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History and Evolution of Digital (Predictive) Soil Mapping (DSM)

B
Bob MacMillan

The document outlines the history and evolution of digital predictive soil mapping (DSM), detailing the unifying framework, key developments before and after 2003, and future trends in modeling soil properties and classes. It discusses various deterministic and stochastic models, past software and methodologies used for DSM, and emphasizes the importance of integrating multiple data inputs and addressing prediction uncertainties. The document also highlights conceptual and statistical models, including the transition from static mapping to dynamic real-time models in the field of soil science.

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History and Evolution of Digital (Predictive) Soil Mapping R. A. MacMillan LandMapper Environmental Solutions Inc.
Outline • Unifying DSM Framework: Universal Model of Variation – Z(s) = Z*(s) + ε(s) + ε • Past: Early History of Development of DSM (pre 2003) – Theory, Concepts, Models, Software, Inputs, Developments – Examples of early methods and outputs • Key Recent Developments in DSM post 2003 – Theory, Concepts, Models, Software, Inputs, Developments – Examples of recent methods and outputs • Future Trends: How do I See DSM Developing? – Theory, Concepts, Inputs, Models, Software, Developments – From Static Maps to Dynamic Real-Time Models • Discussion and Conclusions – Constraints and pitfalls to be avoided, technical/political
Introduction Universal Model of Soil Variation A Unifying Framework for DSM
Source: Burrough, 1986 eq. 8.14 Universal Model of Soil Variation • A Unifying Framework for Digital Soil Mapping Z(s) = Z*(s) + ε(s) + ε Predicted soil type or Deterministic part of Stochastic part of the Pure Noise part of soil property value the predictive model predictive model the predictive model Predicted spatial part of the variation part of the variation part of the variation pattern of some soil that is predictable by that shows spatial that can’t be predicted property or class means of some structure, can be at the current scale including uncertainty statistical or heuristic modelled with a with the available of the estimate soil-landscape model variogram data and models
Deterministic Part of Prediction Model: Z*(s) KLM Series FMN Series • Conceptual Models EOR Series DYD Series COR Series 15 – Conceptual or mental soil-40 landscape models 60 – Produce area-class maps • Statistical Models In d ivid u a l sa lin ity h a za rd ra tin g s for ea ch la ye r 10 0 x 10 0 m g rid – Scorpan – relate soils/soil La ye r w e igh ting s La nd sca pe cu rva ture 2 x properties to covariates Veg eta tion 1 x R a infa ll 2 x – Explain spatial distribution G e olo gy 1 x S oils of soils in terms of known 3 x soil forming factors as La nd su rfa ce represented by covariates To tal salin ity ha za rd ra ting S alin ity h az ard m ap
Stochastic Part of Prediction Model: ε(s) • Geostatistical Estimation – Predict soil properties • Point or block kriging – Predict soil classes • Indicator kriging – Predict error of estimate • Correct Deterministic Part – Error in deterministic part is computed (residuals) – If structure exists in error then krige error & subtract
Pure Noise Part of Prediction Model: ε(s) • Some Variation not Predictable – Have to be honest about this • Should quantify and report it • Deterministic Prediction – Mental and Statistical Models • Not perfect – often lack suitable covariates to predict target variable Sill • Lack covariates at finer resolution Range • Geostatistical Prediction Semi Variance – Insufficient point input data Nugget • Can’t predict at less than the d1 d2 d3 d4 Lag (distance) smallest spacing of input point data
Past Early History of DSM Development (pre 2003) On Digital Soil Mapping McBratney et al., 2003
Early History of Development of DSM Deterministic Stochastic Soil Classes Soil Classes Soil Soil Properties Properties
Past Theory: Deterministic Component Z*(s) Classed Conceptual Models – Jenny (1941) • CLORPT (Note no N=space) – Simonson (1959) • Process Model of additions, removals, translocations, transformations – Ruhe (1975) • Erosional -Depositional surfaces, open/closed basins – Dalrymple et al., (1968) • Nine unit hill slope model – Milne (1936a, 1936b) • Catena concept, toposequences
Past Concepts: Deterministic Component Z*(s) Classed Conceptual Models Soil = f (C, O, R, P, T, …) Climate Organisms Topography Parent Material Soil Time Source: Lin, 2005 Frontiers in Soil Science http://www7.nationalacademies.org/soilfrontiers/
http://solim.geography.wisc.edu/index.htm
Past Models: Deterministic Component Z*(s) Classed Statistical Predictions • Fuzzy Inference In d ivid u a l sa lin ity h a za rd ra tin g s for ea ch la ye r – Zhu, 1997, Zhu et al., 1996 10 0 x 10 0 m g rid La ye r w e igh ting s – MacMillan et al., 2000, 2005 La nd sca pe cu rva ture 2 x • Neural Networks 1 x Veg eta tion – Zhu, 2000 R a infa ll 2 x G e olo gy • Expert Knowledge (Bayesian) 1 x S oils – Skidmore et al., 1991 3 x – Cook et al., 1996, Corner et al., 1997 La nd su rfa ce • Regression Trees – Moran and Bui, 2002, Bui and S alin ity h az ard To tal salin ity m ap ha za rd ra ting Moran, 2003 Source: Jones et al., 2000
Past Software: Deterministic Component Z*(s) Classed Statistical Predictions • Regression Trees • Fuzzy Logic – CUBIST – SoLIM • Rulequest Research , 2000 • Zhu et al., 1996, 1997 – CART – LandMapR, FuzME • Breiman et al., 1984 – C4.5 & See5 • Bayesian Logic • Quinlin, 1992 – Prospector – JMP (SAS) • Duda et al., 1978 • http://www.jmp.com/ – Expector – R • Skidmore et al., 1991 • http://www.r-project.org/ – Netica • Norsys.com/netica
Past Inputs: Deterministic Component Z*(s) Classed Statistical Predictions • C = Climate • R = Relief (topography) – Temp, Ppt, ET, Solar Rad – Primary Attributes • Mean, min, max, variance • Slope, aspect, curvatures • Annual, monthly, indices • Slope Position, roughness • O = Organisms – Secondary Attributes • CTI, WI, SPI, STC – Manual Maps • Land Use • P = Parent Material • Vegetation – Published geology maps – Remotely Sensed Imagery – Gamma radiometrics • Classified RS imagery – Thermal IR, RS Ratios • NDVI, EVI, other ratios • A = Age
Past Inputs: Deterministic Component Z*(s) Classed Statistical Predictions • Common Topo Inputs Profile Curvature Plan Curvature – Profile Curvature – Plan (Contour) Curvature – Slope Gradient (& Aspect) Slope Gradient Wetness Index – CTI or Wetness Index • Sometimes, not always • Less Common Topo Inputs – Surface Roughness Pit 2 Peak Relief Divide 2 Channel – Relief within a window – Relief relative to drainage • Pit, peak, Ridge, channel, Source: MacMillan, 2005
Past Inputs: Non-DEM Airborne Radiometrics • Radiometrics 4 Subsurface • Infer Parent Material Source: Mayr, 2005
Past Inputs: Non-DEM Satellite Imagery Grassland Land Cover Types Alpine Land Cover Types
Past Models: Deterministic Component Z*(s) Examples of Predictions of Soil Class Maps
Approaches to Producing Predictive Area- Class Maps
Knowledge-Based Classification In SoLIM Source: Zhu, SoLIM Handbook
Knowledge-Based Classification Using Boolean Decision Tree in USA Component Soils Gilpin Pineville Laidig Guyandotte Dekalb Craigsville Meckesville Cateache Shouns Source: Thompson et al., 2010 WCSS
Knowledge-Based Classification In LandMapR Source: Steen and Coupé, 1997 Source: MacMillan, 2005
Knowledge-Based Classification In Utah, Knowledge-Based PURC Approach Note: Not simple slope elements but complex patterns Source: Cole and Boettinger, 2004
Approaches to Producing Predictive Area- Class Maps
Source: Zhou et al., 2004, JZUS Supervised Classification Using Regression Trees Note similarity of supervised rules and classes to typical soil-landform conceptual classes Note numeric estimate of likelihood of occurrence of classes
Supervised Classification Using Bayesian Analysis of Evidence/Classification Trees Source: Zhou et al., 2004, JZUS
Predicting Area-Class Soil Maps Using Discriminant Analysis Source: Scull et al., 2005, Ecological Modelling
Predicting Area-Class Soil Maps Using Regression Trees Extrapolation Uncertainty of prediction Bui and Moran (2003) Geoderma 111:21-44 Source: Bui and Moran., 2003
Supervised Classification Using Fuzzy Logic • Shi et al., 2004 Fuzzy likelihood of being a broad ridge – Used multiple cases of reference sites – Each site was used to establish fuzzy similarity of unclassified locations to reference sites – Used Fuzzy-minimum function to compute fuzzy similarity – Harden class using largest (Fuzzy- maximum) value – Considered distance to each reference site in computing Fuzzy-similarity Source: Shi et al., 2004
Approaches to Producing Predictive Area- Class Maps
Credit: J. Balkovič & G. Čemanová Concept of Fuzzy K-means Clustering Source: Sobocká et al., 2003
Example of Application of Fuzzy K-means Unsupervised Classification From: Burrough et al., 2001, Landscsape Ecology Note similarity of unsupervised classes to conceptual classes
Example of Application of Disaggregation of a Soil Map by Clustering into Components Source: Faine, 2001
Developments: Deterministic Component Z*(s) Classed Predictive Maps in Past • Characteristics of Models • Characteristics of Models – Models largely ignored ε – Many use expert knowledge • Seldom estimate error • Data mining is the exception • Rarely correct for error • Training data seldom used – Mainly use DEM inputs – Specialty software prevails • Initially 3x3 windows • Software for DEM analysis • Slope, aspect, curvatures – SoLIM, TAPESG, TOPAZ, TOPOG, TAS, SAGA, • Maybe wetness index ESRI, ISRISI, LandMapR • Later improvements were • Software for extracting rules measures of slope position – Expector, Netica, CART, – Rarely use ancillary data See 5, Cubist, Prospector • Exceptions like Bui, Skull • Software for applying rules – Operate at single scale – ESRI, SoLIM, SIE, SAGA
Past Models: Deterministic Component Z*(s) for Continuous Soil Properties Approaches Aimed at Predicting Continuous Soil Properties
Past Concepts: Deterministic Component Z*(s) Continuous Soil Properties • Same Theory-Concepts • Key Papers as for Classed Maps – Moore et al., 1993 Soil = f (C, O, R, P, T, …) • Linear regression – Except theory applied to – McSweeney et al., 1994 individual soil properties – McKenzie & Austin, 1993 – Initially referred to as environmental correlation – Gessler at al, 1995 – Soil properties related to • GLMs in S-Plus • Landscape attributes – McKenzie & Ryan, 1999 • Climate variables • Regression Trees • Geology, lithology, soil pm
Past Models: Deterministic Component Z*(s) Continuous Soil Properties • Regression Trees – McKenzie & Ryan, 1998, Odeh et al., 1994 • Fuzzy Logic-Neural Networks – Zhu, 1997 • Bayesian Expert Knowledge – Skidmore et al., 1996 – Cook et al., 1996, Corner et al., 1997 • GLMs – General Linear Models – McKenzie & Austin, 1993 – Gessler et al., 1995 Source: McKenzie and Ryan, 1998
Past Inputs: Deterministic Component Z*(s) for Continuous Soil Properties • Similar to Classed Maps But: – Many innovations originated with continuous modelers • Increased use of non-DEM attributes – climate, radiometrics, imagery • Improved DEM derivatives – Wetness Index & CTI – Upslope means for slope, etc. – Inverted DEMs to compute » Down slope dispersal » Down slope means » New slope position data Source: McKenzie and Ryan, 1998
Past Models: Deterministic Component Z*(s) for Continuous Soil Properties Examples of Predictions of Soil Property Maps
Past Models: Deterministic Component Z*(s) Continuous Maps • Aandahl, 1948 (Note Date!) – Regression model • Predicted – Average Nitrogen (3-24 inch) – Total Nitrogen by depth – Total Organic Carbon by depth interval – Depth of profile to loess • Predictor (covariate) – Slope position as expressed by length of slope from shoulder – Lost in the depths of time Source: Aandahl, 1948
Past Models: Deterministic Component Z*(s) for Continuous Soil Properties • Moore et al., 1993 – Seminal paper – Focus on topography • Small sites • Other covariates were assumed constant – Got people thinking • About quantifying environmental correlation, especially soil-topography relationships Source: Moore et al, 1993
Source: McKenzie and Ryan, 1998 Past Models: Deterministic Component Z*(s) for Continuous Soil Properties • McKenzie & Ryan, 1998 – Regression Tree: Soil Depth
Source: McKenzie and Ryan, 1998 Past Models: Deterministic Component Z*(s) for Continuous Soil Properties • Gessler et al., 1995 – GLMs – Largely based • Topo – CTI • Others held – Steady Source: Gessler, 2005
Credit: Minasny & McBratney Past Models: Deterministic Component Z*(s) for Continuous Soil Properties 2.17 160.1 Regression tree Text: C Text: S,LS,L,CL,LiC 1.18 2.84 54.61 27.45 BD<1.43 BD>1.43 Clay<46.5 Clay>46.5 0.64 2.21 2.97 2.04 15.65 13.00 14.59 5.50 BD<1.42 BD>1.42 3.37 2.81 Source: Minasny and McBratney 1.83 8.90
Developments: Deterministic Component Z*(s) Predictive Maps up to 2003 • Main Developments • Main Developments – Better DEM derivatives – Integration of single models • More and better measures of into multi-purpose software landform position or • ArcGIS, ArcSIE, ArcView context • SAGA, Whitebox, IDRISI • Some recognition of scale and resolution effects – Improved processing ability – Different window sizes • Bigger files, faster processing – Different grid resolutions – Emergence of 2 main scales – More non-DEM inputs • Hillslope elements (series) • Increased use of imagery – Quite similar across models • New surrogates for PM • Landscape patterns (domains) – Similar to associations
Early History of Development of DSM Deterministic Stochastic Soil Classes Soil Classes Soil Soil Properties Properties
Past Theory: Stochastic Component ε(s) – Waldo Tobler (1970) • First law of geography – Everything is related to everything else, but near things are more related than distant things – Matheron (1971) • Theory of regionalized variables – Webster and Cuanalo (1975) • clay, silt, pH, CaCO3, colour value, and stoniness on transect – Burgess and Webster (1980 ab) • Soil Property maps by kriging • Universal kriging (drift) of EC
Source: Oliver, 1989 Past Models: Stochastic Component ε(s) – Universal Model of Variation • Matheron (1971) • Burgess and Webster (1980 ab) • Webster and Burrough (1980) • Burrough (1986) • Webster and McBratney (1987) • Oliver (1989)
Past Models: Stochastic Component ε(s) Optimal Interpolation by Kriging Irregular spatial distribution Compute semi-variance (of observed point values) at different lag distances 6 5 6 7 6 7 8 5 6 y 7 Estimate values and error x at fixed grid locations Collect point sample observations Fit Semi-variogram to lag data 6.1 5.7 5.3 5.8 7.0 6.5 6.0 5.2 7.6 7.0 6.0 5.7 7.2 7.0 6.2 5.5
Past Software: Stochastic Component ε(s) • Earlier Stand Alone • Later More Integrated – Pc-Geostat (PC-Raster) – GSTAT • Early version of GSTAT • Pebesma and Wesseling, 1998 – VESPER • Incorporated into ISRISI • Variogram estimation and • Now incorporated into R and spatial prediction with error S-Plus packages • Minasny et al., 2005 – Pebesma, 2004 • http://sydney.edu.au/agricultu • http://www.gstat.org/index.ht re/pal/software/vesper ml – GEOEASE (DOS, 1991) – ArcGIS • http://www.epa.gov/ada/csm • Geostatistical Analyst os/models/geoeas.html – SGeMS (Stanford Univ) • http://sgems.sourceforge.net/
Past Inputs: Stochastic Component ε(s) • Essentially Just x,y,z Values at Point Locations 1. Start with set of soil 2. Locate the regularly property values spaced grid nodes where irregularly distributed in predicted soil property x,y Cartesian space values are to be calculated 3. Locate the n soil 4. Compute a new value property data points for each location as the within a search window weighted average of n around the current grid neighbor elevations with cell for which a value is weights established by to be calculated the semi-variogram
Past Models: Stochastic Component ε(s) for Continuous Soil Properties Examples of Predictions of Soil Property Maps by Kriging
Continuous Soil Property Maps by Kriging • Very Early Alberta Example SEMI-VARIOGRAM FOR A-HORIZON %SAND SEMI-VARIANCE 160 – Lacombe Research Station 140 120 • Sampled soils on a 50 m grid 100 80 60 – Sand, Silt, Clay, 40 20 – pH, OC, EC, others 0 11 13 15 17 19 1 3 5 7 9 – 3 depths (0-15, 15-50, 50-100) LAG (1 LAG = 30 M) • Used custom written software – Compute variograms – Interpolate using the variograms • Only visualised as contour maps – Only got 3D drapes in 1988 – Used PC-Raster to drape – Saw strong soil-landscape pattern LACOMBE SITE: A HORIZON %SAND (1985) Source: MacMillan, 1985 unpublished
Continuous Soil Property Maps by Kriging Source: http://sydney.edu.au/agriculture/pal/software/vesper.shtml
Continuous Soil Property Maps by Kriging • Yasribi et al., 2009 – Simple ordinary kriging of soil properties (OK) • No co-kriging • No regression prediction – Relies on presence of • Sufficient point samples • Spatial structure over distances longer then the smallest sampling interval Source: Yasribi et al., 2009
Continuous Soil Property Maps by Kriging • Shi, 2009 – Comparison of pH by four different methods • a) HASM • b) Kriging • c) IWD • d) Splines Source: Yasribi et al., 2009
Developments: Stochastic Component ε(s) Predictive Maps up to 2003 • Main Developments • Main Developments – Theory – Software • Becomes better understood • From stand alone and single and accepted purpose to integrated software – Concepts • Improvements in • Regression-kriging evolves – Visualization to include a separate part for – Capacity to process large regression prediction data sets – Automated variogram fitting – Models – Ease of use • Understanding and use of universal model grows – Inputs • Developments in sampling • Directional, local variograms designs and sampling theory
Present and Recent Past Key Developments in DSM Since 2003 (2003-2012) On Digital Soil Mapping McBratney et al., 2003
Developments in DSM Since 2003 Increasing Convergence and Interplay Deterministic Stochastic Soil Classes Soil Classes Soil Soil Properties Properties Scorpan (McBratney et al., 2003) elaborates and popularizes universal model of variation
Theory: Key Developments Since 2003 • Deterministic Part • Stochastic Part – Pretty much unchanged – Same underlying theory • Still based on attempting to • Still based on theory of elucidate quantitative regionalized variables relationships between soils – But & environmental covariates • Increasing realization that – But the structural part of • Scorpan elaboration variation (non-stationary highlights importance of mean or drift) can be better the spatial component (n) modelled by a deterministic and of spatially correlated function than by purely error ε(s) spatial calculations
Concepts: Key Developments Since 2003 • Deterministic Part • Factors as predictors – Scorpan Model – Factors explicitly seen as • Explicitly recognizes soil data quantitative predictors in (s) as a potential input to prediction function predict other soil data – Soil inputs can include soil maps, point observations, even expert knowledge • Explicitly recognizes space (n) or location as a factor in predicting soil data – Space as in x,y location – Space as in context, kriging Scorpan (McBratney et al., 2003) elaborates and popularizes universal model of variation
Concepts: Key Developments Since 2003 • Stochastic Part – Emergence of Regression Kriging (RK) • Key difference to ordinary kriging is that it is no longer assumed that the mean of a variable is constant • Local variation or drift can be modelled by some deterministic function – Local regression lowers error, improves predictions – Local regression function can even be a soil map Source: Heuvelink, personal communication
Models: Key Developments Since 2003 • Deterministic Part • Deterministic Part – Improvements in Data – Improvements in Data Mining and Knowledge Mining and Knowledge Extraction Extraction • Supervised Classification • Expert Knowledge Extraction – Training data obtained – Bayesian Analysis of Evidence from both points and maps – Prototype Category Theory » Sample maps at points – Fuzzy Neural Networks – Ensemble or multiple – Tools for Manual Extraction realization models (100 x) of Fuzzy Expert Knowledge » Boosting, bagging » ArcSIE, SoLIM » Random Forests • Unsupervised classification » ANN, Regression tree – Fuzzy k-means, c-means
Models: Key Developments Since 2003 • Stochastic Part • Stochastic Part – Regression Kriging – Regression Kriging • Recognized as equivalent to • Odeh et al., 1995 universal kriging or kriging • McBratney et al., 2003 with external drift • Hengl et al., 2004, 2007, • Use of external knowledge 2003 and maps made easier • Heuvelink, 2006 – Incorporation of soft data • Hengl how to books • Made more accessible – http://spatial- through implementation in analyst.net/book/ commercial (ESRI) and – http://www.itc.nl/library open source software (R) /Papers_2003/misca/hen gl_comparison.pdf
Software: Key Developments Since 2003 • Commercial Software • Non-commercial Software – JMP (SAS) (McBratney) – Fuzzy Logic • http://www.jmp.com/ • SoLIM Zhu et al., 1996, 1997 – S-Plus, Matlab, • ArcSIE Shi, FuzME • Used by soil researchers – Bayesian Logic – See5, CUBIST, CART – Full Range of Options • Regression Trees • R – Netica (Bayesian) – http://www.r-project.org • Norsys.com/netica – Regression Kriging – Improvements – Random Forests – Regression Trees • Better visualization – GLMs • Better interfaces • GSTAT (in R)
Source: Schmidt and Andrew., 2005 Inputs: Key Developments Since 2003 • Terrain Attributes – More and better measures • Primarily contextual and related to landform position – Real advances related to • Multi-scale analysis – varying window size and grid resolution • Window-based and flow- based hill slope context • Systematic examination of relationships of properties and processes to scale Source: Smith et al., 2006
Inputs: Key Developments Since 2003 • Terrain Attributes – Multi-scale analysis • Varying window size and grid resolution • Identifies that some variables are more useful when computer over larger windows or coarser grids – Finer resolution grids not always needed or better – Drop off in predictive power of DEMs after about 30-50 m grid resolution Source: Deng et al., 2007
MrVBF: Multi-scale DEM Analysis Smooth and subsample Source: Gallant, 2012 Original: 25 m Generalised: 75 m Generalised 675 m Flatness Flatness Bottomness Bottomness Valley Bottom Valley Bottom Flatness Flatness
Multiple Resolution Landform Position MrVBF Example Outputs Broader Scale 9” DEM MRVBF for 25 m DEM Source: Gallant, 2012
Developments: Improved Measures of Landform Position • SAGA-RHSP: relative • SAGA-ABC: altitude hydrologic slope position above channel Source: C. Bulmer, unpublished Calculation based on: MacMillan, 2005 Source: C. Bulmer, unpublished
Developments: Improved Measures of Landform Position • TOPHAT – Schmidt • Slope Position – Hatfield and Hewitt (2004) (1996) Source: Schmidt & Hewitt, (2004) Source: Hatfield (1996)
Developments: Improved Measures of Landform Position - Scilands Source: Rüdiger Köthe , 2012
Measures of Relative Slope Length (L) Computed by LandMapR • Percent L Pit to Peak • Percent L Channel to Divide MEASURE OF REGIONAL CONTEXT MEASURE OF LOCAL CONTEXT Image Data Copyright the Province of British Columbia, 2003 Source: MacMillan, 2005
Measures of Relative Slope Position Computed by LandMapR • Percent Diffuse Upslope Area • Percent Z Channel to Divide SENSITIVE TO HOLLOWS & DRAWS RELATIVE TO MAIN STREAM CHANNELS Image Data Copyright the Province of British Columbia, 2003 Source: MacMillan, 2005
Developments: Improved Classification of Landform Patterns Iwahashi & Pike (2006) • Iwahashi landform underlying 1:650k soil map Terrain Classes Fine texture, Terrain Series High convexity 1 5 9 13 Fine texture, Low convexity 3 7 11 15 Coarse texture, High convexity 2 6 10 14 Coarse texture, Low convexity 4 8 12 16 steep gentle Source: Reuter, H.I. (unpublished)
Inputs: Key Developments Since 2003 • Non-Terrain Attributes – Systematic analysis of environmental covariates • Detect distances and scales over which each covariate exhibits a strong relationship with a soil or property to be predicted or just with itself – Vary window sizes and grid resolutions and compute regressions on derivatives – analyse range of variation inherent to each covariate » Functional relationships are dependent on scale Source: Park, 2004
Inputs: Key Developments Since 2003 • Non-Terrain Attributes – Systematic analysis of scale of environmental covariates • Select and use input covariates at the most appropriate scale – Explicitly recognize the hierarchical nature of environmental controls on soils – Select variables at the scales, resolutions or window sizes with the strongest predictive power for each property or class to be predicted. Source: Park, 2004
Inputs: Key Developments Since 2003 Harmonization of soil profile depth data through spline fitting Source: David Jacquier, 2010
Inputs: Key Developments Since 2003 From discrete soil classes to continuous soil properties Clearfield soil series Wapello County, Iowa Harmonization of soil profile Mukey: 411784 data through spline fitting Musym: 230C ‘Modal’ Fit mass- Estimate Fitted Spline profile preserving averages for Spline averages spline spline at at standardised specified depth depth ranges, e.g., ranges globalsoilmap Source: Sun et al., (2010) depth ranges
Source: Hempel et al., 2011 Outputs: Key Developments Since 2003 • From Classes to Properties – Non-disaggregated soil maps • Weighted averages by polygon by soil property and depth – Calling version 0.5 – Disaggregated Soil Class Maps • Estimate soil property values at every grid cell location & depth – Based on weighted likelihood value of occurrence of each of n soils times property value for that soil at that depth – Likelihood value can come from various methods Source: Sun et al, 2010
Outputs: Key Developments Since 2003 • From Classes to Properties – Disaggregated Soil Class Maps • Estimate soil property values at every grid cell location Source: Zhu et al., 1997
Recent Models Recent Examples of Predictions of Soil Class Maps
Predicting Area-Class Soil Maps Clovis Grinand, Dominique Arrouays,Bertrand Laroche, and Manuel Pascal Martin. Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context. Geoderma 143, 180-190 Source: Grinand et al., 2008
Recent Knowledge-Based Classification In Africa, Multi-scale, Hierarchical Landforms Elevation + Slope + UPA + Catena SOTER Soil and landforms ( 2 km support) (1:1 million – 1.5 million Source: Park et al, 2004
Digital Soil Mapping DEM in England & Wales Predicted using Legacy Data soil series TOPAZ TAPES-G LandMapR TRAINING DATA MODELLING OUTPUTS (NETICA) Point Data Detailed soil maps Accuracy Covariates assessment Expert knowledge Source: Mayr, 2010
Predicting Area-Class Soil Maps Using Multiple Regression Trees (100 x) Prepare a database and tables of mapping units & soil series, and covariates Select 1/n of the points systematically (n=100) Repeat n Sample soil series randomly from the multinomial times distribution of mapping unit composites Used See 5, (RuleQuest Research, 2009 Construct decision tree Predict soil series at all pixels Calculate the soil series statistics based on the n predictions for each pixel Calculate the probability for each soil series Generate soil series maps Source: Sun et al., 2010
Predicting Area-Class Soil Maps Using Multiple Regression Trees (100 x) A closer look at the junction point in the middle of 4 combined maps, (a) the original map units, and (b) the most likely soil series map and its associated probability. The length of the image is approximately 14 km. Legend (a) monr_comppct Value High : 100 Low : 7 (b) Source: Sun et al., 2010
Recent Models Recent Examples of Predictions of Continuous Soil Property Maps
Source: Hengl et al., 2004 Continuous Soil Property Maps by Kriging & RK • Hengl et al., 2004 – Comparison of topsoil thickness by four different methods • a) Point locations • b) Soil Map only • c) Ordinary Kriging • d) Plain Regression • e) Regression-kriging – Evidence supports RK
Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) 300 soil point data Assemble field data
Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Assemble covariates for the predictive model
Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Perform regression to build a predictive model Linear Model OC = f(x) + e Predictors Elevation Aspect Landsat band 6 NDVI Land-use Soil-Landscape Unit
Source: Minasny et al., 2010 Recent Example: Regression-Kriging scorpan + ε) Predict both property value and standard error over the entire area
Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Fit a variogram to the residuals
Source: Minasny et al., 2010 Recent Example: Regression-Kriging scorpan + ε) Krige the residuals
Source: Minasny et al., 2010 Recent Example: Regression-Kriging scorpan + ε) Linear Model + Residuals Add interpolated residuals to the prediction from regression Final Prediction
Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Add regression variance and kriging variance to get total variance (Std.err. of (Std. err. of + kriging)2 regression)2 (Total Variance)1/2
Recent Example: Regression-Kriging Regression C predicted for C= 100-1.2EC-5.2REF-0.6REF2-2.1EL sampled locations model C predicted for Mg C/ha Residuals all grid locations 95 85 Mean 64.0 75 Min 27.0 65 Max 87.9 55 Kriging 45 CV% 18.4 35 RMSE 9.8 25 RI (%) 19.7 15 Final C map
Source: Mayr et al., 2010 Continuous Soil Property Maps by Hybrid Bayesian Analysis
Future Trends Personal View of Likely Future DSM Development (Post 2012)
Source: Heuvelink et al., 2004 The Future: Lets Go Back and Talk About the Universal Model of Variation Again Z(s) = Z*(s) + ε(s) + ε Lots of things qualify as regression! Deterministic part of the predictive model Regression just means minimizing variance Stochastic part of the predictive model What is all this talk about optimization?
The Future: A Conceptual Framework for GSIF – A Global Soil Information Facility Collaborative and open production, assembly and sharing of covariate data (World Grids) Collaborative and open collection, Collaborative and input and sharing of open and modelling geo-registered field on an inter-active, evidence web-based server- (Open Soil Profiles) side platform Everything is accessible, transparent and repeatable Maps we can all contribute to, access, use, modify and update, continuously and transparently Source: Hengl et al., 2011
The Future: Functionality for GSIF – A Global Soil Information Facility Possibility to assess Possibility of making error and correct for use of existing it everywhere legacy soil maps (even new soil maps) Possibility of needed for soil rescuing, sharing, prediction anywhere harmonizing and archiving soil profile point data needed for soil prediction anywhere Possibility to Possibility to develop and use develop and use global models (even multi-scale and for local mapping) multi-resolution hierarchical models Source: Hengl et al., 2011
The Future: Conceptual Framework for GSIF – Open Soil Profiles Source: Hengl et al., 2011
The Future: Conceptual Framework for GSIF – World Grids Source: Hengl et al., 2011
The Future: Conceptual Framework for GSIF – World Grids Source: Hengl et al., 2011
The Future: Collaborative Global, Multi- Scale Mapping through GSIF Possibility to develop and use global models (even Possibility for combining for local mapping) Top-Down and Bottom-up mapping through weighted averaging of 2 or more sets of predictions ) Source: Hengl et al., 2011
The Future: Global, Multi-Scale Modeling of Soil Properties through GSIF Possibility to develop and use multi-scale and multi-resolution hierarchical models Possibility to develop and use global models (even for local mapping) Source: Hengl et al., 2011
The Future: Global, Multi-Scale Modeling of Soil Properties through GSIF Global Models inform and improve local mapping Source: Hengl et al., 2011
The Future: Functionality for GSIF – A Global Soil Information Facility Anyone can access and display the maps Source: Hengl et al., 2011
The Future: Functionality for GSIF – A Global Soil Information Facility With Google Earth everyone has a GIS to view free soil maps and data Slide credit: Tom Hengl, 2011 Source: Hengl et al., 2011
The Future: Collaborative Global, Multi- Scale Mapping through GSIF A Global Collaboratory! Working together we can map the world one tile at a time! The next generation of soil surveyors is everyone! Source: Hengl et al., 2011
Possibility to move from single snapshot mapping of static soil properties to continuous update and improvement of maps of both static and dynamic properties within a structured and consistent framework.

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History and Evolution of Digital (Predictive) Soil Mapping (DSM)

  • 1. History and Evolution of Digital (Predictive) Soil Mapping R. A. MacMillan LandMapper Environmental Solutions Inc.
  • 2. Outline • Unifying DSM Framework: Universal Model of Variation – Z(s) = Z*(s) + ε(s) + ε • Past: Early History of Development of DSM (pre 2003) – Theory, Concepts, Models, Software, Inputs, Developments – Examples of early methods and outputs • Key Recent Developments in DSM post 2003 – Theory, Concepts, Models, Software, Inputs, Developments – Examples of recent methods and outputs • Future Trends: How do I See DSM Developing? – Theory, Concepts, Inputs, Models, Software, Developments – From Static Maps to Dynamic Real-Time Models • Discussion and Conclusions – Constraints and pitfalls to be avoided, technical/political
  • 3. Introduction Universal Model of Soil Variation A Unifying Framework for DSM
  • 4. Source: Burrough, 1986 eq. 8.14 Universal Model of Soil Variation • A Unifying Framework for Digital Soil Mapping Z(s) = Z*(s) + ε(s) + ε Predicted soil type or Deterministic part of Stochastic part of the Pure Noise part of soil property value the predictive model predictive model the predictive model Predicted spatial part of the variation part of the variation part of the variation pattern of some soil that is predictable by that shows spatial that can’t be predicted property or class means of some structure, can be at the current scale including uncertainty statistical or heuristic modelled with a with the available of the estimate soil-landscape model variogram data and models
  • 5. Deterministic Part of Prediction Model: Z*(s) KLM Series FMN Series • Conceptual Models EOR Series DYD Series COR Series 15 – Conceptual or mental soil-40 landscape models 60 – Produce area-class maps • Statistical Models In d ivid u a l sa lin ity h a za rd ra tin g s for ea ch la ye r 10 0 x 10 0 m g rid – Scorpan – relate soils/soil La ye r w e igh ting s La nd sca pe cu rva ture 2 x properties to covariates Veg eta tion 1 x R a infa ll 2 x – Explain spatial distribution G e olo gy 1 x S oils of soils in terms of known 3 x soil forming factors as La nd su rfa ce represented by covariates To tal salin ity ha za rd ra ting S alin ity h az ard m ap
  • 6. Stochastic Part of Prediction Model: ε(s) • Geostatistical Estimation – Predict soil properties • Point or block kriging – Predict soil classes • Indicator kriging – Predict error of estimate • Correct Deterministic Part – Error in deterministic part is computed (residuals) – If structure exists in error then krige error & subtract
  • 7. Pure Noise Part of Prediction Model: ε(s) • Some Variation not Predictable – Have to be honest about this • Should quantify and report it • Deterministic Prediction – Mental and Statistical Models • Not perfect – often lack suitable covariates to predict target variable Sill • Lack covariates at finer resolution Range • Geostatistical Prediction Semi Variance – Insufficient point input data Nugget • Can’t predict at less than the d1 d2 d3 d4 Lag (distance) smallest spacing of input point data
  • 8. Past Early History of DSM Development (pre 2003) On Digital Soil Mapping McBratney et al., 2003
  • 9. Early History of Development of DSM Deterministic Stochastic Soil Classes Soil Classes Soil Soil Properties Properties
  • 10. Past Theory: Deterministic Component Z*(s) Classed Conceptual Models – Jenny (1941) • CLORPT (Note no N=space) – Simonson (1959) • Process Model of additions, removals, translocations, transformations – Ruhe (1975) • Erosional -Depositional surfaces, open/closed basins – Dalrymple et al., (1968) • Nine unit hill slope model – Milne (1936a, 1936b) • Catena concept, toposequences
  • 11. Past Concepts: Deterministic Component Z*(s) Classed Conceptual Models Soil = f (C, O, R, P, T, …) Climate Organisms Topography Parent Material Soil Time Source: Lin, 2005 Frontiers in Soil Science http://www7.nationalacademies.org/soilfrontiers/
  • 13. Past Models: Deterministic Component Z*(s) Classed Statistical Predictions • Fuzzy Inference In d ivid u a l sa lin ity h a za rd ra tin g s for ea ch la ye r – Zhu, 1997, Zhu et al., 1996 10 0 x 10 0 m g rid La ye r w e igh ting s – MacMillan et al., 2000, 2005 La nd sca pe cu rva ture 2 x • Neural Networks 1 x Veg eta tion – Zhu, 2000 R a infa ll 2 x G e olo gy • Expert Knowledge (Bayesian) 1 x S oils – Skidmore et al., 1991 3 x – Cook et al., 1996, Corner et al., 1997 La nd su rfa ce • Regression Trees – Moran and Bui, 2002, Bui and S alin ity h az ard To tal salin ity m ap ha za rd ra ting Moran, 2003 Source: Jones et al., 2000
  • 14. Past Software: Deterministic Component Z*(s) Classed Statistical Predictions • Regression Trees • Fuzzy Logic – CUBIST – SoLIM • Rulequest Research , 2000 • Zhu et al., 1996, 1997 – CART – LandMapR, FuzME • Breiman et al., 1984 – C4.5 & See5 • Bayesian Logic • Quinlin, 1992 – Prospector – JMP (SAS) • Duda et al., 1978 • http://www.jmp.com/ – Expector – R • Skidmore et al., 1991 • http://www.r-project.org/ – Netica • Norsys.com/netica
  • 15. Past Inputs: Deterministic Component Z*(s) Classed Statistical Predictions • C = Climate • R = Relief (topography) – Temp, Ppt, ET, Solar Rad – Primary Attributes • Mean, min, max, variance • Slope, aspect, curvatures • Annual, monthly, indices • Slope Position, roughness • O = Organisms – Secondary Attributes • CTI, WI, SPI, STC – Manual Maps • Land Use • P = Parent Material • Vegetation – Published geology maps – Remotely Sensed Imagery – Gamma radiometrics • Classified RS imagery – Thermal IR, RS Ratios • NDVI, EVI, other ratios • A = Age
  • 16. Past Inputs: Deterministic Component Z*(s) Classed Statistical Predictions • Common Topo Inputs Profile Curvature Plan Curvature – Profile Curvature – Plan (Contour) Curvature – Slope Gradient (& Aspect) Slope Gradient Wetness Index – CTI or Wetness Index • Sometimes, not always • Less Common Topo Inputs – Surface Roughness Pit 2 Peak Relief Divide 2 Channel – Relief within a window – Relief relative to drainage • Pit, peak, Ridge, channel, Source: MacMillan, 2005
  • 17. Past Inputs: Non-DEM Airborne Radiometrics • Radiometrics 4 Subsurface • Infer Parent Material Source: Mayr, 2005
  • 18. Past Inputs: Non-DEM Satellite Imagery Grassland Land Cover Types Alpine Land Cover Types
  • 19. Past Models: Deterministic Component Z*(s) Examples of Predictions of Soil Class Maps
  • 20. Approaches to Producing Predictive Area- Class Maps
  • 21. Knowledge-Based Classification In SoLIM Source: Zhu, SoLIM Handbook
  • 22. Knowledge-Based Classification Using Boolean Decision Tree in USA Component Soils Gilpin Pineville Laidig Guyandotte Dekalb Craigsville Meckesville Cateache Shouns Source: Thompson et al., 2010 WCSS
  • 23. Knowledge-Based Classification In LandMapR Source: Steen and Coupé, 1997 Source: MacMillan, 2005
  • 24. Knowledge-Based Classification In Utah, Knowledge-Based PURC Approach Note: Not simple slope elements but complex patterns Source: Cole and Boettinger, 2004
  • 25. Approaches to Producing Predictive Area- Class Maps
  • 26. Source: Zhou et al., 2004, JZUS Supervised Classification Using Regression Trees Note similarity of supervised rules and classes to typical soil-landform conceptual classes Note numeric estimate of likelihood of occurrence of classes
  • 27. Supervised Classification Using Bayesian Analysis of Evidence/Classification Trees Source: Zhou et al., 2004, JZUS
  • 28. Predicting Area-Class Soil Maps Using Discriminant Analysis Source: Scull et al., 2005, Ecological Modelling
  • 29. Predicting Area-Class Soil Maps Using Regression Trees Extrapolation Uncertainty of prediction Bui and Moran (2003) Geoderma 111:21-44 Source: Bui and Moran., 2003
  • 30. Supervised Classification Using Fuzzy Logic • Shi et al., 2004 Fuzzy likelihood of being a broad ridge – Used multiple cases of reference sites – Each site was used to establish fuzzy similarity of unclassified locations to reference sites – Used Fuzzy-minimum function to compute fuzzy similarity – Harden class using largest (Fuzzy- maximum) value – Considered distance to each reference site in computing Fuzzy-similarity Source: Shi et al., 2004
  • 31. Approaches to Producing Predictive Area- Class Maps
  • 32. Credit: J. Balkovič & G. Čemanová Concept of Fuzzy K-means Clustering Source: Sobocká et al., 2003
  • 33. Example of Application of Fuzzy K-means Unsupervised Classification From: Burrough et al., 2001, Landscsape Ecology Note similarity of unsupervised classes to conceptual classes
  • 34. Example of Application of Disaggregation of a Soil Map by Clustering into Components Source: Faine, 2001
  • 35. Developments: Deterministic Component Z*(s) Classed Predictive Maps in Past • Characteristics of Models • Characteristics of Models – Models largely ignored ε – Many use expert knowledge • Seldom estimate error • Data mining is the exception • Rarely correct for error • Training data seldom used – Mainly use DEM inputs – Specialty software prevails • Initially 3x3 windows • Software for DEM analysis • Slope, aspect, curvatures – SoLIM, TAPESG, TOPAZ, TOPOG, TAS, SAGA, • Maybe wetness index ESRI, ISRISI, LandMapR • Later improvements were • Software for extracting rules measures of slope position – Expector, Netica, CART, – Rarely use ancillary data See 5, Cubist, Prospector • Exceptions like Bui, Skull • Software for applying rules – Operate at single scale – ESRI, SoLIM, SIE, SAGA
  • 36. Past Models: Deterministic Component Z*(s) for Continuous Soil Properties Approaches Aimed at Predicting Continuous Soil Properties
  • 37. Past Concepts: Deterministic Component Z*(s) Continuous Soil Properties • Same Theory-Concepts • Key Papers as for Classed Maps – Moore et al., 1993 Soil = f (C, O, R, P, T, …) • Linear regression – Except theory applied to – McSweeney et al., 1994 individual soil properties – McKenzie & Austin, 1993 – Initially referred to as environmental correlation – Gessler at al, 1995 – Soil properties related to • GLMs in S-Plus • Landscape attributes – McKenzie & Ryan, 1999 • Climate variables • Regression Trees • Geology, lithology, soil pm
  • 38. Past Models: Deterministic Component Z*(s) Continuous Soil Properties • Regression Trees – McKenzie & Ryan, 1998, Odeh et al., 1994 • Fuzzy Logic-Neural Networks – Zhu, 1997 • Bayesian Expert Knowledge – Skidmore et al., 1996 – Cook et al., 1996, Corner et al., 1997 • GLMs – General Linear Models – McKenzie & Austin, 1993 – Gessler et al., 1995 Source: McKenzie and Ryan, 1998
  • 39. Past Inputs: Deterministic Component Z*(s) for Continuous Soil Properties • Similar to Classed Maps But: – Many innovations originated with continuous modelers • Increased use of non-DEM attributes – climate, radiometrics, imagery • Improved DEM derivatives – Wetness Index & CTI – Upslope means for slope, etc. – Inverted DEMs to compute » Down slope dispersal » Down slope means » New slope position data Source: McKenzie and Ryan, 1998
  • 40. Past Models: Deterministic Component Z*(s) for Continuous Soil Properties Examples of Predictions of Soil Property Maps
  • 41. Past Models: Deterministic Component Z*(s) Continuous Maps • Aandahl, 1948 (Note Date!) – Regression model • Predicted – Average Nitrogen (3-24 inch) – Total Nitrogen by depth – Total Organic Carbon by depth interval – Depth of profile to loess • Predictor (covariate) – Slope position as expressed by length of slope from shoulder – Lost in the depths of time Source: Aandahl, 1948
  • 42. Past Models: Deterministic Component Z*(s) for Continuous Soil Properties • Moore et al., 1993 – Seminal paper – Focus on topography • Small sites • Other covariates were assumed constant – Got people thinking • About quantifying environmental correlation, especially soil-topography relationships Source: Moore et al, 1993
  • 43. Source: McKenzie and Ryan, 1998 Past Models: Deterministic Component Z*(s) for Continuous Soil Properties • McKenzie & Ryan, 1998 – Regression Tree: Soil Depth
  • 44. Source: McKenzie and Ryan, 1998 Past Models: Deterministic Component Z*(s) for Continuous Soil Properties • Gessler et al., 1995 – GLMs – Largely based • Topo – CTI • Others held – Steady Source: Gessler, 2005
  • 45. Credit: Minasny & McBratney Past Models: Deterministic Component Z*(s) for Continuous Soil Properties 2.17 160.1 Regression tree Text: C Text: S,LS,L,CL,LiC 1.18 2.84 54.61 27.45 BD<1.43 BD>1.43 Clay<46.5 Clay>46.5 0.64 2.21 2.97 2.04 15.65 13.00 14.59 5.50 BD<1.42 BD>1.42 3.37 2.81 Source: Minasny and McBratney 1.83 8.90
  • 46. Developments: Deterministic Component Z*(s) Predictive Maps up to 2003 • Main Developments • Main Developments – Better DEM derivatives – Integration of single models • More and better measures of into multi-purpose software landform position or • ArcGIS, ArcSIE, ArcView context • SAGA, Whitebox, IDRISI • Some recognition of scale and resolution effects – Improved processing ability – Different window sizes • Bigger files, faster processing – Different grid resolutions – Emergence of 2 main scales – More non-DEM inputs • Hillslope elements (series) • Increased use of imagery – Quite similar across models • New surrogates for PM • Landscape patterns (domains) – Similar to associations
  • 47. Early History of Development of DSM Deterministic Stochastic Soil Classes Soil Classes Soil Soil Properties Properties
  • 48. Past Theory: Stochastic Component ε(s) – Waldo Tobler (1970) • First law of geography – Everything is related to everything else, but near things are more related than distant things – Matheron (1971) • Theory of regionalized variables – Webster and Cuanalo (1975) • clay, silt, pH, CaCO3, colour value, and stoniness on transect – Burgess and Webster (1980 ab) • Soil Property maps by kriging • Universal kriging (drift) of EC
  • 49. Source: Oliver, 1989 Past Models: Stochastic Component ε(s) – Universal Model of Variation • Matheron (1971) • Burgess and Webster (1980 ab) • Webster and Burrough (1980) • Burrough (1986) • Webster and McBratney (1987) • Oliver (1989)
  • 50. Past Models: Stochastic Component ε(s) Optimal Interpolation by Kriging Irregular spatial distribution Compute semi-variance (of observed point values) at different lag distances 6 5 6 7 6 7 8 5 6 y 7 Estimate values and error x at fixed grid locations Collect point sample observations Fit Semi-variogram to lag data 6.1 5.7 5.3 5.8 7.0 6.5 6.0 5.2 7.6 7.0 6.0 5.7 7.2 7.0 6.2 5.5
  • 51. Past Software: Stochastic Component ε(s) • Earlier Stand Alone • Later More Integrated – Pc-Geostat (PC-Raster) – GSTAT • Early version of GSTAT • Pebesma and Wesseling, 1998 – VESPER • Incorporated into ISRISI • Variogram estimation and • Now incorporated into R and spatial prediction with error S-Plus packages • Minasny et al., 2005 – Pebesma, 2004 • http://sydney.edu.au/agricultu • http://www.gstat.org/index.ht re/pal/software/vesper ml – GEOEASE (DOS, 1991) – ArcGIS • http://www.epa.gov/ada/csm • Geostatistical Analyst os/models/geoeas.html – SGeMS (Stanford Univ) • http://sgems.sourceforge.net/
  • 52. Past Inputs: Stochastic Component ε(s) • Essentially Just x,y,z Values at Point Locations 1. Start with set of soil 2. Locate the regularly property values spaced grid nodes where irregularly distributed in predicted soil property x,y Cartesian space values are to be calculated 3. Locate the n soil 4. Compute a new value property data points for each location as the within a search window weighted average of n around the current grid neighbor elevations with cell for which a value is weights established by to be calculated the semi-variogram
  • 53. Past Models: Stochastic Component ε(s) for Continuous Soil Properties Examples of Predictions of Soil Property Maps by Kriging
  • 54. Continuous Soil Property Maps by Kriging • Very Early Alberta Example SEMI-VARIOGRAM FOR A-HORIZON %SAND SEMI-VARIANCE 160 – Lacombe Research Station 140 120 • Sampled soils on a 50 m grid 100 80 60 – Sand, Silt, Clay, 40 20 – pH, OC, EC, others 0 11 13 15 17 19 1 3 5 7 9 – 3 depths (0-15, 15-50, 50-100) LAG (1 LAG = 30 M) • Used custom written software – Compute variograms – Interpolate using the variograms • Only visualised as contour maps – Only got 3D drapes in 1988 – Used PC-Raster to drape – Saw strong soil-landscape pattern LACOMBE SITE: A HORIZON %SAND (1985) Source: MacMillan, 1985 unpublished
  • 55. Continuous Soil Property Maps by Kriging Source: http://sydney.edu.au/agriculture/pal/software/vesper.shtml
  • 56. Continuous Soil Property Maps by Kriging • Yasribi et al., 2009 – Simple ordinary kriging of soil properties (OK) • No co-kriging • No regression prediction – Relies on presence of • Sufficient point samples • Spatial structure over distances longer then the smallest sampling interval Source: Yasribi et al., 2009
  • 57. Continuous Soil Property Maps by Kriging • Shi, 2009 – Comparison of pH by four different methods • a) HASM • b) Kriging • c) IWD • d) Splines Source: Yasribi et al., 2009
  • 58. Developments: Stochastic Component ε(s) Predictive Maps up to 2003 • Main Developments • Main Developments – Theory – Software • Becomes better understood • From stand alone and single and accepted purpose to integrated software – Concepts • Improvements in • Regression-kriging evolves – Visualization to include a separate part for – Capacity to process large regression prediction data sets – Automated variogram fitting – Models – Ease of use • Understanding and use of universal model grows – Inputs • Developments in sampling • Directional, local variograms designs and sampling theory
  • 59. Present and Recent Past Key Developments in DSM Since 2003 (2003-2012) On Digital Soil Mapping McBratney et al., 2003
  • 60. Developments in DSM Since 2003 Increasing Convergence and Interplay Deterministic Stochastic Soil Classes Soil Classes Soil Soil Properties Properties Scorpan (McBratney et al., 2003) elaborates and popularizes universal model of variation
  • 61. Theory: Key Developments Since 2003 • Deterministic Part • Stochastic Part – Pretty much unchanged – Same underlying theory • Still based on attempting to • Still based on theory of elucidate quantitative regionalized variables relationships between soils – But & environmental covariates • Increasing realization that – But the structural part of • Scorpan elaboration variation (non-stationary highlights importance of mean or drift) can be better the spatial component (n) modelled by a deterministic and of spatially correlated function than by purely error ε(s) spatial calculations
  • 62. Concepts: Key Developments Since 2003 • Deterministic Part • Factors as predictors – Scorpan Model – Factors explicitly seen as • Explicitly recognizes soil data quantitative predictors in (s) as a potential input to prediction function predict other soil data – Soil inputs can include soil maps, point observations, even expert knowledge • Explicitly recognizes space (n) or location as a factor in predicting soil data – Space as in x,y location – Space as in context, kriging Scorpan (McBratney et al., 2003) elaborates and popularizes universal model of variation
  • 63. Concepts: Key Developments Since 2003 • Stochastic Part – Emergence of Regression Kriging (RK) • Key difference to ordinary kriging is that it is no longer assumed that the mean of a variable is constant • Local variation or drift can be modelled by some deterministic function – Local regression lowers error, improves predictions – Local regression function can even be a soil map Source: Heuvelink, personal communication
  • 64. Models: Key Developments Since 2003 • Deterministic Part • Deterministic Part – Improvements in Data – Improvements in Data Mining and Knowledge Mining and Knowledge Extraction Extraction • Supervised Classification • Expert Knowledge Extraction – Training data obtained – Bayesian Analysis of Evidence from both points and maps – Prototype Category Theory » Sample maps at points – Fuzzy Neural Networks – Ensemble or multiple – Tools for Manual Extraction realization models (100 x) of Fuzzy Expert Knowledge » Boosting, bagging » ArcSIE, SoLIM » Random Forests • Unsupervised classification » ANN, Regression tree – Fuzzy k-means, c-means
  • 65. Models: Key Developments Since 2003 • Stochastic Part • Stochastic Part – Regression Kriging – Regression Kriging • Recognized as equivalent to • Odeh et al., 1995 universal kriging or kriging • McBratney et al., 2003 with external drift • Hengl et al., 2004, 2007, • Use of external knowledge 2003 and maps made easier • Heuvelink, 2006 – Incorporation of soft data • Hengl how to books • Made more accessible – http://spatial- through implementation in analyst.net/book/ commercial (ESRI) and – http://www.itc.nl/library open source software (R) /Papers_2003/misca/hen gl_comparison.pdf
  • 66. Software: Key Developments Since 2003 • Commercial Software • Non-commercial Software – JMP (SAS) (McBratney) – Fuzzy Logic • http://www.jmp.com/ • SoLIM Zhu et al., 1996, 1997 – S-Plus, Matlab, • ArcSIE Shi, FuzME • Used by soil researchers – Bayesian Logic – See5, CUBIST, CART – Full Range of Options • Regression Trees • R – Netica (Bayesian) – http://www.r-project.org • Norsys.com/netica – Regression Kriging – Improvements – Random Forests – Regression Trees • Better visualization – GLMs • Better interfaces • GSTAT (in R)
  • 67. Source: Schmidt and Andrew., 2005 Inputs: Key Developments Since 2003 • Terrain Attributes – More and better measures • Primarily contextual and related to landform position – Real advances related to • Multi-scale analysis – varying window size and grid resolution • Window-based and flow- based hill slope context • Systematic examination of relationships of properties and processes to scale Source: Smith et al., 2006
  • 68. Inputs: Key Developments Since 2003 • Terrain Attributes – Multi-scale analysis • Varying window size and grid resolution • Identifies that some variables are more useful when computer over larger windows or coarser grids – Finer resolution grids not always needed or better – Drop off in predictive power of DEMs after about 30-50 m grid resolution Source: Deng et al., 2007
  • 69. MrVBF: Multi-scale DEM Analysis Smooth and subsample Source: Gallant, 2012 Original: 25 m Generalised: 75 m Generalised 675 m Flatness Flatness Bottomness Bottomness Valley Bottom Valley Bottom Flatness Flatness
  • 70. Multiple Resolution Landform Position MrVBF Example Outputs Broader Scale 9” DEM MRVBF for 25 m DEM Source: Gallant, 2012
  • 71. Developments: Improved Measures of Landform Position • SAGA-RHSP: relative • SAGA-ABC: altitude hydrologic slope position above channel Source: C. Bulmer, unpublished Calculation based on: MacMillan, 2005 Source: C. Bulmer, unpublished
  • 72. Developments: Improved Measures of Landform Position • TOPHAT – Schmidt • Slope Position – Hatfield and Hewitt (2004) (1996) Source: Schmidt & Hewitt, (2004) Source: Hatfield (1996)
  • 73. Developments: Improved Measures of Landform Position - Scilands Source: Rüdiger Köthe , 2012
  • 74. Measures of Relative Slope Length (L) Computed by LandMapR • Percent L Pit to Peak • Percent L Channel to Divide MEASURE OF REGIONAL CONTEXT MEASURE OF LOCAL CONTEXT Image Data Copyright the Province of British Columbia, 2003 Source: MacMillan, 2005
  • 75. Measures of Relative Slope Position Computed by LandMapR • Percent Diffuse Upslope Area • Percent Z Channel to Divide SENSITIVE TO HOLLOWS & DRAWS RELATIVE TO MAIN STREAM CHANNELS Image Data Copyright the Province of British Columbia, 2003 Source: MacMillan, 2005
  • 76. Developments: Improved Classification of Landform Patterns Iwahashi & Pike (2006) • Iwahashi landform underlying 1:650k soil map Terrain Classes Fine texture, Terrain Series High convexity 1 5 9 13 Fine texture, Low convexity 3 7 11 15 Coarse texture, High convexity 2 6 10 14 Coarse texture, Low convexity 4 8 12 16 steep gentle Source: Reuter, H.I. (unpublished)
  • 77. Inputs: Key Developments Since 2003 • Non-Terrain Attributes – Systematic analysis of environmental covariates • Detect distances and scales over which each covariate exhibits a strong relationship with a soil or property to be predicted or just with itself – Vary window sizes and grid resolutions and compute regressions on derivatives – analyse range of variation inherent to each covariate » Functional relationships are dependent on scale Source: Park, 2004
  • 78. Inputs: Key Developments Since 2003 • Non-Terrain Attributes – Systematic analysis of scale of environmental covariates • Select and use input covariates at the most appropriate scale – Explicitly recognize the hierarchical nature of environmental controls on soils – Select variables at the scales, resolutions or window sizes with the strongest predictive power for each property or class to be predicted. Source: Park, 2004
  • 79. Inputs: Key Developments Since 2003 Harmonization of soil profile depth data through spline fitting Source: David Jacquier, 2010
  • 80. Inputs: Key Developments Since 2003 From discrete soil classes to continuous soil properties Clearfield soil series Wapello County, Iowa Harmonization of soil profile Mukey: 411784 data through spline fitting Musym: 230C ‘Modal’ Fit mass- Estimate Fitted Spline profile preserving averages for Spline averages spline spline at at standardised specified depth depth ranges, e.g., ranges globalsoilmap Source: Sun et al., (2010) depth ranges
  • 81. Source: Hempel et al., 2011 Outputs: Key Developments Since 2003 • From Classes to Properties – Non-disaggregated soil maps • Weighted averages by polygon by soil property and depth – Calling version 0.5 – Disaggregated Soil Class Maps • Estimate soil property values at every grid cell location & depth – Based on weighted likelihood value of occurrence of each of n soils times property value for that soil at that depth – Likelihood value can come from various methods Source: Sun et al, 2010
  • 82. Outputs: Key Developments Since 2003 • From Classes to Properties – Disaggregated Soil Class Maps • Estimate soil property values at every grid cell location Source: Zhu et al., 1997
  • 83. Recent Models Recent Examples of Predictions of Soil Class Maps
  • 84. Predicting Area-Class Soil Maps Clovis Grinand, Dominique Arrouays,Bertrand Laroche, and Manuel Pascal Martin. Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context. Geoderma 143, 180-190 Source: Grinand et al., 2008
  • 85. Recent Knowledge-Based Classification In Africa, Multi-scale, Hierarchical Landforms Elevation + Slope + UPA + Catena SOTER Soil and landforms ( 2 km support) (1:1 million – 1.5 million Source: Park et al, 2004
  • 86. Digital Soil Mapping DEM in England & Wales Predicted using Legacy Data soil series TOPAZ TAPES-G LandMapR TRAINING DATA MODELLING OUTPUTS (NETICA) Point Data Detailed soil maps Accuracy Covariates assessment Expert knowledge Source: Mayr, 2010
  • 87. Predicting Area-Class Soil Maps Using Multiple Regression Trees (100 x) Prepare a database and tables of mapping units & soil series, and covariates Select 1/n of the points systematically (n=100) Repeat n Sample soil series randomly from the multinomial times distribution of mapping unit composites Used See 5, (RuleQuest Research, 2009 Construct decision tree Predict soil series at all pixels Calculate the soil series statistics based on the n predictions for each pixel Calculate the probability for each soil series Generate soil series maps Source: Sun et al., 2010
  • 88. Predicting Area-Class Soil Maps Using Multiple Regression Trees (100 x) A closer look at the junction point in the middle of 4 combined maps, (a) the original map units, and (b) the most likely soil series map and its associated probability. The length of the image is approximately 14 km. Legend (a) monr_comppct Value High : 100 Low : 7 (b) Source: Sun et al., 2010
  • 89. Recent Models Recent Examples of Predictions of Continuous Soil Property Maps
  • 90. Source: Hengl et al., 2004 Continuous Soil Property Maps by Kriging & RK • Hengl et al., 2004 – Comparison of topsoil thickness by four different methods • a) Point locations • b) Soil Map only • c) Ordinary Kriging • d) Plain Regression • e) Regression-kriging – Evidence supports RK
  • 91. Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) 300 soil point data Assemble field data
  • 92. Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Assemble covariates for the predictive model
  • 93. Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Perform regression to build a predictive model Linear Model OC = f(x) + e Predictors Elevation Aspect Landsat band 6 NDVI Land-use Soil-Landscape Unit
  • 94. Source: Minasny et al., 2010 Recent Example: Regression-Kriging scorpan + ε) Predict both property value and standard error over the entire area
  • 95. Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Fit a variogram to the residuals
  • 96. Source: Minasny et al., 2010 Recent Example: Regression-Kriging scorpan + ε) Krige the residuals
  • 97. Source: Minasny et al., 2010 Recent Example: Regression-Kriging scorpan + ε) Linear Model + Residuals Add interpolated residuals to the prediction from regression Final Prediction
  • 98. Source: Minasny et al., 2010 Recent Example: Regression-Kriging (scorpan + ε) Add regression variance and kriging variance to get total variance (Std.err. of (Std. err. of + kriging)2 regression)2 (Total Variance)1/2
  • 99. Recent Example: Regression-Kriging Regression C predicted for C= 100-1.2EC-5.2REF-0.6REF2-2.1EL sampled locations model C predicted for Mg C/ha Residuals all grid locations 95 85 Mean 64.0 75 Min 27.0 65 Max 87.9 55 Kriging 45 CV% 18.4 35 RMSE 9.8 25 RI (%) 19.7 15 Final C map
  • 100. Source: Mayr et al., 2010 Continuous Soil Property Maps by Hybrid Bayesian Analysis
  • 101. Future Trends Personal View of Likely Future DSM Development (Post 2012)
  • 102. Source: Heuvelink et al., 2004 The Future: Lets Go Back and Talk About the Universal Model of Variation Again Z(s) = Z*(s) + ε(s) + ε Lots of things qualify as regression! Deterministic part of the predictive model Regression just means minimizing variance Stochastic part of the predictive model What is all this talk about optimization?
  • 103. The Future: A Conceptual Framework for GSIF – A Global Soil Information Facility Collaborative and open production, assembly and sharing of covariate data (World Grids) Collaborative and open collection, Collaborative and input and sharing of open and modelling geo-registered field on an inter-active, evidence web-based server- (Open Soil Profiles) side platform Everything is accessible, transparent and repeatable Maps we can all contribute to, access, use, modify and update, continuously and transparently Source: Hengl et al., 2011
  • 104. The Future: Functionality for GSIF – A Global Soil Information Facility Possibility to assess Possibility of making error and correct for use of existing it everywhere legacy soil maps (even new soil maps) Possibility of needed for soil rescuing, sharing, prediction anywhere harmonizing and archiving soil profile point data needed for soil prediction anywhere Possibility to Possibility to develop and use develop and use global models (even multi-scale and for local mapping) multi-resolution hierarchical models Source: Hengl et al., 2011
  • 105. The Future: Conceptual Framework for GSIF – Open Soil Profiles Source: Hengl et al., 2011
  • 106. The Future: Conceptual Framework for GSIF – World Grids Source: Hengl et al., 2011
  • 107. The Future: Conceptual Framework for GSIF – World Grids Source: Hengl et al., 2011
  • 108. The Future: Collaborative Global, Multi- Scale Mapping through GSIF Possibility to develop and use global models (even Possibility for combining for local mapping) Top-Down and Bottom-up mapping through weighted averaging of 2 or more sets of predictions ) Source: Hengl et al., 2011
  • 109. The Future: Global, Multi-Scale Modeling of Soil Properties through GSIF Possibility to develop and use multi-scale and multi-resolution hierarchical models Possibility to develop and use global models (even for local mapping) Source: Hengl et al., 2011
  • 110. The Future: Global, Multi-Scale Modeling of Soil Properties through GSIF Global Models inform and improve local mapping Source: Hengl et al., 2011
  • 111. The Future: Functionality for GSIF – A Global Soil Information Facility Anyone can access and display the maps Source: Hengl et al., 2011
  • 112. The Future: Functionality for GSIF – A Global Soil Information Facility With Google Earth everyone has a GIS to view free soil maps and data Slide credit: Tom Hengl, 2011 Source: Hengl et al., 2011
  • 113. The Future: Collaborative Global, Multi- Scale Mapping through GSIF A Global Collaboratory! Working together we can map the world one tile at a time! The next generation of soil surveyors is everyone! Source: Hengl et al., 2011
  • 114. Possibility to move from single snapshot mapping of static soil properties to continuous update and improvement of maps of both static and dynamic properties within a structured and consistent framework.