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.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

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.

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

This first pulse DEM for the Gwynns Falls was interpolated at a resolution of 1 meter from LIDAR data with an average post spacing of 1 meter using the Natural Neighbor interpolation method available in the ArcGIS 3D Analyst. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Aim: Geographic variation in metabolic resources necessary for functional trait expression can set limits on species distributions. For species that need to produce and maintain biomineralized traits for survival, spatial variation in mineral macronutrients may constrain species’ distributions by limiting the expression of biomineralized traits. Here, we examine whether threatened, heavily biomineralized Oreohelix land snails are restricted to CaCO3 rock regions, if they incorporate greater amounts of CaCO3 rock carbon in their shell than less biomineralized smooth forms, and if ornamentation increases shell strength. Location: Western United States Methods: We used random forest (RF) classification models at multiple spatial resolutions to evaluate the contribution of topographic, vegetation, climate, and geologic variables in predicting the presence of heavily biomineralized shell ornaments. We then measured and compared shell biometric variables, 14C/12C ratios, and peak force for fr..., RF predictors: Predictor Dataset Creation The predictors used in this study came from a variety of sources (Supplementary Table A1). In this section, we will detail how they were made to facilitate replication of our results. All predictors were reprojected in ArcGIS Pro v.2.6.0 to WGS1984 and clipped to the same raster resolution. Predictor names used in the R code are shown in parentheses. See Supplemental Table A1 for references.

Elevation (elevation): This layer was sourced from the publicly available ASTER Global Digital Elevation data reprojected to 90m resolution using the Project tool and clipped to the desired extent using the Clip Raster tool.

Slope (slope): This layer was created using the Slope tool in ArcGIS on the 90m elevation data using a z-factor of 0.00001171 appropriate for 40 degrees latitude (https://pro.arcgis.com/en/pro-app/latest/tool-reference/3d-analyst/applying-a-z-factor.htm) which is close to the mean latitude of our study area.

Compound topographic inde..., ArcGIS Pro/QGIS to modify layers R for scripts

This New Zealand car detection Deep Learning Package will detect cars from high resolution imagery. This model is re-trained from the Esri Car Detection - USA Deep Learning Package and is trained to work better within the New Zealand geography.The model precision had also improved from 0.81 to 0.89. The package is trained to be more aggressive in terms of car detecting and is able to detect most cars that are fully covered in shade or partially blocked by tree canopy. This deep learning model is used to detect cars in high resolution drone or aerial imagery. Car detection can be used for applications such as traffic management and analysis, parking lot utilization, urban planning, etc. It can also be used as a proxy for deriving economic indicators and estimating retail sales. High resolution aerial and drone imagery can be used for car detection due to its high spatio-temporal coverage.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS ProArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution RGB imagery (7.5 centimetre spatial resolution)OutputFeature class containing detected carsApplicable geographiesThe model is expected to work well with the New Zealand localised data.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS Pro Arcpy.Accuracy metricsThis model has an average precision score of 0.89.Sample resultsHere are a few results from the model.(Post processing are recommended to filter out False Positive Object.e.g (confidence >= x | 0.95) |& ((shape_area/shape_length) >= x | 0.5) |& (class == Car) |& Regularize(feature)3% of detected object will need to be filtered out averagely .To learn how to use this model, see this story

This bare earth DEM for the Gwynns Falls was interpolated at a resolution of 1 meter from LIDAR data with an average post spacing of 1 meter using the Natural Neighbor interpolation method available in the ArcGIS 3D Analyst. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Plan curvature was calculated from the bathymetry surface for each raster cell using the ArcGIS 3D Analyst "Curvature" Tool. Plan curvature describes the rate of change of curvature (perpendicular to the slope direction) within a square 3x3 cell neighborhood. A negative value denotes concavity, while a positive value denotes convexity. The 2x2 meter resolution plan curvature GeoTIFF was exported and added as a new map layer to aid in benthic habitat classification. Please see ESRI's online support center for more information about Plan Curvature. Acoustic imagery was acquired for the VICRNM on two separate missions onboard the NOAA ship, Nancy Foster. The first mission took place from 2/18/04 to 3/5/04. The second mission took place from 2/1/05 to 2/12/05. On both missions, seafloor depths between 14 to 55 m were mapped using a RESON SeaBat 8101 ER (240 kHz) MBES sensor. This pole-mounted system measured water depths across a 150 degree swath consisting of 101 individual 1.5 degree x 1.5 degree beams. The beams to the port and starboard of nadir (i.e., directly underneath the ship) overlapped adjacent survey lines by approximately 10 m. The vessel survey speed was between 5 and 8 kn. In 2004, the ship's location was determined by a Trimble DSM 132 DGPS system, which provided a RTCM differential data stream from the U.S. Coast Guard Continually Operating Reference Station (CORS) at Port Isabel, Puerto Rico. Gyro, heave, pitch and roll correctors were acquired using an Ixsea Octans gyrocompass. In 2005, the ship's positioning and orientation were determined by the Applanix POS/MV 320 V4, which is a GPS aided Inertial Motion Unit (IMU) providing measurements of roll, pitch and heading. The POS/MV obtained its positions from two dual frequency Trimble Zephyr GPS antennae. An auxiliary Trimble DSM 132 DGPS system provided a RTCM differential data stream from the U.S. Coast Guard CORS at Port Isabel, Puerto Rico. For both years, CTD (conductivity, temperature and depth) measurements were taken approximately every 4 hours using a Seabird Electronics SBE-19 to correct for the changing sound velocities in the water column. In 2004, raw data were logged in .xtf (extended triton format) using Triton ISIS software 6.2. In 2005, raw data were logged in .gsf (generic sensor format) using SAIC ISS 2000 software. Data from 2004 were referenced to the WGS84 UTM 20 N horizontal coordinate system, and data from 2005 were referenced to the NAD83 UTM 20 N horizontal coordinate system. Data from both projects were referenced to the Mean Lower Low Water (MLLW) vertical tidal coordinate system. The 2004 and 2005 MBES bathymetric data were both corrected for sensor offsets, latency, roll, pitch, yaw, static draft, the changing speed of sound in the water column and the influence of tides in CARIS Hips & Sips 5.3 and 5.4, respectively. The 2004 data was then binned to create a 1 x 1 m raster surface, and the 2005 data was binned to a create 2 x 2 m raster surface. After these final surfaces were created, the datum for the 2004 bathymetric surfaces was transformed from WGS84 to NAD83 using the "Project Raster" function in ArcGIS 9.1. The 2004 surface was transformed so that it would have the same datum as the 2005 surface. The 2004 bathymetric surface was then down sampled from 1 x 1 to 2 x 2 m using the "Resample" function in ArcGIS 9.1. The 2004 surface was resampled so it would have the same spatial resolution as the 2005 surface. Having the same coordinate systems and spatial resolutions, the final 2004 and 2005 bathymetry rasters were then merged using the Raster Calculator function "Merge" in ArcGIS's Spatial Analyst Extension to create a seamless bathymetry surface for the entire VICRNM area south of St. John. For a complete description of the data acquisition and processing parameters, please see the data acquisition and processing reports (DAPRs) for projects: NF-04-06-VI and NF-05-05-VI (Monaco & Rooney, 2004; Battista & Lazar, 2005).

This dataset contains unified Bathymetric Profile Curvature GeoTiffs with 4x4 meter cell resolution describing the topography of 15 areas along the shelf edge off the South Atlantic Bight where NOAA South East Fisheries Independent Survey (SEFIS) are conducting research on Red Snapper (lutjanus campechanus). The multibeam data covers a total of 232 sq km and was collected in 2010 by the NOAA Ship Nancy Foster with a hull-mounted Kongsberg Simrad EM 1002 multibeam echo sounder (95 kHz). It was processed by NOAA's NOS/NCCOS/CCMA Biogeography Branch using CARIS HIPS 7.1 software. Data has all correctors applied (attitude, sound velocity) and has been reduced to mean lower low water (MLLW) using final approved tides and zoning from NOAA COOPS. The processed CARIS data was used to generate a CARIS BASE surface using CUBE. The CARIS export option "BASE Surface to ASCII" was then used to create a GeoTiff of the priority areas in ArcGIS 10. Curvature was derived from this surface using the profile curvature function in ArcGIS 3D Analyst.

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 …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. You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

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 …Show full descriptionThe 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).

This data layer contains the critical slopes (slopes in excess of 25%) for Albemarle County. The critical slopes were derived from the Y2007 DTM using ArcGIS v. 9.x 3D Analyst.

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.

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

This dataset includes bathymetric sounding points for the near-shore regions of the Chukchi Sea and Elson Lagoon (Beaufort Sea) for the Barrow region of northern Alaska, along with a bathymetric surface interpolated from these sounding points. Bathymetric surveying was conducted during July and August of 2015 with high-resolution dGPS and single beam sonar. Surveys were conducted on an inflatable Achilles boat with the dGPS antenna and echo sounder placed (on the same pole) off to one side of the boat. dGPS and sonar data were logged on different platforms that were then time-synced later. Sounding points follow the path of the survey boat. At any one sounding point; water depth, bottom elevation, bottom ellipsoid height among other fields are given. Sounding point depths were corrected for salinity and temperature. The average horizontal precision for any one sounding point is 2.3 cm. The average vertical precision for any one sounding point is 4.6 cm (dGPS uncertainty) plus 0.1% of depth (sonar manufacturers stated accuracy). The 10m resolution interpolated surface was created with the Topo to Raster tool within the 3D Analyst license for ArcGIS for Desktop. Pixel values reflect water depth in meters. Related datasets include: recent digital shorelines from dGPS survey, long-term shorelines digitized from imagery and results of coastal change analysis (annual and long-term rates of erosion or accretion). This bathymetric data may be of interest to land managers, scientist and engineers for modeling storm surge effects and coastal erosion. This dataset was collected as part of the Barrow Area Information Database (BAID, https://barrowmapped.org) and was originally made available on the BAID project website. In 2021/2022 the dataset was edited in metadata only and repackaged for archival on the Environmental Data Initiative repository by the Beaufort Lagoon Ecosystems LTER.

Let op: deze tool werkt momenteel niet door een wijiziging in de brondata vanuit de BRO.Actualiteit: september 2020Requirements: ArcGIS Pro 2.5 of hoger. ArcGIS 3D Analyst voor publiceren naar ArcGIS OnlineInhoud van de download:

Deze How-to (.pdf)BRO Add-in voor ArcGIS Pro 2.5-2.9 (.esriAddinX)BRO Add-in voor ArcGIS Pro 3.0 (.esriAddinX)File geodatabase met benodigde dataschema’s (.gdb)Layerfile voor de symbologie (.lyrx). De symbologie is gebaseerd op de Robertson classificatieDeze How-to bevat instructies voor het gebruik van de BRO Add-in voor ArcGIS Pro. De Add-in wordt beschikbaar gesteld door Esri Nederland en is bedoeld om geotechnische sonderingen van de Basisregistratie Ondergrond (BRO) in ArcGIS in te lezen en te visualiseren.De geotechnische sonderingsonderzoeken van de BRO vallen onder het Bodem- en grondonderzoek en worden aangeboden in .gef en .xml bestandsformaat. Met behulp van de BRO Add-in kunt u deze bestandsformaten nu eenvoudig in ArcGIS Pro inlezen en visualiseren in 2D en 3D sonderingen, inclusief symbologie. Vervolgens kunt u de data inzichtelijk maken in een klassieke sonderingsgrafiek. Om de 3D weergave van de sonderingen met anderen te delen wordt uitgelegd hoe u de data kunt publiceren naar ArcGIS Online.Gegevens van de BRO zijn voor iedereen beschikbaar om te downloaden via het BROloket en DINOloket. Voor meer informatie over de BRO zie https://basisregistratieondergrond.nl/.Naast deze how-to stelt Esri Nederland andere handleidingen en tools beschikbaar via https://esri.nl/howto en de gebruiker Esri_NL_Tools.Deze how-to wordt aangeboden vanuit Esri Nederland Content. Esri Nederland Content biedt landsdekkende data en services aan die gebruikt kunnen worden in het ArcGIS-platform. Het content-team actualiseert het aanbod en voegt geregeld nieuwe content toe. Door content van Esri Nederland te combineren met andere gegevens creëert u snel en eenvoudig nieuwe informatieproducten. Meer informatie over het content aanbod is te vinden via: esri.nl/content. Heeft u vragen of opmerkingen dan horen wij dat graag via content@esri.nl. Blijf op de hoogte van het laatste content-nieuws via de Esri Nederland Content Hub.

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.