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 2007-08 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, 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 2013-14 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, 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 2011-12 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, 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 2015-16 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, 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 2014-15 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, 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 2012-13 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, 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 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 8 priority regions on National Parks land tenure from 1989/90 to 2015/16, which is based on Landsat satellite imagery. This covers approximately 30% of the NPWS Managed Lands, across a wide range of vegetation types. From 2016/17 to the current fire year, these regions are covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery. Ongoing FESM processing aims to achieve statewide coverage back to 1989/90, in staged released, as resources permit.

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. Metadata

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2018. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and 2018 for the continental United States, Alaska, Hawaii and Puerto Rico. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. Map Service Feature Layer

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period between 1984 and the current MTBS release. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector polygon of the location of all currently inventoried and mappable MTBS fires occurring between calendar year 1984 and the current MTBS release for the continental United States, Alaska, Hawaii and Puerto Rico. Map Service Feature Layer