Terrain Analysis

Hillslope Position by Soil Series

Submitted by dylan on Wed, 2013-06-05 18:34.

Soil survey data are typically built upon a foundation of soil-landscape relationships that have been verified in the field.

Summarizing Circular Data in R: Aspect Angle

Submitted by dylan on Wed, 2012-10-10 15:26.

The orientation of terrain surface (aspect) can have dramatic effects on landscape-scale variation in soil temperature and moisture. Summarizing aspect angle is complicated by the fact that sampled values are measured on a circular scale. The circular package for R can be used to summarize, model, and visualize this type of data.

An example function is presented below that demonstrates several components of the circular package: special data structures, summary functions, plotting functions and their application to circular data derived from measurements collected by compass. Spread and central tendency are depicted with a combination of circular histogram and kernel density estimate. The circular mean, and relative confidence in that mean are depicted with an arrow: longer arrow lengths correspond to greater confidence in the mean.

Circular HistogramCircular Histogram

Using R and r.mapcalc (GRASS) to Estimate Mean Topographic Curvature

Submitted by dylan on Tue, 2010-08-03 20:51.

Recently I was re-reading a paper on predictive soil mapping (Park et al, 2001), and considered testing one of their proposed terrain attributes in GRASS. The attribute, originally described by Blaszczynski (1997), is the distance-weighted mean difference in elevation applied to an n-by-n window of cells:



Equation 4 from (Park et al, 2001)

 
where n is the number of cells within an (odd-number dimension) square window excluding the central cell, z is the elevation at the central cell, z_{i} is the elevation at one of the surrounding cells i, d_{i} is the horizontal distance between the central cell and surrounding cell i. I wasn't able to get a quick answer using r.neighbors or r.mfilter, so I cooked up a simple R function to produce a solution using r.mapcalc. The results are compared with the source DEM below; concave regions are blue-ish, convex regions are red-ish. The magnitude and range are almost identical to mean curvature derived from v.surf.rst, with a Pearson's correlation coefficient of 0.99. I think that it would be of general interest to add functionality to r.neighbors so that it could perform distance-weighted versions of commonly used focal functions.

Elevation surface (left) and resulting mean curvature estimate (right)Elevation surface (left) and resulting mean curvature estimate (right)

Elevation surface (left) and resulting mean curvature estimate (right)

Submitted by dylan on Sun, 2010-08-01 23:23.
Elevation surface (left) and resulting mean curvature estimate (right)
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Soil Depth Mapping at KREW 2

Submitted by dylan on Tue, 2010-03-02 19:58.
Soil Depth Mapping at KREW 2

linear regression with RCS

Soil Depth Mapping at KREW

Submitted by dylan on Tue, 2010-03-02 19:55.
Soil Depth Mapping at KREW

regression tree

Rangeland Soil Management and Hydrology

Submitted by vbullard on Thu, 2009-06-11 18:30.

SFREC-terrain_classification_004

Submitted by dylan on Sat, 2009-05-23 17:24.
SFREC-terrain_classification_004

4-class terrain grouping.