Digital Soil Mapping: Past, Present and Future

1. Digital Soil Mapping: Past, Present and Future Phillip R. Owens Associate Professor, Soil Geomorphology/Pedology

2. Digital Soil Mapping • Also called predictive soil mapping. • Computer assisted production of soils and soil properties. • Digital Soil Mapping makes extensive use of: (1) technological advances, including GPS receivers, field scanners, and remote sensing, and (2) computational advances, including geostatistical interpolation and inference algorithms, GIS, digital elevation model, and data mining

3. Digital Soil Mapping • These techniques are simply tools to apply your knowledge of soil patterns and distributions. The maps can only be as good as your understanding of the soils and landscapes • DSM - Same type of advancement to the Soil Survey as aerial photographs and stereoscopes introduced by Tom Bushnell and others early in the Survey.

4. Key Point • It is impossible to use these products and create good maps if you do not know your soil-landscape relationship.

5. Opportunities • Available soil data are increasingly numerical • Tools (GIS, Scanners, GPS,… • Soil Data Models • Increasing soil data harmonization • The spatial infrastructures are growing • DEMs: Global coverage • Remote Sensing • Web servers • Quantitative mapping methods • Geostatistics (pedometrics) • Data mining • Expert knowledge modeling

6. Models • Essential tools of science • Viewing and organizing thoughts • Conceptual Models – framework to ponder thoughts • Simplify reality • Must generate testable hypothesis to separate cause and effect • New models must be advanced before facts can be viewed differently – break ruling theories

7. Dynamic Nature of Soils • Society perceives soils as static • Pedologists deal with larger time scales – soils are dynamic • Many soil forming factors are active at a site – but only a few will be dominant • Importance of understanding soil dynamics- better predict results of management and evolution of soils

8. Types of Models • Mental and Verbal – Most pedogenic models • Mathematical – Hope for the future • Simulation – Knowledge of rate transfers

9. Energy Model(Runge, 1973) • Similar to Jenny’s model, but emphasizes intensity factors of water (for leaching) and O.M. production • S = f(o, w, t) where: • W = water available for leaching (intensity factor) • O = organic matter production (renewal factor) • T = time

10. Energy Model(Runge, 1973) • Many researchers continue to show that infiltrating water is a source of organizational pedogenic energy. • Many critics say designed for unconsolidated P.M. with prairie vegetation.

11. Factors of Soil Formation • S = (p, c, o, r, t, …) (Jenny, 1941) • Soils are determined by the influence of soil-forming factors on parent materials with time. • Parent material • Climate • Organisms • Relief • Time • …

12. Functional Factorial Model(Jenny, 1941) • Good conceptual model, but not solvable • Factors are interdependent, not independent • Most often used in research by holding for factors constant – i.e. topo-, clino-, bio-, litho-, chronosequences • Has had the most impact on pedologic research • Divide landscapes into segments along vectors of state factors for better understanding

13. Functional Factorial Model(Jenny, 1941) • Climate and organisms are active factors • Relief, parent material and time are passive factors, i.e. they are being acted on by active factors and pedogenic processes • Model has the most utility in field mapping – may be viewed as a field solution to the model • Very useful for DSM!

14. DEM Derived Terrain Attributes • These terrain attributes quantify the relief factor in Jenny’s Model • Some of the most commonly used are: • Slope; • Altitude Above Channel Network; • Valley Bottom Flatness; • Topographic Wetness Index (TWI).

15. Paradigm Shift in Pedology • S = (s, c, o, r, p, a, n, …) (McBratney, 2003) • Reformulation of Jenny 1941 • Soil variability is understood as: • Soil attributes measured at a specific point • Climate • Organisms • Relief • Parent material • Age (time) • Space • … Soils influence each other through spatial location! GIS

16. Paradigm Shift in Pedology • PCORT (Jenny, 1941) • Emphasizes soil column vertical relationships • Considers soils in relative isolation • Descriptive terms used for landscapes (e.g. “noseslope”) • SCORPAN (McBratney, 2003) • Accounts for lateral relationships and movements • Examines spatial relationships between adjacent soils • Terrain attributesused to quantify landscapes(“topographical wetness index”) • Catena – a “chain” of related soils (Milne, 1934) • Have properties that are spatially related by hydropedologicprocesses (Runge’s Model)

19. Digital Elevation Model Dillon Creek, Dubois County, Indiana Elevation m m

20. Aerial Photo draped over 3-d view

21. Altitude Above Channel Dillon Creek, Dubois County, Indiana AACH

22. Topographic Wetness Index Dillon Creek, Dubois County, Indiana TWI

23. Multi Resolution Ridge Top Flatness Dillon Creek, Dubois County, Indiana MRRTF

24. Multi Resolution Valley Bottom Flatness Dillon Creek, Dubois County, Indiana MRVBF

25. Numerical Soil-Landscape Relationships, Indiana Site

26. Hardened SoLIM Map SOLIM map

27. cm cm Dillion Creek – Dubois County Indiana Depth to Limiting Layer

28. Low relief Landscape in the Glaciated Portion of Indiana

29. Slope Slope in Radians

30. Altitude above channel network (m) Altitude above channel network Olaf Conrad 2005 methodology

31. Multi-resolution index of valley-bottom flatness Valley Bottom Flattness Gallant, J.C., Dowling, T.I. (2003): 'A multiresolution index of valley bottom flatness for mapping depositional areas', Water Resources Research, 39/12:1347-1359

32. TWI: 9 Topographic Wetness Index

33. Soils in Howard County • 5 soils cover 80% of the land on Howard County • Are there relationships between these 5 soils and terrain attributes? • Can we use those relationships to improve the survey in an update context? Provide predicted properties?

34. Shaded Relief Elevation Model, 242 to 248 meters Wetness Index, 8 to 20 SSURGO Slope, 0 to 4% Brookston Fincastle

35. Frequency distributions Terrain attribute:Altitude above channel network Terrain attribute:Curvature Frequency Frequency Brookston Fincastle Fincastle Frequency Brookston ABCN Curvature *Data extracted with Knowledge Miner Software

36. Frequency, Wetness Index Terrain attribute:Wetness Index Fincastle Brookston Frequency Wetness index *Data extracted with Knowledge Miner Software

37. Formalize the Relationship • Example: • If the TWI = 14 then assign Brookston • If TWI = 10 then assign Fincastle • Other related terrain attributes (or other spatial data with unique numbers) can be used. • That provides a membership probability to each pixel

38. Terrain-Soil Matching for Brookston Fuzzy membership values (from 0 to 100%) 2% 100% *Information derived from Soil landscape Interface Model (SoLIM)

39. Terrain-Soil Matching for Fincastle Fuzzy membership values (from 0 to 100%) 97% 5% *Information derived from Soil landscape Interface Model (SoLIM)

40. Create Property Map with SoLIM To estimate the soil property SoLIM uses: We already have Skij – the fuzzy membership value used to make the hardened soil map. So we only need to specify Dk, the representative values of the property of interest for each soil Dij: the estimated soil property value at (i, j); Skij: the fuzzy membership value for kth soil at (i, j); Dk: the representative property value for kth soil. In this case, let’s assign values to carbonate depth for Fincastle and Brookston in the east section of the county. Fincastle: 100 cm (low range of OSD) Brookston: 170 cm (high range of OSD)

41. Predicted depth to carbonates 100 to 170 cm 100 to 170 cm

42. Fuzzy vs. Crisp Soil Maps • Imagine a heap of sand… • The Heap Paradox from 4th Century BCE, more than 2,000 years ago posed a problem that can be addressed by fuzzy logic • Take away 1 sand grain. Is it still a heap? Take away 1 more and keep doing it. When is it not a heap? And what is it? Is it a pile, a mound? How many grains of sand does a mound have, a pile, a heap?

43. Heap of Sand vs. Pile of Sand How many grains of sand do you need to remove from a heap to get a pile? How many grains of sand do you need to add to make your pile of sand into a heap?

44. Fuzzy vs. Crisp Soil Maps • Fuzzy logic says that when you keep taking grains of sand away eventually you move from definitely heap, to mostly heap, partly heap, slightly heap, and not heap. • You can express heapness with values from 0 to 1, with 1 being a perfect example of a heap and 0 being nothing at all like a heap. • How can we define a heap? It is a similar question to how can we define a mapping unit. • You can set rules like a perfect heap is 2 tons or more of sand and not heap is less than ½ a ton of sand. You might also want an upper limit to where you say that after a certain amount it becomes more of a dune or mountain than a heap. You can then set a mathematical curve for expressing the decline in heapness as a function of the removal of sand grains.

45. Crisp vs. Fuzzy Soil Maps • Black is Brookston in the map below • Brown is a different soil, but similar to Brookston. • Orange is very different from Brookston and dark green is fairly different. • As we move away from Brookston in geographic space we cross a threshold and suddenly we are in a different soil. There is an abrupt conceptual change from one soil to another. • Black is Brookston in the map below • Orange is soil very different from Brookston. • Here we can express Brookston as values between 1 and 0 • A given spot might have a 0.7 Brookston membership value • As we move up in elevation that membership value may decrease to 0.5, 0.3, 0.1, and 0 when we know we won’t find Brookston

46. Brief History Of Digital Soil Mapping • 1991-1993: publications of pioneer works • 2003: Digital Soil Mapping as a body of soil science • 2004: 1st International workshop on Digital Soil Mapping. Workshops: Rio (2006), Logan (2008), Rome (2010), Sydney (2012) • 2009: GlobalSoilMap.net

47. SoLIM in the US • SoLIM “soil landscape inference model” was developed at the University of Wisconsin by A-Xing Zhu and Jim Burt (late 90’s) • Knowledge based inference model, fuzzy logic, rule based reasoning. What does that mean? • There were Soil Survey pilot projects in Wisconsin and the Smoky Mountains

48. Knowledge Documentation The Polygon-based Model Polygon Maps Soil-Landscape Model Building S <= f ( E ) Photo Interpretation Manual Delineation The Manual Mapping Process Challenges in Conducting Soil Survey (Slide from Zhu)

49. Case-Based Reasoning Data Mining Local Experts’ Expertise Artificial Neural Network Relationships between Soil and Its Environment Spatial Distribution Perceived as Cl, Pm, Og, Tp Inference (under fuzzy logic) Similarity Maps G.I.S. Overcoming the Manual Mapping Process S <= f ( E ) (Zhu., 1997, Geoderma; Zhu, 2000, Water Resources Research)

50. Valton Lamoile Elbaville Dorerton Churchtown Greenridge Urne Norden Gaphill Rockbluff Boone Elevasil Hixton Council Kickapoo Orion