Description: AUS GEEBAM Fire Severity uses Sentinel 2A satellite imagery from before and after fire to estimate the severity of burn within each 40m grid cell. Fire severity is defined as a metric of the loss or change in organic matter caused by fire.The extent of the 2019/2020 fires was derived from the National Indicative Aggregated Fire Extent Dataset (NIAFED). NIAFED was sourced from the national Emergency Management Spatial Information Network Australia (EMSINA) data service, which is the official fire extent currently used by the Commonwealth and adds supplementary data from other sources to form a cumulative national view of fire extent.AUS GEEBAM relies on a vegetation index (Relativised Normalized Burnt Ratio, RNBR) that is calculated for burnt areas and adjacent unburnt areas, before and after the fire season. The result is a map of four fire severity classes that represent how severely vegetation was burnt during the 2019/2020 fires.To determine a reference unburnt condition, the NIAFED extent was buffered by 2km. For each NVIS broad vegetation type, in each IBRA bioregion a reference unburnt RNBR class was determined. That value was available to calculate a standardised offset or a reference unburnt value.Each IBRA bioregion was systematically assessed to correct for obvious errors. For example, the Very High severity class could be adjusted down by one RNBR Value for a fire where its extent extended into an area of lower severity. Conversely, there were areas of shrublands that had clearly burnt at Very High severity where all of the biomass is likely to have been consumed but low pre-fire biomass had given it a lower RNBR Value.Each pixel of AUS GEEBAM contains the raw RNBR Value, the RNBR Class and the GEEBAM Value. This allows an end user to observe which values have been adjusted during the calibration away from the default global RNBR Value and allows for some transparency in the process.GEEBAMValueGEEBAM ClassDescription1No dataNo data indicates areas outside NIAFED or NVIS categories that do not represent native vegetation (e.g. cleared land, water)2UnburntLittle or no change observed between pre-fire and post-fire imagery.3Low and ModerateSome change or moderate change detected when compared to reference unburnt areas outside the NIAFED extent.4HighVegetation is mostly scorched.5Very highVegetation is clearly consumed.Known Issues:The dataset has a number of known issues, both in its conceptual design and in the quality of its inputs. These are outlined below and should be taken into account when interpreting the data and developing any derived analyses.The list of known issues below is not comprehensive, it is anticipated that further issues will be identified, and the Department welcomes feedback on this. We will seek as far as possible to continuously improve the dataset in future versions.1. AUS GEEBAM classes are not based on field data and no confidence interval or report on accuracy has been provided.2. The number of severity classes has been reduced by combining low and moderate severity fires. Single index thresholds are known to feature poor delineation of low fire severity classes.3. AUS GEEBAM classes are calibrated systematically for each bioregion using visual interpretation of Sentinel 2 false colour composites. 4. The limitations associated with the NIAFED are carried through to this dataset. Users are advised to refer to the NIAFED documentation to better understand limitations.5. This continental dataset includes large burnt areas, particularly in northern Australia, which can be considered part of the natural landscape dynamics. For the intended purpose of informing on the potential impact of fire on the environmental, some interpretation and filtering may be required. 6. The NIAFED dataset used as the extent layer for AUS GEEBAM Fire Severity is current as of 24 February 2020. More recent versions were available at the time of creation, however, these would have introduced burnt areas from a second fire season in Northern Australia where fire patterns differ greatly to that of southern Australia.
Copyright Text: The Remote Sensing and Landscape Science Branch, Science Economics and Insights Division, New South Wales Department of Planning, Industry and Environment.