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.

For some time, Remote Sensing Sciences, has produced Foliage Projective Cover (FPC) using a model applied to Landsat surface reflectance imagery, calibrated by field observations. An updated model was developed which relates field measurements of FPC to 2-year time series of Normalized Difference Vegetation Index (NDVI) computed from Landsat seasonal surface reflectance composites. The model is intended to be applied to Landsat and Sentinel-2 satellite imagery, given their similar spectral characteristics. However, due to insufficient field data coincident with the Sentinel-2 satellite program, the model was fitted on Landsat imagery using a significantly expanded, national set of field data than was used for the previous Landsat FPC model fitting. The FPC model relates the field measured green fraction of mid- and over-storey foliage cover to the minimum value of NDVI calculated from 2-years of Landsat seasonal surface reflectance composites. NDVI is a standard vegetation index used in remote sensing which is highly correlated with vegetation photosynthesis. The model is then applied to analogous Sentinel-2 seasonal surface reflectance composites to produce an FPC image at Sentinel-2 spatial resolution (i.e. 10 m) using the radiometric relationships established between Sentinel-2 and Landsat in Flood (2017). This is intended to represent the FPC for that 2-year period rather than any single date, hence the date range in the dataset file name. The dataset is generally expected to provide a reasonable estimate of the range of FPC values for any given stand of woody vegetation, but it is expected there will be over- and under-estimation of absolute FPC values for any specific location (i.e. pixel) due to a range of factors. The FPC model is sensitive to fluctuations in vegetation greenness, leading to anomalies such as high FPC on irrigated pastures or locations with very green herbaceous or grass understoreys. A given pixel in the FPC image, represents the predicted FPC in the season with the least green/driest vegetation cover over the 2-year period assumed to be that with the least influence of seasonally variable herbaceous vegetation and grasses on the more seasonally stable woody FPC estimates. The two-year period was used partly because it represents a period relative to tree growth but was also constrained due to the limited availability of imagery in the early Sentinel-2 time series. The FPC dataset is constrained by the woody vegetation extent dataset for the FPC year.

<p>This data set provides the photosynthetic pathways for 2428 species recorded across 541 plots surveyed by Australia’s Terrestrial Ecosystem Research Network (TERN) between 2011 and 2017 (inclusive). TERN survey plots are 1 ha (100 x 100 m) permanently established sites located in a homogeneous area of terrestrial vegetation. At each plot, TERN survey teams record vegetation composition and structural characteristics and collect a range of plant samples using a point-intercept method. Species were assigned a photosynthetic pathway using literature and carbon stable isotope analysis of bulk tissue collected by TERN at the survey plots. </p><p>The data set is comprised of two data tables and one data descriptor that defines the values in the two data tables. The first table contains a list of each species and its photosynthetic pathway. The second table includes a list of all the peer-reviewed sources used to create this data set. </p><p>This data set will be updated on an annual basis as TERN’s plot network expands and new information becomes available. </p>

<br>The NSW Carbon Monitoring project is a collaboration between the Natural Resources Commission of NSW and Mullion Group to develop a spatial time-series dataset of forest carbon history for the state of NSW at ~25m resolution. The project used FLINTpro software to integrate historical environmental and land management data to model carbon stock and fluxes. Aboveground biomass refers to the amount of carbon stored within aboveground forest components (pools) which includes leaves, branches, bark and stems. Belowground biomass refers to the amount of carbon stored within belowground forest components (pools) which includes coarse and fine roots. Dead Organic Matter refers to the amount of carbon stored within dead forest components (pools) which includes leaf litter, branch litter, bark litter, stem litter, and dead roots. Carbon stored within soil and harvested wood products is not included within any of these datasets.</br> <br>This dataset has been superseeded by <a href="https://portal.tern.org.au/metadata/TERN/b9eab336-0ccc-43cd-9d44-e1e6207a2575">NSW Forest Carbon Stock - Aboveground, Belowground and Dead Organic Matter Carbon Mass 1990-2021</a></br>

<br>Hermitage Research Station (28&deg; 12’ S, 152&deg; 06’ E) situated near Warwick, is the site of a 33 year study of carbon cycling, storage and emissions in a southern Queensland winter cereal system. Mean annual temperature at the site is 17.5&deg;C and mean annual rainfall is 685&nbsp;mm. The soil is a Vertosol containing 65% clay, 24% silt, and 11% sand. Treatments at the trial included stubble burnt (SB), stubble retained (SR), conventional tillage (CT), no tillage (NT), nitrogen fertiliser added (NF) and no nitrogen fertiliser added (N0). It has provided guidance to farmers on optimising nitrogen use efficiency through fine tuning rates to meet crop need, e.g. delivering nitrogen when it is needed by the crop possibly using split applications and coated fertilisers with slower nutrient release profiles. Sourcing nitrogen from pulse crop and pasture was also studied as an option for meeting nitrogen needs with lower emissions and reduced cost.</br>

The woody vegetation extent for Queensland is attributed with an estimated age in years since the last significant disturbance. The method uses a sequential Conditional Random Fields classifier applied to Landsat time series starting 1988 to predict woody cover over the time period. A set of heuristic rules is used to detect and track regrowing woody vegetation in the time series of woody probabilities and record the approximate start and end dates of the most recent regrowth event. Regrowth detection is combined with the Statewide Land and Trees Study (SLATS) Landsat historic clearing data to provide a preliminary estimate of age since disturbance for each woody pixel in the woody extent. The 'last disturbance' may be due to a clearing event or other disturbance such as fire, flood, drought-related death etc. Note that not all recorded disturbances may result in complete loss of woody vegetation, so the estimated age since disturbance does not always represent the age of the ecosystem. The age since disturbance product is derived from multiple satellite image sources and derived products which represent different scales and resolutions: Landsat (30&nbsp;m), Sentinel-2 (10&nbsp;m) and Earth-i (1&nbsp;m).

This dataset contains a series of spatial outputs describing probabilistic species predictive occupancy (Species Occupancy Models, or SOM) & habitat suitability (Maximum Entropy, or Maxent) surfaces, the underlying data used to calculate these models & model projections predicting the impact of climate change on flora Maxent surfaces. <br> Model outputs are combination outputs dependent on known species occurrence in the landscape, the species relationship with environmental variables (covariates) such as temperature, rainfall and topography; and its predicted occurrence based on covariate analysis. Maxent models do not predict actual occupancy, but rather habitat suitability, while SOMs predict actual occupancy. confounding factors such as inter-species competition, geographical barriers and disturbance events play a significant role in species occurrence, and are not considered in Maxent or SOM. Flora Maxent climate change projections used NSW and Australian Regional Climate Modelling (NARCliM) variables to predict habitat suitability for a baseline year 2000 and projections for 2030 and 2070. <br> Covariates, Fauna & Flora survey records used to create the models are included. <br> More detailed information regarding each model, its processes and outputs are included in the dataset. <br> A web mapping application on the NSW Spatial Collaboration Portal depicts Maxent & SOM of a selected group of vulnerable Flora & Fauna from this dataset. Access the webapp through the link below: <br> https://portal.spatial.nsw.gov.au/portal/home/item.html?id=78e6ae3d34aa45d2b8118fd0308d6459

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. 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.

This record is a comprehensive list of vascular plant species from the Biological Survey of South Australia. Preparation from raw data obtained via the Advanced Ecological Knowledge and Observation System (AEKOS; now deprecated) data portal involved the selection of data fields, the removal of intraspecific taxa (only genus and species used to define individual taxa) and removal of duplicate records and those not determined to species.

This data package comprises fire severity scores from Kakadu in 2014. A total of 220 permanent monitoring plots (40&nbsp;m x &nbsp;20 m) were established across three parks (Kakadu, Litchfield and Nitmiluk) in 1994-1995 to monitor biotic change. Of these, 132 plots are located in Kakadu. These sample a variety of landform and vegetation type/habitat conditions. A substantial proportion of plots were positioned deliberately at sites likely to reveal environmental dynamics, especially at ecotones and in patches of fire-sensitive vegetation. For example stands of <i>Callitris</i>, sandstone heaths. As well, many plots are located at, or in the near vicinity of, intensively managed sites such as camp-grounds and other tourist destinations. A synopsis of related data packages which have been collected as part of the Three Park Savanna Fire-effects Plot Network’s full program is provided at <a href="http://www.ltern.org.au/index.php/ltern-plot-networks/three-parks-savanna ">LTERN</a>