I’ve managed to pilot most of a fast high resolution landscape position workflow with PovRay as my magic tool. The final steps I hope to pipe through PostGIS Raster. In the meantime a screenshot and description: blues are riparian, raw ocre, etc upland categories, grey is mostly flat lake plain and mid slopes, all derived from just a high res DEM input (no hydro lines … Continue reading Landscape Position: Conclusion?
Normally, my blog is a bit of a monologue. It’s not a bad thing, but can be a little lonely. Every now and then I get a great (and often doubtful) comments, which enhances things considerably. What follows is some dialog about the LiDAR shootout series, largely between Etienne and Pierre, posted with their permission: Pierre: “Etienne, Stephen, “I really appreciate the benchmark work you … Continue reading Dialog– What qualifies as a benchmark? Part 1
In reviewing the final numbers and charts from Etienne and Pierre, above are the results we see. The only revision is a moderate increase in speed for the PG Raster query. Final results in speed for lastools– ~350,000 points per second. In other words– off-the-charts fast. And the initial RMSE of ~25 feet was a mistake– it is probably closer to 0.2 feet. Stay tuned … Continue reading LiDAR Shootout! — New Chart, Final Results
For a couple of months now I’ve been corresponding with Etienne Racine and Pierre Racine out of Montreal Laval University in Quebec City. They decided to take on the problem of finding the speed and accuracy of a number of different techniques for extracting canopy height from LiDAR data. They have been kind enough to allow me to post the results here. This will be … Continue reading LiDAR Shootout!
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.)
A quick review of costs to search with increasing distances. Reference this original post for the code being run. SELECT DISTINCT ON(g1.gid) g1.gid as gid, g2.gid as gid_ground, g1.x as x, g1.y as y, g2.z as z, g1.z – g2.z as height, g1.the_geom as geometry FROM veg As g1, ground As g2 WHERE g1.gid <> g2.gid AND ST_DWithin(g1.the_geom, g2.the_geom, 3.5) ORDER BY g1.gid, … Continue reading Cost of nearest neighbor search depending on distance
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
Earlier, I posted some simple pictures from this short video clip using LiDAR to determine vegetation shape, animated in PovRay. Now, I post the video itself (all 7 seconds): I did what YouTube says not to do, and that is to upload a vignetted version of video. Such is. I’ll upload a better one shortly eventually. Continue reading YouTube Canopy Video
I won’t offer much prelude. Read this post, and this post first… . Inadvertently I demonstrated the value of spatial indices, i.e. I meant to use them and didn’t. In attempting to sort my tables by their spatial index I got the following errors: ALTER TABLE lidar_ground CLUSTER ON lidar_ground_the_geom_idx; ERROR: “lidar_ground_the_geom_idx” is not an index for table “lidar_ground” ALTER TABLE lidar_veg CLUSTER ON lidar_veg_the_geom_idx; … Continue reading PostGIS and LiDAR– oops!
Something’s been nagging at me as far as the level of optimization (or lack there-of) that I did to my database before my other posts of using PostGIS to analyze LiDAR data (e.g. this post). It seemed my results were remarkably slow, but I couldn’t put my finger on why that was problematic. Then, as I was testing a smaller lidar dataset in order to … Continue reading Rethinking PostGIS Analyses– Remembering to CLUSTER
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