out<-stack(list.files(pattern='*.tiff$)
filename<-rasterTmpFile()
out<-writeStart(out,filename,overwrite=TRUE)
bs<-blockSize(out)
pb<-pbCreate(bs$n)
for (i in 1:bs$n){
v<-getValues(out,row=bs$row[i],nrows=bs$nrows[i])
s<-t(sapply(apply(v,1,low
ution.
My solution in this moment is as follow:
1.- Create the 'rasterStack'
2.- Save with 'writeRaster(rasterStack,filename)
3.- Open it with "brick(filenanme)"
4.- Use the function writeStart
Thats is the better way or exist another option?
2013/5/8 Nagle, Nicholas mai
Dear Francisco,
I am working with the same time series of MOD09 data, but I leave it as a
stack, rather than create the brick.
Why do you need it as a brick? The problem with brick is that it needs the
data to be in one file. That's a big file in this instance (Tb scale?). And,
since you d
Corey,
You might also look at Simon Wood's mgcv package. It is a general purpose
Generalized Additive Model package. There is a way to specify a Conditional
Autoregressive Model in space. And you can interact this with temporal
autocorrelation (or equivalently, with temporal splines). And,
Hi Trent -
I've had some success doing raster-to-vector conversions with GRASS and the
spgrass6 package. But without the fractional allocation of cells that you need
here. Quick and dirty, you might increase the resolution of the raster so that
the area of cells exactly on the boundary is neg
Hi Chris -
I don't know how to do what you want with spplot, but I've done it with ggplot.
Overlays and colors are more intuitive to me in ggplot. Maybe this will help?
I recently had to plot data from the NLCD. It has a colortable in the
metadata, and people are used to seeing the data with
For the first part of your question, interpolation: I've never used it, but
the 'tripack' package seems to do most of what you want. There is a function
tri.find() that returns the three points of the TIN. From there, a linear
interpolation shouldn't be too difficult.
For the second part, re
