The Statewide Landcover and Trees Study (SLATS) monitors woody vegetation extent, clearing and regrowth using Sentinel-2 satellite imagery. This report for the 2019–20 monitoring period is the second change report in the current series of SLATS reporting which monitors and accounts for woody vegetation extent and change in Queensland, annually. The monitoring period is nominally from August 2019 to August 2020. The methodology monitors and reports change in woody vegetation extent against a 2018 woody vegetation extent baseline which is updated annually with clearing and regrowth mapping. Included are data about the clearing activity type and estimates of woody vegetation density and age, to better describe what woody vegetation currently exists, and where and how it is being cleared. Regrowth reporting is included for the first time in the 2019–20 data. The clearing data are directly comparable with the 2018–19 report but are not comparable with previous SLATS reporting up to and including the 2017–18 SLATS report.

The Statewide Landcover and Trees Study (SLATS) monitors woody vegetation extent and changes in Queensland using Sentinel-2 satellite imagery as its primary tool. This dataset provides annual summaries of woody vegetation clearing and regrowth from the 2018–19 reporting period onward, aligning with an updated Sentinel-2-based methodology introduced in 2018. The data is presented as annual time series summaries, with each year’s data corresponding to a nominal August-to-August reporting period. Summary statistics are provided at the state-wide scale, as well as for administrative boundaries, natural resource management regions and divisions, and other authoritative datasets. This multi-year dataset includes data from the 2018–19 onwards SLATS reporting periods. It supersedes and is not directly comparable with SLATS data published for reporting periods up to and including 2017–18, due to a methodological change. Note that regrowth was not reported in 2018–19; values for regrowth in that year are represented as zero in the dataset.

This dataset was derived from the primary "SLATS Landsat woody change data (25m) for 1988 - 2010" raster (grid) layers used to generate the annualised woody vegetation change rates for the 2010 NSW Annual Report of Native Vegetation.(http://www.environment.nsw.gov.au/vegetation/reports.htm); ; This data describes the areas and type of woody vegetation change (loss) based on the analysis of multi-date Landsat imagery covering NSW. This data is based on a biennial LANDSAT coverage between 1988-2006 and annual coverage 2006-2010. LANDSAT Imagery 1988-2008 was processed by Geosciences Australia at 25m resolution. 2008 onwards is based on USGS processed LANDSAT at 30m resolution.; ; Note, this vector data may generate slightly different aerial statistics to those generated from the source raster data. This is due to variation caused by the data transformation and vector cleaning processes applied in generating the vector data. Remotely sensed imagery is routinely collected by DPE and used to map vegetation clearing. This data is spatially explicit and can be used with other datasets to identify activity on individual lots. Please read the privacy collection notice for more information.

The aim of this project is to compile land use and management practices and their observed and measured impacts and effects on vegetation condition. The results provide land managers and researchers with a tool for reporting and monitoring spatial and temporal transformations of Australia’s native vegetated landscapes due to changes in land use and management practices. Following are the details about South Brooman State Forest, NSW. Pre-European reference-analogue vegetation: The site was originally eucalypt tall open forest, multi-aged open, dry sclerophyll forest. The main overstorey species were spotted gum (Corymbia maculata), Eucalyptus muelleriana, E. paniculata, E. pilularis. The main understorey species were Acacia spp., Acmena spp. Brief chronology of changes in land use and management: 1830: Unmodified 1880: Area picked over for high quality sawlogs 1945: Area picked over for high quality sawlogs 1949: Sawlog harvesting - 85% of area 1959: Sawlog harvesting - 85% of area 1968: Commercial Thinning - 25% of area 1969: Area left to rehabilitate 1994: Wildfire - 100% of the area 1996: Pole harvesting - 5% of area 1998: Sawlog harvesting - 20% of the area 1999 and 2003: Hazard reduction 1997: Site was burnt (prescribed fire) followed by drought 2004-2011: Area left to rehabilitate

State-wide vegetation cover datasets of perennial woody vegetation based on Landsat imagery (30m ground pixel) are produced annually with data starting in 1988 to current. The classification of woody perennial vegetation is provided in two classes, one forest category meeting the vegetation structural requirement for 20% cover density and 2m height at maturity, and a sparse woody vegetation category identifying areas with 5- 20% vegetation cover. Click here for more information.

State-wide vegetation cover datasets of perennial woody vegetation based on Landsat imagery (30m ground pixel) are produced annually with data starting in 1988 to current. The classification of woody perennial vegetation is provided in two classes, one forest category meeting the vegetation structural requirement for 20% cover density and 2m height at maturity, and a sparse woody vegetation category identifying areas with 5- 20% vegetation cover. Click here for more information.

State-wide vegetation cover datasets of perennial woody vegetation based on Landsat imagery (30m ground pixel) are produced annually with data starting in 1988 to current. The classification of woody perennial vegetation is provided in two classes, one forest category meeting the vegetation structural requirement for 20% cover density and 2m height at maturity, and a sparse woody vegetation category identifying areas with 5- 20% vegetation cover. Click here for more information.

State-wide vegetation cover datasets of perennial woody vegetation based on Landsat imagery (30m ground pixel) are produced annually with data starting in 1988 to current. The classification of woody perennial vegetation is provided in two classes, one forest category meeting the vegetation structural requirement for 20% cover density and 2m height at maturity, and a sparse woody vegetation category identifying areas with 5- 20% vegetation cover. Click here for more information.

State-wide vegetation cover datasets of perennial woody vegetation based on Landsat imagery (30m ground pixel) are produced annually with data starting in 1988 to current. The classification of woody perennial vegetation is provided in two classes, one forest category meeting the vegetation structural requirement for 20% cover density and 2m height at maturity, and a sparse woody vegetation category identifying areas with 5- 20% vegetation cover. Click here for more information.