Remote Sensing of Fire

Remote sensing scientists at DCCEEW are contributing to several recommendations from the NSW Bushfire Inquiry, to establish NSW as a major world centre of bushfire research and technology development, maximising the information available from remote sensing technologies. 

The Remote Sensing of Fire team are developing capability within the NSW Government to map all components of fire monitoring. The phases are 

  1. fire prediction modelling, 
  2. active fire detection, 
  3. fire progression mapping, 
  4. fire extent and severity mapping (FESM), 
  5. fire heterogeneity mapping, and 
  6. post-fire recovery monitoring. 

Fire extent and severity mapping, and post-fire recovery monitoring have been our priorities to date. However, progress is continually being made across all components of remote sensing of fire.

For more information see https://www.environment.nsw.gov.au/topics/animals-and-plants/native-vegetation/landcover-science/fire-extent-and-severity-maps

Fire Extent and Severity Mapping (FESM)

Using a combination of high-resolution satellite imagery and machine learning, remote sensing scientists at DCCEEW routinely map the severity of fires in NSW, aiding partners in conservation and fire management.

Historical Fire Extent and Severity Mapping (FESM)

The archive of satellite imagery allows us to map fire severity for historical fires. To date mapping has been completed back to 1989–90 for selected NPWS priority regions.  We are continuing to build systematic statewide coverage of FESM for historical fires.

NSW Post-fire Biomass Recovery Monitoring by Remote Sensing

Remote sensing is being used to monitor post-fire recovery of forest biomass. A new metric has been developed to assess recovery by measuring annual spectral change each year after fire. This method does not attempt to measure changes in vertical structure of forest vegetation. However, field validation shows that changes in forest structure correspond with spectral changes indicating that this method has value for modelling post-fire recovery.
 

Linked Datasets