This is a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets from < https://ecocast.arc.nasa.gov/data/pub/gimms/>; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.
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... is an open-access tutorial about the gimms package which is now available from GitBook. The book will be continuously updated as gimms develops further, so make sure to check it out regularly!
I decided to add optional multi-core support to
monthlyComposite. The referring arument is called 'cores' and, if not specified otherwise, defaults to 1 (i.e., parallel computing is disabled). In the course of this, the gimms package version on branch 'develop' has been incremented to 0.4.0 and can be installed via
devtools::install_github (see further below).
It's Friday 13th and an updated version of the gimms package has been published on CRAN. The new version includes
significantTauto calculate pixel-based (and optionally pre-whitened) Mann-Kendall trend tests from a previously processed GIMMS NDVI3g (or any kind of) 'RasterStack/Brick' object. Note that it also works with simple 'numeric' vectors (i.e., univariate time series observations);
downloadGimmsthat enabled 'Date' input;
In response to recent user suggestions, I decided to enable 'Date' input for
downloadGimms which grants the user a finer control over the temporal coverage of the data to be downloaded. The changes are currently available from the 'develop' branch via
devtools::install_github("environmentalinformatics-marburg/gimms",ref = "develop")
and will be submitted to CRAN soon.
The problem was obviously related to
download.file which worked just fine on Linux when using the default settings, but introduced distortions on Windows. In the newest package version 0.2.0 which is now brand-new on CRAN, I therefore specified
download.file(..., mode = "wb") to explicitly enable binary writing mode.
Thanks again for the input!