ArcGIS for Utilities and Telecommunications provides a standard set of templates that include maps, apps, and tools to support water, electric, gas, and telecommunication industry workflows. In this seminar, you will learn how to configure and deploy these templates to support common asset inspection workflows. The presenters also show how to quickly configure the templates to feature your GIS content.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Standard Or Advanced)ArcGIS OnlineCollector for ArcGIS (iOS) 10.3Operations Dashboard for ArcGIS 10.3

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

For more information about this tool see Batch Metadata Modifier Tool Toolbar Help.Modifying multiple files simultaneously that don't have identical structures is possible but not advised. Be especially careful modifying repeatable elements in multiple files that do not have and identical structureTool can be run as an ArcGIS Add-In or as a stand-alone Windows executableExecutable runs on PC only. (Not supported on Mac.)The ArcGIS Add-In requires ArcGIS Desktop version 10.2 or 10.3Metadata formats accepted: FGDC CSDGM, ArcGIS 1.0, ArcGIS ISO, and ISO 19115Contact Bruce Godfrey (bgodfrey@uidaho.edu, Ph. 208-292-1407) if you have questions or wish to collaborate on further developing this tool.Modifying and maintaining metadata for large batches of ArcGIS items can be a daunting task. Out-of-the-box graphical user interface metadata tools within ArcCatalog 10.x are designed primarily to allow users to interact with metadata for one item at a time. There are, however, a limited number of tools for performing metadata operations on multiple items. Therefore, the need exists to develop tools to modify metadata for numerous items more effectively and efficiently. The Batch Metadata Modifier Tools toolbar is a step in that direction. The Toolbar, which is available as an ArcGIS Add-In, currently contains two tools. The first tool, which is additionally available as a standalone Windows executable application, allows users to update metadata on multiple items iteratively. The tool enables users to modify existing elements, find and replace element content, delete metadata elements, and import metadata elements from external templates. The second tool of the Toolbar, a batch thumbnail creator, enables the batch-creation of the graphic that appears in an item’s metadata, illustrating the data an item contains. Both of these tools make updating metadata in ArcCatalog more efficient, since the tools are able to operate on numerous items iteratively through an easy-to-use graphic interface.This tool, developed by INSIDE Idaho at the University of Idaho Library, was created to assist researchers with modifying FGDC CSDGM, ArcGIS 1.0 Format and ISO 19115 metadata for numerous data products generated under EPSCoR award EPS-0814387.This tool is primarily designed to be used by those familiar with metadata, metadata standards, and metadata schemas. The tool is for use by metadata librarians and metadata managers and those having experience modifying standardized metadata. The tool is designed to expedite batch metadata maintenance. Users of this tool must fully understand the files they are modifying. No responsibility is assumed by the Idaho Geospatial Data Clearinghouse or the University of Idaho in the use of this tool. A portion of the development of this tool was made possible by an Idaho EPSCoR Office award.

In this seminar, the presenter introduces essential concepts of ArcGIS Data Reviewer and highlights automated and semi-automated methods to streamline and expedite data validation.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Basic, Standard, or Advanced)ArcGIS Server 10.3 Workgroup (Standard Or Advanced)ArcGIS Data Reviewer for DesktopArcGIS Data Reviewer for Server

DescriptionPotential fens within a 500-m buffer of Colorado highways were identified in ArcGIS 10.3/10.4 using true color aerial photography taken by the National Agricultural Imagery Program (NAIP) in 2004, 2009, 2011, 2013, and 2015, as well as color-infrared imagery from 2013 and 2015. High (but variable) resolution World Imagery from Environmental Systems Research Institute (ESRI) was also used. Using all available imagery and data, potential fen polygons were hand-drawn based on the best estimation of fen boundaries. Each potential fen polygon was attributed with a confidence value of 1 (low confidence), 3 (possible fen), or 5 (likely fen). In addition to the confidence rating, any justifications of the rating or interesting observations were noted, including iron fens, beaver influence, floating mats, and springs. Once all potential fens were mapped, each polygon was assigned a code for tracking throughout the verification process. The code was a combination of the closest highway segment and a running sequential four-digit number (e.g., 082A-0278).After field verification, additional confidence values of 7 (confirmed fen) and 0 (confirmed non-fen) were added.

    Last Update
    2018


    Update FrequencyAs needed


    Data Owner
    Division of Transportation Development


    Data Contact
    Wetland Program Manager


    Collection Method



    Projection
    NAD83 / UTM zone 13N


    Coverage Area
    Statewide


    Temporal



    Disclaimer/Limitations
    There are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. Data collected between 3/29/17 and 5/3/17. Contours created on 12/13/18. The 1-foot elevation contours were presented as a feature class in an Esri ArcGIS 10.3 File Geodatabase. The contours were derived from Bare-Earth (DEMs) created from QL2 LiDAR data. Source Data Description: MI 2016 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of Michigan State Plane South, NAD83(2011), international feet and vertical datum of NAVD1988 (GEOID12A), international feet. Deliverables included classified LAS, Raw LAS, Bare-Earth hydro-flattened DEMs, Breaklines and Intensity images. Ground Conditions: LiDAR was collected in spring or fall, while no snow was on the ground and rivers were at or below normal levels.

Data created for use in planning, design, assessment, research, general mapping and hydrologic modeling.

A hillshade is a grayscale 3D representation of the surface, with the sun's relative position taken into account for shading the image. This function uses the altitude and azimuth properties to specify the sun's position. This dataset is the hillshade used as a background of the basemap on geoportail.lu It is a rendering of a smoothed version of the 2013 Height model (DHM). This tool has been used to generate the hillshade: http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/hillshade-function.htm

In order to use the Romanian color standard for soil type map legends, a dataset of ESRI ArcMap-10 files, consisting of a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files (https://desktop.arcgis.com/en/arcmap/10.3/map/ : saving-layers-and-layer-packages, about-creating-new-symbols, what-are-symbols-and-styles-), have been prepared. The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend.

This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background. The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB, is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international system WRB-2014.

The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colorcode_srts_wrb.lyr, and legend_colorcode_wrb.lyr. The first two of them are built using as value field the “Soil_codes” field, and as labels (explanation texts) the “Soil_name” field (storing the soil types according to SRTS/WRB classification), respectively, the “WRB” field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the “color_code” field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification.

In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_color_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification.

The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and color_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

The presented file set may be used to directly implement the Romanian color standard in digital soil type map legends, or may be adjusted/modified to other specific requirements.

Data collected between 3/29/17 and 5/3/17. Contours created on 12/13/18. The 1-foot elevation contours were presented as a feature class in an Esri ArcGIS 10.3 File Geodatabase. The contours were derived from Bare-Earth (DEMs) created from QL2 LiDAR data. Source Data Description: MI 2016 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of Michigan State Plane South, NAD83(2011), international feet and vertical datum of NAVD1988 (GEOID12A), international feet. Deliverables included classified LAS, Raw LAS, Bare-Earth hydro-flattened DEMs, Breaklines and Intensity images. Ground Conditions: LiDAR was collected in spring or fall, while no snow was on the ground and rivers were at or below normal levels.

Data collected between 3/29/17 and 5/3/17. Contours created on 12/13/18. The 1-foot elevation contours were presented as a feature class in an Esri ArcGIS 10.3 File Geodatabase. The contours were derived from Bare-Earth (DEMs) created from QL2 LiDAR data. Source Data Description: MI 2016 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of Michigan State Plane South, NAD83(2011), international feet and vertical datum of NAVD1988 (GEOID12A), international feet. Deliverables included classified LAS, Raw LAS, Bare-Earth hydro-flattened DEMs, Breaklines and Intensity images. Ground Conditions: LiDAR was collected in spring or fall, while no snow was on the ground and rivers were at or below normal levels.

Public school district boundaries for all districts in New York State as of 2020Sourced from New York State GIS Clearinghouse:https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1326Metadata:Identification_Information:Citation:Citation_Information:Title:SchoolDistricts_2019_v3Geospatial_Data_Presentation_Form: vector digital dataDescription:Abstract:Data is updated when school distrcits merge or otherwise change.Purpose:NYS School DistrictsSpatial_Domain:Bounding_Coordinates:West_Bounding_Coordinate: -79.996911East_Bounding_Coordinate: -71.650182North_Bounding_Coordinate: 45.022656South_Bounding_Coordinate: 40.386493Keywords:Theme:Theme_Keyword_Thesaurus: NoneTheme_Keyword: NYS SchoolTheme_Keyword: School DistrictsAccess_Constraints: NoneUse_Constraints:NoneData_Set_Credit:NYS Education DepartmentNative_Data_Set_Environment:Esri ArcGIS 10.3.1.4959

Background

The soil map of Israel was first published by Rabinovitch et al. in 1969. It was a massive work that took place 5 years. The map was published in a printed format at a 1:250,000 scale. Until now, a digital version of this map was not available. Accordingly, we carefully digitized the soil map of Rabinovitch and provided the map herein.

Materials and Methods

This dataset contains georeferenced raster layers of the soil map (1:250,000) of Israel published by Ravikovitch (1969). The georectification was done using control points located on the borders of Israel. With this information, it was possible to create polygons over the georeferenced raster layers. This was done using the editing tool of ArcGIS 10.3. For each polygon we assigned the same classification provided by Ravikovitch (1969). Once all the polygons were created, topological corrections were applied using the method of Longley et al., (2015) in order to rectify possible inaccuracies in the digitation. To this end, we used the topology tool of ArcGIS 10.3 applying two rules:

• polygons must not have gaps
• polygons must not overlap.

Following these corrections, the polygons were re-evaluated and further edited where necessary to improve accuracy.

This publication contains:

• the northern and the southern sections of the Israel map of soils after the georectification in geotif format
• the final product of the cartographic edition of the Israel map of soils in shapefile format

Please, if you are going to make use of this map, refer to this work:

Francos, N., Karasik, E., Myers, M., Ben-Dor, E., 2025. Soil type classification using Landsat 8: a comparison between the USDA and a local system in Israel. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2025.03.001

The bathymetry raster with a resolution of 5 m x 5 m was processed from unpublished single beam data from the Argentine Antarctica Institute (IAA, 2010) and multibeam data from the United Kingdom Hydrographic Office (UKHO, 2012) with a cell size of 5 m x 5 m. A coastline digitized from a satellite image (DigitalGlobe, 2014) supplemented the interpolation process. The 'Topo to Raster' tool in ArcMap 10.3 was used to merge the three data sets, while the coastline represented the 0-m-contour to the interpolation process ('contour type option').

The Digital Globe 2011-12 Ortho Image Base Map is an aerial-photo reference map that contains visible color, 30-centimeter pixel resolution ortho imagery from Digital Globe captured in spring 2011 and spring 2012. Imagery was captured statewide in spring 2011 in nine delivery areas. In Spring 2012, Berkshire County (the "Pittsfield" delivery area) and the town of Barnstable were re-flown, and the newer data are used in the imagery base map for these two areas.Image acquisition consisted of "high value" and "standard" data, with the high value areas complying with more stringent standards. MassGIS created the ortho imagery base map such that the High Value imagery draws atop any Standard imagery where the areas overlap.The
image base map layer was cached (pre-rendered) for the Web using ArcGIS 10.3. The caching process greatly speeds the display of the
imagery.

This high resolution imagery of Massachusetts during leaf-off conditions in the Spring of 2015 was acquired by DigitalGlobe™ of Longmont, Colorado.

MassGIS had the WorldView-2 and WorldView-3 satellites tasked to collect swaths of panchromatic and multispectral imagery in 43 separate overflights from March 16 - May 7, 2015. WorldView-2 operates at an altitude of 770 km (478 mi.), and WorldView-3 at 617 km (383 mi.).

The pixel resolutions of the delivered data varied due to off-nadir viewing angles and the altitudes of the sensors:

0.46 - 0.73 m panchromatic and 1.87 - 2.94 m multispectral (WorldView-2) 0.40 - 0.46 m panchromatic and 1.60 - 1.83 m multispectral (WorldView-3)

Pixels closest to nadir (the point directly below the sensor) have a better resolution than those farthest from nadir. U.S. regulation requires imagery to be resampled to a minimum of .40 m pan and 1.6m multispectral.This Web service was created in ArcMap 10.3 and published to a tile package and is hosted at MassGIS' organizational ArcGIS Online account. In ArcMap the display settings (stretching, brightness and contrast) were modified for each mosaic dataset in order to achieve the best possible uniform appearance across the state; however, because of the different acquisition dates and satellites seams are visible at smaller scales.See metadata for full details.

This high resolution imagery of Massachusetts during leaf-off conditions in the Spring of 2015 was acquired by DigitalGlobe™ of Longmont, Colorado.

MassGIS had the WorldView-2 and WorldView-3 satellites tasked to collect swaths of panchromatic and multispectral imagery in 43 separate overflights from March 16 - May 7, 2015. WorldView-2 operates at an altitude of 770 km (478 mi.), and WorldView-3 at 617 km (383 mi.).

The pixel resolutions of the delivered data varied due to off-nadir viewing angles and the altitudes of the sensors:

0.46 - 0.73 m panchromatic and 1.87 - 2.94 m multispectral (WorldView-2) 0.40 - 0.46 m panchromatic and 1.60 - 1.83 m multispectral (WorldView-3)

Pixels closest to nadir (the point directly below the sensor) have a better resolution than those farthest from nadir. U.S. regulation requires imagery to be resampled to a minimum of .40 m pan and 1.6m multispectral.This Web service was created in ArcMap 10.3 and published to a tile package and is hosted at MassGIS' organizational ArcGIS Online account. In ArcMap the display settings (stretching, brightness and contrast) were modified for each mosaic dataset in order to achieve the best possible uniform appearance across the state; however, because of the different acquisition dates and satellites seams are visible at smaller scales.See metadata for full details.

Data collected between 3/29/17 and 5/3/17. Contours created on 12/13/18. The 1-foot elevation contours were presented as a feature class in an Esri ArcGIS 10.3 File Geodatabase. The contours were derived from Bare-Earth (DEMs) created from QL2 LiDAR data. Source Data Description: MI 2016 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of Michigan State Plane South, NAD83(2011), international feet and vertical datum of NAVD1988 (GEOID12A), international feet. Deliverables included classified LAS, Raw LAS, Bare-Earth hydro-flattened DEMs, Breaklines and Intensity images. Ground Conditions: LiDAR was collected in spring or fall, while no snow was on the ground and rivers were at or below normal levels.

Data created for use in planning, design, assessment, research, general mapping and hydrologic modeling. These 2' contours were created by Oakland County from the 1' contours provided by the State of Michigan in December, 2018.

description: This ArcGIS 10.3 point feature class contains identification, location, and outfall attributes including outfall size and receiving water body, class, and contributor information for New York City combined sewer outfalls (CSOs). The information is provided by New York City in their draft SPDES Permit with NYSDEC. This information layer and all R2 GIS layers are maintained in a SQLServer 2012 geodatabase. The National Pollutant Discharge Elimination System (NPDES) program is implemented by NYSDEC via the compliance and enforcement elements of the State Pollutant Discharge Elimination System (SPDES) permit program. This GIS layer supports an ArcGIS relationship class with the attribute table titled EPA_FACILITIES_R2_RELATE_NPDES_CSO_DSCH. This table provides information about outfall events in terms of size (million gallons/year) and number of events per year for select CSOs. The National Pollutant Discharge Elimination System (NPDES) permit program is authorized by the Clean Water Act. The Integrated Compliance Information System (ICIS) for NPDES data exchange allows Partners to provide ICIS-NPDES data to EPA in an XML format and provides processing results to assist Partners with correcting common errors that may occur with their submissions.; abstract: This ArcGIS 10.3 point feature class contains identification, location, and outfall attributes including outfall size and receiving water body, class, and contributor information for New York City combined sewer outfalls (CSOs). The information is provided by New York City in their draft SPDES Permit with NYSDEC. This information layer and all R2 GIS layers are maintained in a SQLServer 2012 geodatabase. The National Pollutant Discharge Elimination System (NPDES) program is implemented by NYSDEC via the compliance and enforcement elements of the State Pollutant Discharge Elimination System (SPDES) permit program. This GIS layer supports an ArcGIS relationship class with the attribute table titled EPA_FACILITIES_R2_RELATE_NPDES_CSO_DSCH. This table provides information about outfall events in terms of size (million gallons/year) and number of events per year for select CSOs. The National Pollutant Discharge Elimination System (NPDES) permit program is authorized by the Clean Water Act. The Integrated Compliance Information System (ICIS) for NPDES data exchange allows Partners to provide ICIS-NPDES data to EPA in an XML format and provides processing results to assist Partners with correcting common errors that may occur with their submissions.

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

Abstract

The dataset was derived by the Bioregional Assessment Programme. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains the hand-contoured potentiometric surfaces of the various aquifers within the Galilee subregion. Contour intervals are 20m.

Purpose

To depict the various thickness contours for the Aquifers which lie within the Galilee subregion.

Dataset History

Created using the QLD DNRM bores waterlevel dataset [ff44f450-8fec-486b-b60d-0d53333a478d].

Contours were interpreted and drawn by hand based on the above source data well lithology information. These contours were then scanned and digitized into ArcGIS shape files. Data was then refined using the ArcGIS spatial analyst tool set - 'smooth contour tools' to smooth the contours.

All edits and geoprocessing were performed using ESRI ArcGIS 10.3 software.

QAQC:

Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data.

Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors.

Dataset Citation

Bioregional Assessment Programme (2015) GAL Group 2 contour data. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/803b0f5c-acb8-4adf-9ae1-3cb385ed4061.

Dataset Ancestors

• Derived From QLD DNRM Galilee Mine Groundwater Bores - Water Levels