Thanks all.
Best, Dexter
On Wed, Nov 25, 2020 at 4:13 AM Roger Bivand wrote:
> On Tue, 24 Nov 2020, Dexter Locke wrote:
>
> > Dear list,
> >
> > Is there a way to reduce the spatial extent of an impractically-large
> > raster (~50Gb) on import? Can a crop or mask be applied as the large
> raster
> > is read into R to create a more manageable subset?
>
> rgdal::GDAL.open() simply opens the data source but does not read it,
> returning an object with a handle to the open file - close with
> rgdal::GDAL.close(). rgdal::getRasterData() has a complete set of
> arguments for the kinds of things you mention, see:
> https://rgdal.r-forge.r-project.org/reference/GDALRasterBand-class.html
>
> The same arguments are used by rgdal::readGDAL(): offset, region.dim,
> output.dim, band. These all permit rectangular cropping, and output.dim=
> can decimate/resample/aggregate input cells to coarser output cells. These
> are the same mechanisms that GDAL utility programs use. They are also used
> by raster internally.
>
> The example in rgdal::readGDAL() includes:
>
> SP27GTIF <- readGDAL(system.file("pictures/SP27GTIF.TIF",
> package = "rgdal")[1], output.dim=c(100,100))
>
> doing resampling on-the-fly. Lower down,
>
> fn <- system.file("pictures/erdas_spnad83.tif", package = "rgdal")[1]
> erdas_spnad83 <- readGDAL(fn, offset=c(50, 100), region.dim=c(400, 400),
> output.dim=c(100,100))
>
> uses all of offset=, region.dim= and output.dim=. The example files are
> tiny, but the same GDAL code applies to larger files. Note that the axis
> ordering of the argument vectors is often counter-intuitive and needs some
> patience.
>
> The GDAL utilities can be run file-to-file using the gdalUtils package,
> in addition to being run from the command line.
>
> Otherwise, see the stars package and its proxy=TRUE read functionality,
> which leaves the data on disk until required in possibly subsetted and
> resampled form.
>
> Hope this helps,
>
> Roger
>
> >
> > I've looked at raster and rgdal helpfiles, but have not found this
> > functionality. This would be akin to the sf::st_read() 's new "query"
> > argument that takes SQL statements for filtering records while reading
> data
> > into R.
> >
> > The ultimate use case is to summarize a large categorical raster into
> many
> > thousands of polygons, and I thought reading small subsets in at a time
> > could be looped through or parallelized for summarizing smaller pieces of
> > the data. If there are other approaches to zonal functions (like
> > equivalents of ArcMap's Tabulate Area) that could also work.
> >
> > Thanks for your consideration, Dexter
> >
> > [[alternative HTML version deleted]]
> >
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> >
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: roger.biv...@nhh.no
> https://orcid.org/-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0J&hl=en
>
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