Just another shot of our digital surface model of the forest. This time, I rendered it with all of the trees at base elevation 0 (zero), placed the orthographic camera at height 150, so our 16-bit range we’re rendering to should scale 0-150 feet to 0-65535 values, giving us a precision of 0.002 feet. The original LiDAR data has a precision of 0.01 feet, so … Continue reading Digital Surface Model– a whole forest and more part Deux
Let’s start putting some of the pieces together. Earlier, I presented a method for deriving a digital surface model from LiDAR data of a forest canopy, using a tree-shaped interpolator that scales the individual tree canopy shapes according to the height of the LiDAR return from the ground. Now I present the whole project, forest and all. I wanted to render my DSM at a … Continue reading Digital Surface Model– a whole forest and then some
Warning. What follows is somewhat informed, but I’m no geologist. I just play one on wordpress. Understanding the basic underlying geology and associated topography plus site history helps us achieve a basic understanding of a sites ecological potential. At the most basic level, we expect different wildlife and vegetation dynamics in a floodplain vs. a mountain ridge. Classification of digital elevation models can be done … Continue reading Topographic Position Index and Ecological Land Type (warning completely unrefined not quite Geologic dribble– with bad maps 🙂 …)
One of my goals in modeling canopy height is to estimate sub-canopy biophysical variables that give indications of habitat suitability. For example, suppose we have a rare plant that is canopy gap dependent. How do we model it’s distribution? With a statewide LiDAR dataset from which we can derive canopy location and height, we can identify canopy gaps. While I haven’t gotten to the modeling … Continue reading Modeling (relative) Sub-Canopy Biophysical Variables with PovRay
Yet again, working with that Ohio DEM dataset, I need some reduced resolution versions of the 2.5 foot DEMs. At a grand total of ~600 DEMs for a given county, it was time to batch, this time in Windows Command prompt. The neat thing is the variable substitution in the for-in-do command in the command prompt, where, e.g. for a file variable %f, the file … Continue reading Batch processing resampling of DEMs
Alright, so let’s begin again with some LiDAR processing in PostGIS. I really liked this discussion of multipoint conversions of LiDAR and other point datasets, but we won’t address making things more efficient here (yet). Besides, these aren’t just points– we do want to retain their attribution… . Now, any regular database user will recognize my nubeness reading this post. If you are that person, … Continue reading LiDAR processing and analysis in PostGIS (I hope…).
In general, I like the results from the hillshade. One problem that I forgot to take into account– hillshade algorithms typically leave a 1-pixel border along the edge where calculations cannot take place… . For this Ohio dataset, that means that every 5000 feet or so (the x and y dimension of our DEM chunks), we have a 2-pixel wide line, resulting in a subtle … Continue reading Hillshade (cont.)
Using the same LiDAR DEM from which we generated the contours, we can create hillshade tifs using http://www.perrygeo.net/wordpress/?p=7. It compiles easily on a mac, probably even easier on a Linux machine following his directions. Then another simple loop: #!/bin/bash x=0 for f in $( ls *.txt); do x=`expr $x + 1` echo $x $f hillshade $f $f.tif done I’d like this all in a single … Continue reading Hillshade using PerryGeo’s ported GRASS utility
Well, I made some mistakes in the last post, not the least of which is I used the wrong flag for creating an attribute field with elevation. What follows is a little more sophisticated. It takes us from a series of DEM tiles from which I want 2-foot and 5-foot contours (using gdal_contour), and then dumps those shapefiles into PostgreSQL using shp2pgsql. First we prep … Continue reading GDAL Contours (cont.)
Just another quick vignette. From the Ohio Statewide Imagery Program (OSIP) there is a 1-meter DEM for all of Ohio. To get contours from this dataset, one approach is to use GDAL tools, i.e. gdal_contours. As I’m working on a Mac today, I used Kyng Chaos pre-compiled Frameworks: http://www.kyngchaos.com/software:frameworks Then I needed to update my path variable in the BASH shell: export PATH=/Library/Frameworks/GDAL.framework/Programs:$PATH Now we … Continue reading GDAL Contours
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