20240311 20044100 1.0 FALSE qld_s2_fire_scars_2018 137.811712 153.598574 -29.752631 -10.063306 1 007 Geographic GDA2020 Angular Unit: Degree (0.017453) <GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.0.0'><WKT>GEOGCS[&quot;GDA2020&quot;,DATUM[&quot;GDA2020&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433],AUTHORITY[&quot;EPSG&quot;,7844]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.983152841195215e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>7844</WKID><LatestWKID>7844</LatestWKID></GeographicCoordinateSystem> 20240311 17394700 20240311 17394700 ISO19139 qld_s2_fire_scars_2018 Feature Class 595178 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. MONTH MONTH Integer 4 0 0 MONTH_DESC MONTH_DESC String 25 0 0 SHAPE Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. fire_id fire_id String 255 0 0 state state String 255 0 0 agency agency String 255 0 0 year year Integer 4 0 0 capt_method capt_method String 255 0 0 area_ha area_ha Double 8 0 0 perim_km perim_km Double 8 0 0 SHAPE_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. SHAPE_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. {3DC78649-36FF-45E1-AA05-F3B95D2741E4} {6176DD3B-70E4-4324-99E3-F47177F96AEB} dataset Department of Environment and Science Principal Scientist +61731705660 41 Boggo Road Dutton Park QLD 4102 EOSSDataEnquiries@des.qld.gov.au 20240311 ArcGIS Metadata 1.0 Queensland Spatial Catalogue Download Service Department of Natural Resources and Mines Open the Queensland Spatial Catalogue (QSpatial). Select the large SEARCH button and all available records are displayed. Select one of the four filter options provided and then the further options within the filter. The resultant search is then displayed. Select your record and complete the order. http://qldspatial.information.qld.gov.au/catalogue/ Fire scar mapping Queensland Department of Resources https://spatial-gis.information.qld.gov.au/arcgis/services/Environment/FireScarMapping/MapServer/WMSServer OGC:WMS-1.3.0-http-get-capabilities Web Map Service version 1.3.0 Month This dataset is a statewide monthly composite of fire scars (burnt area) derived from both Sentinel-2 (2018 onwards) and Landsat (to 2017) images acquired over Queensland. Fire scars have been mapped using an automated change detection method, with supplementary manual interpretation. This data contains both automated and manually edited data.1: burnt area detected. 0: NULL/not burnt. For more information go to the Department website https://www.qld.gov.au/environment/land/management/mapping/statewide-monitoring/firescar. Fire scar mapping Queensland Department of Resources https://spatial-gis.information.qld.gov.au/arcgis/rest/services/Environment/FireScarMapping/FeatureServer WWW:LINK-1.0-http--link ArcGIS REST API Month This dataset is a statewide monthly composite of fire scars (burnt area) derived from both Sentinel-2 (2018 onwards) and Landsat (to 2017) images acquired over Queensland. Fire scars have been mapped using an automated change detection method, with supplementary manual interpretation. This data contains both automated and manually edited data.1: burnt area detected. 0: NULL/not burnt. For more information go to the Department website https://www.qld.gov.au/environment/land/management/mapping/statewide-monitoring/firescar. downloadableData 10.0 ESRI ArcGIS Server Geoportal Extension Content Type Department of Environment and Science Remote Sensing Centre From the Queensland Spatial Catalogue as GeoTIFF 140 http://qldspatial.information.qld.gov.au/catalogue/custom/search.page?q=%22Sentinel-2 Annual Fire Scars Queensland 2018%22 Sentinel-2 Annual Fire Scars Queensland 2018 IMG_QLD_SENTINEL2_ANNUAL_FIRESCARS_2018.zip 2023-09-07 Project Name fire scar mapping Department of Environment and Science Science Division https://www.qld.gov.au/environment/land/management/mapping/statewide-monitoring/firescar Department of Environment and Science Science Division Department of Natural Resources and Mines QSpatial Support (07) 3330 4442 Discovery@dnrm.qld.gov.au This dataset is a statewide annual composite of fire scars (burnt area) derived from all available Sentinel-2 images acquired over Queensland in the period January to December 2018. Fire scars have been mapped using an automated change detection method, with supplementary manual interpretation. This data contains both automated and manually edited data. 1-12: month (of Sentinel-2 acquisition) when fire scar was first detected; 254: crop/water masked (using Current Queensland Land Use Mapping) - no fire scar detection conducted. This state-wide composite of fire scars (burnt areas) is to provide regular monitoring and mapping of fire scars across Queensland, useful for managing natural resources, assessing fire hazard and risk, understanding the impacts of fire on grazing production and monitoring ecological impacts over time. © State of Queensland (Department of Environment and Science) 2022 Department of Environment and Science Principal Scientist +61731705660 41 Boggo Road Dutton Park QLD 4102 EOSSDataEnquiries@des.qld.gov.au GeoTIFF 1 HAZARDS-Fire ANZLIC Search Words HAZARDS-Fire &copy State of Queensland (Department of Environment and Science) 2023 This material is licensed under a Creative Commons - Attribution 4.0 International licence. The Department of Resources requests attribution in the following manner: © State of Queensland (Department of Resources) 2023. Resource Access Level 10 Directory 138 155 -29.5 -9 2018-01-01 2018-12-31 true 137.811712 153.598574 -29.752631 -10.063306 Fire scars were automatically detected in Sentinel-2 imagery using differenced bare soil fraction values obtained using the Joint Remote Sensing Research Program’s (JRSRP) fractional cover model. A Sentinel-2 pixel is identified as likely burnt if there has been a significant increment in bare soil fraction relative to the previous fractional cover values. Once areas of likely change are detected, a region growing algorithm is applied to expand the area to capture the whole fire event. Areas are then filtered into burnt and unburnt classes using a decision tree analysis. Subsequent manual interpretation is used to delineate between false positives and true positives. Public Metadata Access Level The Sentinel-2 fire scars product has been validated using 480,000 independent observations selected from a range of environments and periods within the fire season across Queensland. The validation result showed that a high proportion of burned area was correctly classified (f1score = 0.91) with commission and omission error of 13% and 8% respectively. The omission error does not include burned area missed because of missing data (e.g. long periods without cloud-free images) due to the lack of an independent validation data set. The manual editing applied to the 2018 fire scar product should significantly reduce the number of missed fires, although this has not been quantified. Sentinel-2 analysis does not provide a complete record of fire history for this period. Fire scars may be missed or under-mapped due to: - Lack of visibility due to cloud, haze and smoke, and cloud shadow; - Misclassification as non-fire related change or cloud shadow; - Lack of detection due to size or patchiness. Fire scars smaller than 2 ha may not be included; - Lack of detection due to rapid regrowth of vegetation. This is particularly an issue when there have been multiple cloud-affected images in the time series; - Lack of detection for cool grass/understorey fires, obscured by unburnt vegetation; False burned areas or over-mapping may result from: - Omission errors in the cloud/shadow masks, where cloud is classified as fire scar; - Areas of high intensity land-use change where the extent of bare ground increases rapidly (e.g cropping, vegetation clearing); - Areas of inundation (e.g tidal flats, wetlands, ephemeral lakes and channels). All the data described here has been generated from the analysis of Sentinel-2 data acquired as orthorectified images from the European Space Agency. Sentinel-2 imagery has band-dependent spatial resolutions of 10m and 20m. In-house analysis of Sentinel-2 image-to-image registration showed that in over 90% of image pairs, the geometric error was less than 10m. Fire scars may persist and continue to be detected for several months in the image time sequence. Where there has been fire scar persistence or multiple fire scars recorded for a given pixel within the compositing year, the earliest month of detection is recorded. The 2018 fire scar product was produced via five main steps: 1. Conduct pre-processing of Sentinel-2 imagery to convert to surface reflectance, and screen out cloud and cloud shadow, topographic shadow, cropping lands and water. 2. Apply fractional cover algorithm. Fractional cover is a per-pixel quantitative estimation of the photosynthetic vegetation, non-photosynthetic vegetation and bare soil cover fractions. 3. Apply RapidFire algorithm: - Identify core pixels of potentially burned area, based on the temporal difference in bare soil cover fraction. Core pixels are spatial clusters (bigger than 15 pixels) where the change in bare cover fraction exceeds an optimised threshold. - Expand the extent of the pixels classified as potentially burned, using a region growing algorithm on the core pixels. - Use object-oriented classification to discriminate between burned and unburned areas. The classification tree was based on the median values of the temporal difference of NBR (dNBR) and NIR + IR (dNIRIR) of each potentially burned area. 4. Conduct manual editing by trained analysts to reduce the number of false fires and omission errors. 5. Mosaic individual scenes to form an annual composite product for Queensland. This approach has some important consequences: - Not all the pixels of an image are analysed due to cloud and shadow effects; - Time elapsed between observations for different pixels of the same image may differ, again due to cloud and shadow effects over time; and - Burned areas only appear once in the record. If for some reason a burned area is missed in the first unmasked observation it will be missed in the whole record. This new method is a different approach from the previous Landsat-derived fire scar mapping program (1987-2016). That automated time series method identified large negative outliers in reflectance indices (based on NIR and SWIR1 bands) relative to the time series. The following stage code is provided in raster format: - afm: Monthly mosaic Layer containing burned area data for the given time period (pixel value corresponds to month of the year, eg. 1=January and 12=December). References: Bastarrika, A.; Chuvieco, E.; Martín, M. P., 2011. Mapping burned areas from landsat tm/etm+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sensing of Environment., 115, 1003–1012. Goodwin, N. R.; Collett, L. J., 2014. Development of an automated method for mapping fire history captured in Landsat TM and ETM+ time series across Queensland, Australia. Remote Sensing of Environment., 148, 206–221. Guerschman, J. P.; Oyarzábal, M.; Malthus, T.; McVicar, T. R.; Byrne, G.; Randall, L.; Stewart, J., 2012. Evaluation of the MODIS-based vegetation fractional cover product. CSIRO., 31. 595178 EPSG Geodetic Parameter Dataset 2007-07-16 Version 6.13 8.9.4(10.5.0) Projection 8.9.4(10.5.0) Horizontal Datum 8.9.4(10.5.0)