rgee v.1.0.9

rgee_demos.R

# Spatial R packages ------------------------------------------------------
library(cptcity)
library(mapview)
library(leaflet)
library(rgee)
library(sf)

# Initialize Google Earth Engine (Just One time)
ee_Initialize()

# 1. world map ------------------------------------------------------------
image <- ee$Image('CGIAR/SRTM90_V4')

Map$centerObject(image)
Map$addLayer(
  eeObject = image,
  visParams = list(min = 0, max = 5000, palette= cpt(pal = "mpl_inferno")),
  name = 'SRTM90_V4'
)

# 2. Load Geometry --------------------------------------------------------
nc <- st_read(system.file("shape/nc.shp", package="sf"))
nc_elv <- ee_extract(image, nc, sf = TRUE)
plot(nc_elv["elevation"])

# Spatial R packages ---------------------------------------------------------------
library(cptcity)
library(raster)
library(rgee)


# Initialize Google Earth Engine (Just One time)
ee_Initialize()

# 1. world map ------------------------------------------------------------
image <- ee$Image("LANDSAT/LC08/C01/T1/LC08_044034_20140318")
ndvi <- (image[["B5"]] - image[["B4"]])/(image[["B5"]] + image[["B4"]])
names(ndvi) <- "ndvi"

Map$centerObject(image)
Map$addLayer(
  eeObject = ndvi,
  visParams = list(min = 0, max = 0.7, palette = cpt("grass_ndvi")),
  name = 'SRTM90_V4'
)


# 2. Download Image -------------------------------------------------------
ndvi_raster <- ee_as_raster(ndvi, scale = 1000)
plot(ndvi_raster)

# Spatial R packages ------------------------------------------------------
library(cptcity)
library(raster)
library(rgee)


# Initialize Google Earth Engine (Just One time)
ee_Initialize()

# 1. Landsat 08 ----------------------------------------------------------
image <- ee$Image("LANDSAT/LC08/C01/T1/LC08_044034_20140318")
ndvi <- (image[["B5"]] - image[["B4"]])/(image[["B5"]] + image[["B4"]])
names(ndvi) <- "ndvi"

Map$centerObject(image)
Map$addLayer(
  eeObject = ndvi,
  visParams = list(min = 0, max = 0.7, palette = cpt("grass_ndvi")),
  name = 'SRTM90_V4'
)


sf <- ee_as_sf(image$geometry())
sf_split <- sf::st_make_grid(sf, n = c(2,1))
# plot(sf)
# plot(sf_split, add = TRUE)

# 2. Download Image (on a batch way) ------------------------------------
ndvi_raster <- list()
ee_users <- c("aybar1994", "csaybar")
for (index in seq_along(sf_split)) {
  ee_Initialize(ee_users[index])
  ndvi_raster[[index]] <- ee_as_raster(
    image = ndvi,
    dsn = tempfile(),
    region = sf_as_ee(sf_split[index]),
    scale = 1000,
    lazy = TRUE
  )
}
ndvi_raster_split <- lapply(ndvi_raster, ee_utils_future_value)

# 3. Merge Split raster
ndvi_raster_f <- Reduce(function(...) mosaic(..., fun=mean), ndvi_raster_split)
plot(ndvi_raster_f)
dev.off()

# Spatial R packages ------------------------------------------------------
library(raster)
library(rgee)

# 1. Adds a band containing image date as years since 1991.
createTimeBand <-function(img) {
  year <- ee$Date(img$get('system:time_start'))$get('year')$subtract(1991L)
  ee$Image(year)$byte()$addBands(img)
}

# 2. Map the time band creation helper over the night-time lights collection.
collection <- ee$ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS') %>% 
  ee$ImageCollection$select("stable_lights") %>% 
  ee$ImageCollection$map(createTimeBand)

# 3. Compute a linear fit over the series of values at each pixel
col_reduce <- collection$reduce(ee$Reducer$linearFit())
col_reduce <- col_reduce$addBands(col_reduce$select('scale'))
ee_print(col_reduce)

# 4. Create a interactive visualization!
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"
)