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library(rgeedim)
library(terra)
project_id <- Sys.getenv("GOOGLE_CLOUD_QUOTA_PROJECT", "rgeedim-demo")
gd_initialize(project = project_id)
b <- gd_bbox(
xmin = -120.296,
xmax = -120.227,
ymin = 37.9824,
ymax = 38.0071
)
## landsat example
# search collection for date range and minimum data fill (85%)
x <- 'LANDSAT/LE07/C02/T1_L2' |>
gd_collection_from_name() |>
gd_search(
start_date = '2020-11-01',
end_date = '2021-02-28',
region = b,
cloudless_portion = 85
)
# inspect individual image metadata in the collection
gd_properties(x)
# download a single image, with "clouds" masked
y <- gd_properties(x)$id[1] |>
gd_image_from_id() |>
gd_download(
filename = "image.tif",
region = b,
scale = 30,
crs = 'EPSG:5070',
dtype = 'uint16',
overwrite = TRUE,
silent = FALSE
)
plot(rast(y)[[1:4]])
# create composite landsat image near December 1st, 2020 and download
# using q-mosaic method.
z <- x |>
gd_composite(
method = "q-mosaic",
date = '2020-12-01'
) |>
gd_download(
filename = "image.tif",
region = b,
scale = 30,
crs = 'EPSG:5070',
dtype = 'uint16',
overwrite = TRUE,
silent = FALSE
)
plot(rast(z)[[1:4]])
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.