The SLATS star transect field dataset has been compiled as a record of vegetative and non-vegetative fractional cover as recorded in situ according to the method described in Muir et al (2011). The datasets are a combination of vegetation fractions collected in three strata - non-woody vegetation including vegetative litter near the soil surface, woody vegetation less than 2 metres, and woody vegetation greater than 2 metres - at homogeneous areas of approximately 1 hectare. This dataset is compiled from a variety of sources, including available sites from the ABARES ground cover reference sites database.

Field spectroradiometer measurements have been collected at several locations across Australia (formally known as the AusCover Supersites) to relate field based measurements to satellite data products, such as Landsat and MODIS NBAR products. Collected in collaboration with multiple government and research-based institutions.

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.

This data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base. This data set was prepared by SERM-FBIU. The data is a condensed forest cover type map of Saskatchewan at a scale of 1:1,000,000.

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 seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product that predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season across Australia from 1987 to the present. The green and non-green fractions may include a mix of woody and non-woody vegetation. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.

The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product that predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season across Australia from 1987 to the present. The green and non-green fractions may include a mix of woody and non-woody vegetation. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.

Terrestrial laser scans were acquired in native Eucalypt Open Forest (dry sclerophyll Box-Ironbark forest) in Victoria, Australia. Two plots (RUSH06 and RUSH07) with a 40 m radius were established in Rushworth forest and partially harvested in May 2012 to acquire accurate estimates of above-ground biomass. The main tree species in these plots were Eucalyptus leucoxylon, Eucalyptus microcarpa and Eucalyptus tricarpa. Single trees were extracted from the TLS data and quantitative structure models were used to estimate the tree volume directly from the point cloud data. Above-ground biomass (AGB) was inferred from the derived volumes and basic wood density information, and compared with estimates of above-ground biomass derived from allometric equations and destructive sampling. See Calders et al. (2014) and Murphy et al. (2014) for further information.