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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/ File Geodatabase Feature Class 10.0 ESRI ArcGIS Server Geoportal Extension Content Type Department of Science, Information Technology and Innovation opendata@dsiti.qld.gov.au From Queensland Spatial Catalogue available as GeoTIFF 62 http://qldspatial.information.qld.gov.au/catalogue/custom/search.page?q=%22Landsat fire scars 1996 Queensland%22 qld_landsat_fire_scars_1996_x IMG_QLD_LANDSAT_FIRESCARS_1996.zip 2013-11-14 Department of Science, Information Technology and Innovation Assistant Director-General, Science Delivery Department of Science, Information Technology and Innovation Assistant Director-General, Science Delivery This data set is a statewide map of fire scars (burnt area) as captured by all available Landsat imagery over the period January to December 1996. Fire scars are automatically detected and mapped using dense time series of Landsat imagery acquired over the period 1986 - present. In these products, on average, over 80% of fire scars captured in Landsat imagery have been correctly mapped with less than 30% false fire rate. 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. Department of Science, Information Technology and Innovation Principal Scientist (Remote Sensing) (07) 3170 5688 41 Boggo Road Dutton Park Qld 4102 AU dan.tindall@dsitia.qld.gov.au http://www.qld.gov.au/environment/land/vegetation/mapping/firescar/ GeoTIFF GDAL Version 1.10.1 and Python Version 2.7.3. Sciences Queensland Open Data Categories emergency services fire history fire risk Landsat fire regime burnt area emergency services, fire risk, fire regime, fire history, Landsat, burnt area Theme keywords input by metadata publisher HAZARDS-Fire ANZLIC Search Words Sciences emergency services fire history fire risk Landsat fire regime burnt area HAZARDS-Fire Fire and natural resource management, emergency services, property management, ecology, research. This material is licensed under a Creative Commons - Attribution 3.0 Australia licence. The Department of Science, Information Technology and Innovation requests attribution in the following manner:© State of Queensland (Department of Science, Information Technology and Innovation) 2015. © State of Queensland (Department of Science, Information Technology and Innovation) 2015 Resource Access Level 30 Esri ArcGIS 13.0.6.36057 true 137.393086 154.068035 -30.301803 -9.046586 true 137.393086 154.068035 -30.301803 -9.046586 true 137.393086 154.068035 -30.301803 -9.046586 137.5 154 -29.5 -9 ANZLIC Geographic Extent Name Register 2003-09-19 2 Queensland 1996-01-01T00:00:00 1996-12-31T00:00:00 true 137.994861 153.545574 -29.177647 -10.07139 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). Public Metadata Access Level 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. A fire scar signal may not be evident in the image sequence for very long, 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 (2003-now), 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). false 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 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 All the data described here has been generated from the analysis of Landsat TM and ETM+ data acquired as ortho-rectified L1T images from USGS. Imagery has a spatial resolution of 30m. An in-house analysis of the geometric accuracy was conducted using a set of 140 surveyed Ground Control Points from across Queensland. The collection of these GCPS is described in Gill (2010). This shows that the average image registration error is less than a pixel (30m). Fire scars smaller than 1 ha (approximately 3 pixels) may not be resolved. Reference: Gill, T. , Collett, L. , Armston, J. , Eustace, A. , Danaher, T. , Scarth, P. , Flood, N. and Phinn, S. 2010. Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007. Journal of Spatial Science, 55: 2, 273-287 false The 1986-2013 fire scar maps have been validated using a Landsat-derived data set of over 500,000 random points sampling the spatial and temporal variability. The average omission error for the State was measured at 15%. The omission error does not include fire scar missed because of 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). Omission errors are likely to be much 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. These results are valid for the individual fire scar maps derived from the set of Landsat images. These mosaicked composited data sets may have higher rates of false fires and lower rates of omissions due to the compositing process. Note: Annual composites from 2003 - 2013 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. This is increasingly apparent from 2010 onwards as image transmission from the Landsat-5 TM (unaffected by striping) was limited and finally ceased in November 2011 due to sensor malfunction. false Single date fire scar maps have been produced for all Queensland Landsat images (with up to 60% cloud cover), held by the Remote Sensing Centre, covering the period: 1986 (earliest Landsat 5 imagery) to 2013 (time of latest time series production). The automated method for detecting fire scars was applied to complete time series stacks (production unit is WRS path/Row) of Landsat TM and ETM+ imagery. Imagery in each time series stack (comprising 400-600 image dates from 1987-2013; or 5-40 images per year) was pre-processed to convert to surface reflectance, and screen out cloud and cloud shadow, topographic shadow, crop and water (described in Source). Time series change detection was then conducted on stacks of Band 4+5 (B45) and Band 4 reflectance. Large negative outliers in reflectance over time were identified. These pixels show a significant decline in reflectance relative to the time series. To achieve this, median filters were applied to B4 and B45 reflectance to smooth the time series and provide a reference for no change. A threshold of change was then used to determine outliers. A flood-filling watershed filter from the VIGRA Computer Vision Library (http://hci.iwr.uni-heidelberg.de/vigra) was then used to map a larger spatial extent of the change using the initial outliers as seeds. The resulting segmented change objects were then attributed as either burnt or unburnt using their thermal, reflective and contextual characteristics in a classification tree. All required thresholds and rules for classification were derived from a statewide calibration based on random points sampled from ten Path/Rows located strategically across Queensland. The method was validated using 500,000 independent random points from a further 10 Path/Rows across Queensland. Calibrated thresholds may not apply outside of the calibration region (i.e. Qld). Annual composites of fire scar maps for a given Path/Row were compiled from all available fire scar maps for the given year. Where a pixel was classified as burnt more than twice in a year, the earliest date of detection was recorded. Annual composites were mosaicked to produce state-wide annual fire scar maps. References: Goodwin, N., and Collett, L. 2013. Development of an automated method for mapping fire history captured in Landsat TM and ETM+ time series across Queensland, Australia. In Preparation. The set of fire scar maps used to compile the mosaic were produced using dense time series stacks of Landsat TM and ETM+ imagery (from 1987 to 2013 which may include between 400 and 600 images). The Landsat imagery (including SLC-Off and cloud affected) was acquired from the USGS as ortho-corrected L1T images containing scaled radiance values. For further information, see the USGS website http://glovis.usgs.gov/, from where this data was downloaded. Images were converted from radiance to a standardised surface reflectance, to minimise atmospheric, topographic, and bi-directional effects following Flood et al, 2013. The thermal infrared band 6 (10.40-12.50µm) was converted to top-of-atmosphere brightness temperature (Chander, Markham, & Helder, 2009) and resampled to 30m x 30m pixels to match reflectance data. No atmospheric correction for the thermal band was applied. All data were scaled to 16 bit range. Images containing moderate to high levels of cloud cover (up to 60%) were included in the time series stacks. Cloud and cloud shadow affected pixels were screened using time series based cloud and shadow masking described in Goodwin et al. 2013. Topographic shadow affected pixels were masked out using a ray casting technique described in Robertson (1989). The satellite and sun azimuth and zenith angles were calculated per pixel directly from the orbital geometry. Crops and permanent water bodies were masked out of all imagery using the most current Queensland Land Use Mapping (http://qldspatial.information.qld.gov.au/catalogue/custom/search.page?q=%22"Land use mapping - Queensland current", http://www.qld.gov.au/environment/land/vegetation/mapping/qlump/). References: Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893-903. Flood, N., Danaher, T., Gill, T., & Gillingham, S. (2013). An Operational Scheme for Deriving Standardised Surface Reflectance from Landsat TM/ETM+ and SPOT HRG Imagery for Eastern Australia. Remote Sensing, 5(1), 83-109. Goodwin, N. R., Collett, L. J., Denham, R. J., Flood, N., & Tindall, D. (2013). Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM+ time series. Remote Sensing of Environment, 134, 50-65. Robertson, K. Spatial transformation for rapid scan-line surface shadowing, IEEE Computer Graphics and Applications, 1989. 408758 Horizontal Datum 8.9.4(10.5.0) EPSG Geodetic Parameter Dataset 2007-07-16 Version 6.13 8.9.4(10.5.0) Projection 8.9.4(10.5.0)