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MODISTools – downloading and processing MODIS remotely sensed data in R
Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely us...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278818/ https://www.ncbi.nlm.nih.gov/pubmed/25558360 http://dx.doi.org/10.1002/ece3.1273 |
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author | Tuck, Sean L Phillips, Helen RP Hintzen, Rogier E Scharlemann, Jörn PW Purvis, Andy Hudson, Lawrence N |
author_facet | Tuck, Sean L Phillips, Helen RP Hintzen, Rogier E Scharlemann, Jörn PW Purvis, Andy Hudson, Lawrence N |
author_sort | Tuck, Sean L |
collection | PubMed |
description | Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R(2) values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools). |
format | Online Article Text |
id | pubmed-4278818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-42788182015-01-02 MODISTools – downloading and processing MODIS remotely sensed data in R Tuck, Sean L Phillips, Helen RP Hintzen, Rogier E Scharlemann, Jörn PW Purvis, Andy Hudson, Lawrence N Ecol Evol Original Research Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R(2) values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools). Blackwell Publishing Ltd 2014-12 2014-12-02 /pmc/articles/PMC4278818/ /pubmed/25558360 http://dx.doi.org/10.1002/ece3.1273 Text en © 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Tuck, Sean L Phillips, Helen RP Hintzen, Rogier E Scharlemann, Jörn PW Purvis, Andy Hudson, Lawrence N MODISTools – downloading and processing MODIS remotely sensed data in R |
title | MODISTools – downloading and processing MODIS remotely sensed data in R |
title_full | MODISTools – downloading and processing MODIS remotely sensed data in R |
title_fullStr | MODISTools – downloading and processing MODIS remotely sensed data in R |
title_full_unstemmed | MODISTools – downloading and processing MODIS remotely sensed data in R |
title_short | MODISTools – downloading and processing MODIS remotely sensed data in R |
title_sort | modistools – downloading and processing modis remotely sensed data in r |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4278818/ https://www.ncbi.nlm.nih.gov/pubmed/25558360 http://dx.doi.org/10.1002/ece3.1273 |
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