sits_mosaic {sits} | R Documentation |
Mosaic classified cubes
Description
Creates a mosaic of all tiles of a sits cube.
Mosaics can be created from both regularized ARD images or from classified
maps. In the case of ARD images, a mosaic will be produce for each band/date
combination. It is better to first regularize the data cubes and then
use sits_mosaic
.
Usage
sits_mosaic(
cube,
crs = "EPSG:3857",
roi = NULL,
multicores = 2L,
output_dir,
version = "v1",
progress = TRUE
)
Arguments
cube |
A sits data cube. |
crs |
A target coordinate reference system of raster mosaic. The provided crs could be a string (e.g, "EPSG:4326" or a proj4string), or an EPSG code number (e.g. 4326). Default is "EPSG:3857" - WGS 84 / Pseudo-Mercator. |
roi |
Region of interest (see below). |
multicores |
Number of cores that will be used to crop the images in parallel. |
output_dir |
Directory for output images. |
version |
Version of resulting image (in the case of multiple tests) |
progress |
Show progress bar? Default is TRUE. |
Value
a sits cube with only one tile.
Note
The "roi" parameter defines a region of interest. It can be
an sf_object, a shapefile, or a bounding box vector with
named XY values (xmin
, xmax
, ymin
, ymax
) or
named lat/long values (lon_min
, lon_max
,
lat_min
, lat_max
).
When the data cube has tiles that cover different UTM grid zones, the user should specify the CRS of the mosaic. We use "EPSG:3857" (Pseudo-Mercator) as the default.
Author(s)
Felipe Carvalho, felipe.carvalho@inpe.br
Rolf Simoes, rolfsimoes@gmail.com
Felipe Carlos, efelipecarlos@gmail.com
Examples
if (sits_run_examples()) {
# create a random forest model
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6.1",
data_dir = data_dir
)
# classify a data cube
probs_cube <- sits_classify(
data = cube, ml_model = rfor_model, output_dir = tempdir()
)
# smooth the probability cube using Bayesian statistics
bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
# label the probability cube
label_cube <- sits_label_classification(
bayes_cube,
output_dir = tempdir()
)
# create roi
roi <- sf::st_sfc(
sf::st_polygon(
list(rbind(
c(-55.64768, -11.68649),
c(-55.69654, -11.66455),
c(-55.62973, -11.61519),
c(-55.64768, -11.68649)
))
),
crs = "EPSG:4326"
)
# crop and mosaic classified image
mosaic_cube <- sits_mosaic(
cube = label_cube,
roi = roi,
crs = "EPSG:4326",
output_dir = tempdir()
)
}