Spatial Disaggregation – A Primer Tom D’Avello – NRCS-NSSC-GRU contact: [email protected] Travis Nauman – NRCS-NSSC-GRU, WVU contact: [email protected]
Jan 19, 2016
Spatial Disaggregation – A Primer
Spatial Disaggregation – A Primer
Tom D’Avello – NRCS-NSSC-GRUcontact: [email protected]
Travis Nauman – NRCS-NSSC-GRU, WVUcontact: [email protected]
Tom D’Avello – NRCS-NSSC-GRUcontact: [email protected]
Travis Nauman – NRCS-NSSC-GRU, WVUcontact: [email protected]
OverviewOverview
Define ‘Disaggregation’ Approaches and Tools–West Virginia– Illinois– Arizona
Summary Literature list for your reference
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
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 disaggregation?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
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
http://www.soilsurvey.org/tutorial/page1.asp
What is spatial disaggregation?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.
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 demonstrationPurpose 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
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 effortsWest Virginia early efforts
Gilpin
Pineville
Laidig
Guyandotte
Dekalb
Component Soils
Craigsville
Meckesville
Cateache
Shouns
Gilpin-Laidig
Pineville-Gilpin-Guyandotte
Other
SSURGO Map Units
General disaggregation workflowGeneral disaggregation workflow1. Goals2. Scope3. What data is accessible to help4. Choose method5. Implement6. Validate Quality
– (evaluate and iterate earlier steps as needed)
1. Goals2. Scope3. What data is accessible to help4. Choose method5. Implement6. Validate Quality
– (evaluate and iterate earlier steps as needed)
Current workflow in West VirginiaCurrent workflow in West Virginia1. Goals
Soil series map on field scale grid
2. Scope All map units in Pocahontas and Webster Counties, WV
3. What data is accessible to help ~30-meter DEM (NED), Landsat Geocover (Fed. MDA,
2004), lithology, SSURGO
4. Choose method SSURGO-derived expert rule training sets & classification
tree ensemble (100 trees run on random subsets)
5. Implement Run analysis with Access (SQL), GIS, and Python (or R)
6. Validate Quality Independent pedons for ground truth
1. Goals Soil series map on field scale grid
2. Scope All map units in Pocahontas and Webster Counties, WV
3. What data is accessible to help ~30-meter DEM (NED), Landsat Geocover (Fed. MDA,
2004), lithology, SSURGO
4. Choose method SSURGO-derived expert rule training sets & classification
tree ensemble (100 trees run on random subsets)
5. Implement Run analysis with Access (SQL), GIS, and Python (or R)
6. Validate Quality Independent pedons for ground truth
1. & 2. Goals and scope1. & 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
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
Local
Universal
Figures courtesy of Dave Hoover, NSSC
3. What data: SSURGO3. What data: SSURGO
Component
Components (not explicitly mapped)
Inclusion
Legend
Map Unit
Horizon
Geomorphology
Parent material
Landscape attributes
Horizon attributes
Soil physical properties
Soil chemical properties
3. What data: SSURGO3. What data: SSURGO
Most work done on SSURGO or equivalent scale maps
Raster (grids) used for modeling – to match
environmental data
Most work done on SSURGO or equivalent scale maps
Raster (grids) used for modeling – to match
environmental data
West Virginia data
3. What data: environmental3. What data: environmental Raster grids
– Sometimes other polygon layers converted (e.g. geology)
Characterize variation within polygons using data that infer soil forming factors
Raster grids – Sometimes other polygon layers converted (e.g.
geology)
Characterize variation within polygons using data that infer soil forming factors
SSURGO lines over DEMSSURGO lines over LandsatSSURGO lines over landforms(Schmidt & Hewitt, 2004)
Examples from West Virginia
4. Method: model techniques4. 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)
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)
|overstory< 0.2878
PROF_CURV>=-0.002904 NWNESS>=-0.228
inceptisol ultisol
spodosol inceptisolExample Classification Tree Model
Dekalb series training areas in WV
5. & 6. Implement & Validate 5. & 6. Implement & Validate Create raster
disaggregation map
Validate with ground truth data– Different
methods available
Create raster disaggregation map
Validate with ground truth data– Different
methods available
WV example: universal model for Webster and Pocahontas Counties
validation Spatial Support
match type nearest 60-m radius
exact 26% 39%
like soil 45% 66%
any tree 57% 73%
Historical survey of Webster County, WVHistorical survey of Webster County, WV
These folks were pretty good Milton Whitney Curtis Marbut Hugh Bennett Nice map, too!
1. Goals Components or phases within Sable and Ipava units
2. Scope All Sable and Ipava map units within Peoria County
3. What data is accessible to help 3-meter DEM (NED), SSURGO
4. Choose method Expert rule training sets & classification trees
5. Implement Run analysis with R, ArcGIS and ArcSIE
6. Validate Quality Local soil scientist review.
1. Goals Components or phases within Sable and Ipava units
2. Scope All Sable and Ipava map units within Peoria County
3. What data is accessible to help 3-meter DEM (NED), SSURGO
4. Choose method Expert rule training sets & classification trees
5. Implement Run analysis with R, ArcGIS and ArcSIE
6. Validate Quality Local soil scientist review.
Peoria, Illinois investigationPeoria, Illinois investigation
Peoria, Illinois investigation1. Goals Peoria, Illinois investigation1. Goals Identification of Non-ponded and
ponded phases in Sable units Identification of poorly drained
components in Ipava units
Identification of Non-ponded and ponded phases in Sable units
Identification of poorly drained components in Ipava units
Peoria, Illinois investigation2. Scope - study site
Peoria, Illinois investigation2. Scope - study site
~900,000 acres of Sable ~1,186,000 acres of Ipava
The project area is within MLRAs 95B, 108A, 108B, 108C and 115C
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 setting2. Scope - study site
General setting2. Scope - study site
soil slope profile tangential wetness position sinks
Ipava Low Plane Plane High Broad summit/Talf
Some
Sable Lowest Concave-plane
Concave-plane
Highest Dip on talf Yes
Typical cross-section and qualitative description of Sable and Ipava soils
Variables developed3. 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
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 Analysis4. Method Exploratory Data Analysis4. Method
An extensive sample with soil series as a response was developed
Classification Tree in R to determine explanatory variables
An extensive sample with soil series as a response was developed
Classification Tree in R to determine explanatory variables
Purpose of evaluation4. MethodPurpose 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
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 tree5. 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
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
• Altitude above channel network• Relative Elevation (aka Relative position)• Sink Depth
Input variables Important variables
Developed 20+ datasets – 12 showed promise from qualitative review – 3 wereidentified through classification tree as explanatory variables in this example
Results from classification tree5. Implement -Ipava and Sable independently
Results from classification tree5. Implement -Ipava and Sable independently
Altitude above channel network>= 0.25< 0.25
Results from classification tree5. Implement – walk through the splits
Results from classification tree5. Implement – walk through the splits
Relative position>= 0.595< 0.595
Results from classification tree5. Implement – walk through the splits
Results from classification tree5. Implement – walk through the splits
>= 1.472< 1.472
Results from classification tree5. Implement – walk through the splits
Results from classification tree5. Implement – walk through the splits
Sink depth
Results from classification tree5. Implement - Results of rules applied for Sable and Ipava
Results from classification tree5. Implement - Results of rules applied for Sable and Ipava
Results from classification tree5. Implement Rule base compared with SSURGO for Sable
Results from classification tree5. Implement Rule base compared with SSURGO for Sable
Ponded vs. non-ponded Sable6. Validate Local - using depression depth
Ponded vs. non-ponded Sable6. Validate Local - using depression depth
Blue – likely depression/ponded
Red -Yellow – no depression
Ponded vs. non-ponded Sable6. Validate Local - using depression cost surface
Ponded vs. non-ponded Sable6. Validate Local - using depression cost surface
Blue – likely depression/ponded
Red -Yellow – no depression
Ponded vs. non-ponded Sable6. 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
Zonal statistics indicate 41% of the area mapped as Sable is ponded
Based on selected thresholds Verification and tuning of
threshold values is ongoing
Ponded vs. non-ponded Sable6. Validation/Data Local - using 10m USGS NED
Ponded vs. non-ponded Sable6. Validation/Data Local - using 10m USGS NED
Bigger legend
Zonal statistics indicate 17% of the area is ponded
Zonal statistics indicate 17% of the area is ponded
Area “missed” withcoarser 10m DEM
Ponded vs. non-ponded Ipava6. 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
Zonal statistics indicate 9% of the area is ponded
Future effort for Peoria CountyFuture 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
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 exampleArizona – 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)
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 – arid exampleArizona – arid example
ArizonaArizona More methods trials are planned
for northeast AZ Initial spatial data is being
compiled Model runs by late 2013
More methods trials are planned for northeast AZ
Initial spatial data is being compiled
Model runs by late 2013
SummarySummaryWebster and Pocahontas, West Virginia
Peoria, Illinois ORPI, Arizona
Goal Soil series map of entire area Components/phases within Sable and Ipava units
Match environmental raster patterns to MUs
Scope Full extent of both surveys (Webster and Pocahontas)
Sable and Ipava map units within Peoria Co.
Entire ORPI survey area
Data DEM (NED 30m), Landsat, geology
DEM (NED 3m) DEM (NED 30m), ASTER
Method SSURGO component rules and classification trees
Expert rules and classification trees
Clustering (ISODATA)
Implement Access (or SQL), ArcGIS, and Python (or R)
ArcGIS, ArcSIE, R ArcGIS, Erdas Imagine
Validate Independent set of pedons Expert review Compare w/ SSURGO, expert review
Highlights Series map, harmonized surveys, maintained accuracy
Picked out fine scale depressions
Detected components in complex MUs
SummarySummary 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)
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)
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
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
Next StepsNext Steps
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)
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)
Resources – Available TrainingNRCS offers the following courses which provide an introduction to some of these techniques – check AgLearn
Resources – Available TrainingNRCS offers the following courses which provide an introduction to some of these techniques – check AgLearn
LiteratureLiteratureBui, 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.
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.
LiteratureLiteratureMoore, A. 2008. Spatial Disaggregation Techniques for Visualizing and Evaluating Map
Unit Composition. NRCS 2008 National State Soil Scientist’s Workshop. Florence, Kentucky. ftp://ftp-fc.sc.egov.usda.gov/NSSC/NCSS/Conferences/state/2008/moore.pdf
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.
Moore, A. 2008. Spatial Disaggregation Techniques for Visualizing and Evaluating Map Unit Composition. NRCS 2008 National State Soil Scientist’s Workshop. Florence, Kentucky. ftp://ftp-fc.sc.egov.usda.gov/NSSC/NCSS/Conferences/state/2008/moore.pdf
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.
QuestionsQuestions