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<p>Hi Rich</p>
<p>A couple of thoughts, below</p>
<p><br>
</p>
<div class="moz-cite-prefix">On 03/10/2019 01:22, Rich Shepard
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:alpine.LNX.2.20.1910021508010.2493@salmo.appl-ecosys.com">Attached
are two maps using 1m LiDAR data. The annotated map,
<br>
basin-elevations.png, was drawn by individually applying d.rast to
each of
<br>
the 70 maps covering the basin. Sharp breaks can be seen where the
data
<br>
cross quads or flights didn't match up smoothly.
<br>
<br>
</blockquote>
<p>Can you first zoom in closely to one of the discontinuity areas
between two tiles and examine the actual values on both sides of
the "break". As Ken pointed out, it might be just a coloring
problem, and NOT really a discontinuous step in elevation values.</p>
<p><br>
</p>
<blockquote type="cite"
cite="mid:alpine.LNX.2.20.1910021508010.2493@salmo.appl-ecosys.com">The
un-annotated map, nehalem-dem-patched.png, displays the results of
<br>
running r.patch on all 70 maps. Topographically it's quite
different from
<br>
the individual maps; almost flat when the north, east, and south
edges
<br>
should have elevations similar to the other map.
<br>
<br>
I think I should apply r.resamp.stats to aggregate the 1m
resolution to 5m.
<br>
I'd like your thoughts on this.
<br>
<br>
</blockquote>
<p>I would NOT use resampling to try to overcome discontinuity in
the tiles. That won't solve the problem, just smear it out a bit.
If there really are breaks in the data, then (you won't like
this...) back to the data provider to clarify why there are these
breaks in elevation.</p>
<p>If the region is too large to keep data at 1 m, then you can
decide to down-sample to a lower resolution to make the data more
manageable. </p>
<p><br>
</p>
<p>Also, if there are sliver gaps between the tiles, then you'll
want to run <tt>r.fill.nulls</tt> to get these gaps filled by
interpolation. In order to save time, I suggest to recursively set
the region to a very small area surrounding each gap, run the<tt>
r.fill.nulls</tt> and patch the filled area back into the
original. Then move on to the next gap. This will be much faster
than trying to do r.fill.nulls on the whole region. When finished,
don't forget to go back to the full region.<br>
</p>
<p><br>
</p>
<blockquote type="cite"
cite="mid:alpine.LNX.2.20.1910021508010.2493@salmo.appl-ecosys.com">I
assume that I should resample each individual map, then re-run
r.patch on
<br>
the coarser maps because r.slope.aspect and r.info need a single
map as
<br>
input.
<br>
<br>
Would this be an appropriate process? I'm completely open to all
suggestions
<br>
and recommendations.
<br>
<br>
</blockquote>
<p>I think the best approach would be creating a VRT, outside of
GRASS, using the <tt>gdalbuildvrt </tt>utility: Dump the list of
your 70 rasters into a text file. Use the<tt> -input_file_list</tt>
parameter to gdalbuildvrt, and you'll have one virtual raster for
import into GRASS. You can reference it with r.external (to avoid
importing and duplicating the disk space required). Then do
whatever hydrological analysis you need with that.</p>
<p><br>
</p>
<p>Best, Micha<br>
</p>
<p><br>
</p>
<blockquote type="cite"
cite="mid:alpine.LNX.2.20.1910021508010.2493@salmo.appl-ecosys.com">Regards,
<br>
<br>
Rich
<br>
<br>
<fieldset class="mimeAttachmentHeader"></fieldset>
<pre class="moz-quote-pre" wrap="">_______________________________________________
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</blockquote>
<pre class="moz-signature" cols="72">--
Micha Silver
Ben Gurion Univ.
Sde Boker, Remote Sensing Lab
+972-523-665918</pre>
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