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 satellite imagery. The method uses 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). Here we provide historical severity mapping for the Macquarie Marshes region from 1989/90 to 2015/16, which is based on Landsat satellite imagery. From 2016/17 to the current fire year, this region is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, the department's Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on satellite imagery. The method uses 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). Here we provide statewide historical severity mapping of fires >100ha for the 2008-09 fire year, which is based on Landsat satellite imagery (30m pixels). From 2016/17 to the current fire year is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery (10m pixels).

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 satellite imagery. The method uses 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). Here we provide historical severity mapping for the Royal-Heathcote region from 1989/90 to 2015/16, which is based on Landsat satellite imagery. From 2016/17 to the current fire year, this region is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery.

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 satellite imagery. The method uses 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). Here we provide historical severity mapping for Sturt National Park region from 1989/90 to 2015/16, which is based on Landsat satellite imagery. From 2016/17 to the current fire year, this region is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery.

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 satellite imagery. The method uses 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). Here we provide historical severity mapping for the Northern Forests region from 1989/90 to 2015/16, which is based on Landsat satellite imagery. From 2016/17 to the current fire year, this region is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery.

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 satellite imagery. The method uses 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). Here we provide historical severity mapping for the Kosciuszko region from 1989/90 to 2015/16, which is based on Landsat satellite imagery. From 2016/17 to the current fire year, this region is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery.

Remote sensing scientists from the NSW Department of Climate Change, Energy, the Environment and Water (NSW DCCEEW) Science and Insights Division have developed a new approach to mapping the landscape patterns of high severity fire, based on NSW Fire Extent and Severity Mapping (FESM). High severity fire impacts an ecosystem by completely scorching or consuming the canopy biomass. Such impacts can be harmful to biodiversity, although some species benefit or even depend on this level of fire impact. Recent advances in remote sensing of fire and innovative computation solutions by DCCEEW Remote Sensing Scientists offer accessibility to data on fire severity and landscape patterns of fire heterogeneity across broad regions.

Remote sensing scientists from the NSW Department of Climate Change, Energy, the Environment and Water (NSW DCCEEW) Science and Insights Division have developed a new approach to mapping the landscape patterns of high severity fire, based on NSW Fire Extent and Severity Mapping (FESM). High severity fire impacts an ecosystem by completely scorching or consuming the canopy biomass. Such impacts can be harmful to biodiversity, although some species benefit or even depend on this level of fire impact. Recent advances in remote sensing of fire and innovative computation solutions by DCCEEW Remote Sensing Scientists offer accessibility to data on fire severity and landscape patterns of fire heterogeneity across broad regions.

Remote sensing scientists from the NSW Department of Climate Change, Energy, the Environment and Water (NSW DCCEEW) Science and Insights Division have developed a new approach to mapping the landscape patterns of high severity fire, based on NSW Fire Extent and Severity Mapping (FESM). High severity fire impacts an ecosystem by completely scorching or consuming the canopy biomass. Such impacts can be harmful to biodiversity, although some species benefit or even depend on this level of fire impact. Recent advances in remote sensing of fire and innovative computation solutions by DCCEEW Remote Sensing Scientists offer accessibility to data on fire severity and landscape patterns of fire heterogeneity across broad regions.

Remote sensing scientists from the NSW Department of Climate Change, Energy, the Environment and Water (NSW DCCEEW) Science and Insights Division have developed a new approach to mapping the landscape patterns of high severity fire, based on NSW Fire Extent and Severity Mapping (FESM). High severity fire impacts an ecosystem by completely scorching or consuming the canopy biomass. Such impacts can be harmful to biodiversity, although some species benefit or even depend on this level of fire impact. Recent advances in remote sensing of fire and innovative computation solutions by DCCEEW Remote Sensing Scientists offer accessibility to data on fire severity and landscape patterns of fire heterogeneity across broad regions.