Second version of a bathymetric map of the Baker-Martinez fjord complex (Chile, 48°S) constructed from multiple data sets: multibeam echosounder data of Baker channel (Harada et al., 2008) and of Steffen fjord and Baker river delta (Vandekerkhove et al.), single beam echosounder data of Martinez channel (R/V Sur-Austral 2015/2016) and Jorge Montt fjord (Rivera et al., 2012, Moffat, 2014 and additional data from C. Moffat) and individual bathymetry points (digitized using Global Mapper software) from two SHOA nautical charts (SHOA, 2001, 2008). The heterogeneous data with distinct spatial resolution was gridded using the kriging method (3.6 arc-second resolution) in Surfer from Golden Software. Compared to the first version (Piret et al., 2017), this new version features improved bathymetry for the Martinez Channel. We intend to update this map when new data sets become available. The data file is in GeoTIFF format. Geographic (unprojected lat/lon) Coordinate System – World Geodetic System 1984 (WGS84). The previous version of this bathymetry is available at https://doi.org/10.6084/m9.figshare.5285521.v3 (figshare).

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

ELEVATION.contours_2021 Summary The Texas Natural Resources Information System (TNRIS) contracted Sanborn to fly LiDAR in March of 2021. TNRIS then created the contours in the Spring of 2022 using Global Mapper. Description This layer represents contour elevation lines as of the March 2021. The contours are derived from LiDAR data, collected in the March 2021. Contours were generated using Global Mapper, sample spacing used to create the contours is consistent with the Nominal Point Spacing (NPS), of the source LiDAR dataset from which it was derived. Lines were automatically smoothed while being generated by Global Mapper. Important: The LiDAR data was created using UTM zone 14N and was projected in Central Texas State Plane (NAD 83) FIPS 4203. For contour type: 1 = Minor Contour 2 = Intermediate Contour 3 = Major Contour Credits The Texas Natural Resources Information System (TNRIS) Use limitations This map has been produced by the City of Austin for the cartographic purposes. No warranty is made by the City or TNRIS regarding its accuracy or completeness.

Spatial Services LiDAR data for the Wollongong Botanic Gardens from 2013. LiDAR colourised (RGB encoded) with 2012 Vekta Pty Ltd (now part of the AAM Group) aerial photography - using Global Mapper …Show full descriptionSpatial Services LiDAR data for the Wollongong Botanic Gardens from 2013. LiDAR colourised (RGB encoded) with 2012 Vekta Pty Ltd (now part of the AAM Group) aerial photography - using Global Mapper Mapper Version 22.

'The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from five sensors: Operational Land Imager, Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. This newest version combines all of the GLS data into one collection which has all of the combined collections. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. The datasets have been improved in order to give spatial continuity across all decadal collections. Most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This collection provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.'

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

Color hillshade of the original WAC GLD100 stereo DTM. This color scheme is used for the upcoming Lunar WAC mosaic and Topographic maps (with Nomenclature) produced by the USGS. For outreach and graphical purposes. Using the GLD100 topography, a color relief was created using Global Mapper and exported to a GeoTiff. This was brought back into ISIS for use in Map-a-Planet 2. Pixels do not contain any of the original explicit elevation information. The colorized shaded-relief represents approximate elevations based on the original topographic data.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. 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 x,y,z data of the topographic surface at a 200 m resolution. It is based on the 1 second SRTM dataset.

Purpose

The dataset forms one of the critical datasets for the development of three-dimensional geological models in the Clarence-Moreton bioregion.

Dataset History

The x,y,z data were extracted from the 1 second SRTM (see Lineage) using Global Mapper software. The dataset forms one of the critical datasets for the development of three-dimensional geological models in the Clarence-Moreton bioregion.

Dataset Citation

Bioregional Assessment Programme (XXXX) Clarence-Moreton 200m grid DEM. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/cb66145b-ee39-45ee-bb46-a89da99be5fe.

Dataset Ancestors

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

Methods: This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution raster was produced from a ground classified 2020 Quality Level 1 lidar point cloud. This DTM is hyroflattened, meaning that water bodies are represented as flat surfaces. Hydroflattening improves the aesthetics of the DEM and is consistent with USGS’s 3-DEP specifications.

This DTM was derived by Sanborn and Tukman Geospatial using the following process:

QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hydroflattened raster DEM (geotiff) posted on ArcGIS Online (Tukman Geospatial)

The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet.

Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area.

An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.

Uses and Limitations: The DTM provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis. The DTM will be most accurate in open terrain and less accurate in areas of very dense vegetation.

Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

U.S. Geological Survey (USGS) staff created geographic information system (GIS) footprints to show the extent of light detection and ranging (lidar) datasets published by the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC), since 2001. These lidar datasets were published as LAS, XYZ, or Digital Elevation Model (DEM) outputs of coastal, submerged and/or terrestrial topography in USGS Data Series (DS), Open-File Reports (OFR), and data releases (DR). Please see the publications listed in the source information section of this metadata record for details on data acquisition and processing of the datasets included in this data release. Using tools included in Global Mapper (GM) GIS software, polygons were generated to represent the coverage area of data provided in multiple USGS lidar publications. These footprints were later merged into one shapefile containing information about the field activity number (fan), field activity source link (fan_url; added in version 2.0), publication type (pub), publication source link (pub_url), lidar return type (returntype), and year the data were collected (yr_collect) to serve as an easily accessible data inventory. This data release will be updated and versioned, as needed, as more lidar publications are released from the USGS SPCMSC.

These shapefiles present the coastline, 20m depth and 50m depth contour lines for this second version of a bathymetric map of the Baker-Martinez fjord complex (Chile, 48°S). They are intended to be used in Ocean Data View (ODV). The data set is constructed from multiple data sets: multibeam echosounder data of Baker channel (Harada et al., 2008) and of Steffen fjord and Baker river delta (Vandekerkhove et al.), single beam echosounder data of Martinez channel (R/V Sur-Austral 2015/2016) and Jorge Montt fjord (Rivera et al., 2012, Moffat, 2014 and additional data from C. Moffat) and individual bathymetry points (digitized using Global Mapper software) from two SHOA nautical charts (SHOA, 2001, 2008). The heterogeneous data with distinct spatial resolution was gridded using the kriging method (3.6 arc-second resolution) in Surfer from Golden Software. Compared to the first version (Piret et al., 2017), this new version features improved bathymetry for the Martinez Channel. We intend to update this map when new data sets become available. The data files are in ESRI shapefile format (to use them in ODV, see section 3.1 of the manual v 5.0: https://odv.awi.de/fileadmin/user_upload/odv/misc/HowTo.pdf). Geographic (unprojected lat/lon) Coordinate System – World Geodetic System 1984 (WGS84). The previous version of this bathymetry is available at https://doi.org/10.6084/m9.figshare.5285521.v3 (figshare).

The U.S. Geological Survey in cooperation with the Grand River Dam Authority completed a high-resolution multibeam bathymetric survey to compute a new capacity and surface-area table. The capacity and surface-area tables describe the relation between the elevation of the water surface and the volume of water that can be impounded at each given water-surface elevation. The capacity and surface area of Grand Lake O’ the Cherokees were computed from a Triangular Irregular Network (TIN) surface created in Global Mapper Version 21.0.1. The TIN surface was created from three datasets: (1) a multibeam bathymetric survey of Grand Lake O’ the Cherokees in 2019 (Hunter and others 2020), (2) a 2017 USGS bathymetric survey of the Neosho, Spring, and Elk Rivers (Hunter and others, 2017; Smith and others, 2017), and (3) a 2010 lidar-derived digital elevation model (DEM) (USGS, 2016). Where the USGS 2019 and USGS 2017 survey data overlapped, the more recently collected 2019 USGS data were given preference. The DEM data were used in areas with land-surface elevations of more than 744 ft. above NAVD 88 where the multibeam data could not be collected. With the 2019 multibeam data being the predominant source of data this data set reflects lake conditions from 2019 when the multibeam data were collected.

LIDAR (.LAZ x2 files): data from Cape Bruguieres Channel, as published in Benjamin et al (2020) PLOS ONE article. From the Article: “the team deployed a Diamond Aircraft HK36TTC-ECO Dimona motorglider with two LiDAR systems mounted in under-wing pods: a Riegl Q680i-S (topographic) and a Riegl VQ-820-G (topo-bathymetric), each combined with a tactical grade IMU/GPS system (Novatel SPAN ISA/LCI). A Canon 5D Mk4 was fitted with an EF 24 mm (f/1.4LII USM) lens and co-mounted with the Q680i-S. Point cloud density ranged between 10 and 20 points/m2, and data was processed and converted to a Digital Elevation Model (DEM) using the Global Mapper LiDAR module."

GCP (.xls 1 file): Trimble Net R9 dGPS data with Trimble RTX Satellite Subscription. Data acquired by the Deep History of Sea Country Project Team.

Methods: The 2-foot resolution raster was produced from a ground classified 2020 Quality Level 1 lidar point cloud. This DSM was derived by Sanborn and Tukman Geospatial using the following process:QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)First return points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DSM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DSM geotiff (Tukman Geospatial)1-foot hydroflattened DSM (geotiff) resampled to 2-foot hydro-flattened DSM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hydroflattened raster DEM (geotiff) posted on ArcGIS Online (Tukman Geospatial) The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area.An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations:The DSM provides a raster depiction of the first (surface) returns for each 2x2 foot raster cell across Santa Clara County. The DSM will be most accurate in open terrain and less accurate in areas of very dense vegetation.Related Datasets:This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

This data release consists of a video and individual image frames extracted from the original high frame rate video and used to derive remotely sensed estimates of surface flow velocity via particle image velocimetry (PIV). These data were acquired from the Tanana River near Nenana, Alaska, on July 14, 2020. The video was obtained from a satellite operated by Planet Labs as part of the SkySat constellation. The original video was recorded at 30 frames per second and is provided in a compressed, lower-resolution .mp4 format video file for viewing. In addition, Planet Labs provided the individual frames comprising the video as full resolution TIFF images. This data release consists of individual frames extracted at a reduced frame rate of 1 frame per second (1 Hz). The original images were not geo-referenced and had to be stabilized to account for motion of the satellite platform during acquisition. Image stabilization was performed using the TrakEM2 plugin to the ImageJ software package. The stabilized image sequence was then geo-referenced in the Global Mapper software package using tie points selected from an orthophoto of the Tanana River study area available from a related data release. A spatial transformation based on these tie points and control points was derived in MATLAB and then applied to the images to project them into the UTM Zone 6N, NAD83 coordinate system. The resulting geo-referenced images had a spatial resolution (pixel size) of 0.925 m and effectively stabilized the image sequence prior to PIV analysis. The grayscale images were saved as TIF files with corresponding world files (*.tfw) that contain the spatial referencing information for each image. The sequence consists of 17 individual images representing 17 seconds of data collection. The entire sequence of TIF images and world files is contained within a zip archive.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. 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 a shapefile of the West Ipswich fault line. It was digitised from a map in: Wells, AT and O'Brien, PE (compilers and editors) (1994a). Geology and petroleum potential of the Clarence-Moreton Basin, New South Wales and Queensland. Australian Geological Survey Organisation, Bulletin 241, 302pp.

Dataset History

The West Ipswich Fault has been digitised from a registered image of map sheet "Geology of the Clarence-Moreton Basin" by Wells and O'Brien 1994.

The image name in the Bioregional Assessment repository is 'Registered_image_Wells_and_OBrien ' (http://data.bioregionalassessments.gov.au/dataset/3b5b0b66-d0fb-4b79-a0c1-5cd3719b44fc).

The image has been geo-referenced in Global Mapper and imported into GoCAD, where the fault line has been digitised. It was then exported from GoCAD as a shapefile.

Dataset Citation

Bioregional Assessment Programme (2014) CLM - west Ipswich fault line spatial layer. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/243ee82e-565a-4af3-b392-a8ad434497a7.

Dataset Ancestors

• Derived From CLM - Geology and Petroleum potential image

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details.

This is a scanned image of a geological map which is part of the publication by Wells and O'Brien (1994). It shows the geology of the Clarence-Moreton Basin.

Wells, AT and O'Brien, PE (compilers and editors) (1994a) Geology and petroleum potential of the Clarence-Moreton Basin, New South Wales and Queensland. Australian Geological Survey Organisation, Bulletin 241. Australian Geological Survey Organisation, Canberra.

Dataset History

This is a scanned image of a geological map which is part of the publication by Wells and O'Brien (1994). It shows the geology of the Clarence-Moreton Basin.

It was geo-referenced using Global Mapper software.

Reference: Wells, AT and O'Brien, PE (compilers and editors) (1994a) Geology and petroleum potential of the Clarence-Moreton Basin, New South Wales and Queensland. Australian Geological Survey Organisation, Bulletin 241. Australian Geological Survey Organisation, Canberra.

Dataset Citation

Australian Geological Survey Organisation (2015) CLM - Geology and Petroleum potential image. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/3b5b0b66-d0fb-4b79-a0c1-5cd3719b44fc.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including: I. Beaumont Soccer Club II. Corps’ Port Arthur Resident Office III. Halbouty Pump Station comprises its vicinity IV. Lamar University (Including Exxon Power Plants close to Lamar Univ.) V. MLK Boulevard for aerial images of the industry and the ship channel VI. Salt Water Barrier (include some aerial images about the Big Thicket) Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location. The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well. In October 2023, we had our follow-up data collection, including: I. Beaumont Soccer Club II. Shipping and Receiving Center at Lamar University After the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).

This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000. This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details.

This image was scanned from an unpublished report by Parsons Brinckerhoff (2013) and commissioned by Metgasco Lmd. It shows the geology, petroleum exploration licenses and a potential coal seam gas development area west of Casino in north-eastern NSW.

Parsons Brinckerhoff (2013) Preliminary numerical groundwater modelling report. Prepared for Metgasco Limited, Document No: 2193251B-WAT-PRE-001 RevA, dated 16 December 2013.

Dataset History

This image was scanned from an unpublished report by Parsons Brinckerhoff (2013) and commissioned by Metgasco Lmd. It shows the geology, petroleum exploration licenses and a potential coal seam gas development area west of Casino in north-eastern NSW.

It was geo-referenced using Global Mapper software.

Dataset Citation

Parsons Brinckerhoff (2015) CLM - Metgasco WestCasino PB2013. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/b80d3d41-ff50-4220-a357-105445938e74.