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Spatial Disaggregation – A Primer. Tom D’Avello – NRCS-NSSC-GRU c ontact: [email protected] Travis Nauman – NRCS-NSSC-GRU, WVU c ontact: [email protected] Overview. Define ‘Disaggregation’ Approaches and Tools West Virginia Illinois Arizona Summary

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Spatial Disaggregation – A Primer

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Spatial disaggregation a primer

Spatial Disaggregation – A Primer

Tom D’Avello – NRCS-NSSC-GRU

contact: [email protected]

Travis Nauman – NRCS-NSSC-GRU, WVU

contact: [email protected]



  • Define ‘Disaggregation’

  • Approaches and Tools

    • West Virginia

    • Illinois

    • Arizona

  • Summary

  • Literature list for your reference

What is spatial disaggregation

What is spatial disaggregation?

  • The next opportunity for the NCSS

    • Add value to SSURGO

  • “The process of separating an entity into component parts based on implicit spatial relationships or patterns” – (Moore, 2008)

  • Getting more detail

    • Spatially refining maps to reflect the level of detail for current needs

    • Corresponding increased resolution of attributes

  • Trying to meet new types of demands

What is spatial disaggregation1

What is spatial disaggregation?

  • Mapping of components within map units

  • Usually complexes or associations for Order 2 & 3 soil surveys (SSURGO)

  • STATSGO2 effort

    • Alaska (Moore, 2008)

  • New needs served

    • modeling community

    • maintenance and improvement of the product is a primary charge of NCSS

What is spatial disaggregation2

What is spatial disaggregation?

  • Ultimately, it is a refined segmentation of the landscape

  • Along with the spatial, the attributes are equally important

    • Map units have multiple parts with attributes

      • Example: Ponded parts of a larger map unit

    • Related to SDJR

  • Scope driven!

    • Area of Interest

    • Can be relevant to one, some or all map units.

Purpose of the demonstration

Purpose of the demonstration

  • Demonstrate case studies across varying physiographic regions

  • Get feedback from soil scientists on their assessment of current soil maps

  • Investigate different digital techniques

  • Evaluate results

  • Develop materials and guidelines for application by soil scientists

West virginia early efforts

West Virginia early efforts

Component Soils










SSURGO Map Units




General disaggregation workflow

General disaggregation workflow

  • Goals

  • Scope

  • What data is accessible to help

  • Choose method

  • Implement

  • Validate Quality

    • (evaluate and iterate earlier steps as needed)

Current workflow in west virginia

Current workflow in West Virginia

  • Goals

    • Soil series map on field scale grid

  • Scope

    • All map units in Pocahontas and Webster Counties, WV

  • What data is accessible to help

    • ~30-meter DEM (NED), Landsat Geocover (Fed. MDA, 2004), lithology, SSURGO

  • Choose method

    • SSURGO-derived expert rule training sets & classification tree ensemble (100 trees run on random subsets)

  • Implement

    • Run analysis with Access (SQL), GIS, and Python (or R)

  • Validate Quality

    • Independent pedons for ground truth

1 2 goals and scope

1. & 2. Goals and scope

  • Scope is key

    • define what needs to be disaggregated

  • Universal vs within map unit(s) (Local)

    • Local model

      (confined to existing map unit)

      • Keep original lines

    • Universal model

      • uses original survey to create but lines not used for final



Figures courtesy of Dave Hoover, NSSC

3 what data ssurgo

3. What data: SSURGO


Map Unit


Parent material



Landscape attributes

Components (not explicitly mapped)

Horizon attributes


Soil physical properties

Soil chemical properties

3 what data ssurgo1

3. What data: SSURGO

  • Most work done on SSURGO or equivalent scale maps

  • Raster (grids) used for modeling

    • to match environmental data

West Virginia data

3 what data environmental

3. What data: environmental

  • Raster grids

    • Sometimes other polygon layers converted (e.g. geology)

  • Characterize variation within polygons using data that infer soil forming factors

SSURGO lines over Landsat

SSURGO lines over DEM

SSURGO lines over landforms

(Schmidt & Hewitt, 2004)

Examples from West Virginia

4 method model techniques

4. Method: model techniques

  • Training Data

    • Match environmental data to components of interest

    • Use representative areas or pedon locations

  • Model Types

    • Expert landscape rules

      • Hardened or fuzzy

    • Statistical models

    • Area to Point Interpolations (Goovaerts, 2011)

Dekalb series training areas in WV

Example Classification Tree Model

5 6 implement validate

5. & 6. Implement & Validate

  • Create raster disaggregation map

  • Validate with ground truth data

    • Different methods available

WV example: universal model for Webster and Pocahontas Counties

Historical survey of webster county wv

Historical survey of Webster County, WV

These folks were pretty good

Milton Whitney

Curtis Marbut

Hugh Bennett

Nice map, too!

Peoria illinois investigation

Peoria, Illinois investigation

  • Goals

    • Components or phases within Sable and Ipava units

  • Scope

    • All Sable and Ipava map units within Peoria County

  • What data is accessible to help

    • 3-meter DEM (NED), SSURGO

  • Choose method

    • Expert rule training sets & classification trees

  • Implement

    • Run analysis with R, ArcGIS and ArcSIE

  • Validate Quality

    • Local soil scientist review.

Peoria illinois investigation 1 goals

Peoria, Illinois investigation1. Goals

  • Identification of Non-ponded and ponded phases in Sable units

  • Identification of poorly drained components in Ipava units

Peoria illinois investigation 2 scope study site

Peoria, Illinois investigation2. Scope - study site

The project area is within MLRAs 95B, 108A,

108B, 108C and 115C

~900,000 acres of Sable

~1,186,000 acres of Ipava

  • Why here?

  • Availability of high resolution DEMs

  • Representative setting for Sable and Ipava

  • Good test for developing procedures to complete for entire extent of units when LiDAR coverage is complete

General setting 2 scope study site

General setting2. Scope - study site

Typical cross-section and qualitative description of Sable and Ipava soils

Variables developed 3 data all derived from 3m dem with arcgis arcsie saga gis

Variables developed3. Data - all derived from 3m DEM with ArcGIS/ArcSIE/SAGA GIS

  • Altitude above channel network

  • Curvature at numerous neighborhoods

  • Horizontal distance to flow channel

  • Maximum curvature –numerous neighborhoods

  • Minimum curvature –numerous neighborhoods

  • Multi-resolution ridge top flatness index

  • Multi-resolution valley bottom flatness index

  • Profile curvature –numerous neighborhoods

  • Relative position-numerous neighborhoods

  • Sink depth and Depression cost surface

  • Slope

  • Tangential curvature –numerous neighborhoods

  • Topographic position index

  • Vertical distance to flow channel

  • Wetness index

Exploratory data analysis 4 method

Exploratory Data Analysis4. Method

  • An extensive sample with soil series as a response was developed

  • Classification Tree in R to determine explanatory variables

Purpose of evaluation 4 method

Purpose of evaluation4. Method

  • Spatial data needs to be the driver for modeling effort

  • Efficient determination of explanatory variables

  • Efficient determination of thresholds for variables

  • Practical tools are needed to assist soil scientists in this effort

Results from classification tree 5 implement

Results from classification tree5. Implement

  • Altitude above channel network

  • Horizontal distance to channel

  • Minimum curvature 120m neighborhood

  • Multi-resolution ridge top flatness index

  • Profile curvature 150m neighborhood

  • Relative position 90m neighborhood

  • Relative position 60m neighborhood

  • Relative position 30m neighborhood

  • Sink Depth

  • Slope 30m neighborhood

  • Topographic position index

  • Wetness index

Input variables

Important variables

  • Altitude above channel network

  • Relative Elevation (aka Relative position)

  • Sink Depth

Developed 20+ datasets – 12 showed promise from qualitative review – 3 were

identified through classification tree as explanatory variables in this example

Spatial disaggregation a primer

Results from classification tree

5. Implement -Ipavaand Sable independently

Spatial disaggregation a primer

Results from classification tree

5. Implement – walk through the splits

Altitude above channel network

>= 0.25

< 0.25

Spatial disaggregation a primer

Results from classification tree

5. Implement – walk through the splits

Relative position

>= 0.595

< 0.595

Spatial disaggregation a primer

Results from classification tree

5. Implement – walk through the splits

Sink depth

>= 1.472

< 1.472

Spatial disaggregation a primer

Results from classification tree

5. Implement - Results of rules applied for Sable and Ipava

Spatial disaggregation a primer

Results from classification tree

  • 5. Implement Rule base compared with SSURGO for Sable

Ponded vs non ponded sable 6 validate local using depression depth

Ponded vs. non-ponded Sable6. Validate Local - using depression depth

Blue – likely depression/ponded

Red -Yellow – no depression

Spatial disaggregation a primer

Ponded vs. non-ponded Sable6. Validate Local - using depression cost surface

Blue – likely depression/ponded

Red -Yellow – no depression

Ponded vs non ponded sable 6 validate local using 3m usgs ned

Ponded vs. non-ponded Sable6. Validate Local - using 3m USGS NED

  • Zonal statistics indicate 41% of the area mapped as Sable is ponded

  • Based on selected thresholds

  • Verification and tuning of threshold values is ongoing

Spatial disaggregation a primer

Ponded vs. non-ponded Sable

6. Validation/Data Local - using 10m USGS NED

  • Zonal statistics indicate 17% of the area is ponded

Bigger legend

Area “missed” with

coarser 10m DEM

Ponded vs non ponded ipava 6 validate local using 3m usgs ned

Ponded vs. non-ponded Ipava6. Validate Local - using 3m USGS NED

  • Zonal statistics indicate 9% of the area is ponded

Future effort for peoria county

Future effort for Peoria County

  • Populate component table

    - based on verified and validated thresholds

  • Rename map unit phases if needed

  • What is reasonable to improve product?

  • Accept line work and split components within existing map units?

    - A working copy in preparation for phase II of data recorrelation

    makes this feasible

Arizona arid example

Arizona – arid example

  • Goal

    • match environmental classification of soil forming factor raster layers to soil types.

  • Scope

    • Entire soil survey: Organ Pipe Cactus National Monument (ORPI)

  • Data

    • Used DEM and ASTER imagery to represent topography, vegetation, and geology

  • Method

    • Unsupervised classification (clustering)

  • Implement

    • Erdas Imagine and ArcGIS

  • Validate (evaluation)

    • Contingency tables (Chi2 Cramer’s V) to MUs; found separation of components in most complexes in field recon. (Nauman, 2009)

Arizona a rid example

Arizona – arid example



  • More methods trials are planned for northeast AZ

  • Initial spatial data is being compiled

  • Model runs by late 2013





  • Disaggregation is a process that is defined by a need for more detail

    • Needs a directed scope

  • Tremendous amount of new data and computing abilities to incorporate

  • Disaggregating classic soil surveys

    • improves the detail of final maps without loss of accuracy and with no new data

    • more realistic representation of soil distribution (continuous – background probabilities)

    • Can use new field data in future to re-model for easy update (doing this in WV)

Next steps

Next Steps

  • Match disaggregated data to ESDs

  • Further disaggregate to ESD state and transition models

    • Would better match imagery because management (e.g. pasture vs forest) is more easily detected with remote sensing.

    • Could map at state and/or community level for direct use in conservation planning

      • National Range and Pasture Handbook, 2003

    • Currently submitting article for peer review documenting WV case study

      • Nauman, T., J.A. Thompson. (In prep). Semi-Automated Disaggregation of Conventional Soil Maps using Knowledge Driven Data Mining and Classification Trees

Spatial disaggregation a primer

Resources – Available TrainingNRCS offers the following courses which provide an introduction to some of these techniques – check AgLearn

  • Spatial Analysis workshop (distance learning)

  • Introduction to Digital Soil Mapping (distance learning)

  • Digital Soil Mapping with ArcSIE (conventional class)

  • Remote Sensing for Soil Survey Applications (conventional class)



Bui, E., B. Henderson, and K. Viergever. 2009. Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia. Global Biogeochemical Cycles 23.

Bui, E.N. and Moran, C.J., 2001. Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma, 103(1-2): 79-94.

Bui, E.N., A. Loughhead, and R. Corner. 1999. Extracting soil-landscape rules from previous soil surveys. Australian Journal of Soil Research 37:495-508.

de Bruin, S., Wielemaker, W.G. and Molenaar, M., 1999. Formalisation of soil-landscape knowledge through interactive hierarchical disaggregation. Geoderma, 91(1–2): 151-172.

Goovaerts, P., 2011. A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties. European Journal of Soil Science, 62(3): 371-380.

Häring, T., Dietz, E., Osenstetter, S., Koschitzki, T. and Schröder, B., 2012. Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils. Geoderma, 185–186(0): 37-47.

Kerry, R., Goovaerts, P., Rawlins, B.G. and Marchant, B.P., 2012. Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. Geoderma, 170: 347-358.

Li, S., MacMillan, R. A., Lobb, D. A., McConkey, B. G., Moulin, A., & Fraser, W. R. 2011. Lidar DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada. Geomorphology, 129(3), 263-275.

McBratney, A.B., 1998. Some considerations on methods for spatially aggregating and disaggregating soil information. Nutrient Cycling in Agroecosystems, 50(1-3): 51-62.

MDA, Federal. 2004. Landsat Geocover TM 1990 & ETM+ 2000 Edition Mosaics Tile N-17-35 TM-EarthSat-MrSID. USGS, Sioux Falls, South Dakota.



Moore, A. 2008. Spatial Disaggregation Techniques for Visualizing and Evaluating Map Unit Composition. NRCS 2008 National State Soil Scientist’s Workshop. Florence, Kentucky.

Nauman, T.W., 2009. Digital Soil-Landscape Classification for Soil Survey using ASTER Satellite and Digital Elevation Data in Organ Pipe Cactus National Monument, Arizona. MS Thesis. The University of Arizona.

Nauman, T., J.A. Thompson, N. Odgers, and Z. Libohova. 2012. Fuzzy Disaggregation of Conventional Soil Maps using Database Knowledge Extraction to Produce Soil Property Maps, In B. Minasny, et al., (eds.) Digital Soil Assessments and Beyond: 5th Global Workshop on Digital Soil Mapping, Sydney, Australia.

Schmidt, J. and Hewitt, A., 2004. Fuzzy land element classification from DTMs based on geometry and terrain position. Geoderma, 121(3-4): 243-256.

Thompson, J.A. et al., 2010. Regional Approach to Soil Property Mapping using Legacy Data and Spatial Disaggregation Techniques, 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, Australia.

Wei, S. et al., 2010. Digital Harmonisation of Adjacent Soil Survey areas - 4 Iowa Counties, 19th World Congress of Soil Science, Soils Solutions for a Changing World, Brisbane, Australia.

Wielemaker, W.G., de Bruin, S., Epema, G.F. and Veldkamp, A., 2001. Significance and application of the multi-hierarchical landsystem in soil mapping. Catena, 43(1): 15-34.

Yang, L. et al., 2011. Updating Conventional Soil Maps through Digital Soil Mapping. Soil Science Society of America Journal, 75(3): 1044-1053.

Zhu, A.X., 1997. A similarity model for representing soil spatial information. Geoderma, 77(2-4): 217-242.

Zhu, A.X., Band, L., Vertessy, R. and Dutton, B., 1997. Derivation of soil properties using a soil land inference model (SoLIM). Soil Science Society of America Journal, 61(2): 523-533.

Zhu, A.X., Band, L.E., Dutton, B. and Nimlos, T.J., 1996. Automated soil inference under fuzzy logic. Ecological Modelling, 90(2): 123-145.



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