Algorithms for Quantitative Pedology: a toolkit for digital soil morphometrics

D.E. Beaudette, P. Roudier, J.M. Skovlin









This document is based on aqp version 1.8-8

Why do we need (yet) another framework?

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  • soil data are complex, inherently visual
  • reproducibility is increasingly important
  • focus on the interesting questions, not boilerplate
  • a common vocabulary for soil data analysis would be nice

Soil data analysis in theory

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In practice, it is a long road ...

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Improving the workflow

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Why R?

  • repeatable, self-documenting work:

    • 2800+ packages on CRAN: 100+ packages on GIS, ecology, and soils!
    • package system: algorithms + docs + sample data

  • advanced data visualisation capabilities

  • algorithm development by experts, application by trained users

    • testing by eye: simple graphical “grammar” used to plot data
    • formalized testing: correlation, regression, classification, ordination, …
    • I/O capabilities: file, URL, SOAP, SQL, ODBC, PDF, PNG, SHP, KML, …
    • optimizers, matrix operations, custom data structures, …

aqp package: Algorithms for Quantitative Pedology

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  • special data structures: avoids annoying book-keeping code
  • visualization: soil profile sketches, transect diagrams, Munsell →  RGB
  • re-sampling: regular depth-slicing or EA spline (coming soon)
  • aggregation: summary by depth-slice or arbitrary “slabs”
  • classification: pair-wise dissimilarity of profiles
  • utility functions: soil depth, missing data eval., simulation, …

Details: papers, project homepage

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This talk: dog and pony show

SoilProfileCollection objects

Formal class 'SoilProfileCollection' [package "aqp"] with 7 slots
  ..@ idcol     : chr "peiid"
  ..@ depthcols : chr [1:2] "hzdept" "hzdepb"
  ..@ metadata  :'data.frame':  1 obs. of  1 variable:
  ..@ horizons  :'data.frame':  308 obs. of  34 variables:
  ..@ site      :'data.frame':  54 obs. of  59 variables:
  ..@ sp        :Formal class 'SpatialPoints' [package "sp"] with 3 slots
  ..@ diagnostic:'data.frame':  177 obs. of  4 variables:

plot of chunk SPC-2

SoilProfileCollection objects

  • object inspection
idname(sp4) # pedon ID name
horizonDepths(sp4) # colum names containing top and bottom depths
depth_units(sp4) # defaults to 'cm'
metadata(sp4) # data.frame with 1 row
profile_id(sp4) # vector of profile IDs
  • overloads to common functions
length(sp4) # number of profiles in the collection
nrow(sp4) # number of horizons in the collection
names(sp4) # column names from site and horizon data
min(sp4) # shallowest profile depth in collection
max(sp4) # deepest profile depth in collection
sp4[i, j] # get profile "i", horizon "j"
  • getting / setting of components
horizons(sp4) # get / set horizon data
site(sp4)  # get / set site data
diagnostic_hz(sp4) # get / set diagnostic horizons
proj4string(sp4) # get / set CRS
coordinates(sp4) # get / set coordinates
  • coercion to SpatialPointsDataFrame or data.frame
as(sp4, 'SpatialPointsDataFrame')
as(sp4, 'data.frame')

Munsell Color Conversion

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Plotting SoilProfileCollection Objects

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Plotting SoilProfileCollection Objects

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Plotting SoilProfileCollection Objects

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Plotting SoilProfileCollection Objects

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Plotting SoilProfileCollection Objects

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Slice-Wise Aggregation: RIC via quantiles

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Slice-Wise Aggregation: RIC via quantiles

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Slice-Wise Aggregation: RIC via quantiles

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Slice-Wise Aggregation: probabilities

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Slice-Wise Aggregation: probabilities

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Pair-Wise Dissimilarity

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Pair-Wise Dissimilarity

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Recent (last hour) developments, inspired group discussion

  • inspired by Darrell et al. (thanks Pierre)
    • depiction of structure|fabric via unconditional sim. (variogram)
    • not perfect, but illustrates rapid protoyping framework

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  • on deck for next week (thanks Pierre)
    • more efficient SoilProfileCollection implementation + images
    • EA spline methods for SoilProfileCollection
    • integration of hz boundary topography / distinctness into sketches

Concluding remarks

  • managing soils data can be difficult and time-consuming
  • DSM requires considerable data processing
  • DSM requires detailed analysis of soil profile collections or aggregate soils data

AQP can help:

  • simple conversion from common data sources to SoilProfileCollection
  • SoilProfileCollection objects are powerful
  • innovative constructs: slice(), slab(), SPC[i,j]
  • pair-wise dissimilarity: profile_compare()
  • visual comparisons and non-parametric summaries

AQP speaks digital soil morphometrics


Thank You!

http://aqp.r-forge.r-project.org