Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2018/19 fire year, including wildfires >100ha with fire start date between July 2018 and June 2019.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2017/18 fire year, including wildfires >100ha with fire start date between July 2017 and June 2018.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, extent only (grass fires), low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2022/23 fire year including all wildfires >10ha with a fire start date between 1 July 2022 and 30 June 2023.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2019/20 fire year including all wildfires >10ha with a fire start date between July 2019 and June 2020.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2021/22 fire year including all wildfires >10ha with a fire start date between July 2021 and June 2022.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 and Landsat satellite imagery. Fire Extent and Severity Mapping for the 2016/17 fire year is based on Landsat 8 imagery. Fire Extent and Severity Mapping from the 2017/18 fire year onward is based on Sentinel 2 imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2016/17 fire year, including wildfires >100ha with fire start date between July 2016 and June 2017.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2020/21 fire year including all wildfires >10ha with a fire start date between July 2020 and June 2021.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DCCEEW Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, extent only (grass fires), low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2023/24 fire year including all wildfires >10ha with a fire start date between 1 July 2023 and 30 June 2024.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DCCEEW Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on sentinel 2 satellite imagery. The statewide severity map has standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, extent only (grass fires), low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). This dataset represents the 2024/25 fire year including all wildfires and hazard reductions >10ha with a fire start date between 1 July 2024 and 30 June 2025.

The Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program provides assessments of vegetation conditions following large fires on forested lands. Fire effects are represented by three metrics: percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized Composite Burn Index (CBI). These data are derived from moderate resolution multi-spectral imagery (e.g., Landsat 8 Operational Land Imager or Sentinel-2 Multispectral Instrument). The Relative Differenced Normalized Burn Ratio (RdNBR), which is correlated to the variation of burn severity within a fire, is calculated from a pair of images (pre- and postfire), judiciously selected to capture fire effects. The three-severity metrics are in turn calculated from RdNBR using regression equations developed from and calibrated with historical field data. This map layer is a vector points shapefile of the location of all currently inventoried fires occurring between calendar year 2007 and 2024 for CONUS, Alaska, Hawaii, and Puerto Rico. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.