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GCS_GDA_1994
Angular Unit: Degree (0.017453)
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qld_veg_slats_e9597
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Change description that indicates the land cover for the area after the mapped change and derived through image interpretation and sampled field verification.
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1EEAAFC1-8306-4C67-9CC0-E5EF704DB115
dataset
Dave Harris
Department of Science, Information Technology, Innovation, and the Arts
Principal Scientist
david.harris@qld.gov.au
20230606
ArcGIS Metadata
1.0
File Geodatabase Feature Class
Senior Spatial Information Officer (Product Delivery)
Department of Natural Resources and Mines
Client Outcomes (Product Delivery)
AU
SIProductdelivery@dnrm.qld.gov.au
Statewide landcover and trees study 1995-1997 Queensland
SIRQRY.QLD_VEG_SLATS_E9597
2013-06-04
Department of Science, Information Technology, Innovation and the Arts
SIRQRY.QLD_VEG_SLATS_ANNUAL_CHANGE_MAPPING
The Statewide Landcover and Trees Survey (SLATS) has been analysing and reporting on change (Loss) of woody vegetation since 1988 using Landsat imagery. This dataset shows vegetation change which occurred in the 1995-1997 SLATS reporting period only. The vegetation change has been attributed to change classes representing what the replacement land cover is or if the vegetation change was due to natural causes. This dataset is a vector representation of the raster SLATS product, which has a grid resolution of 25m x 25m. Information on the Landsat image dates used in the analysis can be found in the dataset - QLD_VEG_SLATS_LANDSAT_DATES_A. The project description, methodology and all of the reports can be found at http://www.qld.gov.au/environment/land/vegetation/mapping/slats-reports/.
To monitor vegetation change from 1995-1997 to assess compliance with the QLD Vegetation Management Act 2000
Dave Harris
Department of Science, Information Technology, Innovation, and the Arts
Principal Scientist
david.harris@qld.gov.au
State or Territory
ANZLIC GEN
FORESTS
LAND Cover
LAND Use
VEGETATION
ANZLIC
farming
environment
ISO 19115 Topic Category
State or Territory
FORESTS
LAND Cover
LAND Use
VEGETATION
farming
environment
Dataset is wholly created and owned by Department of Science, Information Technology, Innovation and the Arts for the State of Queensland.
While every care is taken to ensure the accuracy of this information, the Department of Natural Resources and Mines 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 incured as a result of the information being inaccurate or incomplete in any way and for any reason.
Unrestricted to all levels of government and community. Dataset is available at cost of transfer (unless otherwise agreed) to all government agencies, community groups and individuals. Dataset will be available via web delivery tools, for example, through DNRM's internet sites.
This material is licensed under a Creative Commons - Attribution 3.0 Australia licence.
To view a copy of this licence, visit http://creativecommons.org/licenses/by/3.0/au/ .
The Department of Science, Information Technology, Innovation and the Arts requests attribution in the following manner:
State of Queensland (Department of Science, Information Technology, Innovation and the Arts) 2013. Updated data available at http://dds.information.qld.gov.au/dds/
© State of Queensland (Department of Science, Information Technology, Innovation and the Arts) 2013
Esri ArcGIS 13.0.4.36057
1995-01-01T00:00:00
1997-12-31T00:00:00
true
138.01823
153.617001
-29.26929
-10.88739
The statewide landcover and trees survey (SLATS) has been analysing and reporting on change (loss) of woody vegetation since 1988 using Landsat imagery. This dataset shows vegetation change which occurred in the 1995-1997 SLATS reporting period only. The vegetation change has been attributed to change classes representing what the replacement land cover is or if the vegetation change was due to natural causes. The classes are identified by the [code] attribute as follows: 1- Pasture; 2- Crop; 3- Settlement; 4- Mine; 5- Infrastructure; 6- Timber plantation; 7- Thinning; 8- Missed clearing in previous era; 10- Natural disaster damage; 11- Natural tree death.
For all of Queensland, all areas identified as vegetation change in this era have been attributed with a change class. Areas of change where heavy cloud or cloud shadow exist in the Landsat image are not likely to have been identified. This is usually identified and attributed as "Missed clearing from previous era" in subsequent SLATS analysis.
Over 95% of the vegetation change is on scenes which are field validated. Each scene which is field validated is finalised after quality control checks completed by senior image interpreters.
All the data described here has been generated from the analysis of Landsat Thematic Mapper (TM) data, which has a resampled spatial resolution of 25m. The imagery is rectified using control points measured with a differential GPS ensuring a maximum root mean square (RMS) error of 20 metres at these control points. However, it is possible that errors up to +/-50 meters occur between these control points. The imagery has been corrected for height displacement using a digital elevation model (DEM) from the Shuttle Radar. It is not recommended that these data sets be used at scales more detailed than 1:100,000.
The SLATS woody change detection uses calibration data, collected from an analysis of previous operator-interpreted and field-checked woody change data sets from previous years to develop the spectral and temporal change indices. The spectral index used a regression-based approach utilizing the extracted training data, and it was optimized to use bands and transforms that minimized the RMSE between predictions and measured change; minimized the sensitivity of the model to extreme values by calculating the mean and maximum of the partial derivatives of each band; and maximized the reduction in error variance per term. All processing was completed on logarithmic transformed reflective bands to minimize the effect of illumination differences. These coefficients were applied to the transformed Landsat bands and were summed to generate the regression model output. The time series FPC data were used to build a time series metric that uses a difference test based on the measured variance around a line fitted to the FPC time series. The final Woody vegetation change detection results in a classification representing change at 1%, 2%, 4%, 5%, 10% and 20% probability levels and values of 3 representing probable cropping change. The details of the approach are outlined in Danaher, T., Scarth, P., Armston, J., Collet, L., Kitchen, J., Gillingham, S., 2010. Ecosystem Function in Savannas: Measurement and Modelling at Landscape to Global Scales. Vol. Section 3. Remote Sensing of Biophysical and Biochemical Characteristics in Savannas. How different remote sensing technologies contribute to measurement and understanding of savannas. Taylor and Francis, Remote sensing of tree-grass systems: The Eastern Australian Woodlands.
A desktop assessment of the difference classification was conducted for each scene to identify actual Woody vegetation change. Field validation and associated corrections as well as final QC checks by senior staff refine the analysis to a uniform standard. The scenes are then mosaiced to state-wide dataset, and filtered to remove clumps less than 3 pixels in size. A final recode is conducted using statewide datasets which identify land as ‘Urban’ or ‘forestry'.
The final raster product was vectorised and attributed to a uniform set of attributes, consistent across all SLATS eras.
Data Custodian nominated.
Data Custodian (A/D-G, Science Delivery)
Department of Science Information Technology Innovation and the Arts
Assistant Director-General, Science Delivery
AU
Christine.Williams@qld.gov.au
Layer Controller nominated.
Gary Finney
Department of Natural Resources and Mines
Senior Spatial Information Officer
Gary.Finney@qld.gov.au
Content Authority nominated.
Dave Harris
Department of Science, Information Technology, Innovation, and the Arts
Principal Scientist
david.harris@qld.gov.au
Information Management Authority nominated.
Senior Spatial Information Officer (Product Delivery)
Department of Natural Resources and Mines
Client Outcomes (Product Delivery)
AU
SIProductdelivery@dnrm.qld.gov.au
Landsat TM and ETM+ L1T images are acquired from the USGS on an annual basis in the mid to late dry season. These values are scaled radiance values, after calibration. Georegistration is also by USGS. For further information, see the USGS website http://glovis.usgs.gov/, from where this data was downloaded. These dry season dates were selected to enhance spectral contrast between evergreen tree and shrub canopies and the predominantly senescent ground cover. All images are corrected to minimise the confounding effects of geometric distortion, radiometric variability and illumination geometry using the procedure described in Danaher et al (2010). Angle adjustment uses a Walthall-like equation, with coefficients tuned from all Landsat overlaps available at the time. Topographic correction uses the simple adjustment described in Shepherd et al. (2003) Correcting satellite imagery for the variance of reflectance and illumination with topography, International Journal of Remote Sensing Vol. 24, Issue. 17, 2003. Image edges are trimmed to remove ragged ends of scanlines, where data is missing for some bands and not others. Each image was masked for cloud, cloud shadow, water and areas with topographic shadow. Dates of capture for the Landsat imagery is found in the SLATS footprint dates dataset [QLD_SLATS-LANDSAT-DATES_X_A]. The cloud, cloud shadow and snow masks are based on the Fmask Landsat TM cloud algorithm. Zhu, Z. and Woodcock, C.E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery Remote Sensing of Environment 118 (2012) 83-94. An extra test is performed to mask saturated cloud. The water mask was developed using Canonical Variates Analysis (CVA) of visually identified water and non-water signatures in radiometrically calibrated Queensland wide Landsat imagery. The index is a linear combination of bands, Log transformations of bands and interactive band terms. See Danaher, T. and Collett, L. 2006. Development, optimisation and multi-temporal application of a simple Landsat-based water index. Proc. of 13th Australasian Remote Sensing and Photogrammetry conference, Nov., 2006, Canberra, Aust. The topographic shadow mask was created by a ray casting technique described in Robertson, K. Spatial transformation for rapid scan-line surface shadowing, IEEE Computer Graphics and Applications, 1989. The Satellite and sun azimuth and zenith angles are calculated per pixel directly from the orbital geometry. Satellite orbit deduced from the orientation of the data region of the image. FPC is calculated using a model for prediction of overstorey FPC based on pre-processed Landsat imagery, a climatological ancillary variable, Vapor Pressure Deficit, and field calibration data collected from over 1900 sites in Queensland, outlined in Armston et al. (2009) Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. Journal of Applied Remote Sensing 3, 033540-28.
178400
EPSG
6.5(3.0.1)