These are the soil attribute products of the Tasmanian Soil Attribute Grids. There are 8 soil attribute products available from the TERN Soil Facility. Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

Attributes: pH - Water (pHw); Electical Conductivity dS/m (ECD); Clay % (CLY); Sand % (SND); Silt % (SLT); Bulk Density - Whole Earth Mg/m3 (BDw); Organic Carbon % (SOC); Coarse Fragments >2mm (CFG).

These products were developed using datasets held by the Tasmanian Department of Primary Industries Parks Water & Environment (DPIPWE) Soils Database. The mapping was made by using spatial modelling and digital soil mapping (DSM) techniques to produce a fine resolution 3 arc-second grid of soil attribute values and their uncertainties, across all of Tasmania.

Note: Previous versions of this collection contained a Depth layer. This has been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using spatial modelling and digital soil mapping techniques.

Soil inventory:

Tasmanian soil site data originates from the DPIPWE soils database, a compilation of various historical soil surveys undertaken by DPIPWE, CSIRO, Forestry Tasmania and the University of Tasmania. This database contains morphological and laboratory data for all the soil sites.

Data Modelling :

A raster stack of all covariates was generated and the target variable (each soil property and depth) individually intersected with the covariate values to provide the calibration and validation data. All modelling was undertaken in ‘R’ (R Development Core Team 2012), using Regression tree (RT), specifically the Cubist R package (Kuhn, Weston et al. 2012; Kuhn, Weston et al. 2013; Quinlan 2005). The RT approach is a popular modelling approach for many disciplines (Breiman, Friedman et al. 1984), and has been widely used with DSM (Grunwald 2009; Kidd, Malone et al. 2014; McKenzie and Ryan 1999). Cubist develops the regression trees by first applying a data mining-approach to partition the calibration and explanatory covariate values into a set of structured ‘classifier’ data. The tree structure is developed by repeatedly partitioning the data into linear models until no significant measure of difference in the calibration data is determined (McBratney, Mendonça Santos et al. 2003). A series of covariate-based rules (conditions) is developed, and the linear model corresponding to the covariate conditions is applied to produce the final modelled surface. For this modelling exercise, the number of rules was set within the model controls to let the Cubist algorithm decide upon the optimum number of rules to generate.

Uncertainty Leave-one-out-cross-validation (LOOCV) was applied to the Cubist model to generate rule-based uncertainties, using only those covariates forming the conditional partitioning of that rule, following Malone et al (2014). The LOOCV, applied to an individual Cubist model for each rule, effectively produced a mean value for each RT partition, with the upper and lower 5 and 95% quantiles of the prediction variation providing the lower and upper prediction uncertainty values respectively, at the 90% Prediction Interval (PI). A 10-fold cross validation was used to run this process 10 times across all data to produce mean modelling diagnostics and validations, and reduce modelling bias due to sensitivity to training data variance.

A collection of high-resolution climate grid surfaces for land areas in Tasmania. There are 76 climate products available that delineate temperature and rainfall parameters specific to crop growing requirements that form part of the enterprise suitability mapping program (refer here: https://dpipwe.tas.gov.au/agriculture/investing-in-irrigation/enterprise-suitability-toolkit/enterprise-suitability-maps). Broadly speaking these products include climate risk parameters including frost risk, heat risk and extreme rainfall risk as well as crop related indices including growing degree days and chill hours. Furthermore, mean monthly climate variables including mean monthly maximum/minimum air temperature and rainfall products are also produced. Refer here for dataset inventory: https://nrmdatalibrary.dpipwe.tas.gov.au/FactSheets/WfW/ListMapUserNotes/Inventory_DCM_Tas.pdf The grids were made by using high resolution spatial modelling techniques with spatial resolution set at 30m grid spacings. All products can be accessed via Web Map Service: https://spatial.dpipwe.tas.gov.au/naturalassets/Climate/wms Or viewed in the following Web Map application: https://arcg.is/vaHDG

A collection of high-resolution climate grid surfaces for land areas in Tasmania based on Climate Futures For Tasmania projection modelling. There are 152 climate products available that delineate …Show full descriptionA collection of high-resolution climate grid surfaces for land areas in Tasmania based on Climate Futures For Tasmania projection modelling. There are 152 climate products available that delineate temperature and rainfall parameters specific to crop growing requirements that form part of the enterprise suitability mapping program (refer here: https://dpipwe.tas.gov.au/agriculture/investing-in-irrigation/enterprise-suitability-toolkit/enterprise-suitability-maps). Broadly speaking these products include climate risk parameters including frost risk, heat risk and extreme rainfall risk as well as crop related indices including growing degree days and chill hours. Furthermore, mean monthly climate variables including mean monthly maximum/minimum air temperature and rainfall products are also produced. Refer here for dataset inventory: https://nrmdatalibrary.dpipwe.tas.gov.au/FactSheets/WfW/ListMapUserNotes/Inventory_DCM_Tas.pdf Climate Futures Tasmania (CFT) projections were incorporated into the modelling framework to simulate projected climate (according to the RCP 8.5 scenario) for years 2030 and 2050. These projections were downscaled, and bias corrected to a spatial grid resolution of 30m. Also, note that these outputs relate to the baseline climate maps defined here: https://www.thelist.tas.gov.au/app/content/data/geo-meta-data-record?detailRecordUID=ba62f124-5906-4471-a01c-9b57b6142055 All products can be accessed via Web Map Service: https://spatial.dpipwe.tas.gov.au/naturalassets/Climate/wms Or viewed in the following Web Map application: https://arcg.is/vaHDG

A statewide grid surface (80m spatial resolution) delineating Degree Day accumulation across the state of Tasmania is produced daily for the Tasmanian growing season (i.e. between months October …Show full descriptionA statewide grid surface (80m spatial resolution) delineating Degree Day accumulation across the state of Tasmania is produced daily for the Tasmanian growing season (i.e. between months October through to April). The outputs are dynamic with the map products updated daily. Three map products are produced: GDD accumulation to date for the current growing season (i.e. heat accumulation tracker updated daily) GDD accumulation to date based on the 5 year average (i.e. averaged GDD units garnered from data 5 years prior to the current growing season; updated daily) GDD deviation from average (the difference between 1 and 2). Negative values indicate that the current growing season is cooler relative to the 5 year average, whereas positive values indicate a warmer season. Map outputs are based on daily records produced from 43 Bureau of Meteorology (BoM) weather station observation sites with further bias correction provided by 267 independent air temperature logger recording sites (courtesy of the Tasmanian Government Department of Primary Industries, Parks, Water and Environment (DPIPWE)). For operational real-time application, the mapping was fully automated in the R programming language and hosted on a cloud-based computing platform courtesy of Sense-T and hosted on the high performance computing cluster provided by the Tasmanian Partnership of Advanced Computing (TPAC) of the University of Tasmania. Refer to the following link for details of the latest map updates: https://www.dropbox.com/s/zcmq7c9aq5isq3s/Air_Temperature_TAS_map_log.txt

URL: https://geoscience.data.qld.gov.au/dataset/mr010516

The TASMANIA map was published to administer permit and permit related spatial information. The map was maintained internally as a provisional office chart.
The map product is available to all government agencies, industry and the public for reference.
Title and Image reference number is TASMANIA _001216. (MR10415)

Tasmania, Petroleum (Submerged Lands) Act 1967. Department of Mines, Tasmania. Graticular Sections Map. S.K.55.

Redmap is a primarily a website that invites the community to spot, log and map marine species that are uncommon in their region, or along particular parts of their coast. The information collected is mapped and displayed on the site, demonstrating, in time, how species distributions may be changing.

Sightings are divided into two categories – those with a photo that can be ‘verified’ by a marine biologist, and sightings without photos that we call community sightings (anecdotal). All the information collected, with and without photos, is mapped and will be used in the following years to map out a ‘story’ of changes occurring in our marine environment.

The main data collected includes the species sighted (normally selected from a list comprising preselected species of interest), the location, date/time and activity being undertaken. Other optional information gathered include biological data such as sex, size and weight and environmental data such as water depth and temperature and habitat.

This record is associated with live data (and will subsequently change over time) and spatial elements have reduced accuracy. It is also subject to a three year embargo (ie. does not contain data less than three years old). If you wish to discuss obtaining a citable, static dataset, that is current and/or contains accurate spatial elements, please see Point of Contact.

This resource includes bathymetry data acquired during the Tasmanian East Coast bathymetry survey collected by Institute for Marine and Antarctic Studies (IMAS) University of Tasmania (UTAS) during the period 12 – 23 April 2021 on the RV Abyss using a Kongsberg Maritime EM2040C multibeam sonar (contracted from CSIRO). The Tasmanian East Coast bathymetry survey was led by Dr. Vanessa Lucieer (IMAS). The purpose of the project was to map the fine-scale spatial distribution of key abalone habitat impacted by urchins in < 25 m water depth using multibeam acoustic imagery. This dataset contains seven 0.5m-resolution 32-bit floating point geotiff files of the bathymetry in study area and transits, derived from the processed EM2040C bathymetry data, using CARIS HIPS and SIPS software. A detailed report on the survey is provided in: Lucieer V, Keane J, Shelamoff V, Nau A, Ling S, Mapping abalone habitat impacted by Centrostephanus on the east coast of Tasmania: Final contracted report for the Abalone Industry Reinvestment Fund (AIRF Project 2021) and Tasmanian Climate Change Office (Climate Research Grants Program 2021), Institute for Marine and Antarctic Studies, UTAS, December (2021) [Contract Report] http://ecite.utas.edu.au/148298. This dataset is not to be used for navigational purposes. This dataset is published with the permission of the CEO, Geoscience Australia.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This is Version 1 of the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This is Version 1 of the Australian Soil Clay product of the Soil and Landscape Grid of Australia. The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (approximately 90 x 90 m pixels). These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia. Attribute Definition: 2 micrometre mass fraction of the less than 2 mm soil material determined using the pipette method; Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 x 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 3.0 (CC By); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Dataset History The National Digital Soil Property Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being: 1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2014a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2014a). Version 1 of the National Digital Soil Property Maps combines mapping from the: 1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps. These individual mapping products are also available in the CSIRO Data Access Portal (https://data.csiro.au). Please refer to these individual products for more detail on the DSM methods used. References: Specifications: Version 1 GlobalSoilMap.net products, Release 2.1, viewed 12/09/2014, http://www.globalsoilmap.net/specifications. Bishop, TFA, McBratney, AB & Laslett, GM 1999, 'Modelling soil attribute depth functions with equal-area quadratic smoothing splines', Geoderma, vol. 91, no. 1-2, pp. 27-45. http://dx.doi.org/10.1016/S0016-7061(99)00003-8. Breiman, L, Friedman, J, Stone, CJ & Olshen, RA 1984, Classification and Regression Trees, Wadsworth statistics/probability series, Wadsworth Belmont, Ca. Clifford, D, Dobbie, MJ & Searle, R 2014, 'Non-parametric imputation of properties for soil profiles with sparse observations', Geoderma, vol. 232-234, pp. 10-8. http://dx.doi.org/10.1016/j.geoderma.2014.04.026. Clifford, D, Searle, R & Holmes, KW 2015, 'Methods to merge disparate spatial estimates of soil attributes', Soil Research, in preparation. de Caritat, P & Cooper, M 2011, National Geochemical Survey of Australia: The Geochemical Atlas of Australia, Geoscience Australia, Record 2011/20 (2 Volumes), Canberra, 557 pp. http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_71973. DEWRN 2014, Mapping soil and land, Department of Environment, Water and Natural Resources, Government of South Australia, viewed 14/04/2014, http://www.environment.sa.gov.au/Knowledge_Bank/Information_data/soil-and-land/mapping-soil-and-land. Grunwald, S 2009, 'Multi-criteria characterization of recent digital soil mapping and modeling approaches', Geoderma, vol. 152, no. 3-4, pp. 195-207. http://dx.doi.org/10.1016/j.geoderma.2009.06.003. Hall, JAS, Maschmedt, DJ & Billing, NB 2009, The Soils of Southern South Australia, The South Australian Land and Soil Book Series, Volume 1; Geological Survey of South Australia, Bulletin 56, Volume 1, Department of Water, Land and Biodiversity Conservation, Government of South Australia. https://data.environment.sa.gov.au/Land/Land-Resources/Pages/Home.aspx. Holmes, KW, Griffin, TG & Odgers, NP 2015, 'Continental scale spatial disaggregation of legacy soil maps: evaluation over Western Australia', Soil Research, in preparation. Jacquier, D, Wilson, P, Griffin, T & Daniel, B 2012, Soil Information Transfer and Evaluation System (SITES) - Database design and exchange protocols, CSIRO Land and Water, Canberra. http://www.clw.csiro.au/aclep/publications/reports.htm. Kidd, D 2015, '80-metre Resolution 3D Soil Attribute Maps for Tasmania', Soil Research, in preparation. Kidd, DB, Malone, BP, McBratney, AB, Minasny, B & Webb, MA 2014, 'Digital mapping of a soil drainage index for irrigated enterprise suitability in Tasmania, Australia', Soil Research, vol. 52, no. 2, pp. 107-19. http://dx.doi.org/10.1071/sr13100. Malone, BP, Minasny, B, Odgers, NP & McBratney, AB 2014, 'Using model averaging to combine soil property rasters from legacy soil maps and from point data', Geoderma, vol. 232, pp. 34-44. http://dx.doi.org/10.1016/j.geoderma.2014.04.033. McBratney, AB, Mendonça Santos, ML & Minasny, B 2003, 'On digital soil mapping', Geoderma, vol. 117, no. 1-2, pp. 3-52. http://dx.doi.org/10.1016/S0016-7061(03)00223-4. McKenzie, NJ, Jacquier, DW, Maschmedt, DJ, Griffin, EA & Brough, DM 2012, The Australian Soil Resource Information System (ASRIS) Technical Specifications, Revised Version 1.6, June 2012, The Australian Collaborative Land Evaluation Program. http://www.asris.csiro.au/downloads/ASRIS_Tech_Specs_201.6.pdf. McKenzie, NJ & Ryan, PJ 1999, 'Spatial prediction of soil properties using environmental correlation', Geoderma, vol. 89, no. 1-2, pp. 67-94. http://dx.doi.org/10.1016/s0016-7061(98)00137-2. Odgers, NP, Holmes, KW, Griffin, T & Liddicoat, C 2015a, 'Derivation of soil attribute estimations from legacy soil maps', Soil Research, in preparation. Odgers, NP, McBratney, AB & Minasny, B 2015, 'Digital soil property mapping and uncertainty estimation using soil class probability rasters', Geoderma, vol. 237-238, pp. 190-8. http://dx.doi.org/10.1016/j.geoderma.2014.09.009. Odgers, NP, Sun, W, McBratney, AB, Minasny, B & Clifford, D 2014, 'Disaggregating and harmonising soil map units through resampled classification trees', Geoderma, vol. 214-215, pp. 91-100. http://dx.doi.org/10.1016/j.geoderma.2013.09.024. Rodríguez, E, Morris, CS & Belz, JE 2006, 'A Global Assessment of the SRTM Performance', Photogrammetric Engineering & Remote Sensing, vol. 72, no. 3, pp. 249-60. Schoknecht, N & Pathan, S 2013, Soil groups of Western Australia: a simple guide to the main soils of Western Australia, 4th ed. Resource Management Technical Report 280, Department of Agriculture and Food Western Australia, Perth. http://archive.agric.wa.gov.au/PC_95446.html. Schoknecht, N, Tille, P & Purdie, B 2004, Soil-landscape mapping in south-western Australia: an overview of methodology and outputs, Resource Management Technical Report 280, Department of Agriculture, Government of Western Australia, Perth. Searle, R 2014, 'The Australian Site Data Collation to Support Global Soil Map', paper presented to GlobalSoilMap Conference 2013, Orleans, France, 7-9 October 2013, https://publications.csiro.au/rpr. Viscarra Rossel, RA 2011, 'Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra', Journal of Geophysical Research: Earth Surface, vol. 116, no. F4, p. F04023. http://dx.doi.org/10.1029/2011JF001977. Viscarra Rossel, RA & Chen, C 2011, 'Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils', Remote Sensing of Environment, vol. 115, no. 6, pp. 1443-55. http://dx.doi.org/10.1016/j.rse.2011.02.004. Viscarra Rossel, RA, Chen, C, Grundy, M, Searle, R, Clifford, D & Campbell, PH 2015a, 'The Australian three-dimensional soil grid: Australia's contribution to the GlobalSoilMap project', Soil Research, in preparation. Viscarra Rossel, RA, Chen, H & Hicks, W 2015b, 'Prediction of spatial distribution of soil attributes to depth from Australian site and covariate data', Soil Research, in preparation. Viscarra Rossel, RA & Webster, R 2012, 'Predicting soil properties from the Australian soil visible-near infrared spectroscopic database', European Journal of Soil Science, vol. 63, no. 6, pp. 848-60. http://dx.doi.org/10.1111/j.1365-2389.2012.01495.x. Viscarra Rossel, RA, Webster, R, Bui, EN & Baldock, JA 2014, 'Baseline map of organic carbon in Australian soil to support national carbon accounting and monitoring under climate change', Global Change Biology, vol. 20, no. 9, pp. 2953-70. http://dx.doi.org/10.1111/gcb.12569. Dataset Citation CSIRO (2014) Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/f8640540-4bb7-42ee-995a-219881e67705.

URL: https://geoscience.data.qld.gov.au/dataset/mr010517

The KING ISLAND map was published to administer permit and permit related spatial information. The map was maintained internally as a provisional office chart.
The map product is available to all government agencies, industry and the public for reference.
Title and Image reference number is KING ISLAND_001216. (MR10413)

King Island, Petroleum (Submerged Lands) Act 1967. Department of Mines, Tasmania. Graticular Sections Map. S.K.54.

Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.

Methods The file 'Serengeti.csv' includes Thomson’s gazelle data used in our study. The original data file is obtained from Hepler et al. (2018), who reported the presence and absence of Thomson’s gazelle at 195 sites within Serengeti National Park, Tanzania. The sites were sampled using a network of 179 motion-sensitive and thermally activated cameras.

The file 'Sugarglider.csv' includes sugar glider data that is used in our study. The original data file is obtained from Stojanovic (2019), who reported the presence and absence of sugar gliders. The data were collected during four or five site visits made to 100 sites in the Southern Forest region of Tasmania.

The zip file 'Serengeti.zip' includes the associated shapefile for the sampling grid in Serengeti National Park, Tanzania where Thomson’s gazelle data were collected.

The zip file 'Sugarglider.zip' includes the associated shapefile for the Southern Forest region of Tasmania where the sugar glider data were collected.

The Natural Resource Management (NRM) Regions dataset is maintained for the purpose of authoritative reporting on the Australian Government's NRM investments. The dataset is designed to cover all Australian territory where Australian Government funded NRM projects might take place and includes major islands, external territories, and state and coastal waters in addition to the NRM regional boundaries. Whilst the boundaries of NRM Regions are defined by legislation in some states and territories, this dataset should not be used to represent legal boundaries in any way. It is an administrative dataset developed for the purpose of reporting and public information. It should be noted that from time to time the states and/or territories may revise their regional boundaries in accordance with local needs and therefore alterations to either the attribution or boundaries of the data may occur in the future.Current VersionAs part of Phase Two of the National Landcare Program (NLP) the Australian Government's natural resource management (NRM) investments will be delivered with Regional Delivery Partners (RDPs) across 56 management units. These replace the previous NLP management units used in NLP Phase One. They are officially referred to as Regional Delivery Partners for Environmental Protection, Sustainable Agriculture and Natural Resource Management Services 2022. The spatial data for RDP management units are derived from the NRM Regions spatial data, as described below.The 2022 dataset defines NRM Region boundaries and Regional Delivery Partner management units in a single dataset, thereby overcoming version control issues with the previous approach of publishing separate data layers for each.To handle a variety of required derivations, a fundamental set of 64 NRM Region map objects was first defined. This can then be compiled using various queries on non-spatial attributes. For example, as set out below, we can define 56 continental NRM Regions and 8 off-shore NRM regions, or island sub components of NRM regions located on the continent. Across these a total of 56 RDP management units can also be defined.To identify those NRM regions located on the Australian continent, a "continental" field (yes/no) has been included, for the first time, in the 2022 dataset. It allows differentiation between off-shore and continental regions, and accommodates that some NRM regions (ie one each in NSW and Tasmania) have both a continental part (eg North Coast, NSW) and an off-shore part (eg North Coast - Lord Howe Island).In accordance with the Australian Government’s Remote Indigenous Procurement Policy (RIPP) and its application to NRM regional investment, we have identified 16 RDP management units with more than 50% overlap with RIPP areas, as defined by the Australian Bureau of Statistics. A RIPP field (yes/no) is included in the attribute table. The data structure allows either NRM Regions, RDP management units and those RDPs overlapping RIPPs to be mapped from the single dataset using the NRM_REGION, RDP_NAME and RIPP fields respectively. NRM_ID, RDP_ID and RIPP fields may also be used.The 2022 version updates the previous version (2020). In total, the 2022 version dataset comprises 64 NRM map objects for 62 NRM regions. These comprise 56 mainland regions (of which two have associated islands as separate map objects), the Torres Strait NRM region, and a further five external territories. Four of these external territories are islands and one is classified as Marine NRM. Using the RDP_NAME or RDP_ID fields to map Regional Delivery Partner management units will result in 56 RDP management unitsThese comprise: 54 mainland RDP management units (two of which have island components); Torres Strait; and a "Marine NRM" management unit. The Marine NRM unit combines Australia's Territorial Sea (from 3 nautical miles to 12 nautical miles) and Australia's Exclusive Economic Zone (to 200 nautical miles) as well as Ashmore and Cartier Islands, Christmas Island, Cocos Keeling Islands and Heard and McDonald Islands. It excludes coastal waters (to 3 nautical miles) which are part of the terrestrial RDP management units. It also excludes the Australian Antarctic Territory and Norfolk Island.The 2022 version was derived from the former NRM regions series (latest version was 2020), originally established in 2006 as the "Natural Heritage Trust II (NHT2) Region Boundaries" dataset. Changes to the 2020 version in creating the 2022 version include the following.Natural Resource Management Regions- 'Adelaide and Mount Lofty Ranges' NRM_Region split into 'Green Adelaide' and 'Hills ad Fleurieu'- Added two new NRM_IDs (4011 for Green Adelaide and 4012 for Hills ad Fleurieu)Regional Delivery Partner management units- Changed 'National Landcare Program Management Units' to 'Regional Delivery Partners for Environmental Protection, Sustainable Agriculture and Natural Resource Management Services' - 'Adelaide and Mount Lofty Ranges' split into 'Green Adelaide' and 'Hills ad Fleurieu'- Added two new RDP_IDs (4011 for Green Adelaide and 4012 for Hills ad Fleurieu)- 'Torres Strait', 'Green Adelaide' and 'Marine NRM' added * to match note *management unit covered through other financial arrangements- 'South West Queensland', 'Maranoa Balonne and Border Rivers' and 'Condamine' combined into 'Southern Queensland' with light grey dotted line to denote NRM borders.- Added the following fields to differentiate RDPs from NRMs- -RDP_ID- -RDP_NAME- -RDP_DESC (Previously AREA_DESC)- -RIPP- 'Torres Strait' and 'Green Adelaide' symbology changed to grey hatched filling. - The management units are coloured based on their overlap with the remote Indigenous Procurement Policy area (RIPP).- Remote management units are orange – to be incl. the management unit needed to have more than 50% overlap with the RIPP. - Non-remote management units are green- *Management units covered through other financial arrangements management units are grey with hatchingPrevious VersionsThe 2020 version NLP Management Units dataset contained 58 separate map objects. These comprised: 56 mainland Management Units; a separate object for Lord Howe Island (part of North Coast, NSW Management Unit); and a "Marine NRM" Management Unit which combined Australia's Territorial Sea (from 3 nautical miles to 12 nautical miles) and Australia's Exclusive Economic Zone (to 200 nautical miles). It excluded coastal waters (to 3 nautical miles) which are part of the terrestrial NLP Management Units. It also excluded Ashmore & Cartier Islands, Australian Antarctic Territory, Christmas Island, Cocos & Keeling Islands, Macquarie Island, Heard & MacDonald Islands and Norfolk Island, and those parts of Australia's Territorial Sea and Exclusive Economic Zone that surround these locations.The 2020 version was derived from the former NRM regions series, originally established in 2006 as the "Natural Heritage Trust II (NHT2) Region Boundaries" dataset. The 2017 version, from which the 2020 version was developed, was itself an update to 2016 v2 in which changes were made to boundaries of six of Western Australia’s seven NRM regions, and region names in Qld, Tas and WA. AttributesThe principle data fields in the 2022 version dataset are:-STATE -NRM_REGION-NRM_ID-NRM_DESC (Previously AREA_DESC)-RDP_ID-RDP_NAME-RDP_DESC (Previously AREA_DESC)-RIPP-CONTINENTALNRM_ID and NRM_REGION Names grouped by state/territory are as follows:New South Wales (11 regions + 1 extra map object for Lord Howe Island)1010 Central Tablelands1020 Central West1030 Greater Sydney1040 Hunter1050 Murray1060 North Coast1061 North Coast - Lord Howe Island1070 North West NSW1080 Northern Tablelands1090 Riverina1100 South East NSW1110 WesternVictoria (10 regions)2010 Corangamite2020 East Gippsland2030 Glenelg Hopkins2040 Goulburn Broken2050 Mallee2060 North Central2070 North East2080 Port Phillip and Western Port2090 West Gippsland2100 WimmeraQueensland (15 regions) 3010 Burnett Mary3020 Cape York3030 Condamine3040 Co-operative Management Area3050 Desert Channels3060 Fitzroy3070 Burdekin3080 Northern Gulf3090 Maranoa Balonne and Border Rivers3100 Mackay Whitsunday3110 South East Queensland3120 South West Queensland3130 Southern Gulf3140 Wet Tropics3150 Torres StraitSouth Australia (9 regions) 4011 Green Adelaide4012 Hills and Fleurieu4020 Alinytjara Wilurara4030 Eyre Peninsula4040 Kangaroo Island4050 Northern and Yorke4060 South Australian Arid Lands4070 South Australian Murray Darling Basin4080 Limestone CoastWestern Australia (7 regions)5010 Northern Agricultural Region5020 Peel-Harvey Region5030 Swan Region5040 Rangelands Region5050 South Coast Region5060 South West Region5070 Avon River BasinTasmania (3 regions + 1 extra map object for Macquarie Island)6010 North West NRM Region6020 North NRM Region6030 South NRM Region6031 South NRM Region - Macquarie IslandsNorthern Territory (1 region)7010 Northern TerritoryAustralian Capital Territory (1 region)8010 ACTExternal Territories (5 regions) 9010 Ashmore and Cartier Islands9020 Christmas Island9030 Cocos Keeling Islands9040 Heard and McDonald Islands9060 Marine NRM

This is Version 1 of the Australian Soil Sand product of the Soil and Landscape Grid of Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

These maps are generated by combining the best available Digital Soil Mapping (DSM) products available across Australia.

Attribute Definition: 200 μm - 2 mm mass fraction of the less than 2 mm soil material determined using the pipette method;
Units: %; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF.

Lineage: The National Soil Attribute Maps are generated by combining the best available digital soil mapping to calculate a variance weighted mean for each pixel. Two DSM methods have been utilised across and in various parts of Australia, these being:

1) Decision trees with piecewise linear models with kriging of residuals developed from soil site data across Australia. (Viscarra Rossel et al., 2015a); 2) Disaggregation of existing polygon soil mapping using DSMART (Odgers et al. 2015a).

Version 1 of the National Digital Soil Property Maps combines mapping from the:

1) Australia-wide three-dimensional Digital Soil Property Maps; 2) Western Australia Polygon Disaggregation Maps; 3) South Australian Agricultural Areas Polygon Disaggregation Maps; 4) Tasmanian State-wide DSM Maps.

These individual mapping products are also available in the Data Access Portal. Please refer to these individual products for more detail on the DSM methods used.

Important: Our technical support team is available to assist you during business hours only. Please keep in mind that we can only address technical difficulties during these hours. When using the product to make decisions, please take this into consideration.

Abstract This spatial product shows consistent ‘near real-time’ bushfire and prescribed burn boundaries for all jurisdictions who have the technical ability or appropriate licence conditions to provide this information. Currency Maintenance of the underlying data is the responsibility of the custodian. Geoscience Australia has automated methods of regularly checking for changes in source data. Once detected the dataset and feeds will be updated as soon as possible. NOTE: The update frequency of the underlying data from the jurisdictions varies and, in most cases, does not line up to this product’s update cycle. Date created: November 2023 Modification frequency: Every 15 Minutes Spatial Extent

West Bounding Longitude: 113° South Bounding Latitude: -44° East Bounding Longitude: 154° North Bounding Latitude: -10°

Source Information The project team initially identified a list of potential source data through jurisdictional websites and the Emergency Management LINK catalogue. These were then confirmed by each jurisdiction through the EMSINA National and EMSINA Developers networks. This Webservice contains authoritative data sourced from:

Australian Capital Territory - Emergency Service Agency (ESA) New South Wales - Rural Fire Service (RFS) Queensland - Queensland Fire and Emergency Service (QFES) South Australia - Country Fire Service (CFS)
Tasmania - Tasmania Fire Service (TFS)
Victoria – Department of Environment, Land, Water and Planning (DELWP)
Western Australia – Department of Fire and Emergency Services (DFES)

The completeness of the data within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Known Limitations:

This dataset does not contain information from the Northern Territory government. This dataset contains a subset of the Queensland bushfire boundary data. The Queensland ‘Operational’ feed that is consumed within this National Database displays a the last six (6) months of incident boundaries. In order to make this dataset best represent a ‘near-real-time’ or current view of operational bushfire boundaries Geoscience Australia has filtered the Queensland data to only incorporate the last two (2) weeks data. Geoscience Australia is aware of duplicate data (features) may appear within this dataset. This duplicate data is commonly represented in the regions around state borders where it is operationally necessary for one jurisdiction to understand cross border situations. Care must be taken when summing the values to obtain a total area burnt. The data within this aggregated National product is a spatial representation of the input data received from the custodian agencies. Therefore, data quality and data completion will vary. If you wish to assess more information about specific jurisdictional data and/or data feature(s) it is strongly recommended that you contact the appropriate custodian.

The accuracy of the data attributes within this webservice is reliant on each jurisdictional source and the information they elect to publish into their Operational Bushfire Boundary webservices. Note: Geoscience Australia has, where possible, attempted to align the data to the (as of October 2023) draft National Current Incident Extent Feeds Data Dictionary. However, this has not been possible in all cases. Work to progress this alignment will be undertaken after the publication of this dataset, once this project enters a maintenance period. Catalog entry: Bushfire Boundaries – Near Real-Time Lineage Statement Version 1 and 2 (2019/20): This dataset was first built by EMSINA, Geoscience Australia, and Esri Australia staff in early January 2020 in response to the Black Summer Bushfires. The product was aimed at providing a nationally consistent dataset of bushfire boundaries. Version 1 was released publicly on 8 January 2020 through Esri AGOL software.
Version 2 of the product was released in mid-February as EMSINA and Geoscience Australia began automating the product. The release of version 2 exhibited a reformatted attributed table to accommodate these new automation scripts. The product was continuously developed by the three entities above until early May 2020 when both the scripts and data were handed over to the National Bushfire Recovery Agency. The EMSINA Group formally ended their technical involvement with this project on June 30, 2020. Version 3 (2020/21): A 2020/21 version of the National Operational Bushfire Boundaries dataset was agreed to by the Australian Government. It continued to extend upon EMSINA’s 2019/20 Version 2 product. This product was owned and managed by the Australian Government Department of Home Affairs, with Geoscience Australia identified as the technical partners responsible for development and delivery. Work on Version 3 began in August 2020 with delivery of this product occurring on 14 September 2020. Version 4 (2021/22): A 2021/22 version of the National Operational Bushfire Boundaries dataset was produced by Geoscience Australia. This product was owned and managed by Geoscience Australia, who provided both development and delivery. Work on Version 4 began in August 2021 with delivery of this product occurring on 1 September 2021. The dataset was discontinued in May 2022 because of insufficient Government funding. Version 5 (2023/25): A 2023/25 version of the National Near-Real-Time Bushfire Boundaries dataset is produced by Geoscience Australia under funding from the National Bushfire Intelligence Capability (NBIC) - CSIRO. NBIC and Geoscience Australia have also partnered with the EMSINA Group to assist with accessing and delivering this dataset. This dataset is the first time where the jurisdictional attributes are aligned to AFAC’s National Bushfire Schema.
Work on Version 5 began in August 2023 and was released in late 2023 under formal access arrangements with the States and Territories. Data Dictionary Geoscience Australia has not included attributes added automatically by spatial software processes in the table below.

Attribute Name Description

fire_id ID attached to fire (e.g. incident ID, Event ID, Burn ID).

fire_name Incident name. If available.

fire_type Binary variable to describe whether a fire was a bushfire or prescribed burn.

ignition_date The date of the ignition of a fire event. Date and time are local time zone from the State where the fire is located and stored as a string.

capt_date The date of the incident boundary was captured or updated. Date and time are local time zone from the Jurisdiction where the fire is located and stored as a string.

capt_method Categorical variable to describe the source of data used for defining the spatial extent of the fire.

area_ha Burnt area in Hectares. Currently calculated field so that all areas calculations are done in the same map projection. Jurisdiction supply area in appropriate projection to match state incident reporting system.

perim_km ) Burnt perimeter in Kilometres. Calculated field so that all areas calculations are done in the same map projection. Jurisdiction preference is that supplied perimeter calculations are used for consistency with jurisdictional reporting.

state State custodian of the data. NOTE: Currently some states use and have in their feeds cross border data

agency Agency that is responsible for the incident

date_retrieved The date and time that Geoscience Australia retrieved this data from the jurisdictions, stored as UTC. Please note when viewed in ArcGIS Online, the date is converted from UTC to your local time.

Contact Geoscience Australia, clientservices@ga.gov.au

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This raster dataset 'NVIS4_1_AUST_MVS_PRE_ALB' provides summary information on Australia's estimated pre-1750 native vegetation classified into Major Vegetation Subgroups. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size.

A comparable Extant (present) vegetation raster dataset is available:

• NVIS4_1_AUST_MVS_EXT_ALB.

State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012.

This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area.

Eighty-five (85) Major Vegetation Subgroups identified were created in v4.1 to summarise the type and distribution of Australia's native vegetation. The classification contains an emphasis on the structural and floristic composition of the dominant stratum (as with Major Vegetation Groups), but with additional types identified according to typical shrub or ground layers occurring with a dominant tree or shrub stratum.

In a mapping sense, the subgroups reflect the dominant vegetation occurring in a map unit from a mix of several vegetation types. Less-dominant vegetation subgroups which are also present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest with a shrubby understorey, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants.

A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Subgroups. These are provided for cartographic purposes, but should not be used for analyses.

This dataset has been provided to the BA Programme for use within the programme only. The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Dataset History

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, each up to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVS. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Subgroup (MVS) by manual interpretation at ERIN and in consultation with data custodians. 12 new MVSs were created for version 4.1 to better represent open woodland formations, more understorey types and forests (in the NT) with no further data available. Also, a number of MVSs were redefined after creation of the new groups to give a clearer and precise description of of the Subgroup e.g. MVS 9 - 'Eucalyptus woodlands with a grassy understorey' became 'Eucalyptus woodlands with a tussock grass understorey' to distinguish it from MVS10 - 'Eucalyptus woodlands with a hummock grass understorey'.. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Warm Temperate Rainforest

• Eucalyptus woodlands with a hummock grass understorey

• Acacia (+/- low) open woodlands and sparse shrublands with a shrubby understorey

• Mulga (Acacia aneura) open woodlands and sparse shrublands +/- tussock grass

• Eucalyptus woodlands with a chenopod or samphire understorey

• Open mallee woodlands and sparse mallee shrublands with a hummock grass understorey

• Open mallee woodlands and sparse mallee shrublands with a tussock grass understorey

• Open mallee woodlands and sparse mallee shrublands with an open shrubby understorey

• Open mallee woodlands and sparse mallee shrublands with a dense shrubby understorey

• Callitris open woodlands

• Casuarina and Allocasuarina open woodlands with a tussock grass understorey

• Casuarina and Allocasuarina open woodlands with a hummock grass understorey

• Casuarina and Allocasuarina open woodlands with a chenopod shrub understorey

• Casuarina and Allocasuarina open woodlands with a shrubby understorey

• Melaleuca open woodlands

• Other Open Woodlands

• Other sparse shrublands and sparse heathlands

• Unclassified Forest

Data values defined as cleared or non-native by data custodians were attributed specific MVS values such as 42 - naturally bare, sand, rock, claypan, mudflat; 43 - salt lakes and lagoons; 44 - freshwater lakes and dams; 46 - seas & estuaries, 90, 91, 92 & 93 - Regrowth Subgroups; 98 - Cleared, non native, buildings; and 99 - Unknown. Note: some of these MVSs are only present in Extant vegetation.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also stored in the NVIS database, but with blank vegetation descriptions. In general, the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M and 1:5M) maps from Commonwealth and other sources. MVSs were then allocated to each description from the available descriptions in accompanying publications and other sources.

Each spatial dataset with joined lookup table (including MVS_NUMBER linked via NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

In the original extant data, parts of New South Wales, South Australia, Tasmania and the ACT have areas of vector "NoData", thus appearing as an inland sea. Where there were gaps in the spatial coverage of Australia, "artificial" estimated pre-1750 layers were created from datasets available to the ERIN Veg Team. These were managed differently based on available information and complexity of work involved. Pre-1750 vector data for other states were supplied for 4.1 or previously, and did not require modelling. The purpose of this artificial pre-1750 modelling was to ensure that the pre-1750 and extant (present) datasets are comparable in the respective MVG and MVS classifications.

Pre1750 Vector Modelling

Large areas in the original South Australia and the ACT extant vector data had 'NoData'. Pre1750 vector layers were created by filling/cutting in these areas with estimated pre-1750 data from other sources such as the Geoscience Australia (AUSLIG,1990) "Natural" vector data layer. This procedure assumes that extant native vegetation has not changed its type since European settlement. Thus, effectively, only the non-native component was modelled/estimated for pre-1750 extent.

All feature classes were then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances e.g. NSW and TAS, areas of 'NoData' had to be modelled in raster (see below).

Raster modelling

For large parts of NSW, the native component of NVIS extant data were cut into the Geoscience Australia (AUSLIG,1990) "Natural" raster data layer and in some smaller areas, existing pre1750 data layers (e.g. Tumut), using a complex series of raster operations. For Tasmania, the NVIS version 2.0 (i.e. the original NVIS with restructured attributes) pre-European layer was rasterised, and used to fill non-native areas of the extant NVIS vegetation