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Defining the Soils Universe

Defining the Soils Universe. The Soil Universe. Objectives: Understand how soil can be viewed as both a “particulate” universe and as a “continuous” universe, Be able to apply these views, conceptually, to geomorpholgy , hydropedology and soil survey, Discussion :

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Defining the Soils Universe

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  1. Defining the Soils Universe

  2. The Soil Universe • Objectives: • Understand how soil can be viewed as both a “particulate” universe and as a “continuous” universe, • Be able to apply these views, conceptually, to geomorpholgy, hydropedology and soil survey, • Discussion: • Technology + Available Data = enhanced visualization and modeling capabilities. • We are being confronted with views and uses of soil data/info that differ from our historic perspectives. • A fundamental, conceptual foundation will help us interpret and apply GIS-based models across diverse settings.

  3. How do we view our Universe? • Soil Individuals and Soil Classification. E.G. Knox, SSSAP 29: pp79-84, 1965. • Particulate Universe • Contains discrete objects that can be counted. • Individuals are natural objects that are independent of the observer’s actions. • Continuous Universe • Does not contain discrete objects. • Individuals are defined arbitrarily, and are therefore dependent on the observer.

  4. Soils as Thermodynamic Systems • Reappraisal of the Soil. C.C. Nikiforoff, Science, Vol 129, Jan 23, 1959. • Presented a now-classic model of soil as a continuum. • Described soil as “an excited skin of the subaerial part of the earth’s crust.” • Concluded that “the very concept of soil species is vague” and is perhaps “just another example of the influence of the biological sciences.” • Described soil as a “patterned spacial (sic) system” and believed that “dissection of this system on the basis of ill-defined and wholly abstract soil species might serve some practical purposes but has no scientific basis or justification.”

  5. Soil Survey:Livin’ in a Particulate World • Discrete individuals: • Pedons • Polypedons • Map Unit Components • Map Units • Classes • Series; Taxonomic classes • Temperature, moisture, diagnostic horizons, etc. • Slope classes (e.g., for map unit phases) • Landforms; geomorphic surfaces; hillslope position; etc. Simonson, 1959 (Reprinted from Brady, 10th ed.)

  6. Soil Survey:plagued by the continuum • Variability. • Where are the “natural” boundaries of a pedon? • Where is the line between map unit A and map unit B? • What are the RV high and low values for properties for map unit components in NASIS? • Water tables: • where, when, how high, etc. • hydropedology

  7. Soil Survey:plagued by the continuum • Landforms: • e.g., Alluvial Fan. • Variability across & down. • Features within the larger landform • “Is it a Flood Plain Step or a Stream Terrace?” • First, define what it is (lateral dimensions of the continuous surface of the Earth) • Then, try to decide what class it fits into (flooding varies in space and time)

  8. How do we view soils? • As a Continuum? • “Soils vary continuously in space and time.” • Do you agree with this? • As discrete individuals? • “Soils are natural bodies that can be described, classified, and mapped.” • Do you agree with this? • Can we hold these views simultaneously? • What are the implications for soil survey, hydropedology, stratigraphy, geomorphology? • Recent technological advances have made these questions relevant, 50+ years after Knox’s paper and 60 years after Nikiforoff’s paper.

  9. “So what… who cares?” • Continuous-surface digital data • e.g., DEMs, earth reflectance • Increasingly available • Quality steadily improving • GIS improving to match data • Soil property maps • Continuous, or classed • Increasing interest among soil scientists/GIS modelers • Site-specific capability (?) • Precise modeling using highly accurate continuous-surface data (?) • Precision farming, etc. • Can we support this in the NCSS? • Expect increasing interest in soil property maps, as continuous surfaces and as classes, in the coming years.

  10. Example: Soil Map Units and Slope • 30031: Armstrong cl, 9-14%, sev. er. • Backslope on dissected till plain (pre-Illinoian); in late Sangamon paleosol. • RV slope: 12% • RV Low: 9% • RV High: 14% • Minor components (from MUD): • Areas <9% and areas >14% (locations not specified). • Gosport (shale), Newcomer (sandstone), Winnegan (till; no paleosol): all downslope. • Variations in surface thickness, wetness, subsoil clay content (locations not specified).

  11. Distribution of MU 30031 in N Missouri MLRA 109 (till plain) MLRA 113 (claypan) 657 Delineations

  12. A single delineation was selected for analysis Chariton Co. Selected Delineation

  13. Slope classes within a single delineation of 30031 MU Delineation: a discrete individual Slope Classes: several discrete individuals Multiple slope individuals within the MU delineation individual Slopes based on a 10m DEM, derived from 1:24,000 hypsography

  14. Slopes within a single delineation of 30031 Redder = steeper Mean: 9.7% Range: 0 to 30% Slope is modeled here as a continuous universe, with no easily identifiable individuals. Slopes based on a 10m DEM, derived from 1:24,000 hypsography

  15. “Continuous” slope surface under the “discrete” individual of a MU delineation Mean for delineation: 9.7% Standard slope class boundaries Pixel count % Slope Histogram of slope values for a delineation of Armstrong cl, 9-14%

  16. Slope classes via 1m LiDAR Slope modeled as a particulate universe. Are these slope classes “natural objects?” If so, why is this map so different from the previous slope class map?

  17. Comparison of slope class maps for a single delineation of 30031 Derived from 10m (hypso source) Derived from 1m LiDAR Modeling slope as a particulate universe of discrete individuals (classes) is problematic, because slope classes depend on the observer.

  18. Slopes within a single delineation of 30031 Redder = steeper Mean: 12.2% Range: 0 to 149% The LiDAR-derived slope as a continuous universe; no readily identifiable individuals. Based on 1m LiDAR

  19. “Continuous” slope surface under the “discrete” individual of a MU delineation Mean for delineation: 12% Histogram of (LiDAR-derived) slope values for the MU delineation

  20. Comparison of slope histograms for a delineation of 30031 Derived via 10m NED (hypso source) Derived via 1m LiDAR Slope modeled as a continuum: why are they different? The “continuous universe” model seems like a good fit for slope… but it’s not quite so easy to define.

  21. “Use DEM-derived slope in NASIS soil interpretations” • A proposal “from the National Office.” • A committee evaluated the feasibility of implementing this. • Basic procedure: • Convert SSURGO to raster • Use the DEM-derived slope instead of the Component RV Slope in interpretations • Combine a particulate universe model (SSURGO soils) with a continuous model (slope). • Output is a continuous surface • Each pixel may have a different value. • Output may be classed (e.g., slight, moderate, severe) to return to a particulate universe.

  22. Example: slope values for this delineation of MU 30031, based on 3 different slope models NASIS RV: 12 10m NED: 9.2 1m LiDAR: 12.6 X X NASIS RV: 12 10m NED: 11.9 1m LiDAR: 18.9 What do you think of this proposal? X NASIS RV: 12 10m NED: 14.1 1m LiDAR: 3.7

  23. Soil Properties as Continuous Variables Classic pedology: Quantitative, but not “spatially distributed” Organic carbon gradient on an E/W regional traverse (Jenny, 1941; reprinted from Brady, 10th ed.)

  24. Soil Properties as Continuous Variables Quantified relationships between soil properties and slope gradient. Relationship differs between hillslope positions (summit & shoulder vs backslope). Particle size gradient on a hillslope traverse (Ruhe & Walker, 1968).

  25. Modeling soil properties: class maps Bell et al., 1992 and 1994. Soil drainage class modeling. S. Central PA (Mifflintown quad). Used 305 sample points to correlate soil drainage class with various environmental covariates. Modeled soil drainage class via GIS

  26. Modeling soil properties: continuous surfaces Moore et al., 1993. Soil attribute modeling. Sterling, CO (NE CO) Used 231 sample points to model various soil properties, based on terrain attributes (slope, wetness index). (Grouped the continuous output into classes for display) Moore et al. proposed a method of estimating the continuous range of soil properties within a map unit, based on soil survey values and relationships to terrain attributes.

  27. “Surface horizon thickness is related to slope in MU 30031” • NASIS RV thickness: • RV Low: 13 • RV: 13 • RV High: 25 • (low RV is maintained by tillage? Surface horizon becomes clayier?) RV low slope RV high thickness RV slope RV thickness RV high slope RV low thickness 25 Very simple model relating surface thickness to slope, based on NASIS RVs Inspired by Moore et al., 1993 I’m making this up! Thickness 13 0 9 12 14 Slope

  28. Previous example: Use 1m LiDAR for slope Use surface thickness vs slope model Build a continuous surface model for surface horizon thickness, within MU 30031 1m LiDAR: 12.6 Thick: 13 X X 1m LiDAR: 18.9 Thick: 13 X 1m LiDAR: 3.7 Thick: 25

  29. The Raster Soil Survey?? • GIS models of: • map units • components • Properties • Don’t convert to vector • Store as a raster data layer • Attach attributes to pixels • Store model rules as metadata • “Beyond” soil series • Continuous-surface soil property maps • User: specify area (AOI) & desired interpretation • Run the interpretive model • Is this in our future? • Will we be ready for it?

  30. Graphic courtesy of Long et al, St. Johnsbury VT Modeling output from Essex county, VT Map Unit 12: a complex. Is the modeled distribution of soils valid? If so, why do we want to “dumb it down” by vectorizing? Is this an early prototype of the Raster Soil Survey??

  31. How does these conceptions and representations of soil (continuous vs particulate) relate to the Soil Geomorphology Institute?

  32. Geomorphology Geomorphic surfaces as discrete individuals; are these “natural bodies”? Is there a gradation in soil properties down/across the Brandywine surface? Doug will present this slide later in the class…

  33. Stratigraphy Peoria Loess stratigraphic unit in NE Interpolated from point observations: a continuous surface model. Grouped into classes for display. Are these classes “natural bodies”? Phil will present this slide later in the class…

  34. = soil line Hydropedology Particulate Universe 4-soil model (A, B, C, D) Each soil w RV wetness depth B B C B C A C C C A D A Water Table Equipotential lines Richardson et al. 1991 Continuous Universe No soil boundaries Soil properties vary across landscape (e.g., dashed line = depth to water). Jimmie will present this slide later in the class…

  35. Soil Survey, SGI, and the Soil Universeaquestion to ponder • How can our soil survey models best capture the processes (continuous) and natural bodies (discrete) implied by geomorphology, stratigraphy & hydropedology? • Current soil survey models • Future soil survey models

  36. Summary & Conclusions • GIS has breathed new life into an old discussion about the nature of soil as “discrete, natural bodies” or as a continuum across the Earth surface. • Every topic covered in this course can, and is, being modeled via GIS. Inputs into these models are typically in raster format, and include some (if not all) continuous variables. • These models are, perhaps, pushing soil survey in new directions: • Raster vs vector; raster/vector interchange • We need to prepare ourselves, conceptually and technically, for a world in which GIS-derived raster models are commonplace. • Get in front of the wave.

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