Elevation

Key: worldpop_elevation | Secondary data | Category: Environmental

Mean elevation above sea level within the respective administrative unit.

Elevation values represent the arithmetic mean of all 100 m grid cells within the administrative unit. Administrative units containing no grid points receive no value. Values do not capture topographic variability within a unit; areas with heterogeneous terrain may require additional analysis. No area-weighted correction is applied.

Data Layer information
Source file is available (Show file)
Data is available locally
Data is loaded into the database
Operation mean
Database unit m
Value type value
Temporal resolution year
Data availability per spatial unit within the integrated time period
Shape type First Last Availability
Country 100.00 %
Region 100.00 %
District 100.00 %
Total 2007 2007 100.00 %

Analytical overview

Source information

[1 of 2] MERIT Elevation 2007, Ghana
Data
DOI10.5258/SOTON/WP00772
Source: WorldPop

MERIT Elevation is a high-accuracy global terrain elevation dataset developed by Yamazaki et al., distributed via the WorldPop Hub as part of their high resolution, harmonised annual global geospatial covariates database (version 1.0). It provides elevation above sea level at a resolution of approximately 100 m (3 arc-seconds), based on the WGS84 coordinate system.

Citation

This source is included in the Data Layer citation.

DataCite information

Authors (7)
  • Thea Woods ;
  • Tom McKeen ;
  • Alexander Cunningham ;
  • Rhorom Priyatikanto ;
  • Alessandro Sorichetta ;
  • Andrew Tatem ;
  • Maksym Bondarenko
[2 of 2] A high-accuracy map of global terrain elevations
Information
DOI10.1002/2017GL072874

Spaceborne digital elevation models (DEMs) are a fundamental input for many geoscience studies, but they still include nonnegligible height errors. Here we introduce a high-accuracy global DEM at 3″ resolution (~90 m at the equator) by eliminating major error components from existing DEMs. We separated absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering techniques. After the error removal, land areas mapped with ±2 m or better vertical accuracy were increased from 39% to 58%. Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill-valley structures, became clearly represented. We found the topography slope of previous DEMs was largely distorted in most of world major floodplains (e.g., Ganges, Nile, Niger, and Mekong) and swamp forests (e.g., Amazon, Congo, and Vasyugan). The newly developed DEM will enhance many geoscience applications which are terrain dependent.

Citation

This source is included in the Data Layer citation.