This product has been superseded and will not be processed from early 2023.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/23885. 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.

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 (dp1) time series. A single band image is produced: persistent green vegetation cover (in percent). The no data value is 255.

The linear seasonal persistent green trend is derived from analysis of the seasonal persistent green product over time. The current version is based on the 1987-2014 period. <br> Seasonal persistent green cover is derived from seasonal fractional cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarized using simple linear regression, and the slope of the fitted line is captured in this product. The original units are percentage points per year. Values are later truncated and scaled.

The Landsat-derived fractional cover layer gives the amount of bare ground, green vegetation, and dead vegetation for each pixel on a specific date. The landscape of NSW undergoes a large variation in greenness throughout the seasonal and drought cycles. Information about the variation in greenness can be useful for a variety of mapping and planning tasks. Areas of green vegetation are important for native species habitat and human recreation activities. Green areas in the landscape are often related to the availability of near surface water or recent inundation, such as bogs, swamps and mires. These green areas are important for native plants and animals as locations of food and water in dry times. The green fraction has been analysed for a sequence of images to show how long an area stays green following a greening event, such as grass growth in response to rainfall. The map of green accumulation for NSW was created from Landsat images from 1988 to 2012. Areas exhibiting the highest values are the areas of NSW that respond with high green cover for a long period after a greening event.

Three maps are available: 1) foliage projective cover, 2) forest extent, attributed with the foliage projective cover and 3) accuracy of the extent maps, which also acts as masks of forest and other wooded lands. Each pixel in map 1 estimates the fraction of the ground covered by green foliage. Each pixel in map 2 shows two pieces of information. The first is a classification of whether the vegetation is forest or not. The pixels classified as forest are attributed with the second piece of information: the foliage projective cover. Each pixel in map 3 is a class that provides information on the classification accuracies of the woody extent. These maps are derived from Landsat.

The climate adjusted linear seasonal persistent green trend is derived from analysis of the linear seasonal persistent green trend, adjusted for rainfall. The current version is based on the 1987-2014 period. <br> Seasonal persistent green cover is derived from seasonal cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarised using simple linear regression, and the slope of the fitted line is captured in the linear seasonal persistent green product. This product is further processed to produce a climate-adjusted version.

Foliage Projective Cover (FPC) is the percentage of ground area occupied by the vertical projection of foliage. The Remote Sensing Centre FPC mapping is based on regression models applied to dry season (May to October) Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI imagery for the period 1988-2014. An annual woody spectral index image is created for each year using a multiple regression model trained from field data collected mostly over the period 1996-1999. A robust regression of the time series of the annual woody spectral index is then performed. The estimated foliage projective cover is the prediction at the date of the selected dry season image for 2014. Where this deviates significantly from the woody spectral index for that date, further tests are undertaken before this estimate is accepted. In some cases, the final estimate is the woody spectral index value rather than the robust regression prediction. The product is further masked to remove areas classified as non-woody.

The data set is a statewide annual composite of fire scars (burnt area) derived from all available Landsat 5, 7 and 8 images acquired over the period January to December using time series change detection. Fire scars are automatically detected and mapped using dense time series of Landsat imagery acquired over the period 1987 - present. In addition, from 2013, products have undergone significant quality assessment and manual editing. The automated Landsat fire scar map products covering the period 1987-2012 were validated using a Landsat-derived data set of over 500,000 random points sampling the spatial and temporal variability. On average, over 80% of fire scars captured in Landsat imagery have been correctly mapped with less than 30% false fire rate. These error rates are significantly reduced in the edited 2013-2016 fire scar data sets, although this has not been quantified. <br> For the 2016 annual fire scar composite, the manual editing stage incorporated Landsat and Sentinel 2A imagery (resampled to match Landsat spatial resolution), allowing for increased cloud-free ground observations, and an associated reduction in the number of missed fires (not quantified). Sentinel 2A images were primarily used to map fire scars that were otherwise undetectable in the Landsat sequence due to cloud cover/Landsat revisit time. Additionally, Landsat-7 SLC-Off imagery (affected by striping) was excluded from the 2016 annual composite. It is expected that these modifications should result in improved mapping accuracy for the 2016 period.<br> A new fire scar detection algorithm has been developed, with a new edited product implemented in 2021.

This dataset indicates the presence and persistence of water across New South Wales between 1988 and 2012. Water is one of the world’s most important resources as it’s critical for human consumption, agriculture, the persistence of flora and fauna species and other ecosystem services. Information about the spatial distribution and prevalence of water is necessary for a range of business, modelling, monitoring, risk assessment, and conservation activities. For example, one of the necessary steps in the NSW State-wide Landcover and Trees Study (SLATS), which monitors vegetation change and is used in the production of vegetation maps, involves removing non-vegetative features such as water bodies through water masking. Water count The water count product is based on water index and water masks for NSW (Danaher & Collett 2006), and represents the proportion of observations with water present across the Landsat time series as a fraction of total number of possible observations in the 25yr period (1 Jan 1988 to 31 Dec 2012). The product has two bands where band 1 is the number of times water was present across the time series, and band 2 is the count of unobscured (i.e. non-null) input pixels, or number of total observations for that pixel. Cloud, cloud-shadow, steep slopes and topographic shadow can obscure the ability to count water presence. Water Prevalence The water prevalence product is extracted from the water count product and provides a measure of the relative persistence of water in the landscape (e.g. from always present to rarely and never present). There are 12 classes representing the percentage of time a pixel has had water present out of the total number of observations for that pixel (i.e Band 1/Band 2 of the water count product). Water prevalence mapping provides information for multiple, wide-reaching applications. For example, distance to locations of persistent water bodies can be modelled as a contributing indicator of potential biodiversity refugia. Files align with Landsat paths and rows (see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-tools), with files for water count denoted 'dd7' and water prevalence 'ddh'.

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.