sits (version 0.13.0)

sits_regularize: Creates a regularized data cube from an irregular one

Description

Creates cubes with regular time intervals using the gdalcubes package. Cubes are composed using "min", "max", "mean", "median" or "first" functions. Users need to provide an time interval which is used by the composition function.

Usage

sits_regularize(
  cube,
  name,
  dir_images,
  period = NULL,
  roi = NULL,
  agg_method = NULL,
  resampling = "bilinear",
  cloud_mask = TRUE
)

Arguments

cube

A cube whose spacing of observation times is not constant and will be regularized by the "gdalcubes" packges

name

Name of the output data cube

dir_images

Directory where the regularized images will be written by gdalcubes.

period

ISO8601 time period for regular data cubes produced by gdalcubes, with number and unit, e.g., "P16D" for 16 days. Use "D", "M" and "Y" for days, month and year..

roi

A region of interest (see above)

agg_method

Method that will be applied by gdalcubes for aggregation. Options: "min", "max", "mean", "median" and "first".

resampling

Method to be used by gdalcubes for resampling in mosaic operation. Options: "near", "bilinear", "bicubic" or others supported by gdalwarp (see https://gdal.org/programs/gdalwarp.html).

cloud_mask

Use cloud band for aggregation by gdalcubes?

References

APPEL, Marius; PEBESMA, Edzer. On-demand processing of data cubes from satellite image collections with the gdalcubes library. Data, v. 4, n. 3, p. 92, 2019. DOI: 10.3390/data4030092.

Examples

# NOT RUN {
{
# }
# NOT RUN {
# --- Access to the AWS STAC
# Provide your AWS credentials as environment variables
Sys.setenv(
    "AWS_ACCESS_KEY_ID" = <your_aws_access_key>,
    "AWS_SECRET_ACCESS_KEY" = <your_aws_secret_access_key>
)

# define an AWS data cube
  s2_cube <- sits_cube(source = "AWS",
                      name = "T20LKP_2018_2019",
                      collection = "sentinel-s2-l2a",
                      bands = c("B08", "SCL"),
                      tiles = c("20LKP"),
                      start_date = as.Date("2018-07-18"),
                      end_date = as.Date("2018-08-18"),
                      s2_resolution = 60
)

# create a directory to store the resulting images
dir.create(paste0(tempdir(),"/images/"))

 # Build a data cube of equal intervals using the "gdalcubes" package
gc_cube <- sits_regularize(cube   = s2_cube,
                     name          = "T20LKP_2018_2019_1M",
                     dir_images   = paste0(tempdir(),"/images/"),
                     period        = "P1M",
                     agg_method    = "median",
                     resampling    = "bilinear",
                     cloud_mask    = TRUE)
# }
# NOT RUN {
}

# }