On 02/10/2012 21:21, nikos ves wrote:
Hi list,
I was browsing the list and I found out about plotKML. It seems a nice
way to present your data, or do a visual inspection.
I tried to create a timeseries visualization from an environmental index
Im studying at the moment (55 geotiffs ~35 MB
On 03/10/2012 00:17, nikos ves wrote:
An update :
After some experiments, I have created a rasterbrick with 55 layers.
( By stitching 55 geotif's into a vrt using gdal [$gdalbuildvrt
-separate -o tmp.vrt *.tif])
mBrick = brick(/path/to/vrt)
Afterwards when I was trying to create the ts
Dear List,
I'm trying to classify a multiband raster with point shapefiles as
training data. I used the following:
vec-readOGR(.,training)
raster-stack(test.tif) #it contains around 10 pixels and 240 layers
outImage-classified.tif
#then extract the training values
train-extract(raster,vec)
summary: Robert Hijmans' advice fixed my hang, but now I'm getting
very wrong output--i.e., input variables are not preserved. How to
get the appropriate output schema (e.g., data variables)?
details:
https://stat.ethz.ch/pipermail/r-sig-geo/2012-October/016215.html
My current [NetCDF] input
Hello,
I'm trying to write out the values of a RasterBrick to a .txt file. Here's what
I have:
b-brick(file1.nc)
vals-getValues(b)
write.table(vals,file1.txt)
Where 'b' is a RasterBrick with dimensions [224,464,33] which corresponds to
[lat,lon,time]. The output, 'vals' has dimensions
I am having the same problem as the OP. I understand Robert's response, but I
don't think it applies to my situation. Any ideas?
Here are my rasters:
from.rast
class : RasterLayer
band: 2
dimensions : 96, 192, 18432 (nrow, ncol, ncell)
resolution : 1.875,
Hi,
the only way I can see is to use ?xyFromCell
r - raster()
r[] - 1:ncell(r)
r - brick(r,r*2)
vals - getValues(r)
coords - xyFromCell(r,1:ncell(r))
res - cbind(coords,vals)
write.table(res,file1.txt)
Matteo
Tiffany Smith 03.10.12 22.34 Uhr
Hello,
I'm trying to write out the
Oh, I see. My 'from.rast' extent is given from (0,360) degrees longitude,
rather than (-180, 180). I fixed that per this thread:
https://stat.ethz.ch/pipermail/r-sig-geo/2011-March/011155.html
And then the projection works fine.
--
View this message in context:
Dear list,
I have two datasets: one is for point samples with lat and long information at
accuracy of 0.001 degrees. Another is grid cells at a resolution of 0.0025
degrees. Both datasets are in txt format. Now I need to extract the lat and
long information from the grid cell dataset for the
Jin,
I'd be willing to wager it can be done in R alone as someone is sure to
point out, but since you're using R (and so, willing to go the open source
route), you may be willing to take another step and use GRASS GIS (
http://grass.osgeo.org) the wonderful thing about this is that your
problem
Hi
I am comparing different interpolation methods on climate data. I have
found quite a few functions (e.g. spline and kriging) but I wonder if
Nearest-neighbor interpolation and Natural neighbor function are
available in an R package?
Thanks,
Camilo
Camilo Mora, Ph.D.
Department of
Tiffany,
You can also use
r - raster()
r[] - 1:ncell(r)
r - brick(r,r*2)
p - rasterToPoints(r)
write.table(p, file2.txt)
This will add the x and y, but omit NA cells. It also allows to you to use
an expression to subset the values you want (e.g. only values 0)
Robert
On Wed, Oct 3, 2012 at
Tom,
I do not know why you say the output is wrong. Why you would expect that
input variables would have to be preserved (copied to the output file)?
For 'raster' there is input data and output data, the input and output
files (formats, attributes) are not related in any way.
You could use the
Hi Robert,
Thank you very much for the reply. Both datasets are in txt format. Lat and
long are in WGS84. So I might need to do something like:
library(raster)
p - read.table('points.txt')
d - read.table('gridcells.txt')
r - rasterFromPoints(d)
values- extract(r, p)
Is anything else I should
Ani,
The raster::predict function has na.rm=TRUE and removes the NA values, but
not -Inf/Inf values and that indeed seems to be the problem here (see
example below). I have added an option inf.rm to the predict function of
the development version of 'raster' (version 2.0-20) you can try to
On Wed, Oct 3, 2012 at 9:20 PM, jin...@ga.gov.au wrote:
** ** ** ** ** **
Hi Robert,
Thank you very much for the reply. Both datasets are in txt format. Lat
and long are in WGS84. So I might need to do something like:
library(raster)
p - read.table('points.txt')
d -
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