Examining Environmental Justice through Open Source, Cloud-Native Tools: R

This Jupyter (ipynb) notebook provides a brief introduction to a cloud-native workflow. A more complete version of this example can be found in the intro.qmd Quarto notebook. Here, we merely include an ipynb version to demonstrate how Jupyter notebooks may be included in quarto and, in this case, execute in an R kernel.

Note: in Codespaces editor, choose “Select Kernel” -> “Jupyter Kernels” -> “R” to get started.

library(rstac)
library(gdalcubes)
library(stars)
library(tmap)
gdalcubes::gdalcubes_options(parallel = TRUE)

box <- c(xmin=-122.51006, ymin=37.70801, xmax=-122.36268, ymax=37.80668) 
start_date <- "2022-06-01"
end_date <- "2022-08-01"
items <-
  stac("https://earth-search.aws.element84.com/v0/") |>
  stac_search(collections = "sentinel-s2-l2a-cogs",
              bbox = box,
              datetime = paste(start_date, end_date, sep="/"),
              limit = 100) |>
  post_request()


col <- stac_image_collection(items$features,
                             asset_names = c("B04", "B08", "SCL"),
                             property_filter = \(x){
                               x[["eo:cloud_cover"]] < 20
                             })

cube <- cube_view(srs = "EPSG:4326",
                  extent = list(t0 = start_date, t1 = end_date,
                                left = box[1], right = box[3],
                                top = box[4], bottom = box[2]),
                  nx = 1200, ny = 1200, dt = "P1D",
                  aggregation = "median", resampling = "average")

mask <- image_mask("SCL", values=c(3, 8, 9)) # mask clouds and cloud shadows


ndvi <- raster_cube(col, cube, mask = mask) |>
  select_bands(c("B04", "B08")) |>
  apply_pixel("(B08-B04)/(B08+B04)", "NDVI") |>
  reduce_time(c("mean(NDVI)"))
plot(ndvi)