I typed that last one too quickly– too many typos, but my wife says I’m not supposed to revise blogs, but move on… . So, for clarity, let’s talk a little more about McNab indices. Field-derived McNab indices are a measure of average angle from the observer to the horizon (mesoscale landform index), or from the observer to another field person a set distance away, … Continue reading Landscape Position and McNab Indices (cont.)
There’s much to be said for knowing your data in order to best optimize the analysis of it. Beyond all other bits of cleverness, having a functional understanding of your problem is the first step toward conceiving an intelligent and efficient solution. One thing that I didn’t do two posts ago was to spend any time deciding how far out the search for nearby points … Continue reading Further optimization of the PostGIS LiDAR Vegetation Height Query
I wanted to put together a quick and dirty map of a biological reserve in Ecuador, sort of a laptop exploration of a place quite distant. At first, I thought I’d use Shuttle Radar Topography Misssion data to get the elevation information. Then I discovered the ASTER Global DEM which is 30m resolution for the whole world. Wow. Cool cool data. (I used the Japanese … Continue reading Mapping places unknown– free global datasets and FOSS GIS are a great combo
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.)
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