The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover, created from a time series of Sentinel-2 imagery. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (10 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation. This model was originally developed for Landsat imagery, but has been adapted for Sentinel-2 imagery to produce a 10m resolution equivalent product.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/TERN/0997cb3c-e2e2-45be-ac82-f5e13d24331c. 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, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation.

The Seasonal ground cover statistics products are long-term temporal statistic products derived from the seasonal ground cover product for each fraction across Queensland for the 30 year timeseries. There is one raster image for each season and each bare and green fraction for the full time series of imagery available. Statistics include: band 1 – 5th percentile minimum; band 2 – mean value for pixel over full time series for that season only (percentage + 100); band 3 – median value for pixel over full time series for that season only (percentage + 100); band 4 – 95th percentile maximum; band 5 – Standard deviation - the temporal standard deviation of the full time-series for that season only; band 6 – Count - the number of observations statistics for that pixel are based on for that season only. Min/max (5th and 95th percentile) products are also made for each fraction using all seasonal ground cover images available during the long term data period (currently 1990-2020).

The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Landsat TM/ETM+/OLI imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/TERN/8d3c8b36-b4f1-420f-a3f4-824ab70fb367. The monthly fractional cover product shows representative values for the proportion of bare ground, green and non-green ground cover across a month. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each month. This dataset consists of medoid-composited monthly fractional cover created from a combined Landsat 8 and Sentinel-2 time series.

This product has been superseded and will not be processed from early 2023. Please find the updated version 3 of this product at https://portal.tern.org.au/metadata/TERN/dd359b61-3ce2-4cd5-bc63-d54d2d0e2509. An estimate of persistent green cover per season. This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. It is derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dim) time series.

The South Australian Land Cover Layers 1987- 2015 spatial land cover datasets for 6 time periods (1987-1990, 1990-1995, 1995-2000, 2000-2005, 2005-2010 and 2010-2015). This dataset can be used to inform spatial and temporal (5 year) summaries of the described land cover types for SA. The capture method and general nature of the classes are most useful for landscape and regional scale assessment.

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.

The data are 475 thematic land cover raster’s at 2m resolution. Land cover classification was to the land cover classes: Tree (1), Water (2), Barren (3), Other Vegetation (4) and Ice & Snow (8). Cloud cover and Shadow were sometimes coded as Cloud (5) and Shadow (6), however for any land cover application would be considered NoData. Some raster’s may have Cloud and Shadow pixels coded or recoded to NoData already. Commercial high-resolution satellite data was used to create the classifications. Usable image data for the target year (2010) was acquired for 475 of the 500 primary sample locations, with 90% of images acquired within ±2 years of the 2010 target. The remaining 25 of the 500 sample blocks had no usable data so were not able to be mapped. Tabular data is included with the raster classifications indicating the specific high-resolution sensor and date of acquisition for source imagery as well as the stratum to which that sample block belonged. Methods for this classification are described in Pengra et al. (2015). A 1-stage cluster sampling design was used where 500 (475 usable), 5 km x 5 km sample blocks were the primary sampling units (note; the nominal size was 5km x 5km blocks, but some have deviations in dimensions due only partial coverage of the sample block with usable imagery). Sample blocks were selected using stratified random sampling within a sample frame stratified by a modification of the Köppen Climate/Vegetation classification and population density (Olofsson et al., 2012). Secondary sampling units are each of the classified 2m pixels of the raster. This design satisfies the criteria that define a probability sampling design and thus serves as the basis to support rigorous design-based statistical inference (Stehman, 2000).

The data are 475 thematic land cover raster’s at 2m resolution. Land cover classification was to the land cover classes: Tree (1), Water (2), Barren (3), Other Vegetation (4) and Ice & Snow (8). Cloud cover and Shadow were sometimes coded as Cloud (5) and Shadow (6), however for any land cover application would be considered NoData. Some raster’s may have Cloud and Shadow pixels coded or recoded to NoData already. Commercial high-resolution satellite data was used to create the classifications. Usable image data for the target year (2010) was acquired for 475 of the 500 primary sample locations, with 90% of images acquired within ±2 years of the 2010 target. The remaining 25 of the 500 sample blocks had no usable data so were not able to be mapped. Tabular data is included with the raster classifications indicating the specific high-resolution sensor and date of acquisition for source imagery as well as the stratum to which that sample block belonged. Methods for this classification are described in Pengra et al. (2015). A 1-stage cluster sampling design was used where 500 (475 usable), 5 km x 5 km sample blocks were the primary sampling units (note; the nominal size was 5km x 5km blocks, but some have deviations in dimensions due only partial coverage of the sample block with usable imagery). Sample blocks were selected using stratified random sampling within a sample frame stratified by a modification of the Köppen Climate/Vegetation classification and population density (Olofsson et al., 2012). Secondary sampling units are each of the classified 2m pixels of the raster. This design satisfies the criteria that define a probability sampling design and thus serves as the basis to support rigorous design-based statistical inference (Stehman, 2000).