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

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 la...

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Autores principales: Amatulli, Giuseppe, Domisch, Sami, Tuanmu, Mao-Ning, Parmentier, Benoit, Ranipeta, Ajay, Malczyk, Jeremy, Jetz, Walter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859920/
https://www.ncbi.nlm.nih.gov/pubmed/29557978
http://dx.doi.org/10.1038/sdata.2018.40
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author Amatulli, Giuseppe
Domisch, Sami
Tuanmu, Mao-Ning
Parmentier, Benoit
Ranipeta, Ajay
Malczyk, Jeremy
Jetz, Walter
author_facet Amatulli, Giuseppe
Domisch, Sami
Tuanmu, Mao-Ning
Parmentier, Benoit
Ranipeta, Ajay
Malczyk, Jeremy
Jetz, Walter
author_sort Amatulli, Giuseppe
collection PubMed
description 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.
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spelling pubmed-58599202018-03-24 A suite of global, cross-scale topographic variables for environmental and biodiversity modeling Amatulli, Giuseppe Domisch, Sami Tuanmu, Mao-Ning Parmentier, Benoit Ranipeta, Ajay Malczyk, Jeremy Jetz, Walter Sci Data Data Descriptor 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. Nature Publishing Group 2018-03-20 /pmc/articles/PMC5859920/ /pubmed/29557978 http://dx.doi.org/10.1038/sdata.2018.40 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Amatulli, Giuseppe
Domisch, Sami
Tuanmu, Mao-Ning
Parmentier, Benoit
Ranipeta, Ajay
Malczyk, Jeremy
Jetz, Walter
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
title A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
title_full A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
title_fullStr A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
title_full_unstemmed A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
title_short A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
title_sort suite of global, cross-scale topographic variables for environmental and biodiversity modeling
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859920/
https://www.ncbi.nlm.nih.gov/pubmed/29557978
http://dx.doi.org/10.1038/sdata.2018.40
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