants|
Maria Lemper; Jan Militzer
Forest fire & risk mapping
# of Pages:
Internal – Project consortium and science partners
External – ESA
This page has no comments.