[R-sig-Geo] Mask from raster with less RAM

[Frederico Faleiro]

Hi guys,

I really need to use the mask without modifying the original resolution of
the rasters, so vectorizing was my first try. I know I can resample first
before applying a raster mask, but this will not work for my objectives
that involve creating some visualisations to the same area with the
original resolution.
Roger, I have tried use the google colab in the cloud (e.g.
https://colab.research.google.com/drive/1BYnnbqeyZAlYnxR9IHC8tpW07EpDeyKR#scrollTo=uaE0kZ0vkqms)
and I think you are right about the restriction about the use of external
softwares like GDAL in this specific case.
Do you know if is it possible to do it in GRASS or QGIS? I only find the
function polygonize (as in raster::rasterToContour) that generates contours
around equal values from the raster.

Cheers,


Em qua., 12 de ago. de 2020 às 13:45, Roger Bivand <Roger.Bivand using nhh.no>
escreveu:

> On Wed, 12 Aug 2020, Frederico Faleiro wrote:
>
> > Hi guys, thank you for your reply.
> >
> > Jaime, I have tried, but I can't install rgdal needed to use raster and
> > apparently other people too (
> >
> https://stackoverflow.com/questions/57617895/how-to-install-rgdal-and-or-upload-raster-on-google-collaboration
> ).
> > Do you have a solution?
>
> Use CRAN Windows or MacOS binaries, or install the system requirements.
> Unless you can do that, for example because you do not control the
> platform you are using (are you working on a cloud instance?), do not
> install packages needing external software
> from source.
>
> > Hugo, I need the mask in vector format because the rasters have different
> > resolutions, so I can't use raster as a mask. I have modified the reprex
> to
> > be more precise about it (see below).
>
> Please use GRASS. What you are trying to do is something that has "just
> worked" in GRASS since it was first created. GRASS can be run from the
> shell, from Python and from R (rgrass7). Or use SAGA, another fast raster
> processor. Both mask from raster directly. Write a shell script for GRASS
> to resample your rasters, mask them, and complete. You can also use GRASS
> and SAGA from QGIS. You might use Python or R to make file name handling
> "easier" than in a script. Neither GRASS nor SAGA use much memory unless
> rasters are huge, and then they are lean.
>
> Roger
>
> > Steve, I think this approach has the same issue of Hugo Costa. I don't
> have
> > the polygon of the mask, so I am trying to create one to apply in the
> other
> > rasters of different resolutions.
> >
> > # reproducible example
> > library(raster)
> > # read data to create mask
> > r <- raster(system.file("external/test.grd", package="raster"))
> > r[!is.na(r)] <- 1
> > pol <- rasterToPolygons(r, dissolve = T) #  a lot of RAM to process the
> data
> > # raster of different resolution
> > res2 <- res(r) + 10
> > r2 <- raster(extent(r), resolution = res2)
> > r2[ ] <- 1
> > # apply the mask
> > r2.mask <- mask(r2, pol)
> > # plot
> > par(mfrow = c(1, 3))
> > plot(r)
> > plot(r2)
> > plot(r2.mask)
> >
> > Cheers!
> >
> > Em ter., 11 de ago. de 2020 às 19:26, Stephen Stewart <
> > stephen.stewart85 using gmail.com> escreveu:
> >
> >> Hi Frederico,
> >>
> >> It may not solve all of your RAM issues, but in this situation I would
> >> skip the rasterToPolygons (which is also usually very slow) and use
> raster
> >> math to propagate NAs.
> >>
> >> r <- raster(system.file("external/test.grd", package="raster"))
> >> r[!is.na(r)] <- 1
> >> # Can also be faster to do r = r / r, but add an offset (that cannot
> >> result in 0) if you have valid 0s.
> >>  r2 <- raster(extent(r), res(r))
> >> r2[ ] <- 1
> >> r2.mask <- r * r2
> >>
> >> If you have a polygon to use as a mask, burn it in using the fasterize
> >> package and then apply the above.
> >>
> >> Hope that helps.
> >>
> >> Cheers,
> >>
> >> Steve
> >>
> >> On Wed., 12 Aug. 2020, 6:43 am Frederico Faleiro, <fvfaleiro using gmail.com>
> >> wrote:
> >>
> >>> Dear all,
> >>>
> >>> I would like to generate a mask from a raster, but my workflow needs a
> lot
> >>> of RAM to process big rasters. I need this polygon mask to use in
> >>> another's
> >>> rasters.
> >>> Do you know another approach that needs less RAM?
> >>>
> >>> # reproducible example
> >>> library(raster)
> >>> # read data to create mask
> >>> r <- raster(system.file("external/test.grd", package="raster"))
> >>> r[!is.na(r)] <- 1
> >>> pol <- rasterToPolygons(r, dissolve = T) #  a lot of RAM to process the
> >>> data
> >>> # apply the mask in another raster
> >>>  r2 <- raster(extent(r), res(r))
> >>> r2[ ] <- 1
> >>> r2.mask <- mask(r2, pol)
> >>>
> >>> Cheers!
> >>>
> >>> --
> >>> Frederico Faleiro
> >>> Postdoctoral Researcher in the INCT-EECBio (https://www.eecbio.ufg.br/
> )
> >>> Department of Ecology | Institute of Biological Science | Federal
> >>> University of Goiás | Brazil
> >>> RG: https://www.researchgate.net/profile/Frederico_Faleiro
> >>> CV: http://lattes.cnpq.br/4926404840659003
> >>>
> >>>         [[alternative HTML version deleted]]
> >>>
> >>> _______________________________________________
> >>> R-sig-Geo mailing list
> >>> R-sig-Geo using r-project.org
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >>>
> >>
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-Geo mailing list
> > R-sig-Geo using r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
> https://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en

	[[alternative HTML version deleted]]