This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

This data is a mosaic of CTX DEM and ORI’s covering the ExoMars rover landing site in Oxia Planum. This data is a basemap for Oxia Planum and will act as a georeferencing base layer for future High resolution datasets of the rover landing site.ContentsThis data set contains 4 directories:03_a Sets of elevation contours at 100 m and 25 m spacing made from the DEM and smoothed for use in map publications.03_b Mosaic of orthorectified CTX images that accompany the DEM. These data are provided in an equirectangular projection centered at 335.45°E 03_c Hillshade model of the CTX DEM mosaic. These data are provided to help assess the variability and quality of the DEM. These data are provided in an equirectangular projection centered at 335.45°E03_d CTX DEM mosaic. These data are provided in an equirectangular projection centered at 335.45°EGuide to individual files03_a_CTX_DEM_contoursNaming convention: CTX_OXIA_DEM = data from which the contours where created, _cont = contour data, _m = vertical separation of the contours (25 or 100.)File name (example) Description CTX_OXIA_DEM_cont_100m.cpg CTX_OXIA_DEM_cont_100m.dbf CTX_OXIA_DEM_cont_100m.prj Projection information CTX_OXIA_DEM_cont_100m.sbx CTX_OXIA_DEM_cont_100m.shp <- Shape file data Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_DEM_cont_100m.shp.xml Geoprocessing history CTX_OXIA_DEM_cont_100m.shx 03_b_CTX_ORINaming convention: CTX = Instrument, OXIA = Location, ORI = Orthorectified image, 6m = pixel sizeFile name Description CTX_OXIA_ORI_6m.tfw World file <- Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_ORI_6m.tif Image data CTX_OXIA_ORI_6m.tif.aux.xml Auxiliary symbology statistics CTX_OXIA_ORI_6m.tif.ovr Image overviews CTX_OXIA_ORI_6m.tif.xml Geoprocessing history These data are provided with the following projection: Equirectangular_Mars_Oxia_Planum, Projections = Equidistant_Cylindrical, Datum = D_Mars_2000 Spheroid, Central meridian = 335.4503_c_CTX_DEM_hsNaming convention: CTX = Instrument, OXIA = Location, DEM = Digital Elevation Model, 20m = Pixel Size, _hs = hill shade model (sun potion 315°, azimuth 45°)File name Description CTX_OXIA_DEM_20m_hs.tfw World file <- Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_DEM_20m_hs.tif Image data CTX_OXIA_DEM_20m_hs.tif.aux.xml Auxiliary symbology statistics CTX_OXIA_DEM_20m_hs.ovr Image overviews CTX_OXIA_ DEM_20m_hs.tif.xml Geoprocessing history 03_d_CTX_DEMNaming convention: CTX = Instrument, OXIA = Location, DEM = Digital Elevation Model, 20m = Pixel SizeFile name Description CTX_OXIA_DEM_20m.tfw World file <- Open this data in GiS with the other supporting files in the same directoryCTX_OXIA_DEM_20m.tif Image data CTX_OXIA_DEM_20m.tif.aux.xml Auxiliary symbology statistics CTX_OXIA_DEM_20m.ovr Image overviews These data are provided with the following projection: Equirectangular_Mars_Oxia_Planum, Projections = Equidistant_Cylindrical, Datum = D_Mars_2000 Spheroid, Central meridian = 335.45Digital elevation models Digital elevation models (DEMs) were produced from CTX stereo images using the USGS Integrated Software for Imagers and Spectrometers (ISIS) software and the BAE photogrammetric package SOCET SET according to the method of Kirk et al. (2008). We selected 6 CTX image pairs to maximise coverage of the canyon. Tie points were automatically populated in SOCET SET between each image pair. In a departure from previous methods, we ran bundle adjustments on adjacent stereo pairs, removing erroneous tie points until the remaining points had an RMS pixel matching error of ≤ 0.6 pixels. This approach resulted in improved coregistration between stereo pairs, and minimal topographic artefacts across stereo pair boundaries. Each resultant DEM was tied vertically to Mars Orbital Laser Altimeter (MOLA; Zuber et al., 1992) topography and exported with a horizontal post spacing of 20 m/pixel. We then exported orthorectified images from SOCET SET at a resolution of 6 m/pixel. The orthorectified images (ORI) and DEMs were then post-processed in ISIS, mosaicked in the software ENvironment for Visualising Images (ENVI), provided by Harris Geospatial, before manual georeferencing in ArcGIS. Finally, the georeferenced image mosaic was blended in Adobe Photoshop to remove seamlines using the Avenza Geographic Imager extension, which retains geospatial information in the blended product.The output from SocetSet® are 18 – 20 m/pix DEM resolving topography of ~50 – 60 m features and 12 orthorectified CTX images at 6 m/pix. The Expected Vertical Precision (EVP) in each CTX DEM can be estimated based on viewing geometry and pixel scale (Randolph L. Kirk et al., 2003, 2008) e.g. EVP = Δp IFOV / (parallax/height). Where: Δp is the RMS stereo matching error in pixel units, assumed to be 0.2 pixels (Cook et al., 1996) and confirmed with matching software for several other planetary image data sets (Howington-Kraus et al., 2002; R. L. Kirk et al., 1999). The pixel matching error is influenced by signal-to-noise ratio, scene contrast and differences in illumination between the images. Pattern noise can also be introduced by the automatic terrain extraction algorithm, especially in areas of low correlation. These can be identified as patches of ‘triangles’ in the hillshade model (e.g., smooth, low contrast slopes and along shadows). IFOV is the instantaneous field of view of the image (pixel size in metres). If the paired images have different IFOV the RMS values is used e.g. IFOV = √(pixel scale image 1 + pixel scale image 2). The parallax/height ratio, calculated from the three-dimensional intersection geometry, reduces to tan(e) for an image with emission angle ‘e’ paired with a nadir image, e.g., parallax/height = tan(e) where e = |emission angle 1 − emission angle 2|.GeoreferencingMars Express High Resolution Stereo Camera (HRSC; Gwinner et al., 2016) MC11- mosaic (Kersten et al., 2018) has been used as the base control mosaic (tile HMC_11W24_co5ps.tif http://hrscteam.dlr.de/HMC30/).. This data is controlled to the Mars Orbital Laser Altimeter (MOLA; Smith et al., 2001) data the most accurate elevation data for Mars.Registration of the CTX DEM mosaic to the HRSC mosaic used manual tie points between the CTX ORI and HRSC mosaic and applying these tie points to the DEM mosaic. Manual tie points were used because automatic methods gave unsatisfactory results. The CTX mosaic data was rectified using the spline transformation. which optimizes for local accuracy but not global accuracy (Esri, 2020). This method provided good results for images with a range of viewing angles and accounts well for local adjustments needed for abrupt elevation changes.Topographic contoursTopographic contours were created at 25 m intervals from a CTX DEM down sampled to 100 m/pix, and contours shorter than 1500 m were removed and the lines smoothed using the PAEK algorithm at a tolerance of 200 m (USGS & MRCTR GIS Lab, 2018).

Dataset for: Regional Correlations in the layered deposits of Arabia Terra, Mars

Overview:

This repository contains the map-projected HiRISE Digital Elevation Models (DEMs) and the map-projected HiRISE image for each DEM and for each site in the study. Also contained in the repository is a GeoPackage file (beds_2019_08_28_09_29.gpkg) that contains the dip corrected bed thickness measurements, longitude and latitude positions, and error information for each bed measured in the study. GeoPackage files supersede shapefiles as a standard geospatial data format and can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS. For more information about GeoPackage files, please use https://www.geopackage.org/ as a resource. A more detailed description of columns in the beds_2019_08_28_09_29.gpkg file is described below in a dedicated section. Table S1 from the supplementary is also included as an excel spreadsheet file (table_s1.xlsx).

HiRISE DEMs and Images:

Each HiRISE DEM, and corresponding map-projected image used in the study are included in this repository as GeoTiff files (ending with .tif). The file names correspond to the combination of the HiRISE Image IDs listed in Table 1 that were used to produce the DEM for the site, with the image with the smallest emission angle (most-nadir) listed first. Files ending with “_align_1-DEM-adj.tif” are the DEM files containing the 1 meter per pixel elevation values, and files ending with “_align_1-DRG.tif” are the corresponding map-projected HiRISE (left) image. Table 1 Image Pairs correspond to filenames in this repository in the following way: In Table 1, Sera Crater corresponds to HiRISE Image Pair: PSP_001902_1890/PSP_002047_1890, which corresponds to files: “PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif” for the DEM file and “PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif” for the map-projected image file. Each site is listed below with the DEM and map-projected image filenames that correspond to the site as listed in Table 1. The DEM and Image files can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

· Sera

o DEM: PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif

o Image: PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif

· Banes

o DEM: ESP_013611_1910_ESP_014033_1910_align_1-DEM-adj.tif

o Image: ESP_013611_1910_ESP_014033_1910_align_1-DRG.tif

· Wulai 1

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Wulai 2

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Jiji

o DEM: ESP_016657_1890_ESP_017013_1890_align_1-DEM-adj.tif

o Image: ESP_016657_1890_ESP_017013_1890_align_1-DRG.tif

· Alofi

o DEM: ESP_051825_1900_ESP_051970_1900_align_1-DEM-adj.tif

o Image: ESP_051825_1900_ESP_051970_1900_align_1-DRG.tif

· Yelapa

o DEM: ESP_015958_1835_ESP_016235_1835_align_1-DEM-adj.tif

o Image: ESP_015958_1835_ESP_016235_1835_align_1-DRG.tif

· Danielson 1

o DEM: PSP_002733_1880_PSP_002878_1880_align_1-DEM-adj.tif

o Image: PSP_002733_1880_PSP_002878_1880_align_1-DRG.tif

· Danielson 2

o DEM: PSP_008205_1880_PSP_008930_1880_align_1-DEM-adj.tif

o Image: PSP_008205_1880_PSP_008930_1880_align_1-DRG.tif

· Firsoff

o DEM: ESP_047184_1820_ESP_039404_1820_align_1-DEM-adj.tif

o Image: ESP_047184_1820_ESP_039404_1820_align_1-DRG.tif

· Kaporo

o DEM: PSP_002363_1800_PSP_002508_1800_align_1-DEM-adj.tif

o Image: PSP_002363_1800_PSP_002508_1800_align_1-DRG.tif

Description of beds_2019_08_28_09_29.gpkg:

The GeoPackage file “beds_2019_08_28_09_29.gpkg” contains the dip corrected bed thickness measurements among other columns described below. The file can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

(Column_Name: Description)

sitewkn: Site name corresponding to the bed (i.e. Danielson 1)

section: Section ID of the bed (sections contain multiple beds)

meansl: The mean slope (dip) in degrees for the section

meanaz: The mean azimuth (dip-direction) in degrees for the section

ang_error: Angular error for a section derived from individual azimuths in the section

B_1: Plane coefficient 1 for the section

B_2: Plane coefficient 2 for the section

lon: Longitude of the centroid of the Bed

lat: Latitude of the centroid of the Bed

thickness: Thickness of the bed BEFORE dip correction

dipcor_thick: Dip-corrected bed thickness

lon1: Longitude of the centroid of the lower layer for the bed (each bed has a lower and upper layer)

lon2: Longitude of the centroid of the upper layer for the bed

lat1: Latitude of the centroid of the lower layer for the bed

lat2: Latitude of the centroid of the upper layer for the bed

meanc1: Mean stratigraphic position of the lower layer for the bed

meanc2: Mean stratigraphic position of the upper layer for the bed

uuid1: Universally unique identifier of the lower layer for the bed

uuid2: Universally unique identifier of the upper layer for the bed

stdc1: Standard deviation of the stratigraphic position of the lower layer for the bed

stdc2: Standard deviation of the stratigraphic position of the upper layer for the bed

sl1: Individual Slope (dip) of the lower layer for the bed

sl2: Individual Slope (dip) of the upper layer for the bed

az1: Individual Azimuth (dip-direction) of the lower layer for the bed

az2: Individual Azimuth (dip-direction) of the upper layer for the bed

meanz: Mean elevation of the bed

meanz1: Mean elevation of the lower layer for the bed

meanz2: Mean elevation of the upper layer for the bed

rperr1: Regression error for the plane fit of the lower layer for the bed

rperr2: Regression error for the plane fit of the upper layer for the bed

rpstdr1: Standard deviation of the residuals for the plane fit of the lower layer for the bed

rpstdr2: Standard deviation of the residuals for the plane fit of the upper layer for the bed

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

These data depict the elevation features of Konza Prairie. Record type 1 is a 2 meter resolution digital elevation model (DEM) of Konza Prairie, generated from 2006 LiDAR DEM data collected to standard USGS specifications (GIS200). Record type 3 is a 2010 10 meter (1/3 arc second) resolution National Elevation Dataset (NED) DEM of Konza Prairie (GIS202). Record type 4 is a 10 meter resolution NED DEM of Konza Prairie with a modified 3 kilometer buffer (GIS203). Record type 5 is a USGS topographic map of Konza Prairie (GIS204). These data are available as zipped (.zip) TIFF files (.tif). Data and metadata derived from DASC (record types 1 and 5), http://www.kansasgis.org/. Additional data and metadata derived from USGS (record types 3 and 4), http://www.nationalmap.gov/viewer.html.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

Remotely sensed topographic (elevation) and bathymetric (depth) information were acquired for the NSW coast (Point Danger to Cape Howe) and southern Queensland (Palm Beach to Point Danger) using Airborne LiDAR Bathymetry (ALB - a combination of Light Detection And Ranging (LiDAR) and Laser Airborne Depth Sounding (LADS) sensors) during July – December 2018. Data were acquired by Fugro Pty Ltd on behalf of NSW Office of Environment and Heritage using a Riegl VQ-820-G ALB (LiDAR) and Fugro LADS High-Definition sensors aboard sub-contracted Corporate Air Cessna C441 (VH-VEH). Funding was provided through the NSW Coastal Reforms package. The objective of the project was to provide high-resolution data better than 3-5 m spaced soundings (0.5 m spot spacing terrestrial; 3.4 m spot spacing marine) from the mean high-water mark to ~200m inland, and from the shore, seaward (LADS - bathymetry) to the point of laser extinction (~20-40m water depth depending on in-water conditions). Positioning data were collected on the ellipsoid ITRF 2014 GRS80 in UTM Z56 and post-processed using local base stations (CORSnet NSW) to provide a Post Processed Kinematic GNSS solution for final aircraft trajectory before being applied to all data. The final data Geotif products are provided on the Geosciences Australia ELVIS website .They are combined gridded terrestrial (elevation) and subtidal marine (bathymetry) data at 5 x 5 m (horizontal resolution) Geotifs exported using ESRI ArcMap from rasters (weighted average of clean soundings) in GDA 2020 (horizontal datum) to Australian Height Datum (vertical datum) and vertical precision to International Hydrographic Order (IHO) 1B. Data covers an area of 6862 km2 provided in 48 sub-datasets the extents of which are generally defined in their alongshore extent by the boundaries of NSW Secondary Sediment Compartments (Geosciences Australia). Other data outputs will include raw and classified LAS format files, aerial imagery and raw seabed reflectance data to be made available shortly on the ELVIS website. Data packages containing Arc Grids (topo-bathy, contours), XYZ, KMZ, tif, pdf maps and Fledermaus SD files will be made publicly available via the AODN (Australian Ocean Data network).\r Metadata, data quality statements and a geographical data coverage ArcGIS shapefile are available via SEED. The data are intended to inform coastal and marine management and should not be used for navigation without additional processing.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

Light Detection and Ranging (LiDAR) measures the return time of a laser light to reflect off objects on the ground to determine surface and ground elevations and topography. Grey County acquired LiDAR for the Georgian Bay lake fringe watersheds of Grey County in 2019 in collaboration with the Grey Sauble Conservation Authority and the Town of the Blue Mountains.

This LiDAR elevation data is publicly available for non-commercial use to agencies such as any municipality, contractor working for a member municipality or a partner(s) for a specific project, educational institution, non-profit agency or individual for research purposes. Any agency who use the data for commercial use can purchase the data at a cost of $250 per 1 square kilometer tile. The data includes Calibrated and Classified Raw Data in LAS format, Digital Elevation Model (DEM) grid files in elevation (TIF format) with cell size of 1m, Digital Surface Model (DSM) grid files in elevation (TIF format) with cell size of 1m, and 0.5m contours (SHP format).

You can view the coverage area and make a request to acquire the data by completing the web form linked below:

Grey County LiDAR Data Request Form

Requests anywhere within the Town of the Blue Mountains should be directed to gismaps@thebluemountains.ca. You can also find more information about requesting LiDAR data for the Town of Blue Mountains linked below:

https://thebluemountains.ca/document_viewer.cfm?doc=1435.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

This dataset consists of a shapefile representing 50 foot contour intervals for Santa Cruz County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.

This dataset consists of a shapefile representing 50 foot contour intervals for Maricopa County, Arizona. Datasets are also available for 100', 250', and 500' intervals. Each file covers an Arizona county or part of a county and as a collection covers the entire state. The data were created by processing hillshade TIF files derived from the U.S. Geological Survey National Elevation Dataset. The processing produced ESRI formatted coverages for each county or part of a county. The U.S. Geological Survey has developed a National Elevation Dataset (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives. The specifications for the NED 1 arc second and 1/3 arc second data are - Geographic coordinate system, Horizontal datum of NAD83, except for AK which is NAD27, Vertical datum of NAVD88, except for AK which is NAVD29, Z units of meters.