raster_cube: Create a data cube from an image collection

Description Usage Arguments Details Value Note Examples

View source: R/cube.R

Description

Create a proxy data cube, which loads data from a given image collection according to a data cube view

Usage

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raster_cube(image_collection, view, mask = NULL, chunking = c(1, 256, 256))

Arguments

image_collection

Source image collection as from image_collection or create_image_collection

view

A data cube view defining the shape (spatiotemporal extent, resolution, and spatial reference), if missing, a default overview is used

mask

mask pixels of images based on band values, see image_mask

chunking

Vector of length 3 defining the size of data cube chunks in the order time, y, x.

Details

The following steps will be performed when the data cube is requested to read data of a chunk:

1. Find images from the input collection that intersect with the spatiotemporal extent of the chunk 2. For all resulting images, apply gdalwarp to reproject, resize, and resample to an in-memory GDAL dataset 3. Read the resulting data to the chunk buffer and optionally apply a mask on the result 4. Update pixel-wise aggregator (as defined in the data cube view) to combine values of multiple images within the same data cube pixels

Value

A proxy data cube object

Note

This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.

Examples

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# create image collection from example Landsat data only 
# if not already done in other examples
if (!file.exists(file.path(tempdir(), "L8.db"))) {
  L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
                         ".TIF", recursive = TRUE, full.names = TRUE)
  create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db")) 
}

L8.col = image_collection(file.path(tempdir(), "L8.db"))
v = cube_view(extent=list(left=388941.2, right=766552.4, 
              bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
              srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
raster_cube(L8.col, v)
 
 # using a mask on the Landsat quality bit band to filter out clouds
 raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
 

Example output

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

A GDAL image collection object, referencing 19 images with 12  bands
Images:
                                      name      left      top   bottom
1 LC08_L1TP_013032_20180131_20180207_01_T1 -74.67898 41.39099 39.25027
2 LC08_L1TP_013032_20180405_20180417_01_T1 -74.70333 41.39106 39.25080
3 LC08_L1TP_013032_20180421_20180502_01_T1 -74.70681 41.39107 39.25098
4 LC08_L1TP_013032_20180710_20180717_01_T1 -74.66854 41.39096 39.24991
5 LC08_L1TP_013032_20180827_20180911_01_T1 -74.67202 41.39097 39.25000
6 LC08_L1TP_013032_20181030_20181115_01_T1 -74.69637 41.39104 39.25062
      right            datetime
1 -71.92546 2018-01-31T00:00:00
2 -71.94695 2018-04-05T00:00:00
3 -71.95411 2018-04-21T00:00:00
4 -71.91114 2018-07-10T00:00:00
5 -71.91472 2018-08-27T00:00:00
6 -71.93979 2018-10-30T00:00:00
                                                srs
1 +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
2 +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
3 +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
4 +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
5 +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
6 +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
[ omitted 13 images ] 

Bands:
   name offset scale unit   nodata image_count
1   B01      0     1      0.000000          19
2   B02      0     1      0.000000          19
3   B03      0     1      0.000000          19
4   B04      0     1      0.000000          19
5   B05      0     1      0.000000          19
6   B06      0     1      0.000000          19
7   B07      0     1      0.000000          19
8   B08      0     1      0.000000          19
9   B09      0     1      0.000000          19
10  B10      0     1      0.000000          19
11  B11      0     1      0.000000          19
12  BQA      0     1                        19

A GDAL data cube proxy object

Dimensions:
       low     high count       pixel_size chunk_size
t  2018-01  2018-12    12              P1M          1
y  4345299  4744931   526 759.756653992395        256
x 388941.2 766552.4   497 759.781086519115        256

Bands:
   name offset scale nodata unit
1   B01      0     1    NaN     
2   B02      0     1    NaN     
3   B03      0     1    NaN     
4   B04      0     1    NaN     
5   B05      0     1    NaN     
6   B06      0     1    NaN     
7   B07      0     1    NaN     
8   B08      0     1    NaN     
9   B09      0     1    NaN     
10  B10      0     1    NaN     
11  B11      0     1    NaN     
12  BQA      0     1    NaN     

A GDAL data cube proxy object

Dimensions:
       low     high count       pixel_size chunk_size
t  2018-01  2018-12    12              P1M          1
y  4345299  4744931   526 759.756653992395        256
x 388941.2 766552.4   497 759.781086519115        256

Bands:
   name offset scale nodata unit
1   B01      0     1    NaN     
2   B02      0     1    NaN     
3   B03      0     1    NaN     
4   B04      0     1    NaN     
5   B05      0     1    NaN     
6   B06      0     1    NaN     
7   B07      0     1    NaN     
8   B08      0     1    NaN     
9   B09      0     1    NaN     
10  B10      0     1    NaN     
11  B11      0     1    NaN     
12  BQA      0     1    NaN     

gdalcubes documentation built on Oct. 28, 2021, 5:06 p.m.