More landscape position pictures — just showing riparianess. See also https://smathermather.wordpress.com/2014/11/22/landscape-position-using-gdal/ and https://smathermather.wordpress.com/2014/11/24/landscape-position-using-gdal-pt-2/ Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL Continue reading Landscape Position using GDAL — PT 3
Just pretty pics today of estimated riparianess. If you prefer a bit of code, see previous post https://smathermather.wordpress.com/2014/11/22/landscape-position-using-gdal/ Continue reading Landscape Position using GDAL — PT 2
Hat tip again to Seth Fitzsimmons. I’ve been looking for a good, easy to use smoothing algorithm for rasters. Preferably something so easy, I don’t even need to write a little python, and so efficient I can run it on 30GB+ datasets and have it complete before I get distracted again by the next shiny project (a few hours). Seth’s solution? Downsample to a low … Continue reading Landscape Position using GDAL
Anyone who has been following along at home knows I don’t think much like a DBA. Sometimes that’s good; mostly it’s probably bad. In this post, I hope it will be interesting. The problem of the day is how to take engineering contours derived from breaklines, a lidar point cloud, and all the lot, and do a good job interpolating that to a DEM. This … Continue reading Proper (ab)use of a database, contour interpolation using #postgresql #postgis #arcgis
Open Source software is not, of course just about warm and fuzzies, great support, rapid development cycles, shared costs, etc., it’s also about getting your hands dirty with someone else’s code and implementing stuff more quickly and more intelligently because of it, and hopefully learning something in the process. You don’t have to poke under the hood to drive the car, but sometimes it’s nice … Continue reading GDAL Slopes– Local Hydrologic Slope vs. the Standard Approach
From earlier post: “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 … Continue reading Landscape Position: Conclusion? (part 2)
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!
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