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    <p>This database contains occurrence data for vertebrates across the Australian Wet Tropics. Species occurrence point data has been collected during field intensive surveys using a variety of sampling methods as well as from the literature and institutional databases. The records are divided into two tables: Misc_records and STD_records. The first contains records collated opportunistically, as well as records collected from literature. The latter is a collection of standardized surveys conducted by Steve E. Williams (JCU). </p> <p> All occurrences were vetted for positional and taxonomic accuracy, and for sensitivity at the state and national levels. Sensitive species records are withheld or have their location generalised following sensitive species rules for processing these records. </p>

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    These datasets consist of soil maps generated to assess baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. <br> The maps are organised into empirical soil maps, digital soil maps, and data cube maps. <br> Empirical soil maps consists of four products. Maps include topsoil pH, carbon, Emerson Aggregate Stability and Soil Profile Quality Confidence. Each map consists of 2,162 units. Maps were generated using the most representative soil profile for each unit available within the Soil and Land Information System (SALIS). The 2008 woody vegetation coverage was used as baseline. Maps reflect values when the sampling occurred with temporal changes not being accounted for. Locations with missing or of poor quality data are identified, providing a confidence rating map as part of the evaluation process.<br> Digital soil maps include map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps of key soil indicators, such as soil carbon, pH, bulk density, hillslope erosion and others, were created at 100 m resolution. For each key soil indicator, maps include baseline (approximately 2008) levels as well as trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. <br> Data cube maps include time series of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Products provide estimates of SOC concentrations and associated trends through time. Modelling was carried out using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Important covariates required to drive this spatio-temporal modelling were identified using the Recursive Feature Elimination algorithm (RFE). <br>

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    We used Digital Soil Mapping (DSM) technologies combined with the real-time collations of soil attribute data from TERN's recently developed Soil Data Federation System, to produce a map of Australian Soil Classification Soil Order classes with quantified estimates of mapping reliability at a 90m resolution.

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    This dataset consists of bare earth covariates designed to indicate the presence of iron oxides, ferrous minerals, quartz/carbonate and hydroxyl minerals, to support soil and lithological modelling across Australia. <br> Bare earth layers (bands) represent the weighted geometric median of pixel values derived from a 30 year time-series of Landsat 5, 7 and 8 imagery converted to at-surface-reflectance, using the latest techniques to reduce the influence of vegetation (see Publications: Roberts, Wilford & Ghattas 2019). Bare earth layers are (BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. <br> Covariates are then derived from principal components analysis and ratios of specific bare earth layers to target identification of elements of surface geochemistry. Layers are available as mosaics or tiles in 30 or 90 metre resolution.<br>

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    This dataset contains maps of woody vegetation extent and woody foliage projective cover (FPC) for New South Wales at 5 metre resolution. <br /><br /> Woody vegetation is a key feature of our landscape and an integral part of our society. We value it because it contributes to the economy, protects the land, provides us with recreation, and gives refuge to the unique and diverse range of fauna that we regard so highly. Yet it poses a significant threat to us in times of fire and storm. So information about trees is vital for a range of business, property planning, monitoring, risk assessment, and conservation activities. <br /><br /> The datasets are: <br /> Woody vegetation extent. A presence/absence map showing areas of trees and shrubs, taller than two metres, that are visible at the resolution of the imagery used in the analysis. This shows the location, extent, and density of foliage cover for stands of woody vegetation, enabling identification of small features such as trees in paddocks and scattered woodlands through to the largest expanses of forest in the State. Woody extent products contain 'bcu' in the file name.<br /><br /> Woody foliage projective cover (FPC). FPC is a measure of the proportion of the ground area covered by foliage (or photosynthetic tissue) held in a vertical plane and is a measure of canopy density. Woody FPC products contain 'bcv' in the file name. <br /><br /> Both mosaics and tiles are available, along with a shape file that identifies the location of the tiles.

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    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'.

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    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.

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    Heatwaves are defined as unusually high temperature events that occur for at least three consecutive days with major impacts to human health, economy, agriculture and ecosystems. This dataset provides time-series of heatwave characteristics such as peak temperature, number of events, frequency and duration from 1950 to 2016 in Australia. The analysis were based on daily minimum and maximum temperature obtained from the Australian Water Availability Project (AWAP). The data is available as spatial time-series (5km grid-cell) and aggregated time-series for all Local Government Areas in Australia.

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    This dataset contains spatial layers describing Forest Loss and Recovery from 1998-2019 in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. <br> These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. <br> For this dataset product and the processing of metrics, aspects of canopy loss and disturbances in the forest estate were investigated. Measures of canopy loss and recovery are seen as one of the multiple indicators of forest health. This is related to agents or pressures that affect the capacity of native forests and commercial operations to maintain normal ecosystem functions and sustainably provide productive capacity. <br> To attribute disturbances, as a driver of change, a Multiple Lines of Evidence (MLE) approach was used that leveraged available spatial datasets. This allowed for a project-wide disturbance and disturbance context layer to be generated. This information can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. <br> The time taken, in terms of years, for areas to recover from losses in forest cover extent can has also been determined. This process identifies the time taken for a patch of forest to return to a 20% canopy cover threshold, and other characteristics such as the forest type and likely disturbance or loss event. <br> Forest Loss and Recovery uses measures of canopy loss and disturbances which can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. Time taken in years for areas to recover for losses has also been determined, as-well as other characteristics such as forest type and likely disturbance/loss event. <br> Base cover extent grids used are from the NSW RFA Historic Forest Cover Extent – 1995 to 2019 product.

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    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.