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https://github.com/JesJehle/earthEngineGrabR
Simplify the acquisition of remote sensing data
https://github.com/JesJehle/earthEngineGrabR
Last synced: 3 months ago
JSON representation
Simplify the acquisition of remote sensing data
- Host: GitHub
- URL: https://github.com/JesJehle/earthEngineGrabR
- Owner: JesJehle
- Created: 2018-01-25T12:20:41.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-06-24T13:58:20.000Z (over 4 years ago)
- Last Synced: 2024-07-17T01:53:29.336Z (3 months ago)
- Language: R
- Homepage: https://jesjehle.github.io/earthEngineGrabR/
- Size: 8.73 MB
- Stars: 52
- Watchers: 11
- Forks: 5
- Open Issues: 8
-
Metadata Files:
- Readme: README.html
Awesome Lists containing this project
- Awesome-GEE - earthEngineGrabR - Simplify the acquisition of remote sensing data. (R / Packages)
- Awesome-GEE - earthEngineGrabR - Simplify the acquisition of remote sensing data. (R / Packages)
README
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earthEngineGrabR
The earthEngineGrabR simplifies the acquisition of remote sensing data by building an interface between R and the Google Earth Engine. The R package extracts data from the Earth Engine Data Catalog according to a target area and a user-defined aggregation process. Any acquiring and processing of the data are entirely outsourced to EE with only the derived datasets, in an analysis-ready format being imported into R. This way, the package uses both, the massive public Data Catalog of available data and the processing resources supplied by EE, to request data in a strongly user-specified approach.
Usage
The example shows how to grab the yearly precipitation sum from the CHIRPS Daily dataset for elephant territories in Africa. To extract data from the EE Data Catalog the earthEngineGrabR uses ee_grab(). The ee_grab() function grabs data from the CHIRPS Daily dataset according to the target area defined by the feature geometries of the territories shapefile and an aggregation process defined by ee_data_collection().
library(earthEngineGrabR)
chirps_data <- ee_grab(data = ee_data_collection(datasetID = "UCSB-CHG/CHIRPS/DAILY",
spatialReducer = "mean",
temporalReducer = "sum",
timeStart = "2016-01-01",
timeEnd = "2016-12-31",
scale = 200
),
targetArea = system.file("data/territories.shp", package = "earthEngineGrabR")
)
chirps_data
Simple feature collection with 53 features and 3 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -13.71389 ymin: -25.52952 xmax: 43.10118 ymax: 16.63924
epsg (SRID): 4326
proj4string: +proj=longlat +datum=WGS84 +no_defs
First 10 features:
id area_sqkm precipitation_s.mean_t.sum_2016.01.01_to_2016.12.31 geometry
1 40 32356 500.2795 MULTIPOLYGON (((37.76223 0....
2 29 42612 914.9900 MULTIPOLYGON (((36.58819 -1...
3 12 47000 1321.1984 MULTIPOLYGON (((37.10833 -7...
4 9 19624 572.1886 MULTIPOLYGON (((31.87845 -2...
The example calculates the yearly precipitation sum for 2016 and aggregates the spatial mean in the polygons of the target area. The calculations are performed on a scale of 200 meters per pixel.
After a short processing time of approximately 1 -2 minutes, ee_grab() returns the data.
Requirements
The earthEngineGrabR R package has some dependencies that need to be satisfied before the installation can run successfully:
Required dependencies
Next, you can install the development version of the earthEngineGrabR with:
library(devtools)
install_github("JesJehle/earthEngineGrabR")
library(earthEngineGrabR)
Installation
The package has additional Python dependencies and connects to several API’s, which each require an individual, user-specific, authentication procedure.
To simplify the installation and authentication process, the earthEngineGrabR includes a function ee_grab_install() that installs Python dependencies and furthermore guides the user through the different authentications.
Before using the earthEngineGrabR, the user has to call ee_grab_install()
ee_grab_install()
Authentication
The earthEngineGrabR connects to 3 Google API’s:
-
Google Fusion Table API for uploading data.
-
Google Earth Engine API for data aquisition and processing.
-
Google Drive} API for data download.
To authenticate to the API’s the user has to log in with his google account and allow the API to access data on googles servers on the user’s behalf. To simplify this procedure the ee_grab_install() function successively opens a browser window to log into the Google account. 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 the R console, which creates persistent credentials. This process is repeated for each API. If the function runs successfully, all needed credentials are stored for further sessions and there should be no need for further authentification.
Before using the earthEngineGrabR, the user has to call ee_grab_install()
To test the installation run:
srtm_data <- ee_grab(data = ee_data_image(datasetID = "CGIAR/SRTM90_V4",
spatialReducer = "mean",
scale = 100,
bandSelection = "elevation"
),
targetArea = system.file("data/territories.shp", package = "earthEngineGrabR")
)
The earthEngineGrabR Workflow
Search for dataset in Earth Engine Data Catalog .
Grab data according to a user defines data reuquest.
Search for data
Use Earth Engine’s Data Catalog to browse and find datasets you want to grab using the earthEngineGrabR. Once you have found a dataset, use the snippet section to obtain the dataset ID and whether the dataset is an image or a collection of images. The snippet section consists of one line of code (don’t open the link) and shows how Earth Engine loads the dataset. If it is an image, the ee.Image(dataset-ID) constructor is used. if it is a collection the ee.ImageCollection(dataset-id) constructor is used instead.
Grab data
ee_grab() requests and imports data from Earth Engine to R. ee_grab() takes two arguments, data and targetArea. data takes a single or a list of ee_data_image() and ee_data_collection() functions, which define the requested data to ee_grab(). If the requested data is an image use ee_data_image(), if it’s a collection use ee_data_collection(). targetArea takes a path to a local geo-file, which defines the spatial target in which the data sould be aggregated.
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