knitr::opts_chunk$set(eval = FALSE)
GEE offers on-the-fly computation for rendering EE spatial objects:
library(rgee) library(rgeeExtra) ee_Initialize() img <- ee$Image$Dataset$CGIAR_SRTM90_V4 Map$addLayer(log1p(img), list(min = 0, max = 7))
However, this interactive map service is temporary, disappearing after a short period of time (~ 4 hours). This makes Map$addLayer
unusable for report generation. In this vignette, we will learn to create a permanent interactive map.
Instead of using GEE API for creating interactive maps, we will use titiler. titiler creates web map tiles dynamically based on COG (STAC) resources. Since an exported EE task to retrieve images can return a COG, we just have to move these results to a storage web service with HTTP GET range requests.
Fortunately, GCS counts with this feature, so if we manage to move our results to GCS, the work would be already done :)
GET /OBJECT_NAME HTTP/1.1 Host: BUCKET_NAME.storage.googleapis.com Content-Length: 0 Authorization: AUTHENTICATION_STRING Range: bytes=BYTE_RANGE If-Match: ENTITY_TAG If-Modified-Since: DATE If-None-Match: ENTITY_TAG If-Unmodified-Since: DATE
First, load rgee
and googleCloudStorageR
and initialize the EE API. You must have correctly configured a service account key, if not check our tutorial "how to integrate Google Cloud Storage and rgee".
library(rgee) library(googleCloudStorageR) # Init the EE API ee_Initialize("csaybar", gcs = TRUE) # Validate your SaK # ee_utils_sak_validate(bucket = "rgee_examples")
Define your study area.
# Define an study area EE_geom <- ee$Geometry$Point(c(-70.06240, -6.52077))$buffer(5000)
Select an ee$Image
, for instance, a Landsat-8 image.
l8img <- ee$ImageCollection$Dataset$LANDSAT_LC08_C02_T2_L2 %>% ee$ImageCollection$filterDate('2021-06-01', '2021-12-01') %>% ee$ImageCollection$filterBounds(EE_geom) %>% ee$ImageCollection$first()
Move l8img
from EE to GCS.
gcs_l8_name <- "l8demo2" # name of the image in GCS. BUCKET_NAME <- "rgee_examples" # set here your bucket name task <- ee_image_to_gcs( image = l8img$select(sprintf("SR_B%s",1:5)), region = EE_geom, fileNamePrefix = gcs_l8_name, timePrefix = FALSE, bucket = BUCKET_NAME, scale = 10, formatOptions = list(cloudOptimized = TRUE) # Return a COG rather than a TIFF file. ) task$start() ee_monitoring()
Titiler needs resources downloadable for anyone. Therefore, we recommend you to work with GCS buckets with fine-grained access. In this way, you can decide individually which objects to make public. On the other hand, if you decide to work with buckets with uniform access, you will have to expose the entire bucket!. The code below makes a specific object in your bucket public to internet.
# Make PUBLIC the GCS object googleCloudStorageR::gcs_update_object_acl( object_name = paste0(gcs_l8_name, ".tif"), bucket = BUCKET_NAME, entity_type = "allUsers" )
Finally, use Map$addLayer
to display the COG resource. By default, Map$addLayer
use the open endpoint: https://api.cogeo.xyz/docs.
library(rgee) gcs_l8_name <- "l8demo2" # name of the image in GCS. BUCKET_NAME <- "rgee_examples" # set here your bucket name
img_id <- sprintf("https://storage.googleapis.com/%s/%s.tif", BUCKET_NAME, gcs_l8_name) visParams <- list(bands=c("SR_B4","SR_B3","SR_B2"), min = 8000, max = 20000, nodata = 0) Map$centerObject(img_id) Map$addLayer( eeObject = img_id, visParams = visParams, name = "My_first_COG", titiler_server = "https://api.cogeo.xyz/" )
If you prefer to use titiler syntax, set the parameter
titiler_viz_convert
as FALSE.
visParams <- list(expression = "B4,B3,B2", rescale = "8000, 20000", resampling_method = "cubic") Map$addLayer( eeObject = img_id, visParams = visParams, name = "My_first_COG", titiler_server = "https://api.cogeo.xyz/", titiler_viz_convert = FALSE )
Any scripts or data that you put into this service are public.
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