hey,
you can also calculate a normalized point cloud by subtract Z(from
point)
from the DTM(Z)
you need a DTM (as raster) and the LiDAR points as ASCII
1. "r.stats -gn in=DTM >temp_elev"
2. create a dictionary in python, from "temp_elev" k=x,y and value=z (x
and y should INT)
3. loop the point cloud and subtract the z from k for each LiDAR point
and
add a nZ column >> nDTM_LiDAR_points
2 and 3 should run in the same script!
e\.g\. In Python:
#read input raster data (ascii from r.stats -gn DTM)
for lineR in iR.readlines():
valR = lineR.strip().split(' ')
x = float(valR[0])
y = float(valR[1])
k = ('%s %s' % (int(x),int(y)))
z = float(valR[2])
value.append(z)
D[k] = value
value =
#read input point cloud as ascii and caclate nZ
for lineP in iP.readlines():
valP = lineP.strip().split(' ')
x = float(valP[0])
y = float(valP[1])
k = ('%s %s' % (int(x),int(y)))
Z = float(valP[2])
output.write('%s %s %s %s\n' %(x,y,Z,Z-D[k][0]))
oF.write(output.getvalue())
then you can 'awk' the data you are interested in 'cat nDTM_LiDAR_points
|
awk '{if($4 < 2 && $4 > 1) print $0}' >LiDAR_1_to_2
r.in.xyz LiDAR_1_to_2 out=LiDAR_1_to_2 method=n
you can see the related reference to a similar workflow:
http://www.isprs.org/proceedings/XXXVIII/5-W12/Papers/ls2011_submission_35.pdf
Michael
Am 2012-03-30 14:52, schrieb Daniel Lee:
Hi Daniel V 
You're right, that might not be the best way to go. I thought that it
might simply be faster to do a topological operation rather than a DB
edit.
To be honest, I'd stay away from any 3D objects like volumes because
it'd
just get pretty complicated if you use them... As far as I know Could
be
wrong. My suggestion would use the following steps:
1. Make terrain raster
2. Make surface raster
-- Here I'm assuming that for you basically have only two height
"layers"
- i.e. no points that contain information between the surface raster and
terrain (like bushes beneath a tree cover).
3. Make a relative digital surface model (rDSM) (r.mapcalc --> rDSM =
surface - terrain)
4. Reclassify the rDSM into the different classes you're interested in
5. Convert the reclassified raster into an area vector file.
-- This makes a 2D map of polygons that cover the same areas as the
raster
classes.
6. Extract the points inside the different different height classes
(v.select)
However, if you're working with regularly spaced points that pass onto a
regular grid (=1 pt. / raster pixel) you could always use r.stats to
tell
you how much area is in each raster category. Then you really wouldn't
need
vectors at all.
Daniel L.
--
B.Sc. Daniel Lee
Geschäftsführung für Forschung und Entwicklung
ISIS - International Solar Information Solutions GbR
Vertreten durch: Daniel Lee, Nepomuk Reinhard und Nils Räder
Deutschhausstr. 10
35037 Marburg
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Bundesministerium für Wirtschaft und Technologie aufgrund eines
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Zusätzliche Unterstützung erhält ISIS von dem Entrepreneurship Cluster
Mittelhessen, der Universität Marburg, dem Laboratory for Climatology
and
Remote Sensing und dem GIS-Lab Marburg.
Am 30. März 2012 14:09 schrieb Daniel Victoria
<daniel.victoria@gmail.com>:
Hi Daniel Lee (two Daniels changing emails is a hard thing to
follow...)
I can create the different height classes raster easily but I'm not
sure how to get the lidar points from the cloud that are in each
vertical slice.
Maybe work with raster volumes? Is there a way to cross points at
different volumes?
The fact is, I'm not seeing how to do this without having to import
the lidar point cloud.
Schematically, what I want to do is count the number of points that
are in the vertical bins 1 2, (g is ground), a vertical slice...
___
| . :
bin 2 | :
| .
-----
bin 1 | ...
| .
ggggg
Cheers and many thanks for the attention
Daniel V
On Fri, Mar 30, 2012 at 8:08 AM, Daniel Lee <lee@isi-solutions.org>
wrote:
> Hi Daniel,
>
> You could try making different rasters with classes (0-5m over
> ground,
> 5-10m
> over ground, etc.) and then convert them into polygons, then check
> how
> many
> points are inside them. I'm not sure if it'd be faster than the way
> you
> suggested originally, though, when you think that you'd have to make
> the
> rasters first, etc.
>
> I ended up giving up on large vector operations with LiDAR point
> clouds
> in
> GRASS 6.4 because it simply took way too long, but I was dealing with
> millions of points. For such a small dataset it's probably fine,
> since
> as
> long as you know your script is okay you can let it run through your
> lunch
> break
>
>
> Daniel
>
> --
>
> B.Sc. Daniel Lee
> Geschäftsführung für Forschung und Entwicklung
> ISIS - International Solar Information Solutions GbR
> Vertreten durch: Daniel Lee, Nepomuk Reinhard und Nils Räder
>
> Deutschhausstr. 10
> 35037 Marburg
> Festnetz: +49 6421 379 6256
> Mobil: +49 176 6127 7269
> E-Mail: Lee@isi-solutions.org
> Web: http://www.isi-solutions.org
>
> ISIS wird gefördert durch die Bundesrepublik Deutschland,
> Zuwendungsgeber:
> Bundesministerium für Wirtschaft und Technologie aufgrund eines
> Beschlusses
> des Deutschen Bundestages, sowie durch die Europäische Union,
> Zuwendungsgeber: Europäischer Sozialfonds.
> Zusätzliche Unterstützung erhält ISIS von dem Entrepreneurship
> Cluster
> Mittelhessen, der Universität Marburg, dem Laboratory for Climatology
> and
> Remote Sensing und dem GIS-Lab Marburg.
>
>
>
>
> Am 30. März 2012 13:00 schrieb Daniel Victoria
> <daniel.victoria@gmail.com>:
>
>> I need to calculate the number of points at different height levels.
>> That
>> is, how many points are from 0 to 5 meters? And from 5 to 10? And so
>> on. So
>> I believe I need to work with vector points and a database. Or is
>> there
>> another way?
>> Thanks
>> Daniel
>>
>> On Mar 30, 2012 6:47 AM, "Daniel Lee" <lee@isi-solutions.org> wrote:
>>>
>>> Hi there,
>>>
>>> I don't work too much with vectors, but my personal experience with
>>> them
>>> has been fairly slow, perhaps due to the topology. If you have
>>> access
>>> to the
>>> raw point cloud, you could try importing the ground and surface
>>> points as
>>> separate rasters using r.in.xyz and then use r.mapcalc to get the
>>> height by
>>> subtracting the ground from the surface raster. Or do you
>>> definitely
>>> need
>>> vector points as an output?
>>>
>>> Best,
>>> Daniel
>>>
>>> --
>>>
>>> B.Sc. Daniel Lee
>>> Geschäftsführung für Forschung und Entwicklung
>>> ISIS - International Solar Information Solutions GbR
>>> Vertreten durch: Daniel Lee, Nepomuk Reinhard und Nils Räder
>>>
>>> Deutschhausstr. 10
>>> 35037 Marburg
>>> Festnetz: +49 6421 379 6256
>>> Mobil: +49 176 6127 7269
>>> E-Mail: Lee@isi-solutions.org
>>> Web: http://www.isi-solutions.org
>>>
>>> ISIS wird gefördert durch die Bundesrepublik Deutschland,
>>> Zuwendungsgeber: Bundesministerium für Wirtschaft und Technologie
>>> aufgrund
>>> eines Beschlusses des Deutschen Bundestages, sowie durch die
>>> Europäische
>>> Union, Zuwendungsgeber: Europäischer Sozialfonds.
>>> Zusätzliche Unterstützung erhält ISIS von dem Entrepreneurship
>>> Cluster
>>> Mittelhessen, der Universität Marburg, dem Laboratory for
>>> Climatology
>>> and
>>> Remote Sensing und dem GIS-Lab Marburg.
>>>
>>>
>>>
>>>
>>> Am 29. März 2012 22:59 schrieb Daniel Victoria
>>> <daniel.victoria@gmail.com>:
>>>>
>>>> Hi all,
>>>>
>>>> I'm trying to calculate the height from the ground of several
>>>> lidar
>>>> points (15million) in order to get the number of points that occur
>>>> at
>>>> different height levels. I read some older posts about using
>>>> r.in.xyz
>>>> (or r.in.lidar in grass 7) but I could not understand how to count
>>>> the
>>>> number or points that have height from 0 to 5 m above the ground,
>>>> for
>>>> instance. So, I'm trying to do the following.
>>>>
>>>> 1) import points using v.in.lidar (Grass 7 ubuntu linux)
>>>> 2) create a column in the database for the ground height and one
>>>> for
>>>> elevation
>>>> 3) populate height column from ground raster (generate in another
>>>> process) using v.what.rast
>>>> 4) calculate elevation as zcoord - ground for each point
>>>> (v.db.update)
>>>>
>>>> As you might imagine, this takes a long time. Just to import a
>>>> 400Mb
>>>> lidar file takes around 50min.
>>>> Is there any easier way that I'm not envisioning?
>>>>
>>>> I'm running Grass 7 with liblas on a Ubuntu Virtual Machine and
>>>> sqlite
>>>> backend.
>>>>
>>>> Thanks
>>>> Daniel
>>>> _______________________________________________
>>>> grass-user mailing list
>>>> grass-user@lists.osgeo.org
>>>> http://lists.osgeo.org/mailman/listinfo/grass-user
>>>
>>>
>
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