Global Mountains

The first two global mountain datasets were derived from 1km DEMs, with the first being produced by Kapos et al., 2000 (herein referred to as K1), and the second by Körner et al., 2011 (herein referred to as K2). The K1 dataset defined six classes of mountains based on a combination of elevation, slope and relative relief derived from a 1 km DEM. The circular neighborhood analysis window (NAW) for computing the relative relief used a 5 pixel (~7 km) radius for an approximate NAW size of 150 km2. The K2 data layer, which was also based on 1 km DEM source, used ruggedness as the determining factor, where any relative relief greater than 200-meter in the approximately 9 km2 NAW was considered mountainous.

The third global mountain dataset, Karagulle et al., 2017 (herein referred to as K3), was developed by 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). This data was generated from a 250-meter DEM and feature-based extraction algorithms with variable NAW sizes to extract Hammond’s 16 global landform types. Then the final K3 product was produced by an automated extraction of the four Hammond landform types used to represent mountain classes.