This dataset (DTM30CONDPR_A) was aggregated from a 10m digital terrain model that was developed for use in Mn/Model4 archaeological predictive model. The source DTM10COND raster feature dataset that was generated from existing statewide LiDAR elevation data, and processed to remove man-made features such as roads and ditches, to the greatest extent possible. Bathymetric data were used to replace level lake places for large lakes with existing bathymetric survey data. Topographic data from 1899, digitized by MGS were used to replace a portion of the Mesabi Iron Range, restoring the pit mine lands to a more natural surface. DTM10CONDPR is a pit-removed 32 bit floating point version of the DTM10COND, that was processed with the TauDEM (Terrain Analysis Using Digital Elevation Models) Pit Removal Tool to fill-in all sinks so that state wide surface hydrology calculations could be performed using other TauDEM Tools. TauDEM is a collection of surface hydrology processing tools available from Utah State University, created by David Tarboton. Version 5 of the software can be accessed here: http://hydrology.usu.edu/taudem/taudem5/index.html

This dataset was developed for use in Mn/Model4 archaeological predictive model. NOTE: The compressed dataset is over 4 GB in size due to the high resolution scale and statewide extent of the data.

The DTM10COND raster feature dataset that was generated from existing statewide LiDAR elevation data, and processed to remove man-made features such as roads and ditches, to the greatest extent possible. Bathymetric data were used to replace level lake places for large lakes with existing bathymetric survey data. Topographic data from 1899, digitized by Minnesota Geological Survey were used to replace a portion of the Mesabi Iron Range, restoring the pit mine lands to a more natural surface. DTM10CONDPR is a pit-removed version of the DTM10COND, that was processed with the TauDEM (Terrain Analysis Using Digital Elevation Models) Pit Removal Tool to fill-in all sinks so that state wide surface hydrology calculations could be performed using other TauDEM Tools. TauDEM is a collection of surface hydrology processing tools available from Utah State University, created by David Tarboton. Version 5 of the software can be accessed here: http://hydrology.usu.edu/taudem/taudem5/index.html

Disclaimer

This is the first release of the Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. A publication describing the methods used has been submitted to PeerJ and is currently under review. This work was funded by the European Union. However, the views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is." The Open-Earth-Monitor project consortium, along with its suppliers and licensors, hereby disclaims all warranties of any kind, express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and non-infringement. Neither the Open-Earth-Monitor project consortium nor its suppliers and licensors make any warranty that the website will be error-free or that access to it will be continuous or uninterrupted. You understand that you download or otherwise obtain content or services from the website at your own discretion and risk.

Description

GEDTM30 is presented as a 1-arc-second (~30m) global Digital Terrain Model (DTM) generated using machine-learning-based data fusion. It was trained using a global-to-local Random Forest model with ICESat-2 and GEDI data, incorporating almost 30 billion high-quality points. To see the documentation, please visit our GEDTM30 GitHub(https://github.com/openlandmap/GEDTM30).

This dataset covers the entire world and can be used for applications such as topography, hydrology, and geomorphometry analysis.

Dataset Contents

This dataset includes:

• GEDTM30
  Represents the predicted terrain height.
• Uncertainty of GEDTM30 prediction
  Provides an uncertainty map of the terrain prediction, derived from the standard deviation of individual tree predictions in the Random Forest model.

Due to Zenodo's storage limitations, the original GEDTM30 dataset and its standard deviation map are provided via external links:

• GEDTM30 30m
• Uncertainty of GEDTM30 prediction 30m

Related Identifiers

• Landform:
  Slope in Degree, Geomorphons
• Light and Shadow:
  Positive Openness, Negative Openness, Hillshade
• Curvature:
  Minimal Curvature, Maximal Curvature, Profile Curvature, Tangential Curvature, Ring Curvature, Shape Index
• Local Topographic Position:
  Difference from Mean Elevation, Spherical Standard Deviation of the Normals
• Hydrology:
  Specific Catchment Area, LS Factor, Topographic Wetness Index

Data Details

• Time period: static.
• Type of data: Digital Terrain Model
• How the data was collected or derived: Machine learning models.
• Statistical Methods used: Random Forest.
• Limitations or exclusions in the data: The dataset does not include data Antarctica.
• Coordinate reference system: EPSG:4326
• Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -65, 180, 85)
• Spatial resolution: 120m
• Image size: 360,000P x 178,219L
• File format: Cloud Optimized Geotiff (COG) format.
• Layer information:

Support

If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here

Naming convention

To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files.

For example, for edtm_rf_m_120m_s_20000101_20231231_go_epsg.4326_v20250130.tif, the fields are:

1. generic variable name: edtm = ensemble digital terrain model
2. variable procedure combination: rf = random forest
3. Position in the probability distribution/variable type: m = mean | sd = standard deviation
4. Spatial support: 120m
5. Depth reference: s = surface
6. Time reference begin time: 20000101 = 2000-01-01
7. Time reference end time: 20231231 = 2023-12-31
8. Bounding box: go = global
9. EPSG code: EPSG:4326
10. Version code: v20250130 = version from 2025-01-30

Objective: To be able to use the Terrain Analysis Using Digital Elevation Models (TauDEM) tools to derive hydrologically useful information from Digital Elevation Models (DEMs).

Jupyter Notebook TauDEM was used for watershed delineation and calculation of Height Above Nearest Drainage in the Logan River Watershed in Utah. To start, "logan.tif" Digital Elevation Model (DEM) data and "LoganOultet.shp" Logan Outlet were used as the main inputs. The final results were "loagnw.tif" subwatershed, "logannet.shp" stream networks, and 'loganhand.tif' HAND map. This resource includes both the inputs to and the outputs from Jupyter Notebook TauDEM used for hydrologic terrain analysis in the Logan River Watershed in Utah.

To use the Jupyter Notebook, click on the "Open With" blue bottom at the top right of this page and choose "Jupyter". Then, click on "TauDEM.ipynb" to see the code and run it.

Most part of this jupyter notebook is adopted from Tarboton and Garousi-Nejad (2017).

Tarboton, D., I. Garousi-Nejad (2017). UCGIS 2017 Hydrologic Terrain Analysis Using TauDEM Start, HydroShare, http://www.hydroshare.org/resource/d4ed65b0c3c5475aa40af88c4d627c63

This dataset contains the Digital Elevation Model (DEM) for South America from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The data were developed and distributed by processing units. There are 10 processing units for South America. The distribution files have the number of the processing unit appended to the end of the zip file name (e.g. sa_dem_3.zip contains the DEM data for unit 3-2). The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

The dataset was used to estimate the relevant range of spatial scales with multi-scale contextual spatial modelling. The modelled soil properties were cation exchange capacity, pH, and water content at field capacity. The soil quality indicator data was modelled and predicted with partial least squares regression models based on NIR and MIR spectroscopy (Pangaea DOI (doi:10.1594/PANGAEA.938522): “Soil spectroscopy data from 130 soil profiles in Lora del Rio, Andalusia, Spain”). The soil samples were taken in an area of 1000 km² around Lora del Rio, Andalusia, Spain, in the Sierra Morena mountain range (Palaeozoic granite, gneiss, and slate), at the Guadalquivir river flood plain (Pleistocene marl, calcarenite, coarse sand, and Holocene sands and loams), and southern tertiary terraces (coarse gravel and cobble with sands and loams). Present soil types according to USDA Soil Taxonomy are Alfisols, Entisols, Inceptisols, and Vertisols. The basis for the multi-scale terrain analysis was a digital terrain model by the Centro Nacional de Information Geográfica (CNIG) of the Spanish government. The digital terrain model was published under the CC-BY 4.0 license via the Centro de Descargas del CNIG (IGN; doi:10.7419/162.09.2020) with the title Digital Terrain Model - DTM05 (EPSG: 25830) and last accessed on March, 31st 2020. The study area is covered by the MTN50 map sheets 0941, 0942, 0963, 0964, 0985, and 0986. The multi-scale contextual spatial modelling and the derivation of the scaled terrain covariates was based on the Gaussian pyramid (doi:10.1016/j.geoderma.2017.09.015 and doi:10.1038/s41598-018-33516-6) and the estimation of the relevant range of scales was based on exhaustive additive and subtractive machine learning sequences (doi:10.1038/s41598-019-51395-3). The models were trained with the multi-scale terrain covariates at each soil profile location extracted from the digital terrain model derivatives. For each soil depth of the soil dataset (0-10, 10-20, 20-30, 40-60, and 70-100 cm) two model sequences (additive and subtractive) were trained.

Dataset Summary:This 3-foot resolution Digital Terrain Model (DTM) depicts topography, while removing all above-ground objects on the earth’s surface, like trees and buildings. The DTM represents the state of the landscape when countywide LiDAR data was collected in 2018 and 2020. Figure 1 shows the vintages of LiDAR contained in this raster. Quality level 1 LiDAR (QL1, red areas in figure 1) was collected in 2018. Quality level 2 LiDAR (QL2) was collected in summer, 2020.Figure 1. Recent LiDAR collections, by Quality Level (QL) in Santa Cruz County Methods:This LiDAR derivative provides information about the bare surface of the earth. The 3-foot resolution raster was produced from 2018 Quality Level 2 and 2020 Quality Level 1 LiDAR point cloud data (already ground classified) using Lastools. The processing steps were as followsCreate Tiles (lastile)Create DTM from ground classified points (las2dem)N Note that this DTM is neither hydro-flattened nor hydro-enforced.Uses and Limitations:The DTM provides a raster depiction of the ground returns for each 3x3 foot raster cell across Santa Cruz County. The layer is useful for hydrologic and terrain-focused analysis. The DTM will be most accurate in open terrain and less accurate in areas of very dense vegetation.Related Datasets:This dataset is part of a suite of LiDAR of derivatives for Santa Cruz County. See table 1 for a list of all the derivatives.Table 1. LiDAR derivatives for Santa Cruz CountyDatasetDescriptionLink to DatasheetLink to DataCanopy Height ModelThis depicts Santa Cruz County’s woody canopy as a Digital Elevation Model.https://vegmap.press/sc_chm_datasheethttps://vegmap.press/sc_chmNormalized Digital Surface ModelThis depicts the height above ground of objects on the earth’s surface, like trees and buildings.https://vegmap.press/sc_ndsm_datasheethttps://vegmap.press/sc_ndsmDigital Surface ModelThis depicts the elevation above sea level atop of objects on the earth’s surface.https://vegmap.press/sc_dsm_datasheethttps://vegmap.press/sc_dsm HillshadeThis depicts shaded relief based on the Digital Terrain Model. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/sc_hillshade_datasheethttps://vegmap.press/sc_hillshadeDigital Terrain ModelThis depicts topography, while removing all above-ground objects on the earth’s surface, like trees and buildings.https://vegmap.press/sc_dtm_datasheethttps://vegmap.press/sc_dtm

The use of remote sensing techniques to identify (geo)archaeological features is wide spread. For archaeological prospection and geomorphological mapping, Digital Terrain Models (DTMs) on based LiDAR (Light Detection And Ranging) are mainly used to detect surface and subsurface features. LiDAR is a remote sensing tool that scans the surface with high spatial resolution and allows for the removal of vegetation cover with special data filters. Archaeological publications with LiDAR data in issues have been rising exponentially since the mid-2000s. The methodology of DTM analyses within geoarchaeological contexts is usually based on “bare-earth” LiDAR data, although the terrain is often significantly affected by human activities. However, “bare-earth” LiDAR data analyses are very restricted in the case of historic hydro-engineering such as irrigation systems, mills, or canals because modern roads, railway tracks, buildings, and earth lynchets influence surface water flows and may dissect the terrain. Consequently, a "natural" pre-modern DTM with high depth accuracy is required for palaeohydrological analyses. In this study, we present a GIS-based modelling approach to generate a pre-modern and topographically purged DTM. The case study focuses on the landscape around the Early Medieval Fossa Carolina, a canal constructed by Charlemagne and one of the major medieval engineering projects in Europe. Our aim is to reconstruct the pre-modern relief around the Fossa Carolina for a better understanding and interpretation of the alignment of the Carolingian canal. Our input data are LiDAR-derived DTMs and a comprehensive vector layer of anthropogenic structures that affect the modern relief. We interpolated the residual points with a spline algorithm and smoothed the result with a low pass filter. The purged DTM reflects the pre-modern shape of the landscape. To validate and ground-truth the model, we used the levels of recovered pre-modern soils and surfaces that have been buried by floodplain deposits, colluvial layers, or dam material of the Carolingian canal. We compared pre-modern soil and surface levels with the modelled pre-modern terrain levels and calculated the overall error. The modelled pre-modern surface fits with the levels of the buried soils and surfaces. Furthermore, the pre-modern DTM allows us to model the most favourable course of the canal with minimal earth volume to dig out. This modelled pathway corresponds significantly with the alignment of the Carolingian canal. Our method offers various new opportunities for geoarchaeological terrain analysis, for which an undisturbed high-precision pre-modern surface is crucial.

This dataset contains the Digital Elevation Model (DEM) for Africa from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The DEM data were developed and distributed by processing units. There are 19 processing units for Africa. The distribution files have the number of the processing unit appended to the end of the zip file name (e.g. af_dem_3_2.zip contains the DEM data for unit 3-2). The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

Colour images of the topography of Australia, generated and manipulated by computer techniques, have been used for analysis of regional geological structures. Discussion is concentrated on lineaments, because some workers have claimed that Australias largest metalliferous deposits are related to systems of lineaments, and much work is being done on them by the mineral and petroleum exploration industries. There is only partial correspondence of topographic lineaments with those based on gravity or aeromagnetic data, at least at the present early stage of development of DTM studies. On the other hand, some major new lineaments have been found in the DTM, for example, one that continues for hundreds of kilometres from the southeast end of the Emu Fault on which the giant HYC ore body occurs. This feature is parallel to the Roxby Downs gravity lineament. DTM images provide a new tool for recognising regional geological structures and are potentially useful for mineral exploration.

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

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

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

This dataset is a Digital Terrain Model (DTM) for the Niwot Ridge Long Term Ecological Research (LTER) project area at 2 m resolution, and is well suited for hydrologic modeling and other analyses of bare-earth terrain. The DTM is derived from the first reflective surface or a Digital Surface Model (DSM) that was created from 12 micron digital stereo aerial photography. Elevation points were both automatically filtered and hand-adjusted to better represent bare earth conditions. Green Lakes Valley LiDAR (Light Detection and Ranging) point data were appended to the filtered and edited data. Breakline information (ie. ridge tops, lake edges, and streams) was added. A final 2 meter gridded DTM and shaded relief model was then generated. The DTM is useful for terrain analysis and derivation of layers such as slope angle, aspect, shaded relief images, and contours. The DTM and shaded relief model covers a total area of 98 km2 and is available in Environmental Systems Research Institute's (ESRI's) GRID format for a total dataset size of 125 MB. They share a UTM zone 13 projection, NAD83 horizontal datum and NAVD88 vertical datum, with FGDC-compliant metadata. The DTM is available through an unrestricted public license, and can be obtained online or on DVD by request (see Distributor contact information below). Imagery available in this series includes orthorectified aerial photography for 1953, 1972, 1985, 1990, 1999, 2000, 2002, 2004, 2006 and 2008. Together, the digital elevation models and imagery will be of interest to land managers, scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

[from EDINA's description of Land-form PANORAMA data: "http://edina.ac.uk/digimap/description/products/panorama.shtml"]

Land-Form PANORAMA is a digital representation of the contours from Ordnance Survey's 1:50 000 scale Landranger maps. Contours are at 10 metre vertical intervals together with breaklines, lakes, coastline and a selection of spot heights to the nearest metre. Digital contour accuracy values are typically better than 3 metres root mean square error.

The Ordnance Survey has used the dataset to derive mathematically a digital terrain-model (DTM) dataset. The dataset consists of a grid of height values at 50 metre intervals interpolated from the contour data. Height values are rounded to the nearest metre. Accuracy varies according to the complexity of the terrain, from 2 metres in a hilly rural area to 3 metres in an urban lowland area. This data is only available for downloading to your machine.

DTM data can be used for terrain analysis of lines of sight and in applications such as visual impact studies, drainage analysis, site planning.

This dataset contains the Digital Elevation Model (DEM) grid for the Asian continent from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The DEM data were developed and distributed by processing units. There are 19 processing units for Asia. The distribution files have the number of the processing unit appended to the end of the zip file name (e.g. as_dem_3_2.zip contains the DEM data for unit 3-2). The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

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

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

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

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

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

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

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

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

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for, and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

This dataset contains the Digital Elevation Model (DEM) for Europe from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The data were developed and distributed by processing units. There are 11 processing units for Europe. The distribution files have the number of the processing unit appended to the end of the zip file name (e.g. eu_dem_3_2.zip contains the DEM data for unit 3-2). The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

This dataset contains the Digital Elevation Model (DEM) for Greenland from the Hydrologic Derivatives for Modeling and Analysis (HDMA) database. The HDMA database provides comprehensive and consistent global coverage of raster and vector topographically derived layers, including raster layers of digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and vector layers of streams and catchment boundaries. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

Citation: Manley, W.F., Parrish, E.G., and Lestak, L.R., 2009, High-Resolution Orthorectified Imagery and Digital Elevation Models for Study of Environmental Change at Niwot Ridge and Green Lakes Valley, Colorado: Niwot Ridge LTER, INSTAAR, University of Colorado at Boulder, digital media. This dataset is a Digital Terrain Model (DTM) for the Niwot Ridge Long Term Ecological Research (LTER) project area at 2 m resolution, and is well suited for hydrologic modeling and other analyses of bare-earth terrain. The DTM is derived from the first reflective surface or a Digital Surface Model (DSM) that was created from 12 micron digital stereo aerial photography. Elevation points were both automatically filtered and hand-adjusted to better represent bare earth conditions. Green Lakes Valley LiDAR (Light Detection and Ranging) point data were appended to the filtered and edited data. Breakline information (ie. ridge tops, lake edges, and streams) was added. A final 2 meter gridded DTM and shaded relief model was then generated. The DTM is useful for terrain analysis and derivation of layers such as slope angle, aspect, shaded relief images, and contours. The DTM and shaded relief model covers a total area of 98 km2 and is available in Environmental Systems Research Institute's (ESRI's) GRID format for a total dataset size of 125 MB. They share a UTM zone 13 projection, NAD83 horizontal datum and NAVD88 vertical datum, with FGDC-compliant metadata. The DTM is available through an unrestricted public license, and can be obtained online or on DVD by request (see Distributor contact information below). Imagery available in this series includes orthorectified aerial photography for 1953, 1972, 1985, 1990, 1999, 2000, 2002, 2004, 2006 and 2008. Together, the digital elevation models and imagery will be of interest to land managers, scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Comparison of Root Mean Square Errors (RMSE) between the Fossa Carolina and Altmühl validation point clusters and between the measured vs. modelled pre-modern surface and the measured pre-modern surface vs. the present DTM.