A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ...
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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ftdatacite:10.1594/pangaea.867115 2024-06-09T07:49:23+00:00 A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ... Amatulli, Giuseppe Domisch, Sami Tuanmu, Mao-Ning Parmentier, Benoit Ranipeta, Ajay Malczyk, Jeremy Jetz, Walter 2018 application/zip https://dx.doi.org/10.1594/pangaea.867115 https://doi.pangaea.de/10.1594/PANGAEA.867115 en eng PANGAEA https://dx.doi.org/10.1038/sdata.2018.40 Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/legalcode cc-by-3.0 Collection article Supplementary Publication Series of Datasets 2018 ftdatacite https://doi.org/10.1594/pangaea.86711510.1038/sdata.2018.40 2024-05-13T12:44:57Z 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 basic research and analytical applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90m 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 ... : Supplement to: Amatulli, Giuseppe; Domisch, Sami; Tuanmu, Mao-Ning; Parmentier, Benoit; Ranipeta, Ajay; Malczyk, Jeremy; Jetz, Walter (2018): A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5, 180040 ... Article in Journal/Newspaper sami DataCite Metadata Store (German National Library of Science and Technology) Jeremy ENVELOPE(-68.838,-68.838,-69.402,-69.402) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
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 basic research and analytical applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90m 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 ... : Supplement to: Amatulli, Giuseppe; Domisch, Sami; Tuanmu, Mao-Ning; Parmentier, Benoit; Ranipeta, Ajay; Malczyk, Jeremy; Jetz, Walter (2018): A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5, 180040 ... |
format |
Article in Journal/Newspaper |
author |
Amatulli, Giuseppe Domisch, Sami Tuanmu, Mao-Ning Parmentier, Benoit Ranipeta, Ajay Malczyk, Jeremy Jetz, Walter |
spellingShingle |
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, links to files in GeoTIFF format ... |
author_facet |
Amatulli, Giuseppe Domisch, Sami Tuanmu, Mao-Ning Parmentier, Benoit Ranipeta, Ajay Malczyk, Jeremy Jetz, Walter |
author_sort |
Amatulli, Giuseppe |
title |
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ... |
title_short |
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ... |
title_full |
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ... |
title_fullStr |
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ... |
title_full_unstemmed |
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format ... |
title_sort |
suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in geotiff format ... |
publisher |
PANGAEA |
publishDate |
2018 |
url |
https://dx.doi.org/10.1594/pangaea.867115 https://doi.pangaea.de/10.1594/PANGAEA.867115 |
long_lat |
ENVELOPE(-68.838,-68.838,-69.402,-69.402) |
geographic |
Jeremy |
geographic_facet |
Jeremy |
genre |
sami |
genre_facet |
sami |
op_relation |
https://dx.doi.org/10.1038/sdata.2018.40 |
op_rights |
Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/legalcode cc-by-3.0 |
op_doi |
https://doi.org/10.1594/pangaea.86711510.1038/sdata.2018.40 |
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1801381881436962816 |