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Soil Systems, Catenas, and SeriesA Spatial Hierarchy for DSM and Disaggregation
Brian SlaterSakthi Subburayalu
Anne VascikSCHOOL OF ENVIRONMENT & NATURAL RESOURCES
Research Objective
• Develop operational techniques for future soil survey– Raster format– ~10m resolution– Soil classes (series)– Measured uncertainty– Large extent
Methods
• Machine learning from ambiguous data– Possibilistic decision trees and random
forests• Optimize incorporation of information from
SSURGO• High resolution SCORPAN covariates• Validation
Rigorous Test
• Quantify the error in prediction against an independent set of validation pedons
• Are identified series at (raster) locations correct?
Prior Efforts
• Nauman and Thompson (2014) reported a prediction accuracy that ranged between 22% and 24%– Better prediction accuracy with additional
spatial support, or allowing taxonomically similar soils
• Similar prediction accuracies (22.5%) were reported by Odgers et al (2014)
• Subburayalu, Jenhani, and Slater, 2013 reported prediction accuracies of 34-39%
LoD
GkC2
Gilpin-Upshur complex, 6 to 12 percent slopes, moderately eroded
Lowell silt loam, 15 to 25 percent slopes
Lowell or Gilpin? or ?
Series PercentLowell 85Gilpin 15
Series PercentGilpin 55Upshur 35Wellston 5Woodsfield 3Coolville 2
Disaggregation of compound map units
POLARIS/DSMART• Chaney et al., 2016• 294,746 validation pedons from NASIS• POLARIS prediction
– 17% match at rank 1– 55% match the first 10 ranks– 68% match first 50 ranks
• Compare to gSSURGO– 48% match at rank 1– 61% match if include all components
• Validation data is not independent
Under prediction of Guernsey
Under prediction of Gilpin 25%
Accuracy
39% Accuracy
POLARIS/DSMART
Possibilistic decisiontrees
Monroe County, Ohio
Source of Inaccuracy?• “… a lack of appropriate environmental covariates to
properly differentiate the location of minor components from the major components” Chaney et al., 2016
Covariates did not
sufficiently separate
soils
(Slater and Subburayal
u, 2013)
Improvement
• Constrain machine learning to predict just a few classes at a time
• Restrict extent of models in geographic and attribute space
• Develop a hierarchy of spatial units– Map each level of the hierarchy sequentially
• Hierarchy also allows better communication and more information e.g. in legends, general maps
Systems
Catenas
Series
SCORPAN covariates
Soil Landscape Systems
Catenas
Series
~15
~100
~445
Sample training set from
gSSURGO and covariates
Train decision tree models for each System, map catenas
Train decision tree models for each Catena, map Series
Large area disaggregation: Ohio
Soil Landscape Systems
Mapping Soil Landscape Systems
• Simplest method: reclassify gSSURGO• Alternatives:
– Synthesize from variety of previous map products and expert knowledge
– Bottom up, build from covariates
Study Area
• North west Ohio• Includes large areas of
MLRAs 111 (Till Plain), and 99 (Lake Plain)
• Generally low relief landscapes provide a good challenge to develop DSM techniques where typical terrain attributes may have low predictive power
Covariates
– State-wide covariate data set at 10m• USGS 3DEP DEM and Ohio LiDAR data compared;
some problems in LidAR data– For initial Soil Landscape System mapping, we
eventually generalized to 30m raster resolution to make processing times more practical
– Terrain• SAGA GIS
– Substrate• Soil Parent Materials – Isee map
– Climate• Annual and season PET
Environmental Covariates for NW Ohio
ELEVATION
Environmental Covariates for NW Ohio
Multiresolution Index of Valley Bottom Flatness
Environmental Covariates for NW Ohio
Multiresolution Index of Ridge Top Flatness
Parent Material• Isee dominant soil
parent material map
Climate
Results• Annual potential evapotranspiration layer
Climate
Results• Seasonal potential
evapotranspiration layers
Systems Mapping
Object-based image analysis uses a series of processes to group similar pixels into homogenous objects
– Search for patterns on an object basis, instead of on a raster by raster basis
– Successfully used in delineating environmental patterns
– Trimble eCognition is an environment for object-based image classification
• involves a two step approach of segmentation and classification
• Both raster and thematic data layers can be used
Object Based Image Classification
STEP 1: SEGMENTATION
252,187 polygons
STEP 2: CLASSIFICATION
9 Systems
Soil Systems Classification
Ohio Catenas
Source: A Key to Soil Series Used in Ohio since 1900 by T.D.Gerber, S.P.Lewis and M.H. Deaton (2001)
Catenas
• Gerber et al. developed preliminary catena grouping of Ohio Soils
• In many cases, catenas have only 1-3 series included– For example for Erie Lake Plain, 60 series
were included and placed in 33 catenas• We have reclassified catenas to have
several associated soils in each (2-12)– For Lake Plain, 11 catenas, 52 series
Catenas
Additional information from expert knowledge,Block diagrams, OSDs
Catena No. SoilSeries1 SoilSeries2 SoilSeries3 SoilSeries4 SoilSeries5 SoilSeries6 SoilSeries7 SoilSeries8 SoilSeries9 SoilSeries10 SoilSeries11 SoilSeries12L1 Colwood Kibbie Tuscola Sisson DixboroL2 Granby Tedrow Ottokee Oakville Rollersville SpinksL3 Tiderishi Vanlue Jenera Renssalaer DarrochL4 Mermill Aurand Haskins Vaughnsville Cygnet ShawtownL5 Sandusky WeyersL6 Kingsville Stafford Elnora Bixler Plumbrook Colonie Tyner Ottisville Painesville Harbor Swanton ConneautL7 Galen Arkport Lamson MinoaL8 Rimer Seward WauseonL9 Ogontz ZurichL10 Paulding Roselms BroughtonL11 Lenowee Del rey Shinrock SaylesvilleL12 Bono Toledo Latty Fulton Lucas
Lake Plain Catenas
Ao-20:Melvin(P),Newark (SP), Lindside (MW), Nolin (W)
Ro-28:Vandalia (>40in colluvium), Upshur (>40in bedrock),
Woodsfield(Loess capped)
Locating Catenas
SCORPAN covariates
Soil Landscape Systems
Catenas
Series
~15
~100
~445
Sample training set from
gSSURGO and covariates
Train decision tree models for each System, map catenas
Train decision tree models for each Catena, map Series
Large area disaggregation: Ohio
