MODIST ools – 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...
Gespeichert in:
Veröffentlicht in: | Ecology and evolution 2014-12, Vol.4 (24), p.4658-4668 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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
MODIST
ools
, an R package designed to improve the accessing, downloading, and processing of remotely sensed
MODIS
data.
MODIST
ools
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
MODIST
ools
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.
MODIST
ools
can be used on Windows, Mac, and Linux platforms, and is available from
CRAN
and GitHub (
https://github.com/seantuck12/MODISTools
). |
---|---|
ISSN: | 2045-7758 2045-7758 |
DOI: | 10.1002/ece3.1273 |