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

By Giuseppe Amatulli, Sami Domisch, Mao-Ning Tuanmu, B. Parmentier, Ajay Ranipeta, J. Malczyk, Walter Jetz. Scientific Data, 2018

771 citations recorded by the source corpus.

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. Design Type(s) source-based data transformation objective Measurement Type(s) Topography Technology Type(s) computational modeling technique Factor Type(s) Sample Characteristic(s) Earth (Planet) • elevation • physiographic feature • slope • Cardinal Direction • curvature Design Type(s) source-based data transformation objective Measurement Type(s) Topography Technology Type(s) computational modeling technique Factor Type(s) Sample Characteristic(s) Earth (Planet) • elevation • physiographic feature • slope • Cardinal Direction • curvature Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/313d1447d80e65a7838bf9a078f160314c3ad5a3

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Citations in the corpus

A 30 m global map of elevation with forests and buildings removed (2022, background): “Topographic information, described here as digital elevation models (DEMs), are crucial inputs for a diverse range of applications in fields such as demography [1], ecology [2, 3], geomorphology [4, 5], glaciology [6, 7], hydrology [8–10], soil science [11] and volcanology [12, 13].”
Vulnerability of global biodiversity hotspots to climate change (2021, methodology): “…Fordham et al. (2019) (https://doi. org/10.1016/j.cub.2019.04.001); contemporary climate stability data sourced from NCEP_Reanalysis 2 by the NOAA/OAR/ESRL PSL, Boulder, CO, USA (https://psl/noaa.gov/); and topographical variables by Amatulli et al. (2018) (http://www.earth env.org/ topog raphy).”
Re-thinking complex orebodies: Consequences for the future world supply of copper (2019, background): “Risk Moderate to high risk threshold Number of Deposits above the threshold Corresponding copper tonnage Grade Copper equivalent grade less than or equal to 0.60%, i.e. the global copper grade average for 2015 (derived from Mudd and Jowitt, 2018) 180 570Mt Water Medium to high Aqueduct Mining Water ”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “…layers represented environmental conditions hypothesized to influence the distribution of many actual species in the region (Inman et al. 2014), and were generalized to a spatial resolution of 1 km, a grain size typical of widely available environmental covariates (e.g., Amatulli et al. 2018).”