Visualization of Soil Properties

SoilWeb Updates

Submitted by dylan on Sat, 2013-02-09 00:16.

New Label StyleNew Label Style!

Some recent updates to SoilWeb. New visualizations will be incorporated into SoilWeb soon.

Basic Simulation of Soil Profile Data in R via AQP

Submitted by dylan on Wed, 2012-12-19 18:59.

Something fun to play with before the new year: experimental code in aqp for simulating soil profile data from a single "template" profile. The basic idea: simulate horizon thickness data using a family of Gaussian functions with mean defined by horizon thickness values found in the template and standard deviation defined by the user. Larger hz.sd values will yield more drastic variation in the simulated results. Note that only "horizonation" (e.g. the sequence of horizons and their respective thickness) is simulated; horizon attributes (e.g. texture, pH, etc.) are copied from the "template" profile.

Basic usage is demonstrated below, see package manual page for details. This function is only available in the version of aqp hosted by R-Forge. It should be on CRAN by the new year.

Simulated ProfilesSimulated Profiles

Dissimilarity Between Soil Profiles: A Closer Look

Submitted by dylan on Fri, 2012-03-23 19:35.

Continuing the previous discussion of pair-wise dissimilarity between soil profiles, the following demonstration (code, comments, and figures) further elaborates on the method. A more in-depth discussion of this example will be included as a vignette within the 1.0 release of AQP.

Profile Dissimilarity Demo: MVO SoilsProfile Dissimilarity Demo: MVO Soils

Profile Dissimilarity Demo: MVO Soils

Submitted by dylan on Fri, 2012-03-23 19:24.
Profile Dissimilarity Demo: MVO Soils

AQP Sample Dataset 5: Profile Sketches

Submitted by dylan on Wed, 2012-01-04 19:42.
AQP Sample Dataset 5: Profile Sketches

Automated OSD lookup and display

Submitted by dylan on Sat, 2011-03-05 01:51.
Automated OSD lookup and display

A Visualization of Soil Taxonomy Down to the Subgroup Level

Submitted by dylan on Wed, 2010-09-29 18:44.

It turns out that you can generate a quasi-numerical distance between soil profiles classified according to Soil Taxonomy (or any other hierarchical system) using Gower's generalized dissimilarity metric. For example, taxonomic distances computed from subgroup membership are based on the number of matches at the order, suborder, greatgroup, and subgroup level. This approach allows for the derivation of a quasi-numerical classification system from Soil Taxonomy, but it is severly limited by the fact that each split in the hierarchy is given equal weight. In other words, the quasi-numerical dissimilarity associated with divergence at the soil order level is identical to that associated with divergence at the subgroup level. Clearly this is not ideal.

Gower's generalized dissimilarity metric is conveniently implemented in the cluster package for R. I have posted some related material in the past, but left out some of the details regarding which clustering algorithms produce the most useful dendrograms. Divisive clustering best represents the step-wise splits within the hierarchy of Soil Taxonomy, as expressed in terms of pair-wise dissimilarities. Code examples are below, along with the data used to generate the figure of California subgroups. Discontinuities in figure below are caused by errors in the underlying data, e.g. mis-matches in soil order vs. suborder membership.

Subgroups from CaliforniaSubgroups from California