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.

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 layer shows non-woody vegetation change based on the analysis of multi-date Sentinel2 and Planet Maps imagery for 2020 (circa January 1-December 31, 2020) Non-woody change is detected though a combination of automated and manual interpretation of the differences between images captured during the year. The non-woody vegetation change is mapped using the NWD (non-woody disturbance) method which applies a visual interpretation of multiple images before and during 2020 by experienced image interpretation staff. Landcover classes reflect the interpreted cause of non-woody vegetation change. Links to reports, factsheets, change statistics 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 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. https://www.environment.nsw.gov.au/research-and-publications/publications-search/woody-and-non-woody-landcover-change-rural-regulated-land-summary-report-2020 Previous reports https://www.environment.nsw.gov.au/research-and-publications/publications-search/woody-and-non-woody-landcover-change-rural-regulated-land-summary-report-2020 https://www.environment.nsw.gov.au/topics/animals-and-plants/native-vegetation/landcover-science/past-landcover-reporting 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.

Vegetation change products are produced annually identifying locations where the perennial woody vegetation from the vegetation cover datasets based on Landsat imagery (30m ground pixel) has changed between the forest, sparse woody and non woody classifications. Change classes include; no change, non-woody to sparse, non-woody to forest, sparse to non-woody, forest to non-woody and forest to sparse. Click here for more information.

Vegetation change products are produced annually identifying locations where the perennial woody vegetation from the vegetation cover datasets based on Landsat imagery (30m ground pixel) has changed between the forest, sparse woody and non woody classifications. Change classes include; no change, non-woody to sparse, non-woody to forest, sparse to non-woody, forest to non-woody and forest to sparse. Click here for more information.

Vegetation change products are produced annually identifying locations where the perennial woody vegetation from the vegetation cover datasets based on Landsat imagery (30m ground pixel) has changed between the forest, sparse woody and non woody classifications. Change classes include; no change, non-woody to sparse, non-woody to forest, sparse to non-woody, forest to non-woody and forest to sparse. Click here for more information.

Vegetation change products are produced annually identifying locations where the perennial woody vegetation from the vegetation cover datasets based on Landsat imagery (30m ground pixel) has changed between the forest, sparse woody and non woody classifications. Change classes include; no change, non-woody to sparse, non-woody to forest, sparse to non-woody, forest to non-woody and forest to sparse. Click here for more information.

Vegetation change products are produced annually identifying locations where the perennial woody vegetation from the vegetation cover datasets based on Landsat imagery (30m ground pixel) has changed between the forest, sparse woody and non woody classifications. Change classes include; no change, non-woody to sparse, non-woody to forest, sparse to non-woody, forest to non-woody and forest to sparse. Click here for more information.

Vegetation change products are produced annually identifying locations where the perennial woody vegetation from the vegetation cover datasets based on Landsat imagery (30m ground pixel) has changed between the forest, sparse woody and non woody classifications. Change classes include; no change, non-woody to sparse, non-woody to forest, sparse to non-woody, forest to non-woody and forest to sparse. Click here for more information.