Landscape Position using GDAL — PT 3

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

Landscape Position using GDAL

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

Kicking the tires of PostGIS 2.0 — Testing ST_MakeValid

The feature in PostGIS 2.0 that excited me most was not topology support, raster support, or 3D functions.  Ok, raster was near the top of my list.  But what I was really excited by was the ST_MakeValid function.  Sad, isn’t it?  Lack of vision probably– excited to try to solve recurring technical snafus in a computationally inexpensive way, rather than being more excited by the … Continue reading Kicking the tires of PostGIS 2.0 — Testing ST_MakeValid

Landscape Position: Conclusion? (part 2)

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)

Landscape Position: Conclusion?

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?

Landscape Position Continued– absolutely relative position calculation

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

Landscape Position and McNab Indices (cont.)

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.)

Landscape Position and McNab Indices

Just a quick teaser post for our forestry/ecology readers out there.  I have a methodology developed for calculating McNab indices that directly corresponds with the field technique (unlike, as far as I know, any previous GIS-based techniques– which are probably adequate proxies). What is a McNab index?  Well there are two kinds, the minor landforms and mesoscale landforms that are field-measured topographic position or terrain … Continue reading Landscape Position and McNab Indices