气象编程| Google Earth Engine for R——提供250+ 实例开发 ...

气象编程 | Google Earth Engine for R——提供250+ 实例

JS (Code Editor)	Python	R
var db = 'CGIAR/SRTM90_V4'var image = ee.Image(db)print(image.bandNames())#> 'elevation'	import eeee.Initialize()db = 'CGIAR/SRTM90_V4'image = ee.Image(db)image.bandNames().getInfo()#> [u'elevation']	library(rgee)ee_Initialize()db <- 'CGIAR/SRTM90_V4'image <- ee$Image(db)image$bandNames()$getInfo()#> [1] "elevation"

remotes::install_github("r-spatial/rgee")

docker pull csaybar/rgee
docker run -d -p 8787:8787 -e USER=rgee -e PASSWORD=rgee --name rgee-dev csaybar/rgee

127.0.0.1:8787

library(rgee)
## It is necessary just once
ee_install()
# Initialize Earth Engine!
ee_Initialize()

ee_check() # Check non-R dependencies
ee_clean_credentials() # Remove credentials of a specific user
ee_clean_pyenv() # Remove reticulate system variables

library(rgee)
ee_Initialize()
#ee_reattach() # reattach ee as a reserve word

createTimeBand <-function(img) {
  year <- ee$Date(img$get('system:time_start'))$get('year')$subtract(1991L)
  ee$Image(year)$byte()$addBands(img)

collection <- ee$
  ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS')$
  select('stable_lights')$
  map(createTimeBand)

col_reduce <- collection$reduce(ee$Reducer$linearFit())
col_reduce <- col_reduce$addBands(
  col_reduce$select('scale'))
ee_print(col_reduce)

Map$setCenter(9.08203, 47.39835, 3)
Map$addLayer(
  eeObject = col_reduce,
  visParams = list(
    bands = c("scale", "offset", "scale"),
    min = 0,
    max = c(0.18, 20, -0.18)
  name = "stable lights trend"
rgee_01
2. Extract precipitation values
Install and load tidyverse and sf R package, after that, initialize the Earth Engine R API.
library(tidyverse)
library(rgee)
library(sf)
# ee_reattach() # reattach ee as a reserve word
ee_Initialize()
Read the nc shapefile.
nc <- st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
Map each image from 2001 to extract the monthly precipitation (Pr) from the Terraclimate dataset
terraclimate <- ee$ImageCollection("IDAHO_EPSCOR/TERRACLIMATE")$
  filterDate("2001-01-01", "2002-01-01")$
  map(function(x) x$reproject("EPSG:4326")$select("pr"))
Extract monthly precipitation values from the Terraclimate ImageCollection through ee_extract. ee_extract works similar to raster::extract, you just need to define: the ImageCollection object (x), the geometry (y), and a function to summarize the values (fun).
ee_nc_rain <- ee_extract(x = terraclimate, y = nc, sf = FALSE)
colnames(ee_nc_rain) <- sprintf("%02d", 1:12)
ee_nc_rain$name <- nc$NAME
Use ggplot2 to generate a beautiful static plot!
ee_nc_rain %>%
  pivot_longer(-name, names_to = "month", values_to = "pr") %>%
  ggplot(aes(x = month, y = pr, group = name, color = pr)) +
  geom_line(alpha = 0.4) +
  xlab("Month") +
  ylab("Precipitation (mm)") +
  theme_minimal()
3. Create an NDVI-animation (JS version)
Install and load sf, after that, initialize the Earth Engine R API.
library(rgee)
library(sf)
ee_Initialize()
# ee_reattach() # reattach ee as a reserve word
Define the regional bounds of animation frames and a mask to clip the NDVI data by.
mask <- system.file("shp/arequipa.shp", package = "rgee") %>% 
  st_read(quiet = TRUE) %>% 
  sf_as_ee()
region <- mask$geometry()$bounds()
Retrieve the MODIS Terra Vegetation Indices 16-Day Global 1km dataset as an ee.ImageCollectionand select the NDVI band.
col <- ee$ImageCollection('MODIS/006/MOD13A2')$select('NDVI')
Group images by composite date
col <- col$map(function(img) {
  doy <- ee$Date(img$get('system:time_start'))$getRelative('day', 'year')
  img$set('doy', doy)
distinctDOY <- col$filterDate('2013-01-01', '2014-01-01')
Define a filter that identifies which images from the complete collection match the DOY from the distinct DOY collection.
filter <- ee$Filter$equals(leftField = 'doy', rightField = 'doy');
Define a join; convert the resulting FeatureCollection to an ImageCollection.
join <- ee$Join$saveAll('doy_matches')
joinCol <- ee$ImageCollection(join$apply(distinctDOY, col, filter))
Apply median reduction among matching DOY collections.
comp <- joinCol$map(function(img) {
  doyCol = ee$ImageCollection$fromImages(
    img$get('doy_matches')
  doyCol$reduce(ee$Reducer$median())
Define RGB visualization parameters.
visParams = list(
  min = 0.0,
  max = 9000.0,
  bands = "NDVI_median",
  palette = c(
    'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
    '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
    '012E01', '011D01', '011301'
Create RGB visualization images for use as animation frames.
rgbVis <- comp$map(function(img) {
  do.call(img$visualize, visParams) %>% 
    ee$Image$clip(mask)
Define GIF visualization parameters.
gifParams <- list(
  region = region,
  dimensions = 600,
  crs = 'EPSG:3857',
  framesPerSecond = 10
Render the GIF animation in the console.
print(rgbVis$getVideoThumbURL(gifParams))
browseURL(rgbVis$getVideoThumbURL(gifParams))
How does rgee work?
rgee is not a native Earth Engine API like the Javascript or Python client, to do this would be extremely hard, especially considering that the API is in active development. So, how is it possible to run Earth Engine using R? the answer is reticulate. reticulate is an R package designed to allow a seamless interoperability between R and Python. When an Earth Engine request is created in R, reticulate will transform this piece into Python. Once the Python code is obtained, the Earth Engine Python API transform the request to a JSON format. Finally, the request is received by the Google Earth Engine Platform thanks to a Web REST API. The response will follow the same path.

workflow
Code of Conduct
Please note that the rgee project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Contributing Guide
? Thanks for taking the time to contribute! ?? Please review our Contributing Guide.
Share the love  ❤️
Think rgee is useful? Let others discover it, by telling them in person via Twitter or a blog post.
Using rgee for a paper you are writing? Consider citing it
citation("rgee")
To cite rgee in publications use:
  C Aybar, Q Wu, L Bautista, R Yali and A Barja (2020) rgee: An R
  package for interacting with Google Earth Engine Journal of Open
  Source Software URL https://github.com/r-spatial/rgee/.
A BibTeX entry for LaTeX users is
  @Article{,
    title = {rgee: An R package for interacting with Google Earth Engine},
    author = {Cesar Aybar and Quisheng Wu and Lesly Bautista and Roy Yali and Antony Barja},

气象编程 | Google Earth Engine for R——提供250+ 实例

气象编程 | Google Earth Engine for R——提供250+ 实例

Google Earth Engine for R

More than 250+ examples using GEE with R are available here

What is Google Earth Engine?

Installation

Docker image

setup

Package Conventions

Quick Demo

1. Compute the trend of night-time lights (JS version)

2. Extract precipitation values

3. Create an NDVI-animation (JS version)

How does rgee work?

Code of Conduct

Contributing Guide

Share the love ❤️