The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

This dataset contains average elevation data at 1-degree resolution for the globe, and at 5-minute resolution for Europe, parts of North Africa, and most of North America.

The USGS and the NGA have collaborated on the development of a notably enhanced global elevation model named the GMTED2010 that replaces GTOPO30 as the elevation dataset of choice for global and continental scale applications. The new model has been generated at three separate resolutions (horizontal post spacing) of 30 arc-seconds (about 1 kilometer), 15 arc-seconds (about 500 meters), and 7.5 arc-seconds (about 250 meters). This new product suite provides global coverage of all land areas from latitude 84 degrees N to 56 degrees S for most products, and coverage from 84 degrees N to 90 degrees S for several products. Some areas, namely Greenland and Antarctica, do not have data available at the 15- and 7.5-arc-second resolutions because the input source data do not support that level of detail. An additional advantage of the new multi-resolution global model over GTOPO30 is that seven new raster elevation products are available at each resolution. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. The systematic subsample product is defined using a nearest neighbor resampling function, whereby an actual elevation value is extracted from the input source at the center of a processing window. Most vertical heights in GMTED2010 are referenced to the Earth Gravitational Model 1996 (EGM 96) geoid (NGA, 2010). In addition to the elevation products, detailed spatially referenced metadata containing attribute fields such as coordinates, projection information, and raw source elevation statistics have been generated on a tile-by-tile basis for all the input datasets that constitute the global elevation model. GMTED2010 is based on data derived from 11 raster-based elevation sources.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) provides a new level of detail in global topographic data. Previously, the best available global DEM was GTOPO30 with a horizontal grid spacing of 30 arc-seconds. The GMTED2010 product suite contains seven new raster elevation products for each of the 30-, 15-, and 7.5-arc-second spatial resolutions and incorporates the current best available global elevation data. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. Metadata have also been produced to identify the source and attributes of all the input elevation data used to derive the output products. Many of these products will be suitable for various regional continental-scale land cover mapping, extraction of drainage features for hydrologic modeling, and geometric and radiometric correction of medium and coarse resolution satellite image data. The global aggregated vertical accuracy of GMTED2010 can be summarized in terms of the resolution and RMSE of the products with respect to a global set of control points (estimated global accuracy of 6 m RMSE) provided by the National Geospatial-Intelligence Agency (NGA). At 30 arc-seconds, the GMTED2010 RMSE range is between 25 and 42 meters; at 15 arc-seconds, the RMSE range is between 29 and 32 meters; and at 7.5 arc-seconds, the RMSE range is between 26 and 30 meters. GMTED2010 is a major improvement in consistency and vertical accuracy over GTOPO30, which has a 66 m RMSE globally compared to the same NGA control points. In areas where new sources of higher resolution data were available, the GMTED2010 products are substantially better than the aggregated global statistics; however, large areas still exist, particularly above 60 degrees North latitude, that lack good elevation data. As new data become available, especially in areas that have poor coverage in the current model, it is hoped that new versions of GMTED2010 might be generated and thus gradually improve the global model.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

This Web Map is a subset of World Elevatuon GMTED image service.The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) dataset provides a 7.5 arcsecond (approximately 250 meter resolution) digital elevation model with world-wide coverage at a resolution suitable for regional to continental scale analyses. Dataset SummaryThis layer provides access to a 250m cell-sized raster created from the Global Multi-resolution Terrain Elevation Data 2010 7.5 arcsecond mean elevation product. The dataset represents a compilation and synthesis of 11 different existing raster data sources. The data were published in 2011 by the USGS and the National Geospatial-Intelligence Agency.The dataset is documented in the publication: Danielson and Gesch. 2011. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). U.S. Geological Survey Open-File Report 2011–1073, 26 p.The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and modeling efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEM ranges from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).

The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) dataset provides a 7.5 arcsecond (approximately 250 meter resolution) digital elevation model with world-wide coverage at a resolution suitable for regional to continental scale analyses. Dataset SummaryThis layer provides access to a 250m cell-sized raster created from the Global Multi-resolution Terrain Elevation Data 2010 7.5 arcsecond mean elevation product. The dataset represents a compilation and synthesis of 11 different existing raster data sources. The data were published in 2011 by the USGS and the National Geospatial-Intelligence Agency.The dataset is documented in the publication: Danielson and Gesch. 2011. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). U.S. Geological Survey Open-File Report 2011–1073, 26 p.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This statistic shows a ranking of the estimated average elevation of the land area in 2020 in Latin America, differentiated by country.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Accurate, high-resolution elevation information is vital to understanding highly dynamic U.S. coastal regions. The new dataset consists of a detailed and highly accurate elevation model incorporating the best available multi-source topographic and bathymetric elevation data. The Coastal National Elevation Database (CoNED) Project - topobathymetric digital elevation models (TBDEMs) integrate hundreds of different data sources including topographic and bathymetric LiDAR point clouds, hydrographic surveys, side-scan sonar surveys, and multibeam surveys obtained from multiple agencies. The LiDAR and bathymetry surveys were sorted and prioritized based on survey date, accuracy, spatial distribution, and point density to develop a model based on the best available elevation data. Because bathymetric data is typically referenced to tidal datums (such as Mean High Water or Mean Sea Level), all tidally-referenced heights were transformed into orthometric heights that are normally used for mapping elevation on land (based on the North American Vertical Datum of 1988).

Difference from Mean Elevation

This data is part of the Global Ensemble Digital Terrain Model (GEDTM30) dataset. Check the related identifiers section below to access other parts of the dataset.

Disclaimer

This is the first release of the Multiscale Land Surface Parameters (LSPs) of Global Ensemble Digital Terrain Model (GEDTM30). Use for testing purposes only. 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

LSPs are derivative products of the GEDTM30 that represent measures of local topographic position, curvature, hydrology, light, and shadow. A pyramid representation is implemented to generate multiscale resolutions of 30m, 60m, 120m, 240m, 480m, and 960m for each LSP. The parametrization is powered by Whitebox Workflows in Python. To see the documentation, please visit our GEDTM30 GitHub (https://github.com/openlandmap/GEDTM30).

Dataset Contents

This dataset includes:

• Global Difference from Mean Elevation 120m
  
• Global Difference from Mean Elevation 240m
  
• Global Difference from Mean Elevation 480m
  
• Global Difference from Mean Elevation 960m
  

Due to Zenodo's storage limitations, the high resolution LSP data are provided via external links:

• Global Difference from Mean Elevation 30m
  
• Global Difference from Mean Elevation 60m
  

Related Identifiers

• Digital Terrain Model:
  GEDTM30
• 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: properties derived from Digital Terrain Model
• How the data was collected or derived: The data was derived using Whitbox Workflows.
• Methods used: LSP algorithms.
• 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, 240m, 480m, 960m
• Image size: 360,000P x 178,219L; 180,000P x 89,110L; 45,000L x 22,282L
• File format: Cloud Optimized Geotiff (COG) format.
• Additional information:
  



Layer	Scale	Data Type	No Data
Difference from Mean Elevation	100	Int16	32,767
Geomorphons	1	Byte	255
Hillshade	1	UInt16	65,535
LS Factor	1,000	UInt16	65,535
Maximal Curvature	1,000	Int16	32,767
Minimal Curvature	1,000	Int16	32,767
Negative Openness	100	UInt16	65,535
Positive Openness	100	UInt16	65,535
Profile Curvature	1,000	Int16	32,767
Ring Curvature	10,000	Int16	32,767
Shape Index	1,000	Int16	32,767
Slope in Degree	100	UInt16	65,535
Specific Catchment Area	1,000	UInt16	65,535
Spherical Standard Deviation of the Normals	100	Int16	32,767
Tangential Curvature	1,000	Int16	32,767
Topographic Wetness Index	100	Int16	32,767

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 twi_edtm_m_120m_s_20000101_20221231_go_epsg.4326_v20241230.tif, the fields are:

1. generic variable name: twi = topographic wetness index
2. variable procedure combination: edtm = derivative direct from global ensemble digital terrain model
3. Position in the probability distribution/variable type: m = measurement
4. Spatial support: 120m
5. Depth reference: s = surface
6. Time reference begin time: 20000101 = 2000-01-01
7. Time reference end time: 20211231 = 2021-12-31
8. Bounding box: go = global
9. EPSG code: EPSG:4326
10. Version code: v20241230 = version from 2024-12-30

This dataset provides a subset of the SRTM30 Digital Elevation Model (DEM) elevation and standard deviation data for the Amazon Basin. SRTM30 is a near-global digital elevation model (DEM) comprising a combination of data from the Shuttle Radar Topography Mission (SRTM), flown in February, 2000, and the earlier U.S. Geological Survey's GTOPO30 data set. The SRTM30 resolution is 30 arc-sec or about 1 km. In processing the SRTM data, to combine with GTOPO30, the data were resampled from 3 arc-sec to 30 arc-sec. Provided here are the mean elevation and the standard deviation (STD) of the data points used in the averaging. The STD is thus an indication of topographic roughness useful in some applications.

Waterbodies that exhibit persistent water and lack trees within the majority of its area are defined as open waterbodies by the National Hydrography Dataset (NHD, USGS, 2011). The data presented here were created to determine the volume of water for open waterbodies on the floodplain of the Atchafalaya River Basin. Open waterbodies and their surficial geometry were identified using a geographic information system (GIS) shapefile acquired from the NHD. The spatial coverage was clipped to areas within the Atchafalaya Basin Floodway System area of interest. Bed elevations of a representative set of channels were determined from bathymetric surveys conducted 2014-2017 during high river stages by the U.S. Geological Survey (USGS) personnel. A Lowrance HDS5 single-beam digital recording chart plotter was used to collect sound navigation and ranging (SONAR) bathymetry at a frequency of 10Hz. Horizontal position was recorded simultaneously from an GPS internal to the SONAR (+/- 2 meters). Most dredged canals and channels within, or to access an oil field were dredged to similar bed elevations so representative surveyed channel bed elevations were extended to similar channels types within a 4-kilometer distance. Mean bed elevations for the Henderson Lake Watershed Management Unit (WMU) were derived from Kelso and others, 2011. Inaccessible waterbody bed elevations were estimated based on GoogleEarth (2019) imagery from 2000 through 2019. Elevations derived from this imagery were based on exposure of the waterbody beds relative to laser imaging, detection, and ranging (lidar) elevations (USGS 2013), and/or similarity of aquatic vegetation relative to surveyed channels. The mean bottom elevations of waterbodies were calculated from channel or lake cross sections in meters North American vertical datum 1988 (NAVD88). Cross-sections were surveyed using SONAR. SONAR measured points along the channel banks were removed from the calculation of mean to avoid debris and bank slopes. The remaining points across the channel or lake bed were averaged to produce a mean elevation. Cross-section mean elevations were weighted by width of cross section and a weighted mean bottom elevation and calculated for each waterbody segment and added as a field value to the shapefile as MeanBotel. This work was partially funded through a cooperative agreement with the US Army Corps of Engineers. References: Kelso, W. E., Harlan, R., Kelley, D., and Pasco, T., 2011, Pre- and Post-Project Monitoring of Water Quality, Habitat, and Fish Communities in the Buffalo Cove and Henderson Lake, Units of the Atchafalaya River Basin: Task I. Final Report Submitted to JESCO Inc. Jennings, LA. U.S. Geological Survey (USGS), 2011, National Hydrography Dataset (NHD), accessed March 23, 2011 at: https://www.usgs.gov/core-science-systems/ngp/national-hydrography. U.S. Geological Survey (USGS), 2013, 2010 U.S. Geological Survey Topographic LiDAR: Atchafalaya Basin, Louisiana, available at https://abp.cr.usgs.gov/Library/DownloadFile.aspx?id=376.

The National Ecological Framework for Canada's "Elevation by Ecodistrict” dataset provides elevation information for ecodistrict framework polygons, in meters. It includes codes and descriptions for minimum elevation, maximum elevation, mean elevation and the difference in elevation.

This data set provides a 100 meter resolution surface topography Digital Elevation Model (DEM) of the Antarctic Peninsula. The DEM is based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) data. The original GDEM data are accurate for coastal regions of the Antarctic Peninsula, but contain large errors on snow- and ice-covered regions. This data set was developed using a new method specifically for smoothing erroneous regions, and has a mean elevation difference of - 4 meters (±25 m Root Mean Square Error [RMSE]) when compared to data from the NASA Ice, Cloud,and Land Elevation Satellite (ICESat), although elevation accuracies are lower on mountain peaks and steep-sided slopes. Data are available via FTP in GeoTIFF and ASCII formats.

This data release provides elevation distribution in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex. Elevation distribution was calculated in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data was based on the 1-meter gridded Digital Elevation Model and supplemented by 1-meter resampled 1/9 arc-second resolution National Elevation Data, where data gaps exist. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Plum Island Estuary and Parker River salt marsh complex, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms o ...

This is a tiled collection of the 3D Elevation Program (3DEP) and is 1 arc-second (approximately 30 m) resolution. The elevations in this Digital Elevation Model (DEM) represent the topographic bare-earth surface. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The seamless 1 arc-second DEM layers are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the continental United States, are referenced to the North American Vertical Datum of 1988 ( ...

The 1/3 arc-second St. Croix, U.S. Virgin Islands Coastal Digital Elevation Model will be used to support NOAA's tsunami forecast system and for tsunami inundation modeling. This DEM encompasses the Virgin Islands and the adjacent off-shore coastal area.The DEM Global Mosaic is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), along with the global GEBCO_2014 grid: http://www.gebco.net/data_and_products/gridded_bathymetry_data. NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service is a general-purpose global, seamless bathymetry/topography mosaic. It combines DEMs from a variety of near sea-level vertical datums, such as mean high water (MHW), mean sea level (MSL), and North American Vertical Datum of 1988 (NAVD88). Elevation values have been rounded to the nearest meter, with DEM cell sizes going down to 1 arc-second. Higher-resolution DEMs, with greater elevation precision, are available in the companion NAVD88: http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042 and MHW: http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799 mosaics. By default, the DEMs are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Please see NCEI's corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. In this visualization, the elevations/depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png.This is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), with vertical units referenced to mean high water (MHW). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service provides data from many individual DEMs combined together as a mosaic. By default, the rasters are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Alternatively, a single DEM or group of DEMs can be isolated using a filter/definition query or using the 'Lock Raster 'mosaic method in ArcMap. This is one of three services displaying collections of DEMs that are referenced to common vertical datums: North American Vertical Datum of 1988 (NAVD88): http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042, Mean High Water (MHW): http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799, and Mean Higher High Water: http://noaa.maps.arcgis.com/home/item.html?id=9471f8d4f43e48109de6275522856696. In addition, the DEM Global Mosaic is a general-purpose global, seamless bathymetry/topography mosaic containing all the DEMs together. Two services are available: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff Elevation Values: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff and Color Shaded Relief: http://noaa.maps.arcgis.com/home/item.html?id=feb3c625dc094112bb5281c17679c769. Please see the corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. This service has several server-side functions available. These can be selected in the ArcGIS Online layer using 'Image Display ', or in ArcMap under 'Processing Templates '. None: The default. Provides elevation/depth values in meters relative to the NAVD88 vertical datum. ColorHillshade: An elevation-tinted hillshade visualization. The depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png. GrayscaleHillshade: A simple grayscale hillshade visualization. SlopeMapRGB: Slope in degrees, visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/SlopeMapLegend_V7b.png. SlopeNumericValues: Slope in degrees, returning the actual numeric values. AspectMapRGB: Orientation of the terrain (0-360 degrees), visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/AspectMapLegendPie_V7b.png. AspectNumericValues: Aspect in degrees, returning the actual numeric values.