[GRASS-user] Re: Importing Dems with r.in.xyz

[Hamish]

Hanlie wrote:
> >> At this point, g.region reports 1146474 cells in the region, while I
> >> have 1146370 lines of coordinates in my file.
...
> > So it looks like there are about 100 coordinates missing from the ASCII
> > ASCII file.

0.01% ..

> Maybe "holes" in the data?

perhaps this:  https://trac.osgeo.org/grass/ticket/123
??


> I was thinking perhaps importing the points as vectors, converting
> them to raster and then doing a nearest neighbour or IDW interpolation
> to fill the gaps. At least then I'll be able to see where the gaps are
> and limit the interpolated pixels using a mask?

No need to do anything different to find the missing pixels. Inspecting
the output of r.univar with r.in.xyz's method=n maps can be very useful
for troubleshooting.


from the help page:

   Gridded data
       If data is known to be on a regular grid  r.in.xyz  can
       reconstruct  the  map perfectly as long as some care is
       taken to set up  the  region  correctly  and  that  the
       data's  native map projection is used. A typical method
       would involve determining the grid resolution either by
       examining  the  data's  associated  documentation or by
       studying  the  text  file.  Next  scan  the  data  with
       r.in.xyz's  -s  (or  -g)  flag to find the input data's
       bounds. GRASS uses the  cell-center  raster  convention
       where  data points fall within the center of a cell, as
       opposed to the grid-node convention. Therefore you will
       need  to  grow  the  region  out  by half a cell in all
       directions beyond what the  scan  found  in  the  file.
       After  the  region  bounds  and resolution are set cor-
       rectly with g.region, run r.in.xyz using the  n  method
       and  verify that n=1 at all places.  r.univar can help.
       Once you are confident that the region exactly  matches
       the data proceed to run r.in.xyz using one of the mean,
       min, max, or median methods. With n=1  throughout,  the
       result should be identical regardless of which of those
       methods are used.


with the "n" map you might use r.mapcalc to extract the NULL cells
as some value, then r.out.xyz or r.to.vect on th extracts to highlight
where they are. Or maybe you get lucky with r.colors with "nv" set to
bright magenta on the original data.



Hamish