Fire Burn Severity - Same Year

Fire Burn Severity - Same Year This layer is the current fire year burn severity classification for large fires (greater than 100 ha). Burn severity mapping is conducted using best available pre- and post-fire satellite multispectral imagery acquired by the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite or the Operational Land Imager (OLI) sensor aboard the Landsat-8 and 9 satellites. Every attempt is made to use cloud, smoke, shadow and snow-free imagery that was acquired prior to September 30th. However, in late fire seasons imagery acquired after September 30th may be used. This layer is considered an interim product for the 1-year-later burn severity dataset (WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SP). Mapping conducted during the following growing season benefits from greater post-fire image availability and is expected to be more representative of tree mortality. #### Methodology: • Select suitable pre- and post-fire imagery or create a cloud/snow/smoke-free composite from multiple images scenes • Calculate normalized burn severity ratio (NBR) for pre- and post-fire images • Calculate difference NBR (dNBR) where dNBR = pre NBR – post NBR • Apply a scaling equation (dNBR_scaled = dNBR*1000 + 275)/5) • Apply BARC thresholds (76, 110, 187) to create a 4-class image (unburned, low severity, medium severity, and high severity) • Apply region-based filters to reduce noise • Confirm burn severity analysis results through visual quality control • Produce a vector dataset and apply Euclidian distance smoothing 2024-02-21 Government of British Columbia marc.rousseau@gov.bc.ca Form DescriptorsNature and EnvironmentScience and TechnologyBurn SeverityFireFire ImpactFire SeverityWildfireburnburneddisturbancefire perimetersfire polygonGovernment information Original metadata (https://catalogue.data.gov.bc.ca)HTML https://catalogue.data.gov.bc.ca/dataset/fire-burn-severity-same-year Download KML Ground Overlay fileKML https://openmaps.gov.bc.ca/kml/geo/layers/WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SAME_YR_SP_loader.kml Fire Burn Severity - Same YearWMS https://openmaps.gov.bc.ca/geo/pub/WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SAME_YR_SP/ows?service=WMS&request=GetCapabilities&layers=pub:WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SAME_YR_SP&legend_format=image/png&feature_info_type=text/plain Fire Burn Severity - Same YearWMS https://openmaps.gov.bc.ca/geo/pub/WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SAME_YR_SP/ows?service=WMS&request=GetCapabilities&layers=pub:WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SAME_YR_SP&legend_format=image/png&feature_info_type=text/plain

This layer is the current fire year burn severity classification for large fires (greater than 100 ha). Burn severity mapping is conducted using best available pre- and post-fire satellite multispectral imagery acquired by the MultiSpectral Instrument (MSI) aboard the Sentinel-2 satellite or the Operational Land Imager (OLI) sensor aboard the Landsat-8 and 9 satellites. Every attempt is made to use cloud, smoke, shadow and snow-free imagery that was acquired prior to September 30th. However, in late fire seasons imagery acquired after September 30th may be used. This layer is considered an interim product for the 1-year-later burn severity dataset (WHSE_FOREST_VEGETATION.VEG_BURN_SEVERITY_SP). Mapping conducted during the following growing season benefits from greater post-fire image availability and is expected to be more representative of tree mortality. #### Methodology: • Select suitable pre- and post-fire imagery or create a cloud/snow/smoke-free composite from multiple images scenes • Calculate normalized burn severity ratio (NBR) for pre- and post-fire images • Calculate difference NBR (dNBR) where dNBR = pre NBR – post NBR • Apply a scaling equation (dNBR_scaled = dNBR*1000 + 275)/5) • Apply BARC thresholds (76, 110, 187) to create a 4-class image (unburned, low severity, medium severity, and high severity) • Apply region-based filters to reduce noise • Confirm burn severity analysis results through visual quality control • Produce a vector dataset and apply Euclidian distance smoothing

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