SilviLaser 2011

Posted on October 27, 2011 by pete_
The Tahune Airwalk From Silvilaser2011

Just returned from SilviLaser 2011 at the University of Tasmania in Hobart. It is an excellent conference attended by academics and professionals in forestry and remote sensing from around the globe. The conference is unique in my experience as it has a comparatively narrow focus: active remote sensing, mostly laser and some radar, of biophysical forest metrics. It addresses this topic in great depth. There were no concurrent sessions which was refreshing and kept us all on the same page. As a young researcher in the field it was great to see the current work and put faces to the names that I cited in my dissertation and the paper Sohail Shafii and I presented . Our presentation was in the Emerging Technologies session with several other excellent presentations.

Terrestrial laser scanning (TLS), regional/national inventory applications, and operationaization of LiDAR inventory in a forest management context seemed to be emerging topics that were well represented, along with a few satellite (ICESat/GLAS) studies, and several papers focusing on statistical methods for imputing area-based stand metrics from point distributions. There seems to be a great deal of interest in this at the moment. I have some reservations about it, mostly because I don’t understand RandomForest that well.

I’ve posted the abstracts, and all the papers. The program is below:

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Graduated

Posted on September 8, 2011 by pete_

As of June, I have graduated. I’m relieved and grateful for the support of family, friends, and advisors. Bruce Hartsough and Debbie Elliott-Fisk have been indispensable, thanks so much Bruce and Debbie!

I’ve posted my dissertation and will be updating the site with publications, etc. soon.

As of August I have taken a position as a postdoctoral researcher at the UC Davis Institute for Transportation Studies/Energy Institute.

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LibLAS to GRASS r.in.xyz

Posted on March 4, 2011 by pete_

I wrote some python to pipe las2txt to GRASS’s r.in.xyz on using subprocess which is what most of the GRASS python scripts use. Its not very sophisticated at all but seems to work decently well however I suspect Howard’s point about it not being much better than the text file approach is true. Its based on the GRASS LIDAR wiki page(GRASS Wiki): Heres an example:

from liblas import file, header
import subprocess as sub
import grass.script as grass

def run(cmd, **kwargs):
    grass.run_command(cmd, quiet = True, **kwargs)

path='../data/las/ucd_01.las'
dbname='ucd_01.las'
h=file.File(path).header
run("g.region", e=h.max[0], w=h.min[0], s=h.min[1], n=h.max[1])
pts=sub.Popen('las2txt -i %s --parse xyz --delimiter "|" --keep-classes 2 --stdout'%path, shell=True, stdout=sub.PIPE)
mkInRast=grass.feed_command("r.in.xyz", input="-", output=dbname, method='mean')
mkInRast.stdin.write(pts.communicate()[0])
mkInRast.stdin.close()
mkInRast.wait()

#this stuff is based on the wiki page for interpolating/filtering the resulting GRASS dataset

run("r.to.vect", flags='z', input=dbname, output=dbname+'_pt', feature='point')
run("v.surf.rst", layer='0', input=dbname+'_pt', elev= dbname+'_rst')
run("g.remove", vect='%s_pt'%dbname)
run("g.remove", rast=dbname)

DEM from LiDAR

DEM from LIDAR


Hillshade from LiDAR

Hillshade from LiDAR using GRASS and LibLAS from python

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