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.

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 *** 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.

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.

This statistic displays the countries with the greatest range between their highest and lowest elevation points. China and Nepal share the highest elevation point worldwide, which ascends to an amount of 8848 meters above sea level. Near the city Turpan Pendi, Xinjiang, China's elevation reaches 154 meters below sea level.

This statistic shows the ten lowest points on earth. The world's lowest point is the Kola Borehole in Russia with a depth of ****** feet. The Kola Borehole is a result of a Soviet Union's drilling project which started in 1970 and was abandoned in 1989 due to temperatures that reached *** degrees Celsius. The only purpose for this project was to drill as deep as possible into the Earth's crust.

description: GLOBE is a project to develop the best available 30-arc-second (nominally 1 kilometer) global digital elevation data set. This version of GLOBE contains data from 11 sources, and 17 combinations of source and lineage. It continues much in the tradition of the National Geophysical Data Center's TerrainBase (FGDC 1090), as TerrainBase served as a generally lower-resolution prototype of GLOBE data management and compilation techniques. The GLOBE mosaic has been compiled onto CD-ROMs for the international user community. It is also available from the World Wide Web (linked from the online linkage noted above and anonymous ftp. Improvements to the global model are anticipated, as appropriate data and/or methods are made available. In addition, individual contributions to GLOBE (several areas have more than one candidate) should become available at the same website. GLOBE may be used for technology development, such as helping plan infrastructure for cellular communications networks, other public works, satellite data processing, and environmental monitoring and analysis. GLOBE prototypes (and probably GLOBE itself after its release) have been used to help develop terrain avoidance systems for aircraft. In all cases, GLOBE data should be treated as any potentially useful but guaranteed imperfect data set. Mission- or life-critical applications should consider the documented artifacts, as well as likely undocumented imperfections, in the data.; abstract: GLOBE is a project to develop the best available 30-arc-second (nominally 1 kilometer) global digital elevation data set. This version of GLOBE contains data from 11 sources, and 17 combinations of source and lineage. It continues much in the tradition of the National Geophysical Data Center's TerrainBase (FGDC 1090), as TerrainBase served as a generally lower-resolution prototype of GLOBE data management and compilation techniques. The GLOBE mosaic has been compiled onto CD-ROMs for the international user community. It is also available from the World Wide Web (linked from the online linkage noted above and anonymous ftp. Improvements to the global model are anticipated, as appropriate data and/or methods are made available. In addition, individual contributions to GLOBE (several areas have more than one candidate) should become available at the same website. GLOBE may be used for technology development, such as helping plan infrastructure for cellular communications networks, other public works, satellite data processing, and environmental monitoring and analysis. GLOBE prototypes (and probably GLOBE itself after its release) have been used to help develop terrain avoidance systems for aircraft. In all cases, GLOBE data should be treated as any potentially useful but guaranteed imperfect data set. Mission- or life-critical applications should consider the documented artifacts, as well as likely undocumented imperfections, in the data.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells ER-CPA1 to ER-CPA6 (six wells with SFA Location ID's). Each file contains the water level elevation and the water temperature. Water level elevation have been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file WellLocation_WLdataArchive2018). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file (WellLocation_WLdataArchive2018). The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements will be added to this data package, so please check back for updates. If you have questions, please contact the author.

Southeast Asian temperate vegetation develops in mountainous areas at high elevations. Due to the elevation-prone distribution pattern, the temperate forest community is officially denoted a vegetation belt, not a zone. In order to model patterns of the temperate vegetation belt, delimiting the specific upper and lower boundaries of the belt is vital. Yet there is no consensus within scientific research regarding specific elevational boundaries for tropical temperate vegetation. An extensive literature research resulted in a wide collection of elevational boundaries for temperate vegetation within the research area. Using a statistical approach a lower minimal lower boundary of 1000m was inferred.

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.

This data release contains topographic information compiled for the Lower Roanoke River corridor located in eastern North Carolina. The Lower Roanoke River corridor includes the mainstem of the Roanoke River from Roanoke Rapids, NC (below the Roanoke Rapids dam) to the mouth of the Roanoke River at its confluence with the Albemarle Sound, and the associated floodplains and wetland areas surrounding the river. All datasets were derived from publicly available airborne light detection and radar (lidar) data collected in years 2003, 2014, and 2020. Data are organized into four categories: Digital Elevation Models (DEMs), Slopes, Digital Elevation Models of Difference (DoDs) in numeric format, and one DoD in categoric format. The DEM and Slope datasets represent static topographic conditions in 2003, 2014, or 2020. The DoD datasets reflect changes to topographic conditions between the years of 2003 and 2014, 2014 and 2020, and 2003 and 2020. These datasets can be used to support future examination of geomorphologic changes in the Lower Roanoke River basin. Due to the Lower Roanoke River corridor's extensive low-lying floodplain network, the lidar data and subsequent estimations of ground elevations (DEMs) were sensitive to differences in river stages across airborne lidar flight dates. Dense canopy cover and changes to lidar collection and processing techniques across the data acquisition years may also have affected the quality of data contained in this release. This data release contains four .zip files: (1) "DEM_3m.zip" contains three digital elevation model raster datasets in GeoTIFF format representing bare earth ground elevations in the years 2003, 2014, and 2020 and one metadata file in .xml format that describes the three digital elevation models. (2) "Slope_3m.zip" contains three slope raster datasets in GeoTIFF format representing bare earth ground slopes in the years 2003, 2014, and 2020 and one metadata file in .xml format that describes the three slope rasters. (3) "DoD_numeric_3m.zip" contains three digital elevation difference model raster datasets in GeoTIFF format representing bare earth ground elevation changes between the years 2003 and 2014, 2014 and 2020, and 2003 and 2020 and one metadata file in .xml format that describes the three digital elevation difference models. (4) "DoD_categoric_3m.zip" contains one digital elevation class difference raster dataset in GeoTIFF format representing bare earth ground class changes between the years 2003 and 2014, 2014 and 2020, and 2003 and 2020 and one metadata file in .xml format that describes the digital elevation class change models.

This collection provides a seamlessly merged, hydrologically robust Digital Elevation Model (DEM) for the Murray Darling Basin (MDB), Australia, at 5 m and 25 m grid cell resolution.

This composite DEM has been created from all the publicly available high resolution DEMs in the Geoscience Australia (GA) elevation data portal Elvis (https://elevation.fsdf.org.au/) as at November 2022. The input DEMs, also sometimes referred to as digital terrain models (DTMs), are bare-earth products which represent the ground surface with buildings and vegetation removed. The DEMs were either from lidar (0.5 to 2 m resolution) or photogrammetry (5 m resolution) and totalled 852 individual DEMs.

The merging process involved ranking the DEMs, pairing the DEMs with overlaps, and adjusting and smoothing the elevations of the lower ranked DEM to make the edge elevations compatible with the higher-ranked DEM. This method is adapted from Gallant 2019 with modifications to work with hundreds of DEMs and have a variable number of gaussian smoothing steps.

Where there were gaps in the high-resolution DEM extents, the Forests and Buildings removed DEM (FABDEM; Hawker et al. 2022), a bare-earth radar-derived, 1 arc-second resolution global elevation model was used as the underlying base DEM. FABDEM is based on the Copernicus global digital surface model.

Additionally, hillshade datasets created from both the 5 m and 25 m DEMs are provided.

Note: the FABDEM dataset is available publicly for non-commercial purposes and consequently the data files available with this Collection are also available with a Creative Commons NonCommercial ShareAlike 4.0 Licence (CC BY-NC-SA 4.0). See https://data.bris.ac.uk/datasets/25wfy0f9ukoge2gs7a5mqpq2j7/license.txt Lineage: For a more detailed lineage see the supporting document Composite_MDB_DEM_Lineage.

DATA SOURCES 1. Geoscience Australia elevation data (https://elevation.fsdf.org.au/) via Amazon Web Service s3 bucket. Of the 852 digital elevation models (DEMs) from the GA elevation data portal, 601 DEMs were from lidar and 251 were from photogrammetry. The latest date of download was Nov 2022. The oldest input DEM was from 2008 and the newest from 2022.

1. FABDEM - Forests and buildings removed DEM based on the 1 arc-second Copernicus global digital surface model. Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17, 024016. https://doi.org/10.1088/1748-9326/ac4d4f

METHODS Part I. Preprocessing The input DEMs were prepared for merging with the following steps: 1. Metadata for all input DEMs was collated in a single file and the DEMs were ranked from finest resolution/newest to coarsest resolution/oldest 2. Tiled input DEMs were combined into single files 3. Input DEMs were reprojected to GA LCC conformal conic projection (EPSG:7845) and bilinearly resampled to 5 m 4. Input DEMs were shifted vertically to the Australian Vertical Working Surface (AVWS; EPSG:9458) 5. The input DEMs were stacked (without any merging and/or smoothing at DEM edges) based on rank so that higher ranking DEMs preceded the lower ranking DEMs, i.e. the elevation value in a grid cell came from the highest rank DEM which had a value in that cell 6. An index raster dataset was produced, where the value assigned to each grid cell was the rank of the DEM which contributed the elevation value to the stacked DEM (see Collection Files - Index_5m_resolution) 7. A metadata file describing each input dataset was linked to the index dataset via the rank attribute (see Collection Files - Metadata)

Vertical height reference surface https://icsm.gov.au/australian-vertical-working-surface

Part II. DEM Merging The method for seamlessly merging DEMs to create a composite dataset is based on Gallant 2019, with modifications to work with hundreds of input DEMs. Within DEM pairs, the elevations of the lower ranked DEM are adjusted and smoothed to make the edge elevations compatible with the higher-ranked DEM. Processing was on the CSIRO Earth Analytics and Science Innovation (EASI) platform. Code was written in python and dask was used for task scheduling.

Part III. Postprocessing 1. A minor correction was made to the 5 m composite DEM in southern Queensland to replace some erroneous elevation values (-8000 m a.s.l.) with the nearest values from the surrounding grid cells 2. A 25 m version of the composite DEM was created by aggregating the 5m DEM, using a 5 x 5 grid cell window and calculating the mean elevation 3. Hillshade datasets were produced for the 5 m and 25 m DEMs using python code from https://github.com/UP-RS-ESP/DEM-Consistency-Metrics

Part IV. Validation Six validation areas were selected across the MDB for qualitative checking of the output at input dataset boundaries. The hillshade datasets were used to look for linear artefacts. Flow direction and flow accumulation rasters and drainage lines were derived from the stacked DEM (step 5 in preprocessing) and the post-merge composite DEM. These were compared to determine whether the merging process had introduced additional errors.

OUTPUTS 1. seamlessly merged composite DEMs at 5 m and 25 m resolutions (geotiff) 2. hillshade datasets for the 5 m and 25 m DEMs (geotiff) 3. index raster dataset at 5 m resolution (geotiff) 4. metadata file containing input dataset information and rank (the rank column values link to the index raster dataset values) 5. figure showing a map of the index dataset and 5m composite DEM (jpeg)

DATA QUALITY STATEMENT Note that we did not attempt to improve the quality of the input DEMs, they were not corrected prior to merging and any errors will be retained in the composite DEM.

Local relief is the amount of elevation change (in meters) within a local area. This layer shows local relief within a 6-km neighborhood. Local relief is a useful component for many environmental assessment models, including terrain analysis, because it gives insight into local variation of soil and vegetation characteristics. This local relief layer provides the amount of elevation change (in meters) within a 6-km neighborhood.Dataset SummaryThis layer provides relief values calculated from GMTED elevation data (250-meter resolution). To produce this layer, the GMTED elevation data was projected to World Equidistant Cylindrical. For each cell in that raster, a neighborhood analysis summarized the elevation range in a 6-km circle. Each cell was then assigned a local relief class based on the difference between the highest and lowest elevation values within a 6-km neighborhood. The cells in this layer are not clipped to the coastlines because local relief is measured to the extent of the neighborhood, which allows for analysis of relief along coasts.This layer is provided using the World Web Mercator (Auxiliary Sphere) coordinate system, and the underlying data was projected from World Equidistant Cylindrical to WGS_1984. The latter coordinate system most easily and correctly supports re-projection into any relevant coordinate system needed for analysis, with the least amount of data loss.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. 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 groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells ER-CPA1 to ER-CPA6 (six wells with SFA Location ID's). Each file contains the water level elevation and the water temperature. Water level elevation have been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file WellLocation_WLdataArchive2018). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file (WellLocation_WLdataArchive2018). The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements will be added to this data package, so please check back for updates. If you have questions, please contact the author.

The bedrock topography map of the Peerless Lake area (NTS 84B) shows the elevation of the bedrock surface. In general, the topography of the land surface reflects the bedrock topography. Thus, bedrock highs underlie the Buffalo Head Hills Upland, Peerless Lake Upland and Utikuma Uplands. Major buried valleys lie within the Loon River Lowland in the west-central part and within the Wabasca Lowlands in the south and northeast parts of the map area. The elevation of the bedrock surface ranges from 780 metres above sea level (masl) in the Buffalo Head Hills to 300 masl in the Loon River Lowland. Segments of three major buried valleys are present: the Muskwa Valley in the south, the Red Earth Valley in the Loon River Lowland and Gods Valley in the northeast. The exact shape of these bedrock valleys and their relationships in the areas where they appear to merge is uncertain as a consequence of the scarcity of relevant drillholes. The Muskwa Valley trends westward towards Lubicon Lake and approximately corresponds with the southern part of the Misaw Channel of Ceroici and part of the L'Hirondelle Channel of Ceroici and Borneuf. The eastern extent of the Muskwa Valley also corresponds with a bedrock low in the northeast corner of the Lesser Slave Lake map area (NTS 83O). The Red Earth Valley partly corresponds to the northerly trending segment of the Misaw Channel of Ceroici, although in the northern part of Loon River Lowland the Red Earth Valley trends north-northeasterly. In the northern part of the Loon River Lowland, abrupt changes in the elevation of stratigraphic markers appear to define a northeasterly trending graben-like structure, which suggests the trend of the Red Earth Valley is partly controlled by bedrock structure. The lowest elevation along the Red Earth Valley is near the town of Red Earth Creek.

The bedrock topography map of the Peerless Lake area (NTS 84B) shows the elevation of the bedrock surface. In general, the topography of the land surface reflects the bedrock topography. Thus, bedrock highs underlie the Buffalo Head Hills Upland, Peerless Lake Upland and Utikuma Uplands. Major buried valleys lie within the Loon River Lowland in the west-central part and within the Wabasca Lowlands in the south and northeast parts of the map area. The elevation of the bedrock surface ranges from 780 metres above sea level (masl) in the Buffalo Head Hills to 300 masl in the Loon River Lowland. Segments of three major buried valleys are present: the Muskwa Valley in the south, the Red Earth Valley in the Loon River Lowland and Gods Valley in the northeast. The exact shape of these bedrock valleys and their relationships in the areas where they appear to merge is uncertain as a consequence of the scarcity of relevant drillholes. The Muskwa Valley trends westward towards Lubicon Lake and approximately corresponds with the southern part of the Misaw Channel of Ceroici and part of the L'Hirondelle Channel of Ceroici and Borneuf. The eastern extent of the Muskwa Valley also corresponds with a bedrock low in the northeast corner of the Lesser Slave Lake map area (NTS 83O). The Red Earth Valley partly corresponds to the northerly trending segment of the Misaw Channel of Ceroici, although in the northern part of Loon River Lowland the Red Earth Valley trends north-northeasterly. In the northern part of the Loon River Lowland, abrupt changes in the elevation of stratigraphic markers appear to define a northeasterly trending graben-like structure, which suggests the trend of the Red Earth Valley is partly controlled by bedrock structure. The lowest elevation along the Red Earth Valley is near the town of Red Earth Creek.

This groundwater level elevation and temperature data package is aimed at improving the predictive understanding of hydro-biogeochemical processes at the lower montane site in the East River Watershed, Colorado. The dataset is obtained using pressure transducers placed in shallow wells in the floodplain. This dataset contains data from wells with Location ID's ER-DOW (alias DO1West), ER-DOE (alias DO2East), ER-MBA1 (alias M1Bend1), ER-MBA2 (alias M1Bend2), ER-UPW (alias UP1West), ER-UPM (alias UP2), ER-UPE (alias UP3East). Another dataset contains the data from wells with Location ID's ER-CPA1 to ER-CPA6. Each file contains the water level elevation and the water temperature. Water level elevation has been obtained using the barometric pressure from the pressure transducer (Hobos sensor) in the well, barometric pressure from a sensor in air located at the same site (lower montane), depth from top-of-casing (TOC) to sensor measurement point, and TOC elevation. Data have been checked with a few measurements of water table depths. A real-time kinematic (RTK) global positioning system (GPS) has been used to survey the TOC (data in file Well_Location.csv). The water level elevation is given in UTM13N Geoid2012AB. While depth to water level is not present in the data files, it can be easily calculated with the TOC and distance to ground provided in the GPS coordinate file. The dataset quality is discussed in Collection/Analysis section of the methods. Time-series of measurements were initially added to the archive for the period 2015 to 2019, and later updated with time-series until 2024 (end of data collection). The dataset contains 8 *.csv data files, and 3 *.csv metadata files. Feel free to contact the author with any questions or collaboration interests.

description: A bare-earth topography Digital Elevation Model (DEM) mosaic for the Beaumont and Lower Neches River Units of Big Thicket National Preserve in Texas, was produced from remotely sensed, geographically referenced elevation measurements collected on January 11, 15, 17, 18, 19, 21, 22, 23, 25, 26, 27, and 29, 2014 by the U.S. Geological Survey, in cooperation with the National Park Service - Gulf Coast Network. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.; abstract: A bare-earth topography Digital Elevation Model (DEM) mosaic for the Beaumont and Lower Neches River Units of Big Thicket National Preserve in Texas, was produced from remotely sensed, geographically referenced elevation measurements collected on January 11, 15, 17, 18, 19, 21, 22, 23, 25, 26, 27, and 29, 2014 by the U.S. Geological Survey, in cooperation with the National Park Service - Gulf Coast Network. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

The highest city in the world with a population of more than one million is La Paz. The Capital of Bolivia sits ***** meters above sea level, and is more than 1,000 meters higher than the second-ranked city, Quito. La Paz is also higher than Mt. Fuji in Japan, which has a height of 3,776 meters. Many of the world's largest cities are located in South America. The only city in North America that makes the top 20 list is Denver, Colorado, which has an altitude of ***** meters.

At 20,310 feet (6.2km) above sea level, the highest point in the United States is Denali, Alaska (formerly known as Mount McKinley). The highest point in the contiguous United States is Mount Whitney, in the Sierra Nevada mountain range in California; followed by Mount Elbert, Colorado - the highest point in the Rocky Mountains. When looking at the highest point in each state, the 13 tallest peaks are all found in the western region of the country, while there is much more diversity across the other regions and territories.

Despite being approximately 6,500 feet lower than Denali, Hawaii's Mauna Kea is sometimes considered the tallest mountain (and volcano) on earth. This is because its base is well below sea level - the mountain has a total height of 33,474 feet, which is almost 4,500 feet higher than Mount Everest.

The Digital SAR Mosaic and Elevation Map of the Greenland Ice Sheet combines the most detailed synthetic aperture radar (SAR) image mosaic available with the best current digital elevation model. The mosaic image shows both the location of the ice edge and the distribution of melt-related 'scatterers' on the surface. These scatterers include ice lenses and complex layered structure in the percolation zone and bare ice of the ablation zone. Other melt-related features that can be seen include lake and surface meltwater stream channels at lower elevations, as well as ice-marginal lakes.

This characterization of the ice sheet provides a reference against which future change can be measured. Changing conditions resulting from climatic variation should show up as changes in the ice margin and shifts in the hydrologic zones. It is hoped that the standard reference provided by this data set can facilitate activities aimed at change detection and promote other work aimed at understanding the processes operating on the ice sheet.

The image data are derived from SAR image swaths acquired by the ERS-1 satellite during August of 1992. The mosaic was assembled at the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). Its component images are a copyrighted product of the European Space Agency. The mosaic, a value-added derived product, is available to individuals and non-profit organizations for research oriented purposes only. The Danish geodetic and cadastral agency Kort-og Matrikelstyrelsen (KMS) compiled the elevation data provided with the product from a number of sources, including field surveys, aerial photographs, and the ERS-1 radar altimeter.