This statistic shows a ranking of the ten lowest places on earth based on elevation below sea level. The world's lowest place on earth is the Dead Sea located in Jordan and Israel, with an elevation amounting to approximately 414 meters below sea level.

At 282 feet below sea level, Death Valley in the Mojave Desert, California is the lowest point of elevation in the United States (and North America). Coincidentally, Death Valley is less than 85 miles from Mount Whitney, the highest point of elevation in the mainland United States. Death Valley is one of the hottest places on earth, and in 1913 it was the location of the highest naturally occurring temperature ever recorded on Earth (although some meteorologists doubt its legitimacy). New Orleans Louisiana is the only other state where the lowest point of elevation was below sea level. This is in the city of New Orleans, on the Mississippi River Delta. Over half of the city (up to two-thirds) is located below sea level, and recent studies suggest that the city is sinking further - man-made efforts to prevent water damage or flooding are cited as one reason for the city's continued subsidence, as they prevent new sediment from naturally reinforcing the ground upon which the city is built. These factors were one reason why New Orleans was so severely impacted by Hurricane Katrina in 2005 - the hurricane itself was one of the deadliest in history, and it destroyed many of the levee systems in place to prevent flooding, and the elevation exacerbated the damage caused. Highest low points The lowest point in five states is over 1,000 feet above sea level. Colorado's lowest point, at 3,315 feet, is still higher than the highest point in 22 states or territories. For all states whose lowest points are found above sea level, these points are located in rivers, streams, or bodies of water.

Introduction

Climate 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.

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This 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 Alaska
Elevation 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.

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.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The Low Elevation Coastal Zone (LECZ) Global Delta Urban-Rural Population and Land Area Estimates, Version 1 data set provides country-level estimates of urban, quasi-urban, rural, and total population (count), land area (square kilometers), and built-up areas in river delta- and non-delta contexts for 246 statistical areas (countries and other UN-recognized territories) for the years 1990, 2000, 2014 and 2015. The population estimates are disaggregated such that compounding risk factors including elevation, settlement patterns, and delta zones can be cross-examined. The Intergovernmental Panel on Climate Change (IPCC) recently concluded that without significant adaptation and mitigation action, risk to coastal commUnities will increase at least one order of magnitude by 2100, placing people, property, and environmental resources at greater risk. Greater-risk zones were then generated: 1) the global extent of two low-elevation zones contiguous to the coast, one bounded by an upper elevation of 10m (LECZ10), and one by an upper elevation of 5m (LECZ05); 2) the extent of the world's major deltas; 3) the distribution of people and built-up area around the world; 4) the extents of urban centers around the world. The data are layered spatially, along with political and land/water boundaries, allowing the densities and quantities of population and built-up area, as well as levels of urbanization (defined as the share of population living in "urban centers") to be estimated for any country or region, both inside and outside the LECZs and deltas, and at two points in time (1990 and 2015). In using such estimates of populations living in 5m and 10m LECZs and outside of LECZs, policymakers can make informed decisions based on perceived exposure and vulnerability to potential damages from sea level rise.

The Everglades Depth Estimation Network (EDEN) produces daily depth estimates for the Greater Everglades. This data release includes geospatial data to produce depth estimates for the EDEN from updated digital elevation models. The data release includes three main types of data: 1) 10-m digital elevation models (DEMs) with elevation uncertainty treatment; 2) 50-m DEMs with elevation uncertainty treatment; and 3) spatial metadata for the DEMs used. Specifically, this dataset includes accuracy information by zone. These data address elevation error by using Monte Carlo simulations with 1,000 iterations with observations of elevation error in vegetated wetlands and assumptions error in vegetated non-wetland areas and non-vegetated areas. On a per-pixel basis, we created raster surfaces that represented the minimum elevation, maximum elevation, and percentiles (1 to 99). We determined the “best” elevation percentiles for each EDEN zone (Haider and others, 2020) based on the mean bias error, which was calculated for the difference between the USGS high-accuracy elevation dataset (HAED; Jones and Price, 2007) and the DEM. In this case, the percentile DEM with the mean bias error closest to zero for each zone was selected. All zones were combined to create a seamless mosaic. For each zone, upper and lower elevation estimates were determined based on a general rule that selected the percentile that was the farthest from the “best” percentile but had a mean bias error that was within (+/-) 5 cm. Areas in lower and upper estimate DEMs that have “NoData” values indicate that the there was no percentile that could be used to satisfy this rule. For example, a zone may not have a lower estimate if the “best” estimate was the minimum raster. A zone may not have an upper estimate if the next percentile had a mean bias error that was greater than 5 cm.

description: 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.; abstract: 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.

Elevation strongly influences soil moisture and patterns of tundra plant communities. Areas less than 100 m above sea level were separated to show low-elevation plains. Areas above 100 m elevation were divided into 333-m intervals to show decreases of about 2 °C, as predicted by the adiabatic lapse rate of 6 °C per 1000 m. This corresponds to the change in mean July temperature between Bioclimate Subzones. Vegetation in mountainous regions changes with elevation, forming distinct elevational belts which correspond approximately to bioclimatic subzones. Vegetation is also modified by local topographic effects such as slope, aspect, and cold-air drainage. This heterogeneity was too detailed to map at this scale, so vegetation in mountainous areas was mapped as a complex, using a diagonal hachure pattern. The background color and the orientation of the hatching represent the pH of the dominant bedrock (magenta for non-carbonate bedrock including sandstone and granite, purple for carbonate bedrock including limestone and dolomite). The color of the hatching represents the bioclimate subzone at the lowest elevation within the polygon (yellow hatching for Subzone D and red hatching for Subzone E). Back to Alaska Arctic Tundra Vegetation Map (Raynolds et al. 2006) Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes AVHRR NDVI , Bioclimate Subzone, Elevation, False Color-Infrared CIR, Floristic Province, Lake Cover, Landscape, Substrate Chemistry, Vegetation References Raynolds, M.K., Walker, D.A., Maier, H.A. 2005. Plant community-level mapping of arctic Alaska based on the Circumpolar Arctic Vegetation Map. Phytocoenologia. 35(4):821-848. http://doi.org/10.1127/0340-269X/2005/0035-0821 Raynolds, M.K., Walker, D.A., Maier, H.A. 2006. Alaska Arctic Tundra Vegetation Map. 1:4,000,000. U.S. Fish and Wildlife Service. Anchorage, AK.

This dataset contains a seamless high resolution, two-meter, topographic lidar digital elevation model (DEM) of the Lower Texas Coast. The elevations in this DEM represent the topographic bare-earth surface. The dataset is a fusion of several airborne topographic light detection and ranging (lidar) surveys acquired by various surveyors between the years 2007 – 2019 where coverage is primarily from 2018 and 2019. The landward extent of the lidar surveys selected for the creation of this DEM is determined by the boundary of the ADvanced CIRCulation (ADCIRC) TX2008_R35H computational mesh obtained from the Computational Hydraulics Group at The University of Texas at Austin. The spatial reference used for the tiles in the DEM is in Universal Transverse Mercator (UTM) Zone 14 in units of meters and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevations are referenced to the North American Datum of 1988 (NAVD88). The 2-meter DEM of the upper Texas coast is available under GRIIDC Unique Dataset Identifier (UDI): HI.x833.000:0009 (DOI: 10.7266/2MYPTJ7Y).

This dataset was created to represent the land surface elevation at 1:24,000 scale for Florida. The elevation contour lines representing the land surface elevation were digitized from United States Geological survey 1:24,000 (7.5 minute) quadrangles and were compiled by South Florida, South West Florida, St. Johns River and Suwannee River Water Management Districts and FDEP. QA and corrections to the data were supplied by the Florida Department of Environmental Protection's Florida Geological Survey and the Division of Water Resource Management. This data, representing over 1,000 USGS topographic maps, spans a variety of contour intervals including 1 and 2 meter and 5 and 10 foot. The elevation values have been normalized to feet in the final data layer. Attributes for closed topographic depressions were also captured where closed (hautchered) features were identified and the lowest elevation determined using the closest contour line minus one-half the contour interval. This data was derived from the USGS 1:24,000 topographic map series. The data is more than 20 years old and is likely out-of-date in areas of high human activity.

Lidar-derived elevation data for lower Serpentine Glacier and adjacent slopes, Southcentral, Alaska, collected October 14, 2022, Raw Data File 2023-14, uses aerial lidar to produce a classified point cloud, a digital terrain model (DTM), and an intensity model of the unstable slope at Serpentine Glacier, located in Prince William Sound in Southcentral Alaska. Aerial and ground control data were collected on October 14, 2022, and subsequently processed using a suite of geospatial processing software. These data support a paraglacial rock slope destabilization study at Serpentine Glacier and will be used to assess and characterize an ongoing landslide hazard. All files can be downloaded from the Alaska Division of Geological & Geophysical Surveys website (http://doi.org/10.14509/31012).

This statistic shows a ranking of the ten lowest dry land points on earth. The lowest land point is the Dead Sea Depression with an elevation amounting to approximately 413 meters below sea level, however, this elevation is an estimate and tends to fluctuate. The shoreline of the Dead Sea is the lowest dry land in the world.

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.

This map shows detailed elevation in Santa Monica, California. With Smart Mapping, two variables are mapped and shown using both size and color. In this map, the elevation variable is mapped twice, once by size and once by color. The brighter green lines show areas with higher elevation while the dark gray lines show areas of low elevation. The higher elevations also have a slightly thicker line width to subtly enhance the distinction between elevations. Also, the higher elevations have a low transparency while the lower elevations have a higher transparency. This data set was downloaded from the City of Santa Monica's Information System.

Elevation strongly influences soil moisture and patterns of tundra plant communities. Areas less than 100 m above sea level were separated to show low-elevation plains. Areas above 100 m elevation were divided into 333-m intervals to show decreases of about 2 °C, as predicted by the adiabatic lapse rate of 6 °C per 1000 m. This corresponds to the change in mean July temperature between Bioclimate Subzones. Vegetation in mountainous regions changes with elevation, forming distinct elevational belts which correspond approximately to bioclimatic subzones. Vegetation is also modified by local topographic effects such as slope, aspect, and cold-air drainage. This heterogeneity was too detailed to map at this scale, so vegetation in mountainous areas was mapped as a complex, using a diagonal hachure pattern. The background color and the orientation of the hatching represent the pH of the dominant bedrock (magenta for non-carbonate bedrock including sandstone and granite, purple for carbonate bedrock including limestone and dolomite). The color of the hatching represents the bioclimate subzone at the lowest elevation within the polygon (yellow for Subzone D, red for Subzone E). Back to Circumpolar Arctic Vegetation Map Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: AVHRR Biomass 2010, AVHRR Biomass Trend 1982-2010, AVHRR False Color Infrared 1993-1995, AVHRR NDVI 1993-1995, AVHRR NDVI Trend 1982-2010, AVHRR Summer Warmth Index 1982-2003, Bioclimate Subzone, Coastline and Treeline, Elevation, Floristic Provinces, Lake Cover, Landscape Physiography, Landscape Age, Substrate Chemistry, Vegetation References Elvebakk, A. 1999. Bioclimate delimitation and subdivisions of the Arctic. Pages 81-112 in I. Nordal and V. Y. Razzhivin, editors. The Species Concept in the High North - A Panarctic Flora Initiative. The Norwegian Academy of Science and Letters, Oslo. Yurtsev, B. A. 1994. Floristic divisions of the Arctic. Journal of Vegetation Science 5:765-776.

The land surface forms were identified using the method developed by the Missouri Resource Assessment Partnership (MoRAP). The MoRAP method is an automated land surface form classification based on Hammond's (1964a, 1964b) classification. MoRAP made modifications to Hammond's classification, which allowed finer-resolution elevation data to be used as input data and analyses to be made using 1 km2 moving window (True, 2002; True et al., 2000). While Hammond's methodology was based on three variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief (True, 2002). Slope is classified as gently sloping or not gently sloping using a threshold value of 8%. Local relief, the difference between the maximum and minimum elevation in a 1km2 neighborhood for analysis, is classified into five classes (0-15m, 16-30m, 31-90m, 91-150m, and >150m). Slope classes and relief classes were subsequently combined to produce eight land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains). In the implementation for the contiguous United States, Sayre et al. (2009) further refined the MoRAP methodology to identify a new land surface form class, "high mountains/deep canyons", by using an additional local relief class (>400 m). This method was implemented for Africa using a void-filled 90m SRTM elevation dataset which was created from the 30m SRTM elevation data provided by the National Geospatial-Intelligence Agency. In the preliminary output, which had nine land surface form classes (flat plains, smooth plains, irregular plains, escarpments, low hills, hills, breaks/foothills, and low mountains, and high mountains/deep canyons), artifacts were identified over flat desert areas affecting the classification between the two lowest relief classes, "flat plains" and "smooth plains." Since this problem was especially pronounced in areas where the input SRTM elevation data originally had data-voids, the problem could have been caused by anomalies or artifacts in the input data, which resulted from the void-filling processes. Instead of further investigating causes of the problem, the two land surface form classes were combined. In addition, the "low hills" class which had a very low occurrence was combined with the "hills" class. As a result, seven land surface form classes were identified in the final dataset (smooth plains, irregular plains, escarpments, hills, breaks/foothills, low mountains, and high mountains/deep canyons). References: Hammond, E.H., 1964a. Analysis of Properties in Land Form Geography - An Application to Broad-Scale Land Form Mapping. Annals of the Association of American Geographers, v. 54, no. 1, p. 11-19. Hammond, E.H. 1964b. Classes of land surface form in the forty-eight states, U.S.A. Annals of the Association of American Geographers. 54(1): map supplement no. 4, 1: 5,000,000. Sayre, R., P. Comer, H. Warner, and J. Cress. 2009. A new map of standardized terrestrial ecosystems of the conterminous United States: U. S. Geological Survey professional Paper 1768, 17 p. True, D. 2002. Landforms of the Lower Mid-West. Missouri Resource Assessment Partnership. MoRAP Map Series MS-2003-001, scale 1:1,500,000. http://www.cerc.usgs.gov/morap/Assets/maps/Landforms_of_the_Lower_Mid-West_MS-2002-01.pdf. True, D., T. Gordon, and D. Diamond. 2000. How the size of a sliding window impacts the generation of landforms. Missouri Resources Assessment Partnership. http://www.cerc.cr.usgs.gov/morap/projects/landform_model/landforms2001_files/frame.htm.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinity of Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

A high-resolution (10-meter per pixel) digital elevation model (DEM) was created for the Sacramento-San Joaquin Delta using both bathymetry and topography data. This DEM is the result of collaborative efforts of the U.S. Geological Survey (USGS) and the California Department of Water Resources (DWR). The base of the DEM is from a 10-m DEM released in 2004 and updated in 2005 (Foxgrover and others, 2005) that used Environmental Systems Research Institute(ESRI), ArcGIS Topo to Raster module to interpolate grids from single beam bathymetric surveys collected by DWR, the Army Corp of Engineers (COE), the National Oceanic and Atmospheric Administration (NOAA), and the USGS, into a continuous surface. The Topo to Raster interpolation method was specifically designed to create hydrologically correct DEMs from point, line, and polygon data (Environmental Systems Research Institute, Inc., 2015). Elevation contour lines were digitized based on the single beam point data for control of channel morphology during the interpolation process. Checks were performed to ensure that the interpolated surfaces honored the source bathymetry, and additional contours and(or) point data were added as needed to help constrain the data. The original data were collected in the tidal datum Mean Lower or Low Water (MLLW), or the National Geodetic Vertical Datum of 1929 (NGVD29). All data were converted to NGVD29. The 2005 USGS DEM was updated by DWR, first by converting the DEM to the current modern datum of National Geodetic Vertical Datum of 1988 (NGVD88) and then by following the methodology of the USGS DEM, established for the 2005 DEM (Foxgrover and others, 2005) for adding newly collected single and multibeam bathymetric data. They then included topographic data from lidar surveys, providing the first DEM that included the land/water interface (Wang and Ateljevich, 2012). The USGS further updated and expanded the DWR DEM with the inclusion of USGS interpolated sections of single beam bathymetry data collected by the COE and USGS scientists, expanding the DEM to include the northernmost areas of the Sacramento-San Joaquin Delta, and by making use of a two-meter seamless bathymetric/topographic DEM from the USGS EROS Data Center (2013) of the San Francisco Bay region. The resulting 10-meter USGS DEM encompasses the entirety of Suisun Bay, beginning with the Carquinez Strait in the west, east to California Interstate 5, north following the path of the Yolo Bypass and the Sacramento River up to Knights Landing, and the American River northeast to the Nimbus Dam, and south to areas around Tracy. The DEM incorporates the newest available bathymetry data at the time of release, as well as including, at minimum, a 100-meter band of available topography data adjacent to most shorelines. No data areas within the DEM are areas where no elevation data exists, either due to a gap in the land/water interface, or because lidar was collected over standing water that was then cut out of the DEM.

This dataset contains a bare-earth digital elevation model (DEM) for Northampton and Accomack Counties, Virginia based on data collected March 25-30, 2010 and processed to yield bare-earth elevations. It was created using LiDAR by Sanborne Geosystems under a contract with the Virginia Information Technologies Agency (VITA) with funding from The Nature Conservancy, USGS and the Virginia Coast Reserve Long-term Ecological Research project of the University of Virginia. Original LiDAR point data (approximately 1 meter spacing) was used to create a digital elevation model (DEM) with a cell resolution of 10 ft. (3.048 m). The DEM data layer is in the State Plane coordinate system (U.S. Feet) and uses the NAVD88 vertical datum with the 2009 Geoid for elevation in feet. As detailed in the included quality report, elevations are accurate to 0.65 feet or better. Water areas have been hydroflattened and may also include salt marsh areas that were inundated at the time of the flights. As a result, water areas were given a default minimum elevation below the minimum elevation measured by LiDAR over a given area and dependent on tidal cycle. Areas at or below this minimum elevation within the water mask may include salt marsh and tidal flats as well as open water. The elevation of the hydroflattening varies spatially. Salt marsh areas within the water mask were later determined by VCRLTER staff based on the following factors: (1) present as marsh (code 18) in the NOAA CCAP 2006 land cover layer and not included as an open water feature in the 2010 USGS National Hydrography Dataset, (2) minimum contiguous area of 1800 square meters, approximately equal to two 30 meter resolution CCAP pixel cells, and (3) includes extensive areas of salt marsh within north-south flight line “stripesâ€, primarily the seaside lagoons and marshes south of Parramore Island and the town of Wachapreague plus Chesapeake Bay marshes immediately north of Tangier Island (and excludes edge-only areas elsewhere). Two polygon shapefiles have been added to this dataset: one shapefile shows the region-specific water masks and includes the elevation of the masks (in meters, not feet); the other shapefile shows areas of saltmarsh hidden within the water mask and includes a recommended replacement elevation (0.404 meters) based on the average above-water-mask LiDAR elevations of all CCAP-minus-NHD-water-determined marshes of all seaside marshes south of Chincoteague Inlet. The two polygon shapefiles have been reprojected to the WGS84 UTM zone 18N coordinate system, and the NAVD88 vertical elevations converted from feet to meters.