The earthEngineGrabR package is supposed to provide an interface of R and the Google Earth Engine to acquire geodata for environmental system modelling. This Interface is supposed to extract data from the Earth Engine data catalogue while providing extensive control over temporal and spatial resolution. The package not only allows to extract specific aspects of the data, like in a regular databank but enables to generate new data by an aggregation process, controlled by the user. This way, the package uses both, the massive public data catalogue of available data products and the processing resources supplied by the Google Earth Engine, to extract data in a strongly user-specified approach.
Dependencies and installation of the earthEngineGrabR
The earthEngineGrabR R package has some dependencies that need to be satisfied before the installation can run sucessfully:
- you need a Google Account
- sign up for Earth Engine access
- you need a Python version >= 2.7, with PYTHONPATH set
- install GDAL
- install sf
Next, you can install the earthEngineGrabR with:
library(devtools) install_github("JesJehle/earthEngineGrabR") library(earthEngineGrabR)
To initialize the earthEngineGrabR run:
ee_grab_init() function installs additionally required dependencies and guides the user through the authentication processes to activate the different API's. To authenticate to the API the user has to log in to his Google account and allow the API to access data on googles servers on the user's behalf.
If the Google account is verified and the permission is granted, the user is directed to an authentification token. This token is manually copied and pasted into a running command line script, which creates persistent credentials. To simplify this procedure, the
ee_grab_init() function successively opens a browser window to log into the Google account and a corresponding command line window to enter the token. The
To test the earthEngineGrabR run:
data <- ee_grab( target = system.file("data/territories.shp", package="earthEngineGrabR"), products = list(eeProduct_modis_treeCover(yearIntervall = c(2008, 2012))), resolution = 1000 )