20220211
20231800
FALSE
20220211
19501800
20220211
19501800
qld_veg_slats_e8891
137.987532
153.542237
-29.139442
-10.595542
1
002
Geographic
GCS_GDA_1994
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/2.7.0'><WKT>GEOGCS["GCS_GDA_1994",DATUM["D_GDA_1994",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4283]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>8.98315284119521e-09</XYTolerance><ZTolerance>2</ZTolerance><MTolerance>2</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4283</WKID><LatestWKID>4283</LatestWKID></GeographicCoordinateSystem>
1.0
ItemDescription
ISO 19139 Metadata Implementation Specification
qld_veg_slats_e8891
Feature Class
334708
OBJECTID
OBJECTID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
ESRI
Coordinates defining the features.
Shape
Geometry
0
0
0
DESCRIPTION
DESCRIPTION
String
31
0
0
Change description that indicates the land cover for the area after the mapped change and derived through image interpretation and sampled field verification.
RULEID
RuleID
Integer
4
0
0
OVERRIDE
Override
Blob
0
0
0
Code
Change code number that is unique to each description in the “Description” field and uniform across all SLATS era change mapping.
CODE
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.
/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a
HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy
MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA
AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA
AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3
ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm
p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA
AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx
BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK
U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3
uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii
gAooooAKKKKACiiigApCwUjOeTjpS0h6dM0ALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU
UUAFFFFABRRRQAUUUUAFFFFABWdbxOmo3UnlzZlbALBSEAUDIJ5wSOgzj0GTWjTSPnyuN2OR3Pp/
WgBRnHOM+1LSAAdOKWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK
Bz2xQAUjKGXBzj2JH8qWgZ70ARvuSHCtl8YUuep9+KjllaFdsUUkrFGIfggEDjOSOv8AnFSSOyFc
JuBz0ODn2z+Peky+/czYj6jgDHHQ/wA/88gBbSGa1ilPV1DfnzUtVLG9gu4y0UhOXbAcEN1PY84/
zxVugAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDOu9U+z3C24tbtmYZ3pCWUc
ngkZxnB/MZq5bytNCsjxPExz8j4yPyp+Mtyc4ORx04xTqAKuoXBtbOSYKxCKWJUjjHP4/SlivVmY
BI5BlNwEilCfYA/56UskZltHjlVZM/eVxkEdccDmnxSLNbh1jYKRwrLg4+lACecXyYijoRhWU5+b
OOcdunf1qAypJZvtdLmQqcBcDzOeBxnjJxmpp4C8b+WxjkI4dQNwI6Yz/X1NVpTP/o5ecDDfvDFh
VfBBHXJ+7nge9AFWwuBCLcDaVuJGMQjXIAxzkjAHOT0H59dqsO80yWa5gfT5VgVG8wqRlWIPpnjP
qPQelatreQ3at5TZZDtdT1U+hoAnooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigB
rlVXLnAHc9qdRRQBCJmFyYSAeAQV6gc8n8sU6YyqoaJFdgeVLYyP8aZNG0k9u6L9xyWJJGBgjp3P
P86m3DcFyNxGQKAIUNyYRuVBKeSP4R7dfwz7dKjaHYxZ5X+ZiQqk85I4OSf6VbIB6jNZ1xIILuRg
8zMu2QxRLnOcrk+o+UHjkY98UAYuu397ayWn2OOQRMJGLeUS4HdSME8nHb0rUKwzJFfSSSqUTLoj
MVI7hhgAH5s54PfoDTtShbUbQLBIYpHjDRM/3TnqCMHPHbv+BrlB4gm094IlVbm1idxJKihJHwM7
cZAG3P4Y9jQB0dvrV0LiMXcUHkSkIskTj5Wzhg2T64GOvIHNbtc1Z67FdrM0bwTW7ZKxquWPTA2n
HPPIyc9gOBW1Z3gnd4iSXUBg20gMp7/XOeKALdFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR
RRQAgAAwB70AYPB4paQMCxUdR1oACM45Iwc8Ux4w7o5HMZJHPXjH9akxjPvSAH+LGQeMUANi37Pn
bJHGSMGmXCM64UkHByBnJGOmQRg8jv8A41SOmWzTXM053b+WcjY+ASfvAg7RwB9M0qX9qs0scMjY
jyrqq5IcAfKM98dBj+E0AVb3zZbq3sknWGB0kLjJyQrD6EdT6jscg88O2lW8WsSwxz7HMpRYkUEE
AKy4AbJBAODj0NdnqV2GgTc6GYZlUB8bo+C2MZwcHAPrg571w1nK8c2oXiQvcQCBkAk3SADpk8A8
AAc469hxQBfv7Syt7aKeJlMdxlpUt2K5DAH5VwMAADjJ79+TuWN8bR1R3meUKeHZnbY3zc7iDkDZ
6dfY1yslvINHiuYJX24aV1ik2pGQxU4HODgqeOuT0xWg6pZGHbPcRXEsKM5d8qu4YJyueMgY74Pt
QB3dhfRTJDbmUvceSHbK4z2J/Or1c3HNKs8l3ZwLPdAMjoxKMy56jjaOmenUkVt2t9Dd5VCVkUAv
E4wy/UfXIz04NAFmiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKQ57Y/Glo6UAZurRSy
2MsKmZ/OG0iID5VHLcE5OR8vHqOnWstdLttM024ubi1NxcIm4mGHJYdBj1PAY9SD3PFdDKY8F3GQ
mCSeABkHPPHGM1E8AuICkxBwWO3sVOcAjHpQByVjdLqEw0z5hHDCyksiEFQSdw3DJBGB0x7Cs7T4
Iry/NjqNy5hTfDBJDLhu+A2cg5Ubs9P6W3sjYRSpDcR/bUHmAQ58wgYC468EKep546E8Zsdx9muo
ikzqsY/eDyxviYEv3GTyCCx65PGBggGxqKabbXVvaIF/0N3mlV8jzhjOckNubGOSQDj3FXb6wsdN
VJiiyRGQuFCFgAVC4wM5zuHbjPQiue02C7vro3D2yXCoXMUcrKp9SBgKD98e3P5X/tVzb3cttJaQ
tbpsUpJIAEJGMjnG3CMBjHB55GKANK01AGWQXJjKBttuzXPDrj5gvdj8w9jxwKl1WKeylXULZmEy
HlQM7lCjK47A4JJ6DGetZdmtvHcrcW1w80y5Dxx2zDkgYOegAyecYyTxzW80sj28lncktPLu2qOw
46HPf1zweO1AF/TNTt9WtBcWzErnByOh/qPerlYHhs/ZnutOdVEsbeawVVUDcWHQf7v6it+gAooo
oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqV9dLbQxHZI4klWMBc8ljxk9hV2qcsRZfsy2
+6EFTlyCODnv9Bzycn8aAKFlFY3ZkDWx+U4XzE2hdwGVHqflB/KuTubG6ih8y3It7ZpHHlTuq5Q7
QWQntj16ZNegSRyPCu5IXlU7gWHCnnBHv+XfmsvV9O1C8t3jgnjUzRmNs7htU7c85POARnH5dwDz
qNrW2aazvC8cyZB8kjbjI7gHOAB+HcHitW1vLZ1hSD7PYIzlshTyoI2qxJO4+2cYHTkVvWnhP7Jc
bxHE7n78rFskEc454YEHBz0b2qrovgqSCHzblkRi+5YZF8wKOx4Iw3AzjtQAunTyXN9NqNqZo4om
BaMyKFfg5zyBtBHHBPOfYdJeRyzvHdWHkyyxggbzwDggfT7xzVW+0F57aQQXbiZgfvn5HOOCw6dR
6dOOwwaPpd1pzrCZWNr5e4/PkmQ43ds9ifxoArWjy2vi+73WzKl7GjCQuNuQCMLzzkBjjrxXS1F9
mh3bjGpIbeMjODjGR6fhUtABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ
AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//Z
04D4BDAA-3F2E-41CB-9E79-7897DC6DFC31
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 1988-1991 Queensland
SIRQRY.QLD_VEG_SLATS_E8891
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 1988-1991 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 1988-1991 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
1988-01-01T00:00:00
1991-12-31T00:00:00
true
137.987532
153.542237
-29.139442
-10.595542
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 1988-1991 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.
334708
EPSG
6.5(3.0.1)