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?
I apologize in advance– this first post will be heavy on code, short on explanation. Landscape position, e.g. previous posts, can be trivial to calculate, but to make the calculations scalable to a large area, some batching is necessary. In this case, instead of a McNab index, we’re calculating the traditional GIS landscape position. Enter my favorite non-geographic tool, PovRay… . To the difference between, … Continue reading Landscape Position Continued– absolutely relative position calculation
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.)
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 completed this project a long time ago and have not blogged on it in over a year. None-the-less, I realized that I hadn’t posted any final images for the viewshed analysis with Povray. So here is the summer viewshed analysis. This is rendered with about (I think) 150,000 trees, each with 100,000+ leaves. The cell tower here is in red. The landscape colored in … Continue reading Viewshed Analyses in Povray– Final Image
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 🙂 …)
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