# 2. stars proxy objects

For a better version of the stars vignettes see https://r-spatial.github.io/stars/articles/

When your imagery or array data easily fits a couple of times in R’s working memory (RAM), consider yourself lucky. This document was not written for you. If your imagery is too large, or for other reasons you want to work with smaller chunks of data than the files in which they come, read on about your options. First, we will discuss the low-level interface for this, then the higher level, using stars proxy objects that delay all reading.

# Preamble: the starsdata package

To run all of the examples in this vignette, you must install a package with datasets that are too large (1 Gb) to be held in the stars package. They are in a drat repo, installation is done by

install.packages("starsdata", repos = "http://gis-bigdata.uni-muenster.de/pebesma", type = "source")
# possibly after: options(timeout = 100)
# or from an alternative repository:
# install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source")

# Reading chunks, change resolution, select bands

read_stars has an argument called RasterIO which controls how a GDAL dataset is being read. By default, all pixels and all bands are read in memory. This can consume a lot of time and require a lot of memory. Remember that your file may be compressed, and that pixel values represented in the file by bytes are converted to 8-byte doubles in R.

The reason for using RasterIO for this is that the parameters we use are directly mapped to the GDAL RasterIO function used (after adapting the 1-based offset index in R to 0-based offset in C++).

An example of using RasterIO is

library(stars)
tif = system.file("tif/L7_ETMs.tif", package = "stars")
rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100, bands = c(1, 3, 4))
(x = read_stars(tif, RasterIO = rasterio))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif    23      54     63 62.05977   73.25  235
## dimension(s):
##      from  to  offset delta                     refsys point x/y
## x       6 105  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       6 105 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   3      NA    NA                         NA    NA
dim(x)
##    x    y band
##  100  100    3

Compare this to

st_dimensions(read_stars(tif))
##      from  to  offset delta                     refsys point x/y
## x       1 349  288776  28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1   6      NA    NA                         NA    NA

and we see that

• the delta values remain the same,
• the offset (x/y coordinates of origin) of the grid remain the same
• the from and to reflect the new area, and relate to the new delta values
• dim(x) reflects the new size, and
• only three bands were read

## Reading at a different resolution

Reading datasets at a lower (but also higher!) resolution can be done by setting nBufXSize and nBufYSize

rasterio = list(nXOff = 6, nYOff = 6, nXSize = 100, nYSize = 100,
nBufXSize = 20, nBufYSize = 20, bands = c(1, 3, 4))
(x = read_stars(tif, RasterIO = rasterio))
## stars object with 3 dimensions and 1 attribute
## attribute(s):
##              Min. 1st Qu. Median     Mean 3rd Qu. Max.
## L7_ETMs.tif    27      53     63 62.09417      74  151
## dimension(s):
##      from to  offset  delta                     refsys point x/y
## x       2 21  288776  142.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
## y       2 21 9120761 -142.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
## band    1  3      NA     NA                         NA    NA

and we see that in addition:

• the delta (raster cell size) values have increased a factor 5, because nBufXSize and nBufYSize were set to values a factor 5 smaller than nXSize and nYSize
• the offset coordinates of the grid are still the same
• the from and to reflect the new area, but relate to the new delta cell size values

We can also read at higher resolution; here we read a 3 x 3 area and blow it up to 100 x 100:

rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3,
nBufXSize = 100, nBufYSize = 100, bands = 1)
x = read_stars(tif, RasterIO = rasterio)
dim(x)
##   x   y
## 100 100
plot(x)

The reason we “see” only three grid cells is that the default sampling method is “nearest neighbour”. We can modify this by

rasterio = list(nXOff = 6, nYOff = 6, nXSize = 3, nYSize = 3,
nBufXSize = 100, nBufYSize = 100, bands = 1, resample = "cubic_spline")
x = read_stars(tif, RasterIO = rasterio)
dim(x)
##   x   y
## 100 100
plot(x)

The following methods are allowed for parameter resample:

resample method used
nearest_neighbour Nearest neighbour (default)
bilinear Bilinear (2x2 kernel)
cubic Cubic Convolution Approximation (4x4 kernel)
cubic_spline Cubic B-Spline Approximation (4x4 kernel)
lanczos Lanczos windowed sinc interpolation (6x6 kernel)
average Average
mode Mode (selects the value which appears most often of all the sampled points)
Gauss Gauss blurring

All these methods are implemented in GDAL; for what these methods exactly do, we refer to the GDAL documentation or source code.

# Stars proxy objects

Stars proxy objects take another approach: upon creation they contain no data at all, but only pointers to where the data can be read. Data is only read when it is needed, and only as much as is needed: if we plot a proxy objects, the data are read at the resolution of pixels on the screen, rather than at the native resolution, so that if we have e.g. a 10000 x 10000 Sentinel 2 (level 1C) image, we can open it by

granule = system.file("sentinel/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.zip", package = "starsdata")
s2 = paste0("SENTINEL2_L1C:/vsizip/", granule, "/S2A_MSIL1C_20180220T105051_N0206_R051_T32ULE_20180221T134037.SAFE/MTD_MSIL1C.xml:10m:EPSG_32632")
(p = read_stars(s2, proxy = TRUE))
## stars_proxy object with 1 attribute in 1 file(s):
## $EPSG_32632 ## [1] "[...]/MTD_MSIL1C.xml:10m:EPSG_32632" ## ## dimension(s): ## from to offset delta refsys values x/y ## x 1 10980 3e+05 10 WGS 84 / UTM zone 32N NULL [x] ## y 1 10980 6e+06 -10 WGS 84 / UTM zone 32N NULL [y] ## band 1 4 NA NA NA B4,...,B8 and this happens instantly, because no data is read. When we plot this object, system.time(plot(p)) ## downsample set to 43 ## user system elapsed ## 0.876 0.263 0.275 This takes only around 1 second, since only those pixels are read that can be seen on the plot. If we read the entire image in memory first, as we would do with p = read_stars(s2, proxy = FALSE) then only the reading would take over a minute, and require 5 Gb memory. ## Methods for stars proxy objects methods(class = "stars_proxy") ## [1] Math Ops [ [<- ## [5] [[<- adrop aggregate aperm ## [9] as.data.frame c coerce dim ## [13] droplevels filter hist initialize ## [17] is.na merge mutate plot ## [21] predict print pull rename ## [25] select show slice slotsFromS3 ## [29] split st_apply st_as_sf st_as_stars ## [33] st_crop st_dimensions<- st_downsample st_mosaic ## [37] st_redimension st_sample st_set_bbox transmute ## [41] write_stars ## see '?methods' for accessing help and source code ## Select attributes We can select attributes as with regular stars objects, by using the first argument to [: x = c("avhrr-only-v2.19810901.nc", "avhrr-only-v2.19810902.nc", "avhrr-only-v2.19810903.nc", "avhrr-only-v2.19810904.nc", "avhrr-only-v2.19810905.nc", "avhrr-only-v2.19810906.nc", "avhrr-only-v2.19810907.nc", "avhrr-only-v2.19810908.nc", "avhrr-only-v2.19810909.nc") file_list = system.file(paste0("netcdf/", x), package = "starsdata") y = read_stars(file_list, quiet = TRUE, proxy = TRUE) names(y) ## [1] "sst" "anom" "err" "ice" y["sst"] ## stars_proxy object with 1 attribute in 9 file(s): ##$sst
## [1] "[...]/avhrr-only-v2.19810901.nc:sst" "[...]/avhrr-only-v2.19810902.nc:sst"
## [3] "[...]/avhrr-only-v2.19810903.nc:sst" "[...]/avhrr-only-v2.19810904.nc:sst"
## [5] "[...]/avhrr-only-v2.19810905.nc:sst" "[...]/avhrr-only-v2.19810906.nc:sst"
## [7] "[...]/avhrr-only-v2.19810907.nc:sst" "[...]/avhrr-only-v2.19810908.nc:sst"
## [9] "[...]/avhrr-only-v2.19810909.nc:sst"
##
## dimension(s):
##      from   to         offset  delta  refsys x/y
## x       1 1440              0   0.25      NA [x]
## y       1  720             90  -0.25      NA [y]
## zlev    1    1          0 [m]     NA      NA
## time    1    9 1981-09-01 UTC 1 days POSIXct

Note that this selection limits the reading from 4 to 1 subdataset from all 9 NetCDF files.

## Select an area

Another possibility is to crop, or select a rectangular region based on a spatial object. This can be done by passing a bbox object, or an sf, sfc or stars object from which the bounding box will be taken. An example:

bb = st_bbox(c(xmin = 10.125, ymin = 0.125, xmax = 70.125, ymax = 70.125))
ysub = y[bb]
st_dimensions(ysub)
##      from  to         offset  delta  refsys x/y
## x      41 281              0   0.25      NA [x]
## y      80 360             90  -0.25      NA [y]
## zlev    1   1          0 [m]     NA      NA
## time    1   9 1981-09-01 UTC 1 days POSIXct
class(ysub) # still no data here!!
## [1] "stars_proxy" "stars"
plot(ysub, reset = FALSE) # plot reads the data, at resolution that is relevant
plot(st_as_sfc(bb), add = TRUE, lwd = .5, border = 'red')

## Lazy evaluation, changing evaluation order

Some other actions can be carried out on stars_proxy objects, but their effect is delayed until the data are actually needed (plot, write_stars). For instance, range selections on dimensions other than shown above first need data, and can only then be carried out. Such functions are added to the object, in an attribute called call_list:

yy = adrop(y)
yyy = yy[,1:10,1:10,]
class(yyy) # still no data
## [1] "stars_proxy" "stars"
st_dimensions(yyy) # and dimensions not adjusted
##      from   to         offset  delta  refsys x/y
## x       1 1440              0   0.25      NA [x]
## y       1  720             90  -0.25      NA [y]
## zlev    1    1          0 [m]     NA      NA
## time    1    9 1981-09-01 UTC 1 days POSIXct
attr(yyy, "call_list") # the name of object in the call (y) is replaced with x:
## [[1]]
## adrop(x = x, drop = drop)
## attr(,".Environment")
## <environment: 0x5624f7a20b98>
##
## [[2]]
## x[i = i, 1:10, 1:10, , drop = drop, crop = crop]
## attr(,".Environment")
## <environment: 0x5624f794ccf8>

Doing this allows for optimizing the order in which operations are done. As an example, for st_apply, reading can be done sequentially over the dimensions over which the function is applied:

• If for example a function is applied to each band (such as: compute band quantiles), bands can be read sequentially, and discarded after the quantiles have been computed.
• If a time series function is applied to pixel time series and the result is plotted on a map, the time series function is only evaluated on the pixels actually plotted. This means that e.g. in
plot(st_apply(x, c("x", "y"), range))

the order of evaluation is reversed: plot knows which pixels are going to be shown, and controls how x is downsampled before st_apply is carried out on this subset.

### Fetching the data

Fetching the data now involves reading the whole array and then evaluating the call_list on it, sequentially:

(x = st_as_stars(yyy)) # read, adrop, subset
## stars object with 3 dimensions and 4 attributes
## attribute(s):
##                Min. 1st Qu. Median       Mean 3rd Qu.  Max.
## sst [C*°]     -1.28   -1.17  -1.11 -1.1163555   -1.06 -0.95
## anom [C*°]     0.48    0.62   0.69  0.6649555    0.72  0.77
## err [C*°]      0.30    0.30   0.30  0.3000000    0.30  0.30
## ice [percent]  0.76    0.79   0.81  0.8062889    0.82  0.85
## dimension(s):
##      from to         offset  delta  refsys x/y
## x       1 10              0   0.25      NA [x]
## y       1 10             90  -0.25      NA [y]
## time    1  9 1981-09-01 UTC 1 days POSIXct

### Plotting with changed evaluation order

For the Sentinel 2 data, band 4 represents NIR and band 1 red, so we can compute NDVI by

# S2 10m: band 4: near infrared, band 1: red.
#ndvi = function(x) (x[4] - x[1])/(x[4] + x[1])
ndvi = function(x1, x2, x3, x4) (x4 - x1)/(x4 + x1)
rm(x)
(s2.ndvi = st_apply(p, c("x", "y"), ndvi))
## stars_proxy object with 1 attribute in 1 file(s):
## $EPSG_32632 ## [1] "[...]/MTD_MSIL1C.xml:10m:EPSG_32632" ## ## dimension(s): ## from to offset delta refsys values x/y ## x 1 10980 3e+05 10 WGS 84 / UTM zone 32N NULL [x] ## y 1 10980 6e+06 -10 WGS 84 / UTM zone 32N NULL [y] ## band 1 4 NA NA NA B4,...,B8 ## call_list: ## [[1]] ## st_apply(X = X, MARGIN = MARGIN, FUN = FUN, CLUSTER = CLUSTER, ## PROGRESS = PROGRESS, FUTURE = FUTURE, rename = rename, .fname = .fname) ## attr(,".Environment") ## <environment: 0x56250179b9f8> ## ## This object has pending lazy operations: dimensions as printed may not reflect this. system.time(plot(s2.ndvi)) # read - compute ndvi - plot ## downsample set to 43 ## user system elapsed ## 0.883 0.208 0.200 # Multi-resolution proxy objects This sections shows some examples how stars_proxy objects deal with the situation where the different maps have dissimilar resolution. The assumptions here are: • all maps need to have the same origin coordinates (typically upper-left corner) and CRS. • the first map determines the “working” resolution, to which e.g. native or downsampled resolutions refer We’ll create four maps with cells size 1, 2 and 3: s1 = st_as_stars(matrix(1:16, 4)) s2 = st_as_stars(matrix(1:16, 4)) s3 = st_as_stars(matrix(1:16, 4)) attr(s1, "dimensions")$X1$offset = 0 attr(s1, "dimensions")$X2$offset = 4 attr(s2, "dimensions")$X1$offset = 0 attr(s2, "dimensions")$X2$offset = 4 attr(s3, "dimensions")$X1$offset = 0 attr(s3, "dimensions")$X2$offset = 4 attr(s1, "dimensions")$X1$delta = 1 attr(s1, "dimensions")$X2$delta = -1 attr(s2, "dimensions")$X1$delta = 2 attr(s2, "dimensions")$X2$delta = -2 attr(s3, "dimensions")$X1$delta = 3 attr(s3, "dimensions")$X2$delta = -3 plot(s1, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s2, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s3, axes = TRUE, text_values = TRUE, text_color = 'orange') We created three rasters with identical cell values and dimensions, but different cell sizes, and hence extents. If we bind them in a single proxy object, with fn1 = paste0(tempdir(), .Platform$file.sep, "img1.tif")
fn2 = paste0(tempdir(), .Platform$file.sep, "img2.tif") fn3 = paste0(tempdir(), .Platform$file.sep, "img3.tif")
write_stars(s1, fn1)
write_stars(s2, fn2)
write_stars(s3, fn3)
(r1 = read_stars(c(fn1, fn2, fn3), proxy = TRUE))
## multi-resolution stars_proxy object with 3 attributes in 3 file(s):
## $1 ## [1] "[...]/img1.tif" ## ##$2
## [1] "[...]/img2.tif"
##
## $3 ## [1] "[...]/img3.tif" ## ## dimension(s): ## from to offset delta x/y ## x 1 4 0 1 [x] ## y 1 4 4 -1 [y] We see that multi-resolution is mentioned in the printed summary. When converting this to a stars object, the secondary rasters are resampled to the cellsize + extent of the first: st_as_stars(r1) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) If we do this for a sub-range, defined for the object resolutions, we get: st_as_stars(r1[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) We now create four maps, all over the same region ([0,4] x [0,4]), with different resolutions (cell size 1, 1/2 and 1/3): s4 = st_as_stars(matrix(1: 16, 4)) s5 = st_as_stars(matrix(1: 64, 8)) s6 = st_as_stars(matrix(1:144,12)) attr(s4, "dimensions")$X1$offset = 0 attr(s4, "dimensions")$X2$offset = 4 attr(s5, "dimensions")$X1$offset = 0 attr(s5, "dimensions")$X2$offset = 4 attr(s6, "dimensions")$X1$offset = 0 attr(s6, "dimensions")$X2$offset = 4 attr(s4, "dimensions")$X1$delta = 1 attr(s4, "dimensions")$X2$delta = -1 attr(s5, "dimensions")$X1$delta = 1/2 attr(s5, "dimensions")$X2$delta = -1/2 attr(s6, "dimensions")$X1$delta = 1/3 attr(s6, "dimensions")$X2$delta = -1/3 plot(s4, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s5, axes = TRUE, text_values = TRUE, text_color = 'orange') plot(s6, axes = TRUE, text_values = TRUE, text_color = 'orange') fn4 = paste0(tempdir(), .Platform$file.sep, "img4.tif")
fn5 = paste0(tempdir(), .Platform$file.sep, "img5.tif") fn6 = paste0(tempdir(), .Platform$file.sep, "img6.tif")
write_stars(s4, fn4)
write_stars(s5, fn5)
write_stars(s6, fn6)
(r2 = read_stars(c(fn4, fn5, fn6), proxy = TRUE))
## multi-resolution stars_proxy object with 3 attributes in 3 file(s):
## $4 ## [1] "[...]/img4.tif" ## ##$5
## [1] "[...]/img5.tif"
##
## $6 ## [1] "[...]/img6.tif" ## ## dimension(s): ## from to offset delta x/y ## x 1 4 0 1 [x] ## y 1 4 4 -1 [y] st_as_stars(r2) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) st_as_stars(r2[,2:4,2:4]) %>% merge() %>% plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE) Finally, an example where the first raster has the higher resolution: (r3 = read_stars(c(fn6, fn5, fn4), proxy = TRUE)) ## multi-resolution stars_proxy object with 3 attributes in 3 file(s): ##$6
## [1] "[...]/img6.tif"
##
## $5 ## [1] "[...]/img5.tif" ## ##$4
## [1] "[...]/img4.tif"
##
## dimension(s):
##   from to offset     delta x/y
## x    1 12      0  0.333333 [x]
## y    1 12      4 -0.333333 [y]

st_as_stars(r3) %>%
merge() %>%
plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)

st_as_stars(r3[,2:6,3:6]) %>%
merge() %>%
plot(breaks = "equal", text_values = TRUE, text_color = 'orange', axes = TRUE)