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Angular Unit: Degree (0.017453)
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dataset
Department of Environment, Science and Innovation
Principal Scientist
61731705660
41 Boggo Road
Dutton Park
QLD
4102
EOSSDataEnquiries@des.qld.gov.au
20250226
ArcGIS Metadata
1.0
Enterprise Geodatabase Feature Class
Sentinel-2 Annual Fire Scars Queensland 2024
Offline Data
2024-02-23
Project Name
fire scar mapping
Department of Environment, Science and Innovation
Science Division
https://www.qld.gov.au/environment/land/management/mapping/statewide-monitoring/firescar
Department of Environment, Science and Innovation
Science Division
This collection of data sets are state-wide composites of fire scars (burnt area) derived from all available Sentinel-2 images acquired over Queensland for 2024. Fire scars have been mapped using an automated change detection method, with supplementary manual interpretation.
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.
Department of Environment, Science and Innovation
Principal Scientist
41 Boggo Road
Dutton Park
QLD
4102
EOSSDataEnquiries@des.qld.gov.au
GeoTIFF
1
HAZARDS-Fire
ANZLIC Search Words
HAZARDS-Fire
Resource Access Level
© State of Queensland (Department of Environment, Science and Innovation) 2024
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 Environment, Science and Innovation) 2024.
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2024-12-31
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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.
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.
These 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.
995024
EPSG Geodetic Parameter
Dataset
2007-07-16
Version 6.13
8.9.4(10.5.0)
EPSG
8.9.4(10.5.0)
EPSG
8.9.4(10.5.0)
qld_s2_fire_scars_2024
Feature Class
995024
OBJECTID
OBJECTID
OID
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Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
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Feature geometry.
Esri
Coordinates defining the features.
month
Month
Integer
4
10
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month_desc
Month of year
String
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0
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fire_id
fire_id
String
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0
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state
state
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agency
agency
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year
year
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capt_method
capt_method
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0
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area_ha
area_ha
Double
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perim_km
perim_km
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8
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st_area(shape)
st_area(shape)
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0
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st_perimeter(shape)
st_perimeter(shape)
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0
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FALSE
995024
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FALSE