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 new global mountains raster GIS data layer was recently 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.
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 GNOME, GEO’s Global Network for Observations and Information in Mountain Environments (http://www.earthobservations.org/activity.php?id=117). The work specifically addresses Task 1.0 in GEO GNOME’s workplan: Accurately delineate mountain regions using best available data.
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) and mountain bioclimatic belts (K2) 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. While the original K1 layer was derived from a 1 km DEM, we used a global 250 m DEM (GMTED2010) and recalculated to derive a new, finer resolution K1, using the same algorithms and parameters used to calculate the original K1.
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). We then downsampled the original K2 datalayer to a 250 m resolution to match the K3 and the new K1 resolution.
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.
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.
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, firstname.lastname@example.org
Deniz Karagulle, Esri, email@example.com
Jürg Krauer, Center for Development and Environment, University of Bern, firstname.lastname@example.org
Davnah Payne, Global Mountain Biodiversity Assessment, University of Bern, email@example.com
Carolina Adler, Mountain Research Initiative, University of Bern, firstname.lastname@example.org
GME Developer and Webmaster
Jill Cress, U.S. Geological Survey, email@example.com