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A suite of global, cross-scale topographic variables for environmental and biodiversity modeling.


ABSTRACT: Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250?m GMTED2010 and near-global 90?m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100?km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at Data Citation 1 and for download and visualization at http://www.earthenv.org/topography.

SUBMITTER: Amatulli G 

PROVIDER: S-EPMC5859920 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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A suite of global, cross-scale topographic variables for environmental and biodiversity modeling.

Amatulli Giuseppe G   Domisch Sami S   Tuanmu Mao-Ning MN   Parmentier Benoit B   Ranipeta Ajay A   Malczyk Jeremy J   Jetz Walter W  

Scientific data 20180320


Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a  ...[more]

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