From DataNSW
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Historical Fire Extent and Severity Mapping (FESM) - Blue Mountains Region

Created 06/05/2025

Updated 06/05/2025

JSON RDF ISO19115-3 XML

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 Blue Mountains 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.

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Additional Info

Field Value
Title Historical Fire Extent and Severity Mapping (FESM) - Blue Mountains Region
Language English
Licence Creative Commons Attribution 3.0 Australia
Landing Page https://devweb.dga.links.com.au/data/dataset/a79080c1-ec8e-4de9-9686-87816950913a
Remote Last Updated 26/02/2024
Contact Point
NSW Department of Climate Change, Energy, the Environment and Water
test@email.loc
Reference Period 01/07/1989
Geospatial Coverage { "type": "Polygon", "coordinates": [ [ [ 149.501953, -34.358647 ], [ 149.501953, -32.330561 ], [ 151.358643, -32.330561 ], [ 151.358643, -34.358647 ], [ 149.501953, -34.358647 ] ] ]}
Data Portal data.gov.au

Data Source

This dataset was originally found on DataNSW "Historical Fire Extent and Severity Mapping (FESM) - Blue Mountains Region". Please visit the source to access the original metadata of the dataset:
https://data.nsw.gov.au/data/dataset/historical-fire-extent-and-severity-mapping-fesm-blue-mountains-region

Original dataset
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No duplicate datasets found.