20240311 20240000 1.0 FALSE qld_s2_fire_scars_2020 137.813721 153.478874 -29.629459 -10.852175 1 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 qld_s2_fire_scars_2020 Feature Class 385987 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. MONTH MONTH Integer 4 0 0 MONTH_DESC Month of year String 25 0 0 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. e963274f-0b5e-49da-aa25-8b97797d93ac Sentinel-2 Queensland Fire Scars Series dataset Deanna Vandenberg Principal Scientist (Remote Sensing) Deanna.Vandenberg@des.qld.gov.au Department of Environment and Science Thomas Franz Scientist (Remote Sensing) thomas.franz@des.qld.gov.au 20200118 ArcGIS Metadata 1.0 HTML simplified 1 Deanna Vandenberg Principal Scientist (Remote Sensing) Deanna.Vandenberg@des.qld.gov.au Sentinel-2 Annual Fire Scars Mapping 2020 1.0 HTML simplified https://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={CAEA8CAE-C0C8-46BE-B4FA-F072CE594A1C} Sentinel-2 Annual Fire Scars Queensland 2020 Sentinel-2 Annual Fire Scars Queensland 2020 2021-01-17T01:49:35 2021-10-13T14:00:00 Thomas Franz - Scientist (Remote Sensing) 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 2020. 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. These state-wide composites of fire scars (burnt areas) are 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 FIRE HISTORY FIRE OCCURRENCE fire scar FIRE HISTORY FIRE OCCURRENCE fire scar © State of Queensland (Department of Environment and Science) 2022 Creative Commons Attribution 4.0 International OFFICIAL-PUBLIC 10 Esri ArcGIS 13.0.6.36057 true 137.813721 153.478874 -29.629459 -10.852175 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. Completeness (omission): 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. Consistency (conceptual): 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). Positional accuracy (external, absolute): 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. Attribute accuracy (non quantitative): 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. These 2020 fire scar data sets have been 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. Source: 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). 385987 EPSG 8.9.4(10.5.0)