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) is a scientific monitoring program which monitors, maps and reports on woody vegetation change in Queensland. Since the 1990's and up to the 2017-18 reporting period, SLATS has been monitoring woody vegetation loss due to land clearing, applying a methodology which used Landsat satellite imagery. From 2018 onwards, woody vegetation change is mapped using Sentinel-2 imagery. The vegetation change has been attributed to change classes representing human induced woody clearing or regrowth.

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 statewide landcover and trees study (SLATS) has been analysing and reporting on change (loss) of woody vegetation since 1988 using Landsat imagery. 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.

These layers show woody vegetation change based on the analysis of multi-date Sentinel2 imagery for 2017, 2018, 2019 and 2020. Woody change is detected though a combination of automated and manual interpretation of the differences between images captured during summer of each year. Satellite images are selected as close as possible to the 1st of January each year with minimal smoke or cloud. This requirement can result in a range of image dates for each SLATS year. To reflect this, SLATS data naming previously included both years (the era of change) in which imagery was captured, for example era 2015-16 (e1516). For clarity data is now named using the year in which the majority of the clearing has taken place for example 2015, some files contain the old naming convention. The woody vegetation change is mapped using the SLATS (Statewide Land and Tree Survey) method which applies an automated change analysis process followed by visual interpretation of the results by experienced image interpretation staff. Landcover classes reflect the interpreted cause of woody vegetation change. The woody change data is also used for vegetation compliance analysis. Change statistics are available. Links to reports, factsheets and other information below: Landcover monitoring and reporting https://www.environment.nsw.gov.au/topics/animals-and-plants/native-vegetation/landcover-monitoring-and-reporting Current NSW Vegetation clearing report (2021) https://www.environment.nsw.gov.au/topics/animals-and-plants/native-vegetation/landcover-science/2021-nsw-vegetation-clearing-report#:~:text=Statewide%20vegetation%20clearing%20by%20landcover%20class&text=In%202021%20across%20the%20state,clearing%20has%20occurred%20for%20agriculture. Previous reports and data https://www.environment.nsw.gov.au/topics/animals-and-plants/native-vegetation/landcover-science Woody vegetation change: Statewide Landcover and Tree Study method https://www.environment.nsw.gov.au/topics/animals-and-plants/native-vegetation/landcover-science/statewide-landcover-tree-study 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.

This layer shows woody vegetation change based on the analysis of multi-date SPOT5 and Sentinel2 imagery. This analysis was done for the period 2015 and 2016. Woody change is detected though a combination of automated and manual interpretation of the differences between images captured during summer of each year. Satellite images are selected as close as possible to the 1st of January each year and must have a clear view of the ground not impacted by smoke or cloud cover. This requirement can result in a range of imagery dates being selected for each SLATS year. To reflect this, SLATS data naming previously included both years in which imagery was captured, for example 2015-16. For clarity data is now named using the year in which the majority of the clearing has taken place for example 2015 The woody vegetation change is mapped using the SLATS (Statewide Land and Tree Survey) method which applies an automated change analysis process followed by visual interpretation of the results by experienced image interpretation staff. Landcover classes reflect the interpreted cause of woody vegetation change. The woody change data is also used for vegetation compliance analysis. Change statistics are available. 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.

These layers show areas of woody vegetation change based on the analysis of multi-date SPOT5 imagery. Woody change is detected though a combination of automated and manual interpretation of the differences between images captured during summer of each year. Satellite images are selected as close as possible to the 1st of January each year and must have a clear view of the ground not impacted by smoke or cloud cover. This requirement can result in a range of imagery dates being selected for each SLATS year. To reflect this, data naming previously included both years in which imagery was captured, for example 2008-2009, 2009-2010, 2010-2011, 2011-2012, 2012-2013, 2013-14 and 2014-15. For clarity data is now named using the year in which the majority of the clearing has taken place i.e. 2008, 2009, 2010, 2011, 2012, 2013, 2014. The woody vegetation change is mapped using the SLATS (Statewide Land and Tree Study) method which applies an automated change analysis process followed by visual interpretation of the results by experienced image interpretation staff. Landcover classes reflect the interpreted cause of woody vegetation change. Each change year has a single statewide point and polygon layer derived from approximately 310 SPOT scenes covering NSW. Vector point format is preferred for analysis to prevent double counting when undertaking regional calculations. Points can only fall into a single region unlike raster pixels which can sit astride a vector boundary. This often occurs with analysis based on vector regions such as Local Government Areas or Bioregions. The woody change data is also used for vegetation compliance analysis. Change statistics are available for all change periods. Contact the data broker on data.broker@environment.nsw.gov.au for further information. 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.

"This map and report (cited below) present the results of research on the rate of change of native woody vegetation in the Central New South Wales wheatbelt. The study was carried out over three years and analysed vegetation change between the 1980s and 2000. ; ; The project tested methods to map changes in native woody vegetation using direct visual inspection of readily available Landsat TM satellite imagery. NPWS mapping of native woody vegetation types within the wheatbelt provided the 1980s baseline information for the study. Clearing was identified on the satellite images and digitised. The resulting clearing maps were used to produce updated maps of remaining native woody vegetation for each monitoring period.; Systematic validation of the mapping was done by comparison with specially flown, fine-scale aerial photography. Validation results showed that the mapping consistently and accurately distinguished between clearing and areas of no-change with typical accuracy rates of approximately 95%."

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.