NYC 1foot Digital Elevation Model: A bare-earth, hydro-flattened, digital-elevation surface model derived from 2010 Light Detection and Ranging (LiDAR) data. Surface models are raster representations derived by interpolating the LiDAR point data to produce a seamless gridded elevation data set. A Digital Elevation Model (DEM) is a surface model generated from the LiDAR returns that correspond to the ground with all buildings, trees and other above ground features removed. The cell values represent the elevation of the ground relative to sea level. The DEM was generated by interpolating the LiDAR ground points to create a 1 foot resolution seamless surface. Cell values correspond to the ground elevation value (feet) above sea level. A proprietary approach to surface model generation was developed that reduced spurious elevation values in areas where there were no LiDAR returns, primarily beneath buildings and over water. This was combined with a detailed manual QA/QC process, with emphasis on accurate representation of docks and bare-earth within 2000ft of the water bodies surrounding each of the five boroughs. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_DigitalElevationModel.md

A bare-earth, hydro-flattened, digital-elevation surface model derived from 2010 Light Detection and Ranging (LiDAR) data. Surface models are raster representations derived by interpolating the LiDAR point data to produce a seamless gridded elevation data set. A Digital Elevation Model (DEM) is a surface model generated from the LiDAR returns that correspond to the ground with all buildings, trees and other above ground features removed. The cell values represent the elevation of the ground relative to sea level. The DEM was generated by interpolating the LiDAR ground points to create a 1 foot resolution seamless surface. Cell values correspond to the ground elevation value (feet) above sea level. A proprietary approach to surface model generation was developed that reduced spurious elevation values in areas where there were no LiDAR returns, primarily beneath buildings and over water. This was combined with a detailed manual QA/QC process, with emphasis on accurate representation of docks and bare-earth within 2000ft of the water bodies surrounding each of the five boroughs.

In this joint demonstration project for the Tampa Bay region, NOAA's National Ocean Service (NOS) and the U.S. Geological Survey (USGS) have merged NOAA bathymetric and USGS topographic data sets into a hybrid digital elevation model (DEM) with all data initially referenced to the ellipsoid, but transformable to any of 28 orthometric, 3-D, or tidal datums.A seamless bathymetric/topographic digital elevation model (DEM) was developed by merging the "best available" bathymetric data from NOAA and topographic data for USGS. Each of the datasets was initially processed independently to apply the "best available" criteria to select the data to be merged. Prior to merging, the selected data were transformed to a common reference coordinate system, both horizontally and vertically.The selected topography points within the shoreline buffer zone and the bathymetry points were gridded to produce a raster surface model with a 1-arc-second (30-meter) grid spacing to match the resolution of NED. The points were input to an implementation of the ANUDEM thin plate spline interpolation algorithm, which is optimized for generation of topographic surfaces. The bathymetry points could have been gridded independently of the topographic data, but the shoreline zone land elevations were included in the interpolation to ensure a better match of the bathymetric and topographic surfaces for the subsequent mosaicing step. To avoid introduction of any interpolation edge effects into the merged elevation model, the output grid from the interpolation was clipped to include only land elevations within 300 meters of the shoreline.The final processing step involved the mosaicing of the bathymetry grid and the NED elevation grid. The values in the 300-meter overlap area were blended by weighted averaging, where the weights for each grid are determined on a cell-by-cell basis according to the cell's proximity to the edges of the overlap area. The resulting final merged product is a seamless bathymetric/topographic model covering the Tampa Bay region at a grid spacing of 1-arc-second (30-meter). The vertical coordinates represent elevation in decimal meters relative to the GRS80 ellipsoid, and the horizontal coordinates are decimal degrees of latitude and longitude referenced to the NAD83 datum.This dataset is intended for geospatial applications that require seamless land elevation and water depth information in coastal environments.

Topographic Position Index (TPI) is a topographic position classification identifying upper, middle and lower parts of the landscape. This dataset includes a mask that identifies where topographic position cannot be reliably derived in low relief areas.

The TPI product was derived from Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. A masked version of the TPI product was derived using the slope relief classification product.

The TPI data are available at 1 arc-second and 3 arc-second resolution.

The 3 arc-second resolution dataset was generated from the 1 arc-second TPI product and masked by the 3” water and ocean mask datasets.

Lineage: Source data 1.\t1 arc-second SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016). 2.\t1 arc-second slope relief product 3.\t3 arc-second resolution SRTM water body and ocean mask datasets.

Topographic position index calculation TPI is a measure of topographic position, classified into three classes corresponding to upper slopes, mid-slopes and lower slopes. The method follows that of the "Drainage Channels Class" section of Warner, Cress and Sayre (2008) which is based on the TPI method of Jenness (2006) and Weiss (2001).

The TPI classification uses relative elevation as a fraction of local relief; where the relative elevation is high compared to the local relief the class is upper slope, and where the relative elevation is low compared to local relief the class is lower slope. Intermediate values are classified as mid-slopes. This use of residuals compared to a smoothed elevation model to produce relative elevations is similar to the method described by McRae (1992).

Relative elevation is the difference between local (cell) elevation and the mean elevation over a 300 m radius circle (approximately: the calculation actually uses 10 grid cells at 1 arc-second resolution). Local relief is calculated as the standard deviation of elevation over the same circular region. The classification is:

TPI = \t1 if relative_elevation < -0.5 * local relief (lower slopes) \t3 if relative_elevation > 0.5 * local relief (upper slopes) 2 otherwise (mid slopes)

In relatively flat areas the finite accuracy of a DEM limits its ability to discriminate topographic position. The mask included with the TPI layer identifies areas that are too flat to reliably identify upper, middle and lower landscape positions. It is based on the 'Slope-Relief' classification and the TPI mask has values of 1 where there is sufficient relief for TPI to be meaningful and 0 where TPI should not be used.

The TPI calculation was performed on 1° x 1° tiles, with overlaps to ensure correct values at tile edges.

The 3” arc-resolution version was generated from the 1” TPI class and mask products. This was done by aggregating the 1” data over a 3 x 3 grid cell window and taking the mean of the nine values that contributed to each 3” output grid cell. The result was then converted to integer format, avoiding truncation errors and ensuring that (for example) values between 1.5 and 2 were assigned to class 2, and values between 2.5 and 3 were assigned to class 3. The 3” TPI and TPI mask data were then masked using the SRTM 3” ocean and water body datasets.

Presentation for AWRA Geospatial Technologies Conference held Virtually August 4-13, 2020. This presentation on August 12. https://www.eventscribe.com/2020/AWRAGIS/

Flood inundation is difficult to map, model, and forecast because of the data needed and the computational demand. Recently an approach based on the relative elevation, or Height Above Nearest Drainage (HAND), which is derived from a digital elevation model (DEM), has been suggested for both flood mapping and obtaining reach hydraulic properties and synthetic rating curves. These products are only as good as the underlying DEM from which they are derived and better DEMs offers the potential for improving model representations of streams and parameters derived from DEMs used in hydrologic and flood inundation modeling and mapping. This presentation will review the approach for using relative elevation in flood modeling, describing how HAND is calculated, how it is used to map flood inundation for stream reach catchments and how it is used to determine stream reach properties, identifying shortcomings and giving ideas for improvements. As we obtain more detailed information on bathymetry, topography and hydrography it is important to establish a consistent data model for the river bed that is used in HAND related work that aligns and reconciles elevation and hydrography. This presentation will discuss approaches for using hydrography to remove DEM obstacles, the segmentation of streams used in deriving HAND reach average hydraulic properties and ideas for quantifying reach average roughness based on HAND and flood inundation mapped from remote sensing of previous floods with measured discharges.

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.

Note: Geoscience Australia no longer supports users' external hard drives. The data can either be downloaded from the ELVIS Portal or from the Related links. The 1 second Shuttle Radar Topography Mission (SRTM) Digital Elevation Models Version 1.0 package comprises three surface models: the Digital Elevation Model (DEM), the Smoothed Digital Elevation Model (DEM-S) and the Hydrologically Enforced Digital Elevation Model (DEM-H). The DEMs were derived from the SRTM data acquired by NASA in February 2000 and were publicly released under Creative Commons licensing from November 2011 in ESRI Grid format.

DEM represents ground surface topography, with vegetation features removed using an automatic process supported by several vegetation maps. This 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 identified in supplementary layers distributed with the data, and described in the User Guide.

DEM-S represents ground surface topography, excluding vegetation features, and has been smoothed to reduce noise and improve the representation of surface shape. An adaptive smoothing process applied more smoothing in flatter areas than hilly areas, and more smoothing in noisier areas than in less noisy areas. This DEM-S supports calculation of local terrain shape attributes such as slope, aspect and curvature that could not be reliably derived from the unsmoothed 1 second DEM because of noise.

DEM-H is a hydrologically enforced version of the smoothed DEM-S. The DEM-H captures flow paths based on SRTM elevations and mapped stream lines, and supports delineation of catchments and related hydrological attributes. The dataset was derived from the 1 second smoothed Digital Elevation Model (DEM-S) by enforcing hydrological connectivity with the ANUDEM software, using selected AusHydro V1.6 (February 2010) 1:250,000 scale watercourse lines and lines derived from DEM-S to define the watercourses. The drainage enforcement has produced a consistent representation of hydrological connectivity with some elevation artefacts resulting from the drainage enforcement.

Further information can be found in the supplementary layers supplied with the data and in the User Guide.

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.

Dunes with a high relative topography can often be easily distinguished in high-resolution lidar-based digital elevation models (DEMs). Thus, researchers have begun using relative topography metrics, such as the topographic position index (TPI; Weiss, 2001), to identify ridges and upper slopes for extracting dunes from lidar-based DEMs (Wernette et al., 2016; Halls et al. 2018). DEMs are often used for automated delineations of intertidal and supratidal habitats in coastal applications despite issues related to vertical uncertainty. However, the level of vertical uncertainty from data collected with conventional aerial topographic lidar systems has been found to be as high as 60 cm in densely vegetated emergent wetlands throughout the United States (Medeiros et al., 2015; Buffington et al., 2016; Enwright et al., 2018). This uncertainty can also impact elevations in other habitats such as dunes due to vegetation cover and slope (Su and Bork, 2006). Another challenge when mapping geomorphology-based habitats (e.g., dune, beach, intertidal marsh, forest) on dynamic barrier islands is the need for standardized methods that are efficient and repeatable. In response, we developed an approach that builds on recent efforts using relative topography to identify ridges and upper slopes for dune delineation (Wernette et al. 2016; Halls et al. 2018) by also applying Monte Carlo simulations to treat elevation uncertainty in coastal settings when extracting elevation-dependent habitats from a DEM (Liu et al. 2007; Enwright et al. 2018) for a case study on Dauphin Island, Alabama. Beyond just the application of uncertainty, we refined ridges and upper slopes extracted from a DEM by removing small noisy polygons and using manual refinement. This data release contains each of these iterations to show the importance of uncertainty analyses and manual refinement when using automated extraction of elevation-dependent habitats from a DEM. This data release includes a TPI directory, which contains four polygon shapefiles that represent each step in the TPI-based dune delineation process, which includes: 1) step1_raw_ridges_upper_slopes.shp; 2) step2_refinement_extreme_water_level.shp; 3) step3_refinement_via_noise removal.shp; and 4) step4_final_refinement_from_visual_inspection.shp. Since this a step-wise process, each step includes the prior steps. A second component of this data release is a raster named “Prob_Abv_Storm” that estimates the probability of a pixel being above the extreme water level with a 10-percent annual exceedance probability for National Oceanic and Atmospheric Administration’s Dauphin Island tide gauge (station ID: 8735180).

This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW.

This dataset contains four alternative digital elevation models (DEMs) at 1 m resolution and model performance statistical metrics for the Global Change Research Wetland (GCReW) site on the Rhode River, a tributary of the Chesapeake Bay in Maryland, USA, for the year 2016. Three DEMs were created by using different strategies for correcting positive biases in Light Detection and Ranging (LiDAR)-based DEMs that are common in tidal wetlands. These included (1) applying a single average offset based on a literature review, (2) using the LiDAR Elevation Correction with NDVI (LEAN)-method, and (3) applying plant community-specific offsets using a local vegetation cover map. Existing LiDAR data at 1 m resolution collected in 2011 was the basis for these DEMs. The fourth DEM was created by using Empirical Bayesian Kriging to extrapolate between measured ground points. The elevation is provided in meters relative to the North American Vertical Datum of 1988 (NAVD 88). To calibrate the four approaches, the elevation of the entire marsh complex was surveyed at 20 m x 20 m resolution to document the distribution of elevation relative to tidal datums from a single year. Two Trimble R8 real-time kinematic (RTK) GPS receivers were used to survey 525 points over the complex from July 26, 2016, to August 15, 2016. Relative plant cover was also documented. Tidal datums were calculated from the nearby Annapolis, MD tidal gauge located 13 km from GCReW.

These Bathymetric Digital Elevation Models (DEM) were generated from original point soundings collected during hydrographic surveys conducted by the National Ocean Service and its predecessors. Mean High Water shoreline as defined by NOAA nautical charts was used as a constraining boundary and assigned its local elevation relative to the local datum (typically Mean Low Water). In the event of multiple surveys in a region, the most recent survey soundings were retained. A Digital Elevation Model (DEM) contains a series of elevations ordered from south to north with the order of the columns from west to east. The 7.5-minute DEM (30- by 30-m data spacing) is cast on the Universal Transverse Mercator (UTM) projection. It provides coverage in 7.5- by 7.5-minute blocks. Each product provides the same coverage as a standard USGS 7.5-minute quadrangle but the DEM contains over edge data. Coverage is available for many estuaries of the contiguous United States but is not complete.

Bathymetric data were collected by the U.S. Geological Survey (USGS) in 2019 for approximately 2.2 square kilometers of the Nehalem Bay between the Highway 101 bridge and Nehalem Bay State Park (about 6.5 kilometers) near Wheeler, Oregon. The data were collected using a Trimble R8 Global Navigation Satellite System (GNSS) receiver combined with a Seafloor Systems Hydrolite TM single-beam 200 kilohertz echosounder mounted to a motorized boat. GPS positions received corrections in real time from the Oregon Real-Time GNSS Network. Sound velocity profiles were recorded at 19 different locations evenly spaced throughout the survey area using an AML Oceanographic Base X2 100 meter sound velocity profiler in order to quantify the spatial and temporal variation in sound velocity. Data was collected using the UTM Zone 10 North (meters) (NAD83[2011]) coordinate system, with elevations relative to NAVD88 using Geoid 12B. Data was post-processed to remove spurious data points. Raw depths were corrected using the mean value of the recorded sound velocity profiles, then converted to bed elevations. The mean vertical and horizontal uncertainties are 0.04145 and 0.00945 meters, respectively. This product is a GeoTiff raster digital elevation model (DEM) based on the bed elevation data. Bed elevations were used to create a triangulated irregular network (TIN) surface which was edited by adding lines delineating breaks in slope, then converted into a raster with a 20-meter cell size.

'The USGS National Unmanned Aircraft Systems (UAS) Project Office utilizes UAS technology for collecting remote sensing data on a local scale. Typical UAS projects encompass areas that are too large to cover on foot and too small for traditional aircraft missions. The flexibility of operations and relative low cost of UAS allow scientists to support a range of activities including monitoring environmental conditions, analyzing the impacts of climate changes, responding to natural hazards, understanding landscape change rates and consequences, conducting fire assessments, tracking wildlife inventories, aiding search and rescue, and supporting related land management and emergency response missions. The USGS EROS Center manages and distributes data for the UAS Project Office. '

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.

This raster dataset contains a 10-meter resolution digital elevation model (DEM) of the Elk River watershed in Humboldt County, California. This dataset was generated from 7.5-minute topographic quadrangle maps originally produced by the US Geological Survey. This dataset is the product of the Department of Conservation, California Geological Survey’s (CGS) investigation of landslides in the Elk River watershed. The 52 square mile study area is located in Humboldt County in northwestern California. The investigation was based on interpretation of 1940, 1941, 1948, 1954, 1962, 1965, 1984, 1988, 1996 and 2000 aerial photos, findings from CGS’s landslide mapping conducted in the early 1980s (Kilbourne, R.T. 1982-84, Manson, M. W. 1984), as well as other sources. Mapping was conducted at 1:24,000 scale. The resulting maps are titled “Geologic and Geomorphic Features Related to Landsliding, Elk River Watershed” (Plate 1) and “Relative Landslide Potential with Geologic and Geomorphic Features, Elk River Watershed” map (Plate 2). This study was conducted at a regional scale of mapping using ten sets of aerial photos combined with a compilation of earlier published and unpublished work. Other photo sets may reveal additional landslides. The regional nature of the study makes the data and maps, including the relative landslide potential zones, inappropriate as a substitute for site-specific analysis.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the 1 second SRTM Digital Elevation Model (DEM) dataset. The source dataset is 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.

A clipped version of the Australia wide 1 second -S DEM, version 1, which limits the size to the rectangular extent of the Galilee Basin Subregion, enhancing speed and efficiency for visualisation and processing.

The metadata for the Geoscience Australia 1 sec SRTM is below:

The 1 second DSM, DEM, DEM-S and DEM-H are national elevation data products derived from the Shuttle Radar Topography Mission (SRTM) data. The SRTM data is not suitable for routine application due to various artefacts and noise.

The data has been treated with several processes to produce more usable products:

* A cleaned digital surface model (DSM)

* regular grid representing ground surface topography as well as other features including vegetation and man-made structures

* A bare-earth digital elevation model (DEM)

* regular grid representing ground surface topography, and where possible, excluding other features such as vegetation and man-made structures.

* A smoothed digital elevation model (DEM-S)

* A smoothed DEM based on the bare-earth DEM that has been adaptively smoothed to reduce random noise typically associated with the SRTM data in low relief areas.

* A hydrologically enforced digital elevation model (DEM-H)

* A hydrologically enforced DEM is based on DEM-S that has had drainage lines imposed and been further smoothed using the ANUDEM interpolation software.

The last product, a hydrologically enforced DEM, is most similar to the DEMs commonly in use around Australia, such as the GEODATA 9 Second DEM and the 25 m resolution DEMs produced by State and Territory agencies from digitised topographic maps.

For any analysis where surface shape is important, one of the smoothed DEMs (DEM-S or DEM-H) should be used. DEM-S is preferred for shape and vertical accuracy and DEM-H for hydrological connectivity. The DSM is suitable if you want to see the vegetation as well as the land surface height. There are few cases where DEM is the best data source, unless access to a less processed product is necessary.

The 1 second DEM (in its various incarnations) has quite different characteristics to DEMs derived by interpolation from topographic data. Those DEMs are typically quite smooth and are based on fairly accurate but sparse source data, usually contours and spot heights supplemented by drainage lines. The SRTM data is derived from radar measurements that are dense (there is essentially a measurement at almost every grid cell) but noisy.

Version 1.0 of the DSM was released in early 2009 and version 1.0 of the DEM was released in late 2009. Version 1.0 of the DEM-S was released in July 2010 and version 1.0 of the hydrologically enforced DEM-H was released in October 2011. These products provide substantial improvements in the quality and consistency of the data relative to the original SRTM data, but are not free from artefacts. Improved products will be released over time.

The 3 second products were derived from the 1 second data and version 1.0 was released in August 2010. Future releases of these products will occur when the 1 second products have been improved. At this stage there is no 3 second DEM-H product, which requires re-interpolation with drainage enforcement at that resolution.

Purpose

To enhance the speed and efficiency for visualisation and processing of the smoothed 1 second DEM data within the Galilee Basin Subregion

Dataset History

The original, Australia wide, 1 second smoothed DEM was clipped to rectangular extents of the Galilee subregion using the Spatial Analyst 'Extract By Rectangle' tool in ESRI ArcCatalog v10.0 with the following parameters:

Input raster: source 1 second SRTM

Extent: Galilee Basin subregion polygon

Extraction Area: INSIDE

'no data' values are created outside the clip extent therefore the extent of the dataset may still reflect the national DEM extent in ArcCatalog. Check the tool details for more info.

The lineage of the source 1 second SRTM is below:

The following datasets were used to derive this version of the 1 second DEM products:

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 were produced by NASA from radar data collected by the Shuttle Radar Topography 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.

Full metadata, methodologies and lineage descriptions can be found in the PDF userguide within this dataset.

Dataset Citation

Bioregional Assessment Programme (2014) Smoothed Digital Elevation Model (DEM) - 1 arc second resolution - Clipped to Galilee Subregion extent. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/0fe257aa-8845-4183-9d05-5b48edd98f34.

Dataset Ancestors

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

This product is superseded by version 4.2. See: https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4-2

Citation and Main Description:

This product is described in Chapter 5 of the 2018 DWR Delta Modeling Section annual report, produced jointly with USGS.

https://water.ca.gov/-/media/DWR-Website/Web-Pages/Library/Modeling-And-Analysis/Files/Modeling-and-Analysis-PDFs/FINAL6BayDelta39thProgress-Report071918.pdf

Domain and Product

Changes between 4.0 and 4.1 are documented in the change log below and are most pronounced in the Suisun Marsh region and in the incorporation of some improvements in the South Delta.

Changes in version 4 relative to prior products are limited to the region east of the Carquinez Strait (starting around Carquinez Bridge). To facilitate compatibility between products released by DWR and USGS/NOAA partners, DWR distributes the region west of the active work at 10m resolution but does not actively work in this region. The San Pablo Bay boundary of active revision in the present product in a place where its source data matches that of other Bay elevation models, e.g., the 2m seamless high-resolution bathymetric and topographic DEM of San Francisco Bay by USGS Earth Resources Observation and Science Center (EROS) (https://topotools.cr.usgs.gov/coned/sanfrancisco.php ), the 2010 San Francisco Bay DEM by National Oceanic and Atmospheric Administration (https://www.ngdc.noaa.gov/metaview/page?xml=NOAA/NESDIS/NGDC/MGG/DEM/iso/xml/741.xml&view=getDataView&header=none ) or the prior (version 3) 10m digital elevation model (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3 ).The 10m DEM for the Bay-Delta is based on the first on the list, i.e. EROS’ 2m DEM for the Bay

New work reported here was done at 2m resolution, although the improvements have been incorporated into the 10m products as much as possible. Relative to the previous DWR release (https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-v3), the 2m DEM product reported here consolidates work at this resolution into a small number of larger surfaces representing approximately one-third of the Delta (link to the Coverage Areas page). Laterally, the 2m models now extend over the levee crest as needed to match well with Delta LiDAR (http://www.atlas.ca.gov/download.html#/casil/imageryBaseMapsLandCover/lidar2009 ), the main terrestrial source of data used in this work. The 10m product (link to the Coverage Areas page) is based on the updated USGS DEM (https://www.sciencebase.gov/catalog/item/58599681e4b01224f329b484 ). In places where updated 2m models overlap the 10 meters, the 10m base elevation model was updated by resampling the new 2m model and adding levee enforcement. At the border between the 2m and 10m models, the two resolutions were locally edge-matched over a small region to maintain smoothness. For more information, please refer to the report: A Revised Continuous Surface Elevation Model for Modeling (link to Chapter 5 in the 2018 Delta Modeling Section Annual Report (https://water.ca.gov/-/media/DWR-Website/Web-Pages/Library/Modeling-And-Analysis/Files/Modeling-and-Analysis-PDFs/FINAL6BayDelta39thProgress-Report071918.pdf). Please note that by agreement with our data providers we distribute only our own integrated maps, not the original source point data. (https://water.ca.gov/-/media/DWR-Website/Web-Pages/Library/Modeling-And-Analysis/Files/Modeling-and-Analysis-PDFs/FINAL6BayDelta39thProgress-Report071918.pdf

Version: 4.1

• Time Completed: December 2018
• Horizontal Datum: NAD83
• Spheroid:GRS1980
• Projection:UTM_Zone_10N (meters)
• Vertical Datum:NAVD88 (meters)

Data Sources

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4/resource/981a57b5-e16f-4632-b7e0-4a533534a79c

Coverage Areas

https://data.cnra.ca.gov/dataset/san-francisco-bay-and-sacramento-san-joaquin-delta-dem-for-modeling-version-4/resource/2e3f2b6e-9f0d-4719-b32a-dff449be18af

Changes since 4.0

• correct the year of survey for COE single-beam (OBJECTID 25) from 1988 to 1988
• update 2m DEM for Montezuma Slough based on newly connected multibean data by NCRO (August 2018 for Montezuma Slough and Hunter Cut)
• cut the portion of DEM for Calaveras from v3 2m San Joaquin River (dem_calaveras_river_2m_20120902
• Changes only occurs in part 1 of "dem_north_delta_2m_v4_20171220" (v4 final): 1.apply patch at the intersection of Sacramento River and Feather River; 2.add Fremont Weir from the 2m Yolo DEM
• The following modifications are applied to "dem_yolo_merge3_mod_20161202" (v4 final; D:\dem_list_review\yolo_bypass):
• Improved merge with Sacramento River on the north:
• Improved merge with 2m Shag/Stairstep dem (dem_shag_stairstep_toe_drain_2m_v4_beta_20150518.tif ) which goes beyond multibeam data, derived based on mixed single-beam datasets using GAM
• Improved patching and compatibility with USGS 10m release
• Create 2m DEM for the Old River between SWP/CVP to Five points based on new bathymetry data collected in June 2018
• Add the newly developed 2m Montazuma Slough DEM as part of v4 release. Update 10m Delta DEM to be consistent with this addition and update data source polygon accordingly.
• Create 2m DEM for the Old River near Coney Island based on new bathymetry data collected in June 2018
• Incorporate modifications done for Yolo and North Delta into 10m DEM for Delta and Bay-Delta.
• The 20181120 version of the 2m South Delta DEM missed a small region that is included in the old version
• Add LiDAR to Shag Slough in 2m Yolo DEM (dem_yolo_2m_20181012), The existing DEM for Shag Slough did not extend sufficiently over banks.

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 …Show full descriptionThe 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.

Relative elevation data was extracted from the map of residual topography over 25-40 km swaths extending from the Rio Bermejo channel centerline. We quantified the superelevation and incision of the Rio Bermejo relative to the surrounding topography by making a residual elevation map (sensu Cohen et al. (2015, doi:10.1130/2015.1212(16); McGlue et al. (2016, doi:10.2110/jsr.2016.82)). We created a series of 1 km wide swaths set perpendicular to the Rio Bermejo flow direction and extracted the minimum elevation of the active Rio Bermejo channel within each swath using the TanDEM-X DEM. Variability in DEM values for water surfaces created ~2-4 m noise in elevation values of the active channel which we smoothed by fitting a 4th order polynomial to the extracted active channel elevations to estimate the downstream change in elevation. We subtracted the polynomial fit from the DEM to yield a DEM that was de-trended from the Rio Bermejo channel slope, with values representing the local relief between the active channel and the surrounding terrain. Relative elevation is the elevation of the flood basin surface relative to the river channel. Positive values represent channel incision, negative values represent channel superelevation.