Hi Richard,<br><br>the lidar system emits a pulse of focused light in the near infrared region of the electromagnetic spectrum and detects the radiation reflected back from the target. Lidar intensity, is the intensity of each return pulse (ive seen it referred to as laser amplitude), it represents the reflected energy and provides a concentrated measurements of the object's reflectance unaffected by shadows or occlusions. This reflectance may vary based on the reflectance properties of the targeted material.<br>
<br>Most often in Australia the data suppliers provide a flatfile containing x,y,x,i . where i is the intensity of the pulse. Each pulse is then classified as ground or non ground (or vegetation in my case). Ive simply taken the non-ground hits and removed the ground elevation (DTM) to get a relative canopy height of my vegetation.<br>
<br>RE Stats package. I would like to use R but I have 1200 2kmby2km tiles with approximately 3 billion points (approximately 700 Gb). r.in.xyz and its statistics is an amazingly quick way to calculate summary statistics in a grid format. Using a stats package just seems to have too much overhead. Ive started to write a python program to read the flat files and calculate summary statistics for the grid cells.<br>
<br>My problem is I can calculate 13 canopy height percentiles for my 20m resolution grid cells really quickly with r.in.xyz- but then I wanted for the same height percentiles the mean intensity values (basically a two classification table on height percentiles and intensity) and was wondering if it was possible to do this in grass easily.<br>
<br><br>cheers<br><br>Andy<br><br><br><br><div class="gmail_quote">On Thu, May 29, 2008 at 7:18 PM, Richard Chirgwin <<a href="mailto:rchirgwin@ozemail.com.au">rchirgwin@ozemail.com.au</a>> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
------------------------------<br>
<br>
Message: 7<br>
Date: Thu, 29 May 2008 13:16:04 +1000<br>
From: "andrew haywood" <<a href="mailto:ahaywood3@gmail.com" target="_blank">ahaywood3@gmail.com</a>><br>
Subject: [GRASS-user] newbie: processing lidar with r.in.xyz<br>
To: <a href="mailto:grass-user@lists.osgeo.org" target="_blank">grass-user@lists.osgeo.org</a><br>
Message-ID:<br>
<<a href="mailto:7a4569180805282016s3212c78ck2c35344b95650a7a@mail.gmail.com" target="_blank">7a4569180805282016s3212c78ck2c35344b95650a7a@mail.gmail.com</a>><br>
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<br>
Dear List,<br>
<br>
i am currently enjoying the flexibility of using r.in.xyz to process lidar<br>
data over native forests in Victoria, Australia.Thank you hamish for such a<br>
great tool.<br>
At this stage I have 'binned' my data into 20m cells and calculated a number<br>
of metrics (including 13 vegetation height percentiles p5 p10 p20 .. p90,<br>
p95, p99) based on the z values.<br>
What I was wondering is how can I calculate the mean intensity for the<br>
associated height percentiles.<br>
<br>
To get around this I have written a script in a proprietary stats package to<br>
create the intensity metrics. However, I would prefer to use Grass and<br>
opensource tools to do this binning<br>
<br>
Any suggestions would be appreciated!!!!<br>
<br>
My data is in the following format<br>
<br>
x|y|ground|intensity|class|canopy_top|height<br>
410774.45|5820999.93|773.3|23|10|766.0983886719|7.20161<br>
410763.07|5820999.9|802.27|2|10|763.9434814453|38.3265<br>
410765.47|5820999.94|773.11|90|10|764.2877807617|8.82222<br>
410758.09|5820999.95|807.99|47|10|762.8372192383|45.1528<br>
410748.12|5820999.93|760.89|8|10|760.5731811523|0.316819<br>
<br>
</blockquote>
Andrew,<br>
At the risk of sounding thick, I'd like to know what "intensity" represents in your context ... Regarding statistical packages, since R is grass-linked, why not do the calcs in R?<br>
<br>
Richard Chirgwin<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
...<br>
...<br>
...<br>
<br>
I suspect I should start to learn python.<br>
<br>
<br>
regards<br>
<br>
Andy<br>
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Message: 8<br>
Date: Thu, 29 May 2008 11:17:48 +0800<br>
From: "maning sambale" <<a href="mailto:emmanuel.sambale@gmail.com" target="_blank">emmanuel.sambale@gmail.com</a>><br>
Subject: Re: [GRASS-user] Triangles polygons with Arc*<br>
To: grass-user <<a href="mailto:grass-user@lists.osgeo.org" target="_blank">grass-user@lists.osgeo.org</a>><br>
Message-ID:<br>
<<a href="mailto:f902f9840805282017y2bae1830y786aa244a5dc60f6@mail.gmail.com" target="_blank">f902f9840805282017y2bae1830y786aa244a5dc60f6@mail.gmail.com</a>><br>
Content-Type: text/plain; charset=UTF-8<br>
<br>
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Just curious if anybody encountered this issue with Arc*:<br>
<br>
</blockquote>
it's a common problem.<br>
<br>
</blockquote>
It does seem to be.<br>
<br>
<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
short answer: TINs stink. Raster maps created from TINs stink more.<br>
<br>
</blockquote>
<br>
My colleague used SRTM DEM converted to grid format of Arc<br>
He mentioned that it is fairly common when you convert raster grids in<br>
arc to vectors (shapefiles). Especially with very small areas like a<br>
2-3 pixels. Will look into this further.<br>
<br>
To all the others who gave me a list of Arc mailing list thanks! I am<br>
reading a few right now.<br>
<br>
cheers,<br>
<br>
maning<br>
<br>
<br>
</blockquote>
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