20220211 20274300 1.0 FGDC CSDGM Metadata FALSE qld_veg_slats_e1314 0.000 137.997147 153.517892 -29.125201 -10.747033 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[&quot;GCS_GDA_1994&quot;,DATUM[&quot;D_GDA_1994&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433],AUTHORITY[&quot;EPSG&quot;,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> 20220211 19502300 20220211 19502300 FGDC qld_veg_slats_e1314 Feature Class 156831 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape SHAPE Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. Code Code Double 8 0 0 RULEID RULEID Integer 4 0 0 OVERRIDE OVERRIDE Blob 0 0 0 Descr Descr String 31 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. {F38F8837-2874-4114-B526-51CB1B9762EB} {5D7F5911-9CF3-413D-B2DE-C1F99A3AFCAA} dataset Department of Science, Information Technology and Innovation Principal Scientist (Remote Sensing) (07) 3170 5686 41 Boggo Road Dutton Park Qld 4102 AU Dave.Harris@dsiti.qld.gov.au http://www.qld.gov.au/environment/land/vegetation/mapping/slats 20170803 ArcGIS Metadata 1.0 Feature Class Download Service Department of Science, Information Technology and Innovation Open the Queensland Spatial Catalogue (QSpatial). 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/ 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 as ESRI Shape file and MapInfo Tab file. 10 http://qldspatial.information.qld.gov.au/catalogue/custom/search.page?q=%22Statewide landcover and trees study 2013 to 2014 Queensland%22 Statewide landcover and trees study 2013-2014 Queensland DP_QLD_VEG_SLATS_E1314_A.zip 2015-11-12 Project Name SLATS Department of Science, Information Technology and Innovation Assistant Director-General, Science Delivery http://www.qld.gov.au/environment/land/vegetation/mapping/slats/ Department of Natural Resources and Mines QSpatial Support (07) 3330 4442 Discovery@dnrm.qld.gov.au Department of Science, Information Technology and Innovation Assistant Director-General, Science Delivery The statewide landcover and trees study (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 2013 - 2014 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. To monitor vegetation change from 2013-2014 to assess compliance with the QLD Vegetation Management Act 2000 Department of Science, Information Technology and Innovation Principal Scientist (Remote Sensing) (07) 3170 5686 41 Boggo Road Dutton Park Qld 4102 AU Dave.Harris@dsiti.qld.gov.au SHP 1 LAND-Use LAND-Cover AGRICULTURE FORESTS VEGETATION ANZLIC Search Words Sciences Queensland Open Data Categories Change SLATS clearing Landsat Common policy Theme keywords input by metadata publisher LAND-Use LAND-Cover AGRICULTURE FORESTS VEGETATION Sciences Change SLATS clearing Landsat &copy State of Queensland (Department of Science, Information Technology and Innovation) 2015 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. Updated data available at http://qldspatial.information.qld.gov.au/catalogue// Resource Access Level DP_QLD_VEG_SLATS_LANDSAT_DATES_POST2012_A 30000 Esri ArcGIS 10.4.1.5686 137.5 155.5 -29.5 -9 ANZLIC Geographic Extent Name Register 2003-09-19 2 Queensland 2013-01-01T00:00:00 2014-01-01T00:00:00 true 137.997147 153.517892 -29.125201 -10.747033 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. Due to the nature of capturing satellite imagery with maximal usable data the SLATS analysis is not over a finite 365 day interval, in order to make SLATS reports comparable across all eras an annualised rate (hectares per year) is calculated and used for all figures in the report. This is calculated by using the relevant dates in the SLATS footprint dates dataset [Satellite image footprints and dates for SLATS analysis post-2012], and the methods spelled out in section 3 of the report (http://www.qld.gov.au/environment/land/vegetation/mapping/slats-reports/). Public Metadata Access Level 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. false Over 95% of the vegetation change is on Landsat scenes which we identified as 'priority scenes'. Each priority scene underwent a rigorous quality control checks procedure completed by senior image interpreters. false All the data described here has been generated from the analysis of Landsat TM, ETM+ and OLI data, which has a resampled spatial resolution of 30m. 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. false 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. Landsat Thematic Mapper (TM) Enhanced Thematic Mapper plus (ETM+) or Operational Land Imager (OLI) 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 [Satellite image footprints and dates for SLATS analysis post-2012]. 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. Due to the failure of the Landsat 5 satellite, Landsat TM imagery was unavailable for 2012 the only imagery available was Landsat ETM+ with approximately 22% of data missing from the known "SLC-off" error. A method was developed to composite multiple 2012 images together to fill in the missing data. Refer to the SLATS report for further information. Due to the success of this method in providing additional data in clouded areas, the multi-image compositing method has been applied to cloud affected Landsat OLI images for 2013 and beyond. All other imagery corrections listed above were applied to these images. 156831 EPSG 8.3.4(3.0.1) EPSG Geodetic Parameter Dataset 2007-07-16 Version 6.13 8.3.4(3.0.1) EPSG 8.3.4(3.0.1)