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Product pages » Annual Fire Scars - Landsat, QLD DSITI algorithm, QLD coverage

Annual Fire Scars - Landsat, QLD DSITI algorithm, QLD coverage

Last modified by Bec Trevithick on 2017/09/04 14:43

Annual Fire Scars - Landsat, QLD DSITI algorithm, QLD coverage

fire.jpg

Link to the data

DescriptorData linkLayer name
Persistent URLhttp://www.auscover.org.au/purl/landsat-fire-scars-qld
GeoNetwork recordNot Available Yet

Sub-setting tool (experimental 'clip and ship')http://vegcover.com/chopper/Fire Annual Mosaics
QLD tiff mosaics - FTP accessftp://qld.auscover.org.au/landsat/burnt_area/qld_annual/burnt_area/qld_annual

Data licence and Access rights

ItemDetail
RightsCopyright State of Queensland (Department of Science, Information Technology and Innovation) 2010 - 2020. Rights owned by the Queensland Department of Science, Information Technology and Innovation (DSITI) 
LicenceCreative Common Attribution (CC-BY) 4.0
AccessWhile every care is taken to ensure the accuracy of this information, the Department of Science, Information Technology and Innovation (DSITI) makes no representations or warranties about its accuracy, reliability, completeness or suitability for any particular purpose and disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which might be incurred as a result of the information being inaccurate or incomplete in any way and for any reason.

Alternate title

IMG_QLD_LANDSAT_FIRESCARS

Abstract or Summary

The data set is a statewide annual composite of fire scars (burnt area) derived from all available Landsat 5, 7 and 8 images acquired over the period January to December using time series change detection. Fire scars are automatically detected and mapped using dense time series of Landsat imagery acquired over the period 1987 - present. In addition, from 2013, products have undergone significant quality assessment and manual editing. The automated Landsat fire scar map products covering the period 1987-2012 were validated using a Landsat-derived data set of over 500,000 random points sampling the spatial and temporal variability. On average, over 80% of fire scars captured in Landsat imagery have been correctly mapped with less than 30% false fire rate.  These error rates are significantly reduced in the edited 2013-2016 fire scar data sets, although this has not been quantified.

For the 2016 annual fire scar composite, the manual editing stage incorporated Landsat and Sentinel2A imagery (resampled to match Landsat spatial resolution), allowing for increased cloud-free ground observations, and an associated reduction in the number of missed fires (not quantified). Sentinel2A images were primarily used to map fire scars that were otherwise undetectable in the Landsat sequence due to cloud cover/Landsat revisit time. Additionally, Landsat-7 SLC-Off imagery (affected by striping) was excluded from the 2016 annual composite.  It is expected that these modifications should result in improved mapping accuracy for the 2016 period.

Characterising historic patterns of burning and changing fire regimes over time (spatial extent, timing, patchiness, frequency and intensity) is important for improving our understanding and management of fire, climate, land-use and vegetation interactions. These products may assist the development of appropriate fire management practices and benefit a range of conservation and resource management objectives, as well as ongoing scientific research.

Spatial and Temporal extents

ItemDetail
Spatial resolution (metres)30
Spatial coverage (degrees)north:-10; south:-29; west:138 east:155  
Temporal resolutionAnnually
Temporal coverage1987 ongoing
Sensor & platformLandsat 5&7&8; Sentinel2A (from 2016 onwards)
ItemDetail
Spatial representation typegrid
Spatial reference systemAustralian Albers. EPSG:3577

Point of contact

ItemDetail
NameLisa Collett
OrganisationQLD Department of Science, Information Technology and Innovation
PositionScientist (Remote Sensing)
Emaillisa.collett@science.dsiti.qld.gov.au
RolePoint of Contact
AddressRemote Sensing Centre, DSITI, EcoSciences Precinct
Telephone+61 7 3170 5673
URL http://www.qld.gov.au/environment/land/vegetation/mapping/firescar/

Credit

  • Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI images were acquired from United States Geologic Survey.
  • Contains modified Copernicus Sentinel2 data [2017]. Thanks to European Union and European Space Agency Copernicus Program.

Keywords

ThesauriKeyword
GCMDEARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION COVER
CFvegetation_area_fraction
FoREnvironmental Sciences > Ecological Applications = 0501

There are three main thesauri that AusCover recommends:

  1. Global Change Master Directory (http://gcmd.nasa.gov)
  2. Climate and Forecast (CF) convention standard names (http://cfconventions.org/standard-names.html).
  3. Fields of Research codes (http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E?
    opendocument).

Data quality

Horizontal Positional Accuracy

All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.

Vertical Positional Accuracy

All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a spatial resolution of approximately 30 m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 m at these control points. However, it is possible that errors up to ±50 m occur between these control points. The imagery has been corrected for height displacement using a 3" digital elevation model (DEM) based on the National Aeronautics and Space Administration (NASA), Shuttle Radar Topography Mission (SRTM). It is not recommended that these data sets be used at scales more detailed than 1:100,000.

Algorithm Accuracy
Landsat does not provide a complete record of fire history for this period. This is mostly due to the sensor revisit time of 8-16 days which may be further limited by cloud and cloud shadow obstruction and striping in the imagery.

Annual composites from 2003 onwards are affected by data loss due to systematic striping in the Landsat-7 ETM+ imagery. This is due to the failure of the instrument's Scan Line Corrector (SLC). This is increasingly apparent in imagery acquired in the period 2010-2013, prior to the launch of Landsat-8, as image transmission from the Landsat-5 TM (unaffected by striping) was limited and finally ceased in November 2011 due to sensor malfunction. This has resulted in striping and fragmentation in the fire scar maps derived from SLC-Off imagery.

Omissions
In the 1987-2012 automated fire scar products, the average fire scar omission error for the State was measured at 15%. The omission error does not include fire scars missed due to Landsat data loss e.g. SLC-Off striping, or gaps in the Landsat record e.g. due to cloud or revisit time. This has not been quantified due to the lack of  a validation data set which is independent from the sensor being used (Landsat).  In addition, omission errors are likely to be higher for fire scar composites containing Landsat-7 SLC-Off striping and for wet season periods (Nov-February) in tropical and coastal regions where cloud cover may obscure the view of the surface for months at a time.

A fire scar signal may not be evident in the image sequence for long time periods, particularly in savanna regions in North Queensland. Ash/char can be blown or washed away over short periods of time (~weeks) and the fire scar is often rapidly masked by green-flush and vegetation resprouting in subsequent images.
Data loss from SLC-Off image striping in Landsat-7 imagery (since 2003), cloud cover, haze and smoke, as well as errors in removing cloud and shadow (fire scars which are mapped as cloud shadow) can result in missed fires.

Additionally, fires may be captured in the Landsat imagery but missed or under-mapped by the classifier for the following reasons: the fire may be too small or patchy to detect; cool grass/understorey fires may be obscured by the unburnt tree canopy; or the fire may be misclassified as non-fire related change or cloud shadow.
An assumption that burnt areas decline in reflectance over time may not always be true and missed fire scars have been noted (e.g. spinifex grasses).

The additional step of manual editing applied to the 2013-2016 fire scar data sets should reduce the number of missed fires (due to misclassification) to well below the measured omission rate of 15%, although the edited mapping has not been validated.

From 2016, the incorporation of Sentinel2A into the Landsat image sequence at the manual editing stage has allowed for increased cloud-free ground observations, improved interpretation, and mapping of fire scars which might otherwise be missed in the Landsat sequence (due to cloud etc). This should further reduce the number of missed fires.

False Fires

In the 1987-2012 automated fire scar products, the average rate of false fires across Queensland was measured at 30%. This is likely to be less in some regions and more in others.  False fires are far more common in image dates affected by SLC-Off striping as the data is fragmented and less reliable.

False fires or over-mapping of fire scars may result from the presence of cloud shadows, areas of high land-use change (e.g. cropping), black soils, and inundation e.g. tidal flats, wetlands, ephemeral lakes and channels. These features often spectrally and temporally resemble fire scars.

The additional step of manual editing applied to the 2013-2016 fire scar data sets should reduce the number of false fires to well below the measured rate of 30%, although the edited mapping has not been validated.

From 2016, the incorporation of Sentinel2A into the Landsat image sequence at the manual editing stage has allowed increased cloud-free ground observations and improved interpretation and mapping accuracy (not quantified).

Filenaming convention

Filenames for the seasonal fractional cover deciles product conforms to the AusCover standard naming convention. The standard form of this convention is:

<satellite category code><instrument code><product code>_<where>_<when>_<processing stage code>_<additional dataset specific tags>

Details for the unique codes used for this dataset can be found in the following table.

 Data Naming Element Possible
Code(s)
Descriptor 
Standard Elements  
satellite categorylzLandsat - all possible
instrumenttmthematic
productrereflective
whereqld queensland
when yyyy year 
processing stagedkaannual burnt area composite (unedited; no QA)
 dkdannual burnt area composite (significant manual editing and QA)
 dkgannual burnt area composite (significant manual editing and QA; derived from Landsat-8 and Sentinel2 imagery)
data projectiona2Australian Albers Equal Area


Related products

References

ItemDetail or link
Publication

Algorithm summary

Raster pixel values correspond to the month of detection (often different from the date of active fire). A pixel is mapped as burnt if there has been a significant change in reflectance relative to the time series due to the effects of fire e.g. presence of charcoal or ash, removal of foliage, scorch. Pixel values:0: no fire scar was detected during this period;1-12: month (of Landsat acquisition) when fire scar was first detected;254: crop/water masked (using Current Queensland Land Use Mapping) - no fire scar detection conducted;255: no data value.Note: 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.Data sets are 8 Bit GeoTiff with LZW compression and tiling (BigTIFF).

Product version history

Version labelDetail
1.0Initial release
1.1Updated to incorporate Sentinel2 imagery (2016+ mosaics)

Metadata history

DateDetail
2014-10-14Metadata creation date
2017-04-06Metadata updated
Tags:
Created by Bec Trevithick on 2014/10/09 15:50

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