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Global Mountain Explorer 2.0
Esri Group On Earth Observations Centre for Development and Environment Global Mountain Biodiversity Assessment Mountain Research Initiative


Idaho Borah Peak

Although the answers to the questions 1) "what is a mountain?" and 2) "where are the mountains of the world?" might seem obvious and intuitive to many, there are surprisingly few attempts to rigorously and consistently define and map the mountains of the Earth. Two geographic information systems (GIS)-based characterizations derived from global digital elevation models (DEMs) at a spatial resolution of approximately 1 km have considerably advanced our understanding of the global distribution of mountains. The first global mountains raster GIS datalayer was produced by Kapos et al. (2000), herein referred to as K1. The second global mountains raster GIS datalayer was produced by Körner et al. (2011), herein referred to as K2. A third global mountains raster GIS layer was produced by Karagulle et al. (2017), herein referred to as K3. This Global Mountain Explorer (GME) was developed for web-based browsing and visual comparisons of the K1, K2, and K3 characterizations of global mountain extents. In addition to browsing and comparing K1, K2, and K3, the GIS datalayers are available for download.

Global Mountain Explorer (GME) 2.0

Global Mountain Explorer Video Tutorial

This resource was developed by the U.S. Geological Survey (USGS), in partnership with Esri, the Center for Development and Environment of the University of Bern (CDE), the Global Mountain Biodiversity Assessment (GMBA), and the Mountain Research Initiative (MRI). The work is part of a Group on Earth Observations (GEO) initiative called GEO Mountains, GEO’s Global Network for Observations and Information in Mountain Environments. The work specifically addresses a task in GEO Mountains initial workplan to accurately delineate and compare global mountain extent using data from three established approaches.

The Global Mountain Explorer allows for visualization and query of K1, K2, and K3 either separately or in pairwise comparisons. The K1, K2, and K3 resources are accessible as either mountains only in one color, or as mountain classes (K1, K3) in various colors. Pan, zoom, and query functionality are included, and a query anywhere on the map returns the binary values for K1, K2, and K3 in a pop-up query results box. The data are being served as image services, and the GIS data files are available for download.

Background: K1, K2, and K3

The K1 resource used a 1 km DEM which was processed using a combination of elevation, slope and relative relief. Relative relief, or ruggedness, is the difference between maximum and minimum elevation in a moving neighborhood analysis window (NAW) and is computed for every raster cell. The K1 layer defined six classes of mountains, where the upper three classes were defined by elevation ranges. The lower three classes were defined either by a combination of elevation and slope, or elevation and relative relief. The circular NAW for computing the relative relief used a 5 pixel (~7 km) radius for an approximate NAW size of 150 km2.

The K2 resource was developed using ruggedness as the determining factor, where any relative relief greater than 200m in the NAW was considered mountainous. The original K2 resource used a 1 km DEM and an approximately 9km2 NAW to determine relative relief and then generalized the relative relief surface to an approximately 4.5 km grid (at the equator).

The K3 resource was developed using a finer spatial resolution (250 m) DEM and feature-based extraction algorithms with variable NAW sizes used to extract a set of global Hammond landforms with 16 landform types, of which four were mountain classes. E. H. Hammond was a pioneer of landform mapping and described three parameters for distinguishing different types of plains, hills, mountains, and tablelands. The three classification parameters are slope, relative relief, and profile, where the profile parameter assesses the amount of relatively flat terrain in upland locations to delineate tablelands. The 250 m global Hammond landforms product was based on an automated extraction of classes in a GIS environment, and the K3 mountains product was an export of the four mountain classes into a global mountains datalayer.

**IMPORTANT GME Version Note**
This web resource represents version 2.0 (2021) of the Global Mountain Explorer. The previous version of the GME used downsampled (increased spatial resolution) versions of the original K1 and K2 layers in an attempt to make them more comparable in granularity with the high spatial resolution K3 data. It has since been discovered that the downsampling process increased the global area of both K1 and K2 mountains considerably from what had been reported in the original research papers, due to inadequate calibration of raster processing parameters. A decision was therefore taken to replace the GME version 1 layers for K1 and K2, in both the viewer and from the download site, with the identical layers that were used by the original authors and their institutions. The GME version 2.0 therefore contains the original (or recreations thereof), non-resampled versions of K1 and K2 in the viewer and provides these resources for download. The K2 layer used in the viewer and available for download is neither downsampled from the original 1 km resolution nor generalized to the 4.5 km grid as was done in the original analysis to decrease the computational burden. Users of the K1 and K2 resources downloaded from the GME prior to June 2021 need to be aware that those layers were downsampled and identify higher amounts of global mountain areas as compared with the published results. Importantly, when using DEMs to compute terrain characteristics, the choice of the neighborhood analysis window (NAW) size for raster processing influences the area calculated as mountainous by any definition. Selection and evaluation of NAW sizes, elevation criteria, ruggedness thresholds, etc. is always arbitrary and empirical, and the interactions of these parameters with increasingly finer DEM spatial resolutions is complex, rendering terrain-based determination of global mountain extent a very nuanced undertaking.

Global Mountain K1, K2, and K3 Datafiles


Kapos, V., J. Rhind, M. Edwards, M. Prince, and C. Ravilious. 2000. Developing a map of the world’s mountain forests. In: M. Price and N. Butt (eds), Forests in Sustainable Mountain Development, IUFRO Research Series 5, CABI Publishing, Wallingford, UK; pp. 4-9.

Körner, C., J. Paulsen, and E. Spehn. 2011. A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alpine Botany 121;73-78 (10.1007/s00035-011-0094-4).

Karagulle, D., C. Frye, R. Sayre, S. Breyer, P. Aniello, R. Vaughan, and D. Wright. 2017. Modeling global Hammond landform regions from 250-m elevation data. Transactions in GIS, DOI: 10.1111/tgis.12265


Roger Sayre, U.S. Geological Survey
Deniz Karagulle, Esri,
Davnah Urbach, Global Mountain Biodiversity Assessment, University of Bern,
Carolina Adler, Mountain Research Initiative, University of Bern,
Mark Snethlage, Global Mountain Biodiversity Assessment, University of Bern,
James Thornton, Mountain Research Initiative, University of Bern,


Roger Sayre, U.S. Geological Survey
Jill Cress, U.S. Geological Survey, GME Developer and Webmaster