This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

Digital terrain models offer a representation of the relief south of the 52nd parallel, in the form of an elevation matrix. This matrix makes it possible to visualize the territory in perspective and to perform three-dimensional spatial analyses, using appropriate software. A module specialized in three-dimensional data processing, such as 3D Analyst or Spatial Analyst, is required to visualize the digital altitude model in three dimensions. This digital altitude model (10-meter pixel matrix) is obtained by processing altimeter data (level curves and elevation points) from ** topographic databases on a scale of 1/20,000 .This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Seabed Landform Classification Toolset is a GIS toolbox designed to classify seabed landforms on continental and island shelf settings. The user is guided through a series of classification steps within an ArcGIS toolbox to classify prominent seabed features termed ‘seabed landforms’, which characterise the morphology of the seabed surface. Seabed landforms include reefs/banks, peaks, plains, scarps, channels and depressions. Plain areas can additionally be classified into high and low features at localised and broad scales to capture features within plain surfaces. Common variables for seabed classification are utilised, including slope, bathymetric position index and ruggedness, and a series of procedures are applied to identify reef outcrops and minimise noise. The classification approach applies a whole-seascape classification which is aimed to offer a flexible and user-friendly approach to extract key seabed features from high-resolution shelf bathymetry data.

This toolset was developed using ESRI ArcGIS Desktop 10.8 and requires an Advanced licence with Spatial Analyst and 3D Analyst and extensions. It utilises scripts within the Benthic Terrain Modeler toolset (Walbridge et al. 2018) and Geomorphometry and Gradients Metrics Toolbox (Evans et al., 2014).

Please read the User Guide and supporting documentation for information on how to run the toolset. A web explainer is available at: https://arcg.is/1Tqmv50

The Seabed Landform Classification Toolset is also available for download on GitHub (https://github.com/LinklaterM/Seabed-Landforms-Classification-Toolset/).

The toolset was developed by the Coastal and Marine Team, NSW Department of Climate Change, Energy, the Environment and Water (formerly NSW Department of Planning and Environment), funded by NSW Climate Change Fund through the Coastal Management Funding Package and the Marine Estate Management Authority.

Please cite this toolset as: Linklater, M, Morris, B.D. and Hanslow, D.J. (2023) Classification of seabed landforms on continental and island shelves. Frontiers of Marine Science, 10, https://doi.org/10.3389/fmars.2023.1258556.

Other toolsets utilised by the Seabed Landform Classification Toolset include: Benthic Terrain Modeler: Walbridge, S., Slocum, N., Pobuda, M., and Wright, D. J. (2018). Unified geomorphological analysis workflows with Benthic Terrain Modeler. Geosciences 8, 94. Geomorphometry and Gradients Metrics Toolbox: Evans, J., Oakleaf, J., and Cushman, S. (2014). An ArcGIS Toolbox for Surface Gradient and Geomorphometric Modeling, Version 2.0-0. https://github.com/jeffreyevans/GradientMetrics.

Digital terrain models offer a representation of the relief south of the 52nd parallel, in the form of an elevation matrix. This matrix makes it possible to visualize the territory in perspective and to perform three-dimensional spatial analyses, using appropriate software. A module specialized in three-dimensional data processing, such as 3D Analyst or Spatial Analyst, is required to visualize the digital altitude model in three dimensions. This digital altitude model (10-meter pixel matrix) is obtained by processing altimeter data (level curves and elevation points) from topographic databases on a scale of 1/20,000 .This third party metadata element was translated using an automated translation tool (Amazon Translate).

The Petrel Sub-basin Marine Environmental Survey GA-0335, (SOL5463) was undertaken by the RV Solander during May 2012 as part of the Commonwealth Government's National Low Emission Coal Initiative (NLECI). The survey was undertaken as a collaboration between the Australian Institute of Marine Science (AIMS) and GA. The purpose was to acquire geophysical and biophysical data on shallow (less then 100m water depth) seabed environments within two targeted areas in the Petrel Sub-basin to support investigation for CO2 storage potential in these areas. This dataset comprises an interpreted geomorphic map.

Interpreted local-scale geomorphic maps were produced for each survey area in the Petrel Sub-basin using multibeam bathymetry and backscatter grids at 2 m resolution and bathymetric derivatives (e.g. slope; 1-m contours). Five geomorphic units; bank, plain, ridge, terrace and valley, were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Maps and polygons were manual digitised in ArcGIS using the spatial analyst and 3D analyst toolboxes.

The Petrel Sub-basin Marine Environmental Survey GA-0335, (SOL5463) was undertaken by the RV Solander during May 2012 as part of the Commonwealth Government's National Low Emission Coal Initiative …Show full descriptionThe Petrel Sub-basin Marine Environmental Survey GA-0335, (SOL5463) was undertaken by the RV Solander during May 2012 as part of the Commonwealth Government's National Low Emission Coal Initiative (NLECI). The survey was undertaken as a collaboration between the Australian Institute of Marine Science (AIMS) and GA. The purpose was to acquire geophysical and biophysical data on shallow (less then 100m water depth) seabed environments within two targeted areas in the Petrel Sub-basin to support investigation for CO2 storage potential in these areas. This dataset comprises an interpreted geomorphic map. Interpreted local-scale geomorphic maps were produced for each survey area in the Petrel Sub-basin using multibeam bathymetry and backscatter grids at 2 m resolution and bathymetric derivatives (e.g. slope; 1-m contours). Five geomorphic units; bank, plain, ridge, terrace and valley, were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Maps and polygons were manual digitised in ArcGIS using the spatial analyst and 3D analyst toolboxes.

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling.

Methods:

The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis.

The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes.

The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps.

Format:

Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model

All GIS files are in GDA 94 Albers Australia coordinate system.

1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution.

Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution.

SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA.

UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons.

Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope.

Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope.

Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing.

Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry.

Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing.

Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.

This dataset uses daily temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015). It is generated by the dual-index (TB, 37v, SG) freeze-thaw discrimination algorithm. The classification results include the frozen surface, the thawed surface, the deserts and water bodies. The data coverage is the main part of China’s mainland, with a spatial resolution of 25.067525 km via the EASE-Grid projection method, and it is stored in ASCIIGRID format. All the ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the head file, the body content is numerically characterized by the freeze/thaw status of the surface soil: 1 for frozen, 2 for thawed, 3 for desert, and 4 for precipitation. If you want to use the icon for display, we recommend using the ArcView + 3D or Spatial Analyst extension module for reading; in the process of reading, a grid format file will be generated, and the displayed grid file is the graphical expression of the ASCII file. The read method comprises the following. [1] Add the 3D or Spatial Analyst extension module to the ArcView software and then create a new View. [2] Activate View, click File menu, and select the Import Data Source option. When the Import Data Source selection box pops up, select ASCII Raster in the Select import file type box. When the dialog box for selecting the source ASCII file automatically pops up, click to find any ASCII file in the data set, and then press OK. [3] Type the name of the Grid file in the Output Grid dialog box (it is recommended that a meaningful file name is used for later viewing) and click the path to store the Grid file, press OK again, and then press Yes (to select integer data) and Yes (to put the generated grid file into the current view). The generated files can be edited according to the Grid file standard. This completes the process of displaying an ASCII file into a Grid file. [4] In the batch processing, the ASCIGRID command of ARCINFO can be used to write AML files, and then use the Run command to complete the process in the Grid module: Usage: ASCIIGRID

China long-sequence surface freeze-thaw dataset——decision tree algorithm (1987-2009), is derived from the decision tree classification using passive microwave remote sensing SSM / I brightness temperature data. This data set uses the EASE-Grid projection method (equal cut cylindrical projection, standard latitude is ± 30 °), with a spatial resolution of 25.067525km, and provides daily classification results of the surface freeze-thaw state of the main part of mainland China. The data set is stored by year and consists of 23 folders, from 1987 to 2009. Each folder contains the day-to-day surface freeze-thaw classification results for the current year. It is an ASCII file with the naming rule: SSMI-frozenYYYY ***. Txt, where YYYY represents the year and *** represents the Julian date (001 ~ 365 / 366). The freeze-thaw classification result txt file can be opened and viewed directly with a text program, and can also be opened with ArcView + Spatial Analyst extension module or Arcinfo's Asciigrid command. The original frozen and thawed surface data was derived from daily passive microwave data processed by the National Snow and Ice Data Center (NSIDC) since 1987. This data set uses EASE-Grid (equivalent area expandable earth grid) as a standard format . China's surface freeze-thaw long-term sequence data set-The decision tree algorithm (1987-2009) attributes consist of the spatial-temporal resolution, projection information, and data format of the data set. Spatio-temporal resolution: the time resolution is day by day, the spatial resolution is 25.067525km, the longitude range is 60 ° ~ 140 ° E, and the latitude is 15 ° ~ 55 ° N. Projection information: Global equal-area cylindrical EASE-Grid projection. For more information about EASE-Grid projection, see the description of this projection in data preparation. Data format: The data set consists of 23 folders from 1987 to 2009. Each folder contains the results of the day-to-day surface freeze-thaw classification of the year, and is stored as a txt file on a daily basis. File naming rules: For example, SMI-frozen1994001.txt represents the surface freeze-thaw classification results on the first day of 1994. The ASCII file of the data set is composed of a header file and a body content. The header file consists of 6 lines of description information such as the number of rows, the number of columns, the coordinates of the lower left point of the x-axis, the coordinates of the lower left point of the y-axis, the grid size, and the value of the data-less area. Array, with columns as the priority. The values ​​are integers, from 1 to 4, 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. Because the space described by all ASCII files in this data set is nationwide, the header files of these files are unchanged. The header files are extracted as follows (where xllcenter, yllcenter and cellsize are in m): ncols 308 nrows 166 xllcorner 5778060 yllcorner 1880060 cellsize 25067.525 nodata_value 0 All ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the header file, the main content is a numerical representation of the surface freeze-thaw state: 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. If you want to display it with an icon, we recommend using ArcView + 3D or Spatial Analyst extension module to read it. During the reading process, a grid format file will be generated. The displayed grid file is the graphic representation of the ASCII code file. Reading method: [1] Add 3D or Spatial Analyst extension module in ArcView software, and then create a new View; [2] Activate View, click the File menu, select the Import Data Source option, the Import Data Source selection box pops up, select ASCII Raster in Select import file type: in this box, and a dialog box for selecting the source ASCII file automatically pops up Find any ASCII file in the data set and press OK; [3] Type the name of the Grid file in the Output Grid dialog box (a meaningful file name is recommended for later viewing), and click the path where the Grid file is stored, press Ok again, and then press Yes (to select an integer) Data), Yes (call the generated grid file into the current view). The generated file can be edited according to the Grid file standard. This completes the process of displaying the ASCII file as a Grid file. [4] During batch processing, you can use ARCINFO's ASCIIGRID command to write an AML file, and then use the Run command to complete in the Grid module: Usage: ASCIIGRID

The Oceanic Shoals survey (SOL5650, GA survey 339) was conducted on the R.V. Solander in collaboration with Geoscience Australia, the Australian Institute of Marine Science (AIMS), University of Western Australia and the Museum and Art Gallery of the Northern Territory between 12 September - 5 October, 2012. This dataset comprises an interpreted geomorphic map. Interpreted local-scale geomorphic maps were produced for each survey area in the Oceanic Shoals Commonwealth Marine Reserve (CMR) using multibeam bathymetry and backscatter grids at 2 m resolution and bathymetric derivatives (e.g. slope; 1-m contours). Six geomorphic units; bank, depression, mound, plain, scarp and terrace were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Maps and polygons were manual digitised in ArcGIS using the spatial analyst and 3D analyst toolboxes. For further information on the geomorphic mapping methods please refer to Appendix N of the post-survey report, published as Geoscience Australia Record 2013/38: Nichol, S.L., Howard, F.J.F., Kool, J., Stowar, M., Bouchet, P., Radke, L., Siwabessy, J., Przeslawski, R., Picard, K., Alvarez de Glasby, B., Colquhoun, J., Letessier, T. & Heyward, A. 2013. Oceanic Shoals Commonwealth Marine Reserve (Timor Sea) Biodiversity Survey: GA0339/SOL5650 Post Survey Report. Record 2013/38. Geoscience Australia: Canberra. (GEOCAT #76658).

S is a probability of cultivation based on a series of environmental conditions on a global scale. Here, S is created to compare settlement locations throughout Utah to explain initial Euro-American settlement of the region. S is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. The data are presented as a raster file where any one pixel represents the probability of cultivation from zero to one, normalized on a global scale (Ramankutty et al., 2002). Because S is normalized on a global scale, the range of values of S for Utah U.S.A does not cover the global spectrum of S, thus the highest S value in the data is 0.51. S was originally created by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we created a higher resolution dataset of S specific to the state of Utah. S is composed of actual and potential evapotranspiration rates from 2000-2013, growing degree days, soil carbon density, and soil pH. The Moisture Index is calculated as: MI = ETact /PET Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI was calculated for the study area (Utah) using a raster of annual actual ETact and PET evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometers.containing values representative of the average Moisture Index for Utah over a period of fourteen years (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water were interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015). The probability of cultivation (S) is calculated as a normalized product of growing degree days (GDD), available moisture (MI), soil carbon density (Csoil), and soil pH (pHsoil). The equation is divided into two general components: S = Sclim * Ssoil where Sclim = f1(GDD) f2(MI) and Ssoil = g1(Csoil) g2(pHsoil) Climate suitability (Sclim) is calculated as a normalized probability density function of cropland area to Growing Degree-days (f1[GDD]) and probability density function of cropland area to Moisture Index (f2[MI]) (Ramankutty et al. 2002). Soil suitability (Ssoil) is calculated using a sigmoidal function of the soil carbon density and soil acidity/alkalinity. The optimum soil carbon range is from 4 to 8 kg of C/m2 and the optimum range of soil pH is from 6 to 7 (Ramankutty et al. 2002). The resulting S value varies from zero to one indicating the probability of agricultural on a global scale. To implement the equation for S, growing degree-days (GDD) are calculated using usmapmaker.pl Growing Degree-days calculator and PRISM climate maps with a minimum temperature threshold of 50 degrees Fahrenheit (Coop, 2010; Daly, Gibson, Taylor, Johnson, & Pasteris, 2002; Willmott & Robeson, 1995; “US Degree-Day Map Maker,” n.d.). Moisture Index data is calculated as described above. To calculate the overall climate suitability (Sclim), the resulting raster datasets of Growing Degree-days and Moisture Index are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create climate suitability (Sclim) raster dataset with a resolution of 2.6 kilometers sq. To calculate soil suitability, the functions provided by Ramankutty et al. (2002) are applied to soil data derived from the SSURGO soil dataset compiled using NRCS Soil Data Viewer 6.1 to create thematic maps of average soil pH within the top 30 centimeters and average carbon density within the top 30 centimeters ( Soil Survey Staff, 2015; NRCS Soils, n.d.). However, there are missing values in the SSURGO soil dataset for the state of Utah, resulting in datasets using soil pH to have null values in portions of the state (Soil Survey Staff, 2015). The resulting raster datasets of soil pH and carbon density are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) to create a soil suitability (Ssoil) raster dataset with a resolution of 9.2 kilometers sq (ESRI, 2015). The climate suitability raster dataset and soil suitability raster dataset are combined in ArcMap 10.3.1 using the Raster Calculator (Spatial Analyst) generating a S raster dataset with a resolution of 9.2 kilometers (ESRI, 2015). Projection: GCS_WGS_1984 Citations Coop, L. B. (2010). U. S. degree-day...

Thickness of Paleogene-Neogene sequence overlying the Great Artesian Basin

Data is available as isopachs and raster. Isopachs are in Shapefile format. Rasters are in both ESRI grid and ASCII grid formats.

This GIS data set was produced for the Great Artesian Basin Water Resource Assessment and used in: Figure 3.2 of Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. Figure 3.3 of Smerdon BD, Ransley TR, Radke BM and Kellett JR (2012) Water resource assessment for the Great Artesian Basin. A report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia.

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 76538.

LINEAGE (Continued from Lineage field due to space constraints) METHOD:

Data covering the areas of Upper Darling, Lower Namoi was supplied by the NSW government.

Contours in the Macquarie region NSW were interpreted from the Cenozoic isopachs taken from Macaulay, S. & Kellett, J. (2009)

Lower Balonne Deep Lead tertiary isopach contours captured from a National Action Plan for Salinity and Water Quality report (Chamberlain, T. & Wilkinson, K., 2004; Kellett et.al. 2004).

Isopachs in the southern portion of the GAB were captured from the Cainozoic Structural Features page 22 of Palaeogeographic Atlas of Australia: Cainozoic (Langford & Wilford, 1995)

Isopachs over the Poolowanna Trough and Cooper Basin region were taken from Tertiary Stratigraphy and Tectonics, Eromanga Basin (Moussavi-Harami, R. & Alexander, E., 1998)

Isopachs in the central Eromanga Basin, Queensland came from Senior 1978.

Position and boundary of the Condamine Basin from Klohn, Crippen & Berger, 2011 - feasibility of injecting CSG water into the central Condamine Alluvium - Summary. Report prepared for department of Environment and Resource Management, Queensland, 8p. Isopachs came from the Cainozoic Structural Features page 22 of Palaeogeographic Atlas of Australia: Cainozoic (Langford & Wilford, 1995)

Drill-hole data sourced from PIRSA (2007) and GABLOG (Habermehl, 2001) databases, Gibson et al 1974, and well completion reports from GSQ (Queensland Department of Natural Resources and Mines, 2012).

Data were used to interpolate a surface using the Topo to Raster tool in the ArcGIS Spatial analyst toolset and the resulting raster was clipped to the Great Artesian Basin Water Resource project boundary.

Isopach contours were generated from the raster, using the Contour tool in the 3d analyst toolset in ArcGIS.

METHOD

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.

Thickness of Cenozoic weathering in the Great Artesian Basin. Data is available as isopachs and raster. Isopachs are in Shapefile format. Rasters are in both ESRI grid and ASCII grid formats. This GIS data set was produced for the Great Artesian Basin Water Resource Assessment and used in Figure 3.3 of Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia. This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 76539.

Dataset History

SOURCE Thickness data sourced from GABLOG (Habermehl 2001), PIRSA (2007), QDEX (Queensland Department of Natural Resources and Mines, 2012), Gibson et al 1974 and Geoscience Australia's 1:250K Geological Map series (Geoscience Australia, 2010) REFERENCES 1. Geoscience Australia. 2010. 1:250 000 scale Geological Map series - Explanatory Notes (1960-1980). Geoscience Australia, Canberra 2. Gibson, D. L., B. S. Powell, et al. (1974). Shallow stratigraphic drilling, northern Cape York Peninsula, 1973. Record 1974/76. Australia, Bureau of Mineral Resources. 3. Habermehl, M. A. (2001). Wire-line logged water bores in the Great Artesian Basin, Australia - digital data of logs and water bore data acquired by AGSO. Australian Geological Survey Organisation Bulletin 245. Canberra, Bureau of Rural Sciences: ix, 98 p. 4. PIRSA (2007). Petroleum and geothermal in South Australia. 19th Edition (DVD). Adelaide, Primary Industries and Resources South Australia, Division of Minerals and Energy Resources. 5. Queensland Department of Natural Resources and Mines (2012) "Queensland Digital Exploration Reports (QDEX)". http://mines.industry.qld.gov.au/geoscience/company-exploration-reports.htm METHOD For the western Eromanga Basin, an averaged weathering thickness for each 1:250 000 Sheet area was derived from the explanatory notes or published geological maps. For Central Eromanga Basin, Carpentaria Basin, Laura Basin and Surat Basin, weathering depth were derived from reports contained in descriptive lithological logs from Well Completion Reports. Weathering thicknesses were derived from the QPED stratigraphic database where the logged information reported the alluvium and weathering thickness. Thickness data were imported into ArcGIS as point sets and interpolated to create a surface using the Topo to Raster tool in the ArcGIS Spatial analyst toolset. The raster was clipped to the Great Artesian Basin Water Resource Assessment project boundary - offshore included (Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia). Isopachs were generated from the raster, using the Contour tool in the 3d analyst toolset in ArcGIS.

Dataset Citation

Geoscience Australia (2013) Thickness of Cenozoic weathering in the Great Artesian Basin. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/788a990e-77d5-4341-954d-aae72fc3d8b6.

Moisture Index (MI) for the state of Utah is calculated from a spatial raster of annual actual (ETact) and potential (PET) evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Moisture Index (MI) was created to compare the suitability of settlement locations throughout Utah to explain initial Euro-American settlement of the region. MI is one of two proxies created specifically for Utah for comparison of environmental productivity throughout the state. Moisture index (MI) was originally used by Ramankutty et al. (2002) on a global scale to understand probability of cultivation based on a series of environmental factors. The Ramankutty et al. (2002) methods were used to build a regional proxy of agricultural suitability for the state of Utah. Adapting the methods in Ramankutty et al. (2002), we were able to create a higher resolution dataset of MI specific to the state of Utah. Unlike S, MI only accounts for evapotranspiration rates.The Moisture Index is calculated as: MI = ETact / PET Where ETact is the actual evapotranspiration and PET is the potential evapotranspiration. This calculation results in a zero to one index representing global variation in moisture. MI is calculated for the study area (Utah) using a raster of annual actual (ETact) and potential (PET) evapotranspiration data from 2000 to 2013 derived from the MODIS instrumentation (Mu, Zhao, & Running, 2011; Mu, Zhao, & Running, 2013; Numerical Terradynamic Simulation Group, 2013). Using the ArcMap 10.3.1 Raster Calculator (Spatial Analyst), a raster dataset is created at a resolution of 2.6 kilometer square, which contain values representative of the average Moisture Index for Utah over a fourteen year period (ESRI, 2015). The data were collected remotely by satellite (MODIS) and represents reflective surfaces (urban areas, lakes, and the Utah Salt Flats) as null values in the dataset. Areas of null values that were not bodies of water are interpolated using Inverse Distance Weighting (3d Analyst) in ArcMap 10.3.1 (ESRI, 2015). Download the moisture index (MI) data below. If you have any questions or concerns, please contact me at PYaworsky89@gmail.com. Citations ESRI. (2015). ArcGIS Desktop: Release (Version 10.3.1). Redlands, CA: Environmental Systems Research Institute. Mu, Q., Zhao, M., & Running, S. W. (2013). MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD16A2/A3). Algorithm Theoretical Basis Document, Collection, 5. Retrieved from http://www.ntsg.umt.edu/sites/ntsg.umt.edu/files/MOD16_ATBD.pdf Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800. Numerical Terradynamic Simulation Group. (2013, July 29). MODIS Global Evapotranspiration Project (MOD16). University of Montana. Ramankutty, N., Foley, J. A., Norman, J., & Mcsweeney, K. (2002). The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11(5), 377–392. http://doi.org/10.1046/j.1466-822x.2002.00294.x

Techsalerator’s Sound and Audio Data for Chile

Techsalerator’s Sound and Audio Data for Chile provides an essential and detailed collection of information crucial for businesses, researchers, and technology analysts. This dataset delivers an in-depth analysis of sound and audio trends across various industries in Chile, capturing and categorizing data related to sound frequencies, acoustic environments, and audio technology applications.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Sound and Audio Data for Chile

Techsalerator’s Sound and Audio Data for Chile offers a comprehensive overview of key information for businesses, researchers, and technology analysts. This dataset provides a thorough examination of sound and audio data across diverse sectors in Chile, detailing data related to audio frequencies, decibel levels, sound environments, and emerging trends in audio technology.

Top 5 Key Data Fields

• Sound Frequency Analysis: Captures detailed data on audio frequency ranges from low bass to high treble, essential for audio engineering, music production, and telecommunications.
• Decibel Level Measurements: Provides noise level readings across different environments, supporting industries such as urban planning, construction, and health & safety compliance.
• Acoustic Environment Classification: Identifies and categorizes sound environments, such as urban noise pollution, natural soundscapes, and industrial sound zones.
• Audio Technology Adoption: Tracks the implementation of emerging audio technologies, including smart speakers, AI-driven sound enhancement, and 3D audio.
• Broadcast and Streaming Audio Data: Collects and analyzes audio data from radio, podcasts, and streaming services, providing insights into content trends and listener preferences.

Top 5 Sound and Audio Trends in Chile

• Smart Audio Devices Growth: Increasing adoption of smart speakers and AI-driven voice recognition technology in Chilean households and businesses.
• Noise Pollution Monitoring: Rising government and private-sector efforts to measure and mitigate urban noise pollution in major cities.
• Podcast and Digital Radio Boom: A surge in podcast creation and digital radio streaming, reflecting changing media consumption habits.
• 3D and Spatial Audio Development: Growing interest in immersive audio experiences for gaming, VR, and entertainment industries.
• Hearing Health and Acoustic Safety: Increased focus on workplace and environmental noise exposure, leading to advancements in hearing protection and soundproofing solutions.

Top 5 Companies Leveraging Sound and Audio Data in Chile

• Chile AudioTech Solutions: Specializing in AI-powered audio analysis and enhancement for various industries.
• SonicChile Acoustics: A leader in acoustic consulting, working on noise reduction and soundproofing projects.
• LatinoPod Networks: One of Chile’s fastest-growing podcast production and distribution platforms.
• VR Soundscape Studios: Innovating 3D and spatial audio experiences for virtual reality applications.
• SoundHealth Chile: Focusing on noise pollution research and hearing health solutions to improve urban living conditions.

Accessing Techsalerator’s Sound and Audio Data

To obtain Techsalerator’s Sound and Audio Data for Chile, contact info@techsalerator.com with your specific requirements. Techsalerator will provide a customized quote based on the required data fields and records, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Sound Frequency Ranges
• Decibel Level Readings
• Acoustic Environment Categories
• Audio Technology Usage Trends
• Broadcast and Streaming Audio Metrics
• Noise Pollution Analysis
• Hearing Health and Safety Data
• Smart Audio Device Adoption
• 3D and Spatial Audio Applications
• Contact Information

For detailed insights into sound and audio trends in Chile, Techsalerator’s dataset is an invaluable resource for researchers, businesses, and technology analysts seeking informed, strategic decisions.

Thickness of the basal Jurassic-Cretaceous sandstone aquifers in the Carpentaria and Laura basins.

Data is available as isopachs and raster. Isopachs are in Shapefile format. Rasters are in both ESRI grid and ASCII grid formats.

This GIS data set was produced for the Great Artesian Basin Water Resource Assessment and used in:

Figure 2.12 of Ransley TR and Smerdon BD (eds) (2012) Hydrostratigraphy, hydrogeology and system conceptualisation of the Great Artesian Basin. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia.

Figure 5.8 of Smerdon BD, Welsh WD and Ransley TR (eds) (2012) Water resource assessment for the Carpentaria region. A report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment. CSIRO Water for a Healthy Country Flagship, Australia.

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 76536.

LINEAGE (continued from Lineage field)
------------------------------------------------------
REFERENCES (continued)

Meyers, N. A. (1969). Carpentaria Basin. GSQ Report 34. Queensland, Geological Survey of Queensland.

Mines Administration Pty Ltd. (1962). Cabot-Blueberry Marina No. 1, Authority to Prospect 61P, Queensland. Well Completion report. Report Q/61P/112. Company report 976. Brisbane, Geological Survey of Queensland.

Perryman, J. C. (1964). Midwood Exploratory Proprietary Ltd., Completion report, Burketown No.1, A-P 91P, Queensland. Company Report 1480. Brisbane, Geological Survey of Queensland.

Smart J, Grimes KG, Doutch HF and Pinchin J (1980) The Carpentaria and Karumba Basins, north Queensland. Bulletin 202. Bureau of Mineral Resources, Geology and Geophysics, Australia.

Williams, L. J. (1976). GSQ Ebagoola 1 - Preliminary lithologic and composite log. Record 1988/14. Brisbane, Queensland Department of Mines and Geological Survey of Queensland.

Williams, L. J. and L. M. Gunther (1989). GSQ Dobbyn 1 - Preliminary lithologic and composite log. Record 1989/22. Brisbane, Geological Survey of Queensland.

METHOD:

Data was imported into ArcGIS as point sets. The isopach value field was used to interpolate a surface using the Topo to Raster tool in the Spatial analyst toolset.
Isopachs were generated from the raster using the Contour tool in the 3d analyst toolset in ArcGIS.

The raster and isopachs were clipped to a boundary created from :
1. Gilbert River Formation and equivalents sourced from inset C of Plate 2 The Geology of the Carpentaria and Karumba Basins Queensland 1980 which is part of the Carpentaria and Karumba Basins, North Queensland Bureau of Mineral Resources, Geology and Geophysics Bulletin 202. J.Smart, K.G.Grimes, H.F.Doutch & J.Pinchin. ISBN 0642046182
2. Great Artesian Basin Water Resource Assessment project boundary.

Les modèles numériques de terrain offrent une représentation du relief au sud du 52e parallèle, sous forme d’une matrice d’élévation. Cette matrice permet de visualiser le territoire en perspective et d’effectuer des analyses spatiales tridimensionnelles, à l’aide des logiciels appropriés. Un module spécialisé en traitement de données tridimensionnelles, tel que 3D Analyst ou Spatial Analyst, est nécessaire pour visualiser le modèle numérique d'altitude en trois dimensions. Ce modèle numérique d’altitude (matrice de pixels de 10 mètres) est obtenu par le traitement des données altimétriques (courbes de niveau et points d’élévation) issues des bases de données topographiques à l'échelle de 1/20 000.