This data set provides a photogrammetry-based digital elevation model (DEM) that covers ~90% of the surface trace of the Eastern Denali Fault between the Alaska-Yukon international border and the village of Haines Junction, Yukon, Canada. The DEM has an average resolution of 4 m/pixel.

description: This publication updates previous fault maps of Oregon as a contribution to the larger U.S. Geological Survey effort to produce digital maps of active faults in the Pacific Northwest region. The new map is derived from the 1992 fault map of Pezzopane, Nakata, and Weldon that has seen wide distribution and has been reproduced in essentially all subsequent compilations of active faults of Oregon. This publication provides a substantial update of known active or suspected active faults east of the Cascades. Improvements in the new map include (1) many newly recognized active faults, (2) a linked ArcInfo map and reference database, (3) more precise locations for previously recognized faults on shaded relief quadrangles generated from USGS 30-m digital elevations models (DEM), (4) more uniform coverage resulting in more consistent grouping of the ages of active faults, and (5) a new category of "possibly" active faults that share characteristics with known active faults, but have not been studied adequately to assess their activity. The distribution of active faults has not changed substantially from the original Pezzopane, Nakata and Weldon map. Most faults occur in the south-central Basin and Range tectonic province that is located in the backarc portion of the Cascadia subduction margin. These faults occur in zones consisting of numerous short faults with similar rates, ages, and styles of movement. Many active faults strongly correlate with the most active volcanic centers of Oregon, including Newberry Craters and Crater Lake.; abstract: This publication updates previous fault maps of Oregon as a contribution to the larger U.S. Geological Survey effort to produce digital maps of active faults in the Pacific Northwest region. The new map is derived from the 1992 fault map of Pezzopane, Nakata, and Weldon that has seen wide distribution and has been reproduced in essentially all subsequent compilations of active faults of Oregon. This publication provides a substantial update of known active or suspected active faults east of the Cascades. Improvements in the new map include (1) many newly recognized active faults, (2) a linked ArcInfo map and reference database, (3) more precise locations for previously recognized faults on shaded relief quadrangles generated from USGS 30-m digital elevations models (DEM), (4) more uniform coverage resulting in more consistent grouping of the ages of active faults, and (5) a new category of "possibly" active faults that share characteristics with known active faults, but have not been studied adequately to assess their activity. The distribution of active faults has not changed substantially from the original Pezzopane, Nakata and Weldon map. Most faults occur in the south-central Basin and Range tectonic province that is located in the backarc portion of the Cascadia subduction margin. These faults occur in zones consisting of numerous short faults with similar rates, ages, and styles of movement. Many active faults strongly correlate with the most active volcanic centers of Oregon, including Newberry Craters and Crater Lake.

This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a "bald Earth" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory.

Spatial Data Organization Information -

Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1

Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet

Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222

This data set is derived from the original 2005 B4 lidar dataset collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief), "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format. The reprocessing of the 2005 B4 dataset was performed by Dr. Stephen B DeLong, USGS Earthquake Hazards Program, as a service to the community. The data available here were originally published on the USGS ScienceBase website as Classified point cloud and gridded elevation data from the 2005 B4 Lidar Project, southern California, USA.

Original B4 project description: The B4 Lidar Project collected lidar point cloud data of the southern San Andreas and San Jacinto Faults in southern California. Data acquisition and processing were performed by the National Center for Airborne Laser Mapping (NCALM) in partnership with the USGS and Ohio State University through funding from the EAR Geophysics program at the National Science Foundation (NSF). Optech International contributed the ALTM3100 laser scanner system. UNAVCO and SCIGN assisted in GPS ground control and continuous high rate GPS data acquisition. A group of volunteers from USGS, UCSD, UCLA, Caltech and private industry, as well as gracious landowners along the fault zones, also made the project possible. If you utilize the B4 data for talks, posters or publications, we ask that you acknowledge the B4 project. The B4 logo can be downloaded here. More information about the B4 Project.

Publications associated with this dataset can be found at NCALM's Data Tracking Center

This data set provides 13 digital air photo orthomosaics that cover ~90% of the surface trace of the Eastern Denali Fault between the Alaska-Yukon international border and the village of Haines Junction, Yukon, Canada.

First release of the Luangwa Rift Active Fault Database for the submission of a manuscript to EGU Solid Earth.

Active fault database for the Luangwa Rift, Zambia compiled by Tess Turner, Luke Wedmore and Juliet Biggs at University of Bristol.

The Luangwa Rift Active Fault Database (LRAFD) is a freely available open-source geospatial database of active fault traces within the Luangwa Rift, Zambia.

The active fault database has been designed and released in line with the Global Earthquake Model standards. Full details of the criteria used to assess activity will be released in a publication that is currently in preparation.

Citation
Please cite the latest release of this database on Zenodo in addition to the following manuscript:
Turner, T. Wedmore, L.N.J., Biggs, J. Williams, J.N., Sichingabula, H.M., Kabumbu, C., Banda, K. The Luangwa Rift Active Fault Database and fault reactivations along the southwestern branch of the East African Rift. _Submitted to EGU Solid Earth_

Data Format
The LRAFD is a geospatial database containing a collection of active fault traces in GIS vector format. Each fault is mapped as a single continuous GIS feature, and has associated metadata that describe the geometry of the fault and various aspects of its exposure and the methodology used to map the fault.

The list below describes the attributes within the LRAFD. These attributes are based on the Global Earthquake Model Global Active Faults Database (GEM-GAFD; Styron and Pagani, 2020). Note, we do not currently include all attributes from the GEM-GAFD as these data have not been collected in the Luangwa Rift. It is the intention that future versions of this database will include more attributes. No assessment is made of the seismogenic properties of the faults in the LRAFD as this is subjective. These data have been compiled in the publication associated with this database.

Data Table

Luangwa Rift Active Fault Database Attributes

Attribute	Data Type	Description	Notes
LRAFD_ID	integer	Unique Fault IDentification number assigned to each fault trace
Fault_Name	string	Name of Fault	Assigned using local geographic features or towns
Dip_Direction	string	Compass quadrant of fault dip direction
slip_type	string	kinematic type of fault	e.g. normal, reverse, sinistral-strike slip, dextral-strike slip
Fault_Length	decimal	Straight line distance between the tips of the fault
GeomorphicExpression	string	Geomorphic feature/features used to identify the fault trace and its extent	e.g. escarpment, fault scarp, offset sedimentary feature
Method	string	DEM or geologic dataset used to identify and map the fault trace	e.g. digital elevation model hillshade, slope map
Confidence	integer	Confidence of recent (Quaternary) activity	Ranges from 1-4, 1 if high certainty, 4 if low certainty
ExposureQuality	integer	Fault exposure quality	1 if high, 2 if low
EpistemicQuality	integer	Certainty of whether a fault exists there	1 if high, 2 if low
Accuracy	integer	Coarsest scale at which fault trace can be mapped, expressed as the denominator of the map scale	reflects the prominence of the fault's geomorphic expression
GeologicalMapExpression	string	extent of correlation between fault traces and legacy geological map	whether faults have been previously mapped and/or follow geological contacts
Notes	string	Any additional or relevant information regarding the fault
References	string	Relevant literature/geological maps where the fault is mentioned/described

File Formats
Following the GEM-GAFD, this database is provided in a variety of GIS vector file formats. GeoJSON is the version of record, and any changes should be made in this version, before they are converted to other filed formats using the convert.sh shell script available in this repository. This script uses the GDAL tool ogr2ogr and is adapted from a script posted by Richard Styron (https://github.com/cossatot/central_am_carib_faults/blob/master/convert.sh), who we thank for making this publicly available. The other versions available are ESRI Shapefile, KML, GMT and Geopackage.

Note that in the ESRI Shapefile format, the length of the attribute are restricted in length by the format, so we advise against using this format.

Version Control
This version of the database is v1.0 and is associated with the release of the data for submission of the associated manuscript.

It is intended that this database is updated in future versions by both the authors and other users. As such we encourage edits of the [GeoJSON] file and the submission of pull requests on the associated github site. Please contact Luke Wedmore (<luke.wedmore@bristol.ac.uk>) for information or to report errors in the database.

References
Styron, Richard, and Marco Pagani. “The GEM Global Active Faults Database.” Earthquake Spectra, vol. 36, no. 1_suppl, Oct. 2020, pp. 160–180, doi:10.1177/8755293020944182.

description: This is the 1st release of the fourth version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. Collectively, these data are referred to as "High Accuracy Elevation Data" (HAED). This version differs from the previous elevation model (EDEN_EM_OCT07) in several ways. First, the kriging algorithm applied to newly modeled subareas was changed from ordinary to universal kriging - resulting in slightly lower errors during cross-validation and accuracy assessment. Second, a previously omitted area in the southern portion of the Big Cypress National Preserve (BCNP) and the northwestern corner of the Everglades National Park (ENP) has been filled. Third, to increase accuracy in Water Conservation Area 1 (WCA1), the most challenging EDEN subarea from an elevation modeling standpoint, the Conservation area is subdivided into 3 zones (North, Central, South). Boundaries between the North, Central and South zones are based upon landscape units defined in the CERP Monitoring and Assessment Plan, Part 1, Figure 3-20 on p. 3-38 (p. 36 in the pdf file) at http://www.evergladesplan.org/pm/recover/recover_docs/map/MAP_3.1_GE.pdf. The South landscape unit (representing approximately the southern third of WCA1) was further divided into two zones (east and west, termed "Southeast" and "Southwest") based on marked changes in slope and aspect data generated from a DEM of the South landscape unit as a whole. Division of WCA1 into 4 zones reduces errors estimated by comparing DEM modeled water depths with those measured by EDEN Principal Investigators in the field. Subdivision of the South landscape unit into east and west zones resulted in lower error estimates for the Southeast zone without significantly affecting (i.e., improving or degrading) the quality of the Southwest zone - an area where DEM modeling is most challenging. To reduce artificial breaks in elevation along WCA1 subarea boundaries, models were overlapped by 1 cell at these boundaries and, for the North, Central and South zone boundaries, overlapping model values were averaged. For the boundaries between the Southwest and Southeast zones, cell values were "blended" based on weighted distance from the boundary edge. Finally, points along the North / Central and Central / South zone edges were subjectively selected and changed by adding or subtracting 0.03 meters (3 cm) to particular cells based on nearby cell values. This slightly reduces apparent artifacts without drastically affecting the integrity of the model. The EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface.; abstract: This is the 1st release of the fourth version of an Everglades Depth Estimation Network (EDEN) digital elevation model (DEM) generated from certified airborne height finder (AHF) and airboat collected ground surface elevations for the Greater Everglades Region. Collectively, these data are referred to as "High Accuracy Elevation Data" (HAED). This version differs from the previous elevation model (EDEN_EM_OCT07) in several ways. First, the kriging algorithm applied to newly modeled subareas was changed from ordinary to universal kriging - resulting in slightly lower errors during cross-validation and accuracy assessment. Second, a previously omitted area in the southern portion of the Big Cypress National Preserve (BCNP) and the northwestern corner of the Everglades National Park (ENP) has been filled. Third, to increase accuracy in Water Conservation Area 1 (WCA1), the most challenging EDEN subarea from an elevation modeling standpoint, the Conservation area is subdivided into 3 zones (North, Central, South). Boundaries between the North, Central and South zones are based upon landscape units defined in the CERP Monitoring and Assessment Plan, Part 1, Figure 3-20 on p. 3-38 (p. 36 in the pdf file) at http://www.evergladesplan.org/pm/recover/recover_docs/map/MAP_3.1_GE.pdf. The South landscape unit (representing approximately the southern third of WCA1) was further divided into two zones (east and west, termed "Southeast" and "Southwest") based on marked changes in slope and aspect data generated from a DEM of the South landscape unit as a whole. Division of WCA1 into 4 zones reduces errors estimated by comparing DEM modeled water depths with those measured by EDEN Principal Investigators in the field. Subdivision of the South landscape unit into east and west zones resulted in lower error estimates for the Southeast zone without significantly affecting (i.e., improving or degrading) the quality of the Southwest zone - an area where DEM modeling is most challenging. To reduce artificial breaks in elevation along WCA1 subarea boundaries, models were overlapped by 1 cell at these boundaries and, for the North, Central and South zone boundaries, overlapping model values were averaged. For the boundaries between the Southwest and Southeast zones, cell values were "blended" based on weighted distance from the boundary edge. Finally, points along the North / Central and Central / South zone edges were subjectively selected and changed by adding or subtracting 0.03 meters (3 cm) to particular cells based on nearby cell values. This slightly reduces apparent artifacts without drastically affecting the integrity of the model. The EDEN offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan. To produce historic and near-real time maps of water depths, the EDEN requires a system-wide DEM of the ground surface.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The 3 second (~90m) Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) version 1.0 was derived from resampling the 1 arc second (~30m) gridded DEM (ANZCW0703013355). The DEM represents ground surface topography, and excludes vegetation features. The dataset was derived from the 1 second Digital Surface Model (DSM; ANZCW0703013336) by automatically removing vegetation offsets identified using several vegetation maps and directly from the DSM. The 1 second product provides substantial improvements in the quality and consistency of the data relative to the original SRTM data, but is not free from artefacts. Man-made structures such as urban areas and power line towers have not been treated. The removal of vegetation effects has produced satisfactory results over most of the continent and areas with defects are identified in the quality assessment layers distributed with the data and described in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010). A full description of the methods is in progress (Read et al., in prep; Gallant et al., in prep). The 3 second DEM was produced for use by government and the public under Creative Commons attribution.

The 3 second DSM and smoothed DEM are also available (DSM; ANZCW0703014216,

DEM-S; ANZCW0703014217).

Dataset History

Source data

1. SRTM 1 second Version 2 data (Slater et al., 2006), supplied by Defence Imagery and Geospatial Organisation (DIGO) as 813 1 x 1 degree tiles. Data was produced by NASA from radar data collected by the Shuttle Radar Topographic Mission in February 2000.

2. GEODATA 9 second DEM Version 3 (Geoscience Australia, 2008) used to fill voids.

3. SRTM Water Body Data (SWBD) shapefile accompanying the SRTM data (Slater et al., 2006). This defines the coastline and larger inland waterbodies for the DEM and DSM.

4. Vegetation masks and water masks applied to the DEM to remove vegetation.

5. 1 second DEM resampled to 3 second DEM.

1 second DSM processing

The 1 second SRTM-derived Digital Surface Model (DSM) was derived from the 1 second Shuttle Radar Topographic Mission data by removing stripes, filling voids and reflattening water bodies. Further details are provided in the DSM metadata (ANZCW0703013336).

1 second DEM processing (vegetation offset removal)

Vegetation offsets were identified using Landsat-based mapping of woody vegetation. The height offsets were estimated around the edges of vegetation patches then interpolated to a continuous surface of vegetation height offset that was subtracted from the DSM to produce a bare-earth DEM. Further details are provided in the 1 second DSM metadata (ANZCW0703013355).

Void filling

Voids (areas without data) occur in the data due to low radar reflectance (typically open water or dry sandy soils) or topographic shadowing in high relief areas. Delta Surface Fill Method (Grohman et al., 2006) was adapted for this task, using GEODATA 9 second DEM as infill data source. The 9 second data was refined to 1 second resolution using ANUDEM 5.2 without drainage enforcement. Delta Surface Fill Method calculates height differences between SRTM and infill data to create a "delta" surface with voids where the SRTM has no values, then interpolates across voids. The void is then replaced by infill DEM adjusted by the interpolated delta surface, resulting in an exact match of heights at the edges of each void. Two changes to the Delta Surface Fill Method were made: interpolation of the delta surface was achieved with natural neighbour interpolation (Sibson, 1981; implemented in ArcGIS 9.3) rather than inverse distance weighted interpolation; and a mean plane inside larger voids was not used.

Water bodies

Water bodies defined from the SRTM Water Body Data as part of the DSM processing were set to the same elevations as in the DSM.

Edit rules for land surrounding water bodies

SRTM edit rules set all land adjacent to water at least 1m above water level to ensure containment of water (Slater et al., 2006). Following vegetation removal, void filling and water flattening, the heights of all grid cells adjacent to water was set to at least 1 cm above the water surface. The smaller offset (1cm rather than 1m) could be used because the cleaned digital surface model is in floating point format rather than integer format of the original SRTM.

Some small islands within water bodies are represented as voids within the SRTM due to edit rules. These voids are filled as part of void filling process, and their elevations set to a minimum of 1 cm above surrounding water surface across the entire void fill.

Overview of quality assessment

The quality of vegetation offset removal was manually assessed on a 1/8 ×1/8 degree grid. Issues with the vegetation removal were identified and recorded in ancillary data layers. The assessment was based on visible artefacts rather than comparison with reference data so relies on the detection of artefacts by edges.

The issues identified were:

* vegetation offsets are still visible (not fully removed)

* vegetation offset overestimated

* linear vegetation offset not fully removed

* incomplete removal of built infrastructure and other minor issues

DEM Ancillary data layers

The vegetation removal and assessment process produced two ancillary data layers:

* A shapefile of 1/8 × 1/8 degree tiles indicating which tiles have been affected by vegetation removal and any issue noted with the vegetation offset removal

* A difference surface showing the vegetation offset that has been removed; this shows the effect of vegetation on heights as observed by the SRTM radar

instrument and is related to vegetation height, density and structure.

The water and void fill masks for the 1 second DSM were also applied to the DEM. Further information is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Resampling to 3 seconds

The 1 second SRTM derived Digital Elevation Model (DEM) was resampled to 3 seconds of arc (90m) in ArcGIS software using aggregation tool. This tool determines a new cell value based on multiplying the cell resolution by a factor of the input (in this case three) and determines the mean value of input cells with the new extent of the cell (i.e. Mean value of the 3x3 input cells). The 3 second SRTM was converted to integer format for the national mosaic to make the file size more manageable. It does not affect the accuracy of the data at this resolution. Further information on the processing is provided in the User Guide (Geoscience Australia and CSIRO Land & Water, 2010).

Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_aac46307-fce9-449d-e044-00144fdd4fa6/SRTM-derived+3+Second+Digital+Elevation+Models+Version+1.0

Dataset Citation

Geoscience Australia (2010) Geoscience Australia, 3 second SRTM Digital Elevation Model (DEM) v01. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/12e0731d-96dd-49cc-aa21-ebfd65a3f67a.

The repository includes:

1. The Auto_Throw code

2. The topographic data (folder “DEMs”). Note that Les Saintes bathymetric data are available at https://doi.org/10.17882/96053).

3. Analysis Auto_Throw outputs obtained in the study (folder “Analysis_outputs”); namely, the topographic profiles as analyzed by Auto_Throw (along with raw topographic profiles for comparison). Each file concerns a given fault, as indicated in the file name.

4. Result Auto_Throw outputs obtained in the study (folder “Results_outputs”); namely the interpreted results obtained for each fault and each calculation. Each file concerns a given fault, as indicated in the file name.

Each Fi_Synthesis file includes:

(a) A Table synthesizing the Fault, DEM, and Run parameters

(b) Results from far-field slope measurements: we show maps of average far-field slope along each profile (far-field slopes are averaged over all calculations, and maps of far-field slope differences either side of the target scarp (averaged on each side over all calculations per profile;)

(c) The final measurements obtained on the fault, for each run. Each page includes several figures: (A1): manual fault map (not used in the calculations); (A2): map of the final vertical offset measurements. Profiles are shown (red), along with the polygon (black) within which measurements are considered as concerning the target fault; (A3): 3D vision of the topographic profiles; (A4): Bivariate histogram showing the relative difference between any offset value and the final best offset along each profile (for a given scarp; i.e., within the polygon in A2), as a function of Th. Inset shows these differences globally; (B1): fault scarp and extracted topographic profiles. Fault tips are indicated in yellow; (B2): Final vertical offset profile, with measurements undiscriminated or discriminated based on expert assessment. Uncertainties are standard deviation of offsets among the dense population of “modal offsets”. If this population is small (e.g., Th < 40), the uncertainty is calculated as indicated in the corresponding pages (example in F5-Run 4); (B3): Final steepest (red) and mean (black) scarp slope profiles, with measurements undiscriminated or discriminated based on expert assessment. Uncertainties on steepest slopes are aleatory errors derived from the code, while uncertainties on mean slope are standard deviation of slope values among the dense population of “modal offsets”; (B4): Final fault (black) and scarp (green) width profiles, with measurements undiscriminated or discriminated based on expert assessment. Uncertainties on fault width are the standard deviation of the averaged values; (C1): Fault width measured at 2 lowest Th values; (C2): Fault width measured at 10 lowest Th values; (C3): Fault width measured at all Th values for population of “modal offsets”.

When several runs have been done, or several faults combine into a larger-scale system, or our results need to be compared to prior measurements, the compared or combined results are generally presented in the last few pages of the Fi_Synthesis file. For F5-Run 4, results are shown per individual fault. For Fish Slough, results are presented slightly differently as the objective is their comparison with those of Scott et al., 2022.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The State of Utah, including the Utah Automated Geographic Reference Center, Utah Geological Survey, and the Utah Division of Emergency Management, along with local and federal partners, including Salt Lake County and local cities, the Federal Emergency Management Agency, the U.S. Geological Survey, and the U.S. Environmental Protection Agency, have funded and collected over 8380 km² (3236 mi²) of high-resolution (0.5 or 1 meter) Lidar data across the state since 2011, in support of a diverse set of flood mapping, geologic, transportation, infrastructure, solar energy, and vegetation projects. The datasets include point cloud, first return digital surface model (DSM), and bare-earth digital terrain/elevation model (DEM) data, along with appropriate metadata (XML, project tile indexes, and area completion reports).

This 0.5-meter 2013-2014 Wasatch Front dataset includes most of the Salt Lake and Utah Valleys (Utah), and the Wasatch (Utah and Idaho), and West Valley fault zones (Utah).

Other recently acquired State of Utah data include the 2011 Utah Geological Survey Lidar dataset covering Cedar and Parowan Valleys, the east shore/wetlands of Great Salt Lake, the Hurricane fault zone, the west half of Ogden Valley, North Ogden, and part of the Wasatch Plateau in Utah.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Topography provides information about the structural controls of the Great Basin and therefore information that may be used to identify favorable structural settings for geothermal systems. Specifically, local relative topography gives information about locations of faults and fault intersections relative to mountains, valleys, or at the transitions between. As part of U.S. Geological Survey efforts to engineer features that are useful for predicting geothermal resources, we construct a detrended elevation map that emphasizes local relative topography and highlights features that geologists use for identifying geothermal systems (i.e., providing machine learning algorithms with features that may improve predictive skill by emphasizing the information used by geologists). Herein, we provide the trend and local relative elevation maps documented in DeAngelo and others (2023), describing the process of removal of the regional trend and the resulting detrended elevation maps that emphasize basin-and-range scale structural features. Regional elevation trends were estimated using a local linear regression and subtracted from a 30-m digital elevation model (DEM) of topography to create the detrended elevation (i.e., local relative topography) map; therefore one could add the detrended surface to the corresponding trend surface to construct the original DEM. In an effort to optimize the detrended surface, alternate versions were produced with different rates of smoothness resulting in three detrended elevation maps. The resulting detrended elevation maps emphasize geologic structure and relative displacement, and these products may be useful for other geologic research including mineral exploration, hydrologic research, and defining geologic provinces. References DeAngelo, J., Burns, E.R., Lindsey, C.R., and Mordensky, S.P., (2023), Detrending Great Basin elevation to identify structural patterns for identifying geothermal favorability, Geothermal Rising Conference Transactions, 47, Reno, Nevada, October 1-5, 2023.

The San Andreas fault at Dry Lake Valley data set comprises high-resolution topography and an orthomosaic of part of the creeping central section of the San Andreas fault (SAF) at Dry Lake Valley, California, USA. The data set covers ~3 km of the SAF and ~2.7 km² area. The data were created using small UAS-derived low-altitude aerial photographs, Structure-from-Motion processing, and georeferencing from dGNSS onboard the sUAS and ground control points.

The motivation for producing the data set was to difference the topography against EarthScope lidar of the area collected ten years earlier, in 2007, to measure strain along and across strike of the fault (Scott et al., 2020). The study site was chosen because it is the location of a previous paleoseismology study (Toke et al., 2015), and creep-induced fracturing was mapped in detail at the location in 2014 and used to infer deformation rate localized on the fault (Scott et al., 2020).

For more information see: Scott, C., Bunds, M., Shirzaei, M., & Toke, N. (2020). Creep along the Central San Andreas Fault from Surface Fractures, Topographic Differencing, and InSAR. Journal of Geophysical Research: Solid Earth. https://doi.org/10.1029/2020JB019762

Layers include: various DEM derivatives computed using SAGA GIS at 250 m and using MERIT DEM (Yamazaki et al., 2017) as input. Antartica is not included. MERIT DEM was first reprojected to 6 global tiles based on the Equi7 grid system (Bauer-Marschallinger et al. 2014) and then these were used to derive all DEM derivatives. To access original DEM tiles please refer to MERIT DEM download page.

To access and visualize maps use: OpenLandMap.org

If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:

Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues

General questions and comments: https://disqus.com/home/forums/landgis/

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

dtm = theme: digital terrain models,

twi = variable: SAGA GIS Topographic Wetness Index,

merit.dem = determination method: MERIT DEM,

m = mean value,

1km = spatial resolution / block support: 1 km,

s0..0cm = vertical reference: land surface,

2017 = time reference: year 2017,

v1.0 = version number: 1.0,

The USGS Western Region Coastal and Marine Geology division contains extensive bathymetry data through InfoBank ("http://walrus.wr.usgs.gov/infobank/gazette/html/bathymetry/gl.html") Data includes bathymetry, magnetics, gravity, multibeam, subbottom profiler data, and sample data.

 Older bathymetric and elevation data is still available from the
 walrus anonymous ftp server (available on the web only), at:
 "http://walrus.wr.usgs.gov/ftp/"

A high resolution DEM of Tongziba River around the MD Fault

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

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 …Show full descriptionAbstract 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 is a single realisation of the stochastic fault network generated for the Gloucester AEM model, used as an illustrative visualisation in report GLO262 (Peeters et al. 2016). Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia. Dataset History Dataset GLO_AEM_Model_v2 contains 1000 stochastically generated fault networks. This dataset is one, randomly chosen, realisation that is used to illustrate the fault network in report GLO262 (Peeters et al, 2016) Peeters L, Dawes W, Rachakonda P, Pagendam D, Singh R, Pickett T, Frery E, Marvanek S and McVicar T (2016) Groundwater numerical modelling for the Gloucester subregion. Product 2.6.2 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., Dataset Citation Bioregional Assessment Programme (XXXX) GLO AEM Faults v01. Bioregional Assessment Derived Dataset. Viewed 11 July 2018, http://data.bioregionalassessments.gov.au/dataset/89aa53bd-e697-49c1-aa00-1b7a2cc11cbe. Dataset Ancestors Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW) Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014 Derived From Gloucester digitised coal mine boundaries Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From GLO Geological Model Extracted Horizons Final Grid XYZ V01 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Gloucester subregion on 12 September 2014 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From National Groundwater Information System (NGIS) v1.1 Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved Derived From Asset database for the Gloucester subregion on 8 April 2015 Derived From R-scripts for uncertainty analysis v01 Derived From New South Wales 2 kilometers Residential Exclusions Zone Derived From Geofabric Surface Cartography - V2.1 Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From EIS Gloucester Coal 2010 Derived From Report for Director Generals Requirement Rocky Hill Project 2012 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Geological Maps Combined for NSW Derived From Asset database for the Gloucester subregion on 28 May 2015 Derived From Gloucester Deep Wells Completion Reports - Geology Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014 Derived From EIS for Rocky Hill Coal Project 2013 Derived From GLO AEM dmax v01 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From GLO Deep Well Locations and Depths of Formations V01 Derived From GLO DEM 1sec SRTM MGA56 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From GLO RMS Model Depth Structure Eroded v01 Derived From Asset database for the Gloucester subregion on 29 August 2014 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Groundwater Modelling Report for Stratford Coal Mine Derived From AGL Gloucester Gas Project AECOM report location map features Derived From Groundwater Economic Assets GLO 20150326 Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Freshwater Fish Biodiversity Hotspots Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014 Derived From GLO AEM Model v02 Derived From Australia - Species of National Environmental Significance Database Derived From Gloucester - Additional assets from local councils Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From GLO Receptors 20150828 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

description: ABSTRACT: This data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors. There are 15 data files with this data set which includes 12 compressed *.zip files containing ArcInfo shape files and 3 GeoTIFFS.This data set contains vector data showing roads, rivers, and hypsography for each study area in ESRI ArcGIS shapefile format. The vectors were hand-digitized by the Images Company in Brazil from paper maps produced by the Brazilian government. Depending on the scale of the original maps, the digitization errors vary. For some maps, some vectors are missing. Data were manually checked for duplicate or extra vectors. These data sets were derived from several map sheets produced from aerial coverages dating from 1974 to 1978.The DEM images were encoded from the hypsography vectors and are provided in GeoTIFF format. The attribute value associated with each line and point in the vector segment is encoded into the image channel; the image channel is then filled in by interpolating image data between encoded vector data. For each DEM: 1 image channel with pixel resolution = 25m x 25m. DEM images are provided for Manaus, Tapajos National Forest, and Rondonia. The files for Rio Branco were unusable due to a documentation error.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS:The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data. - Any use of the derived data is not recommended because the results have not been validated.- However, the DEM, vectors, and orthorectified SAR data (related data set) can be used if the user understands how these were produced and accepts the limitations.; abstract: ABSTRACT: This data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors. There are 15 data files with this data set which includes 12 compressed *.zip files containing ArcInfo shape files and 3 GeoTIFFS.This data set contains vector data showing roads, rivers, and hypsography for each study area in ESRI ArcGIS shapefile format. The vectors were hand-digitized by the Images Company in Brazil from paper maps produced by the Brazilian government. Depending on the scale of the original maps, the digitization errors vary. For some maps, some vectors are missing. Data were manually checked for duplicate or extra vectors. These data sets were derived from several map sheets produced from aerial coverages dating from 1974 to 1978.The DEM images were encoded from the hypsography vectors and are provided in GeoTIFF format. The attribute value associated with each line and point in the vector segment is encoded into the image channel; the image channel is then filled in by interpolating image data between encoded vector data. For each DEM: 1 image channel with pixel resolution = 25m x 25m. DEM images are provided for Manaus, Tapajos National Forest, and Rondonia. The files for Rio Branco were unusable due to a documentation error.DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products. KNOWN PROBLEMS:The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data. - Any use of the derived data is not recommended because the results have not been validated.- However, the DEM, vectors, and orthorectified SAR data (related data set) can be used if the user understands how these were produced and accepts the limitations.