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Digital soil mapping
This document provides an overview of Phillip Owens' career and research interests in digital soil mapping. It outlines his educational background in soil science and highlights some of his past roles, including working as a Congressional Science Fellow and USDA-ARS Research Scientist. It also summarizes some of his current interests and involvement in projects related to digital soil mapping, the development of soil classification systems, and using geospatial tools and data to better understand soil-landscape relationships.
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Digital soil mapping
- 1. Digital Soil Mapping:New Ideas and Technologies to Explore Soil-Landscape Relationships Phillip R. Owens Soil Geomorphologist/Pedologist - Associate Professor, Purdue University
- 2. “Evolution of PhillipOwens”
- 3. 1 person per260 ha 10,000 people in 2,450 km2
- 4. Education • B.S. -Soil Science – University of Arkansas • M.S. - Soil Science - University of Arkansas – Research on soils and septic systems • Ph.D. – Texas A&M University – Reduction-oxidation gradients along soil toposequences
- 5. Timeline • Congressional ScienceFellow • USDA-ARS Research Scientist – Mississippi State University • Assistant Professor – Purdue University • Associate Professor – Purdue University
- 6. Currently…. • In-Coming ChairSSSA Pedology Division • Associate Editor for SSSA Journal • USDA – National Soil Survey Advisory Committee • Member of GlobalSoilMap.net project • Facilitator for development of the Universal Soil Classification System
- 7. Congressional Science Fellowship Sponsored by- American Association For the Advancement of Science and the Tri-Societies Office of Senator Blanche L. Lincoln - Arkansas
- 9. Senator Lincoln • Chairof the Senate Agriculture Committee • Member - Taxation and IRS oversight and International Trade • Member of Committee on Aging
- 10. Issues I WorkedOn • Farm Bill - Passed February 8, 2002 • Bioterrorism - Agriculture and human • Agriculture Civil Rights Concerns • Biodiesel amendment to Energy Bill • ANWR • Conservation Title of the Farm Bill • Implementation of the Farm Bill • Agriculture Appropriations
- 11. Back to Science…. •Steeplands of Southern Honduras • Structural Heterogeneity verses Functional Homogeniety • Digital soil mapping tools and practices • Applications to solve problems
- 12. Characterization of SlumpingPotential and Soils in the Steeplands of Southern Honduras
- 13. Mass Movement Infrastructure Damages Domestic Hazards
- 14. Namasigue watershed (Honduras) Soils Map Soil Great Groups Fluventic Haplustolls / Pachic Argiustolls Typic Haplustalfs (mod. deep) Typic Haplustalfs (deep) Typic Haplustepts (mod. deep) Typic Ustiorthents (shallow) Typic Ustiorthents & Haplustepts (shallow)
- 15. Namasigue watershed soilsmap and landslides Soil great groups Soil great groups vs. landslides Fluventic Haplustolls / Pachic Argiustolls Typic Haplustalfs (mod. deep) Typic Haplustalfs (deep) Typic Haplustepts (mod. deep) Typic Ustiorthents (shallow) Typic Ustiorthents & Haplustepts (shallow) Landslides
- 16. Namasigue Watershed: Slopeand Landscape Position Classification Soil Slope Slump Landscape (Great Groups) Depth Range % Position Fluventic Haplustolls >1m 0 - 10% 0 Drainage Pachic Argisutolls, deep Ways Typic Haplustalf, deep >1m 10 - 45% <10 Backslopes Typic Haplustalfs, 0.5 - 1m 45 - 60% 20-50 Backslopes moderately deep Typic Haplustepts, 0.5 - 1m 60 - 90% 10-35 Near Summit moderately deep Typic Ustiorthents, 0.25 - 1m >90% <10 Summit shallow Typic Ustiorthents, 0.25 - 1m 0 - 20% 0 Ridge Tops Typic Haplustepts, shallow
- 17. Soil Factors Contributing to Slope Instability • Deep soils on 45-60% • Rapid infiltration and permeability • Moderately low water holding capacity • DEM/GIS a powerful tool to extend site- specific data to watershed landscape models • Soil attributes interactive with land use, socioeconomic pressures, & extreme storm events
- 18. Evolution of ResearchContinued • Initial research focus on geostatistics and pedometrics • Example: Potassium availability
- 19. Potassium variability acrossa drainage catena • No-till past ~10 yr • Soils differ by drainage • No tile drainage in the field
- 20. Statistical procedure • Linearmodel with correlated residuals (best linear unbiased predictors) • Comparisons made with Bonferroni correction • Spatial autocorrelation modeled with a three-dimensional anisotropic structure (lat, long, depth): − Variance: c 2 pk exp k d i, j , k k 1 − Where σ2 is the sample variance, d(i,j,k) is the absolute distance between the kth coordinate, k = 1, …, c, of the ith and jth observations in the input data set, and geometric anisotropy is corrected by applying the rotation θ to the coordinate system (SAS Institute Inc., 2003).
- 21. Topographical wetness index,TWI TWI Low : High : Value sph_anis 6.5 - 7 N 6.0 - 6 5.5 - 6 5.0 - 5 High TWI: 4.5 - 5 4.0 - 4 --flat areas < 4.0 --areas of convergent overland flow Related to potassium availability? PH3 labobs LegendTWI = ln(a/tan(B)) (Quinn et al 1995).
- 22. • Surface Exchangeable K runoff • Leaching K mg/kg 250 50 5 cm 30 cm 60 cm
- 23. • Differential Nonexchangeable K weathering • Fixation • Ferrolysis K mg/kg 2600 800 5 cm 30 cm 60 cm
- 24. Results • Exchangeable K −Related to TWI, and negative (p < 0.05) −10 cm exchangeable K related to elevation (p < 0.001) −Higher in better drained soils at all depths • Nonexchangeable K −Strongly related to TWI, and negative (p < 0.001) −Negatively related to extremes in elevation −Higher in better drained soils at all depths*
- 26. Normalized yield mapsaveraged from all crops (1995 to 2003), greens and blues represent areas where the yield was above average, red/yellow below average, blue- highest yields, yellow- lowest yields
- 27. Yield Index -Corn: 1995, 1998, 2001 Yield Index - Soybeans: 1996, 1999, 2002 Yield Index - Winter Wheat: 1997, 2000, 2003
- 28. What I learnedfrom these projects? • Topography was the major predictor for soil functional differences. • Topography controls the water which is the energy driving the system - Hydropedology. • Geostatistical approaches require much data – expensive.
- 29. What did Iwant to know? • Functional properties of soils and how to represent that function spatially. • Paradigm shift for a pedologist! • We think of structural heterogeneity rather than functional homogeneity.
- 30. Classification People – Age,sex, race, income, etc. • That is a way to describe the structure of a community, but it doesn’t describe how it functions. Soil – color, structure, texture, horizons, etc. • No real description of how the soil functions for crop growth, carbon sink, etc. • Digital mapping can do that!
- 31. Thoughts on DigitalSoil Mapping • Most of the worlds information on soils are in taxonomic class maps or as tacit knowledge with soil scientists • We need useable information now. Point data takes time and money. • Soil mapping with knowledge-based inference mapping based on fuzzy logic.
- 32. Factors of SoilFormation • 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 • …
- 33. Terrain Attributes DerivedFrom DEM Slope Gradient Slope Curvature Aspect Hillshade Contour Altitude Above Channel Network Valley Bottom Flatness Topographic Wetness Index (TWI)
- 34. Slope 760 Km2 Slope in Radians
- 35. Altitude above channelnetwork (m) 760 Km2 Altitude above channel network Olaf Conrad 2005 methodology
- 36. Multi-resolution index ofvalley-bottom flatness 760 Km2 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
- 37. TWI: 9 760 Km2 Topographic Wetness Index
- 38. Topographic Wetness Index(TWI) • TWI is a measure of the potential for water to accumulate in certain landscape positions: • Where a = the upslope area in m2, per unit contour length, contributing flow to a pixel, and b = slope angle acting on a cell measured in radians (Quinn et al., 1995); • There are 4 methods to calculate TWI, best methods are variable and site specific (Sorensen et al., 2006); • Assumption – vertical water flow is restricted.
- 39. Soil Survey –Illustrates soil taxonomic/morphologic differences Fc RuB2 Fc – Fincastle: Fine- Fc silty, mixed, superactive, mesic Aeric Epiaqualfs · Fc Kk RuA Bs – Brookston: Fine- Loamy, mixed, superactive, mesic Typic Argiaquolls Kk – Kokomo: Fc Fine, mixed, superactive, mesic, Typic Bs Argiaquolls Pa – Patton: Fine- silty, mixed, superactive, mesic, Typic Fc Ca Fc Endoaquolls Bs Ca – Carlisle muck: Euic, mesic, Typic Haplosaprist Limitations •Soil Survey has hard boundaries • Up to 0.8 Ha inclusions Fc •Created using best available Fc technology at the time Pa Fc 0 45 90 180 270 360 Meters
- 40. Digital Soil Mappingwith Knowledge-Based Inference • Define the area within a common geomorphic unit • Develop terrain attributes from a digital elevation model (terrain attributes – algorithms that describe topography) • Determine the soil-landscape relationship (any information you can find) • Determine the centroids (central concepts) to determine soil property terrain attribute relationship • Set the rules in ArcSIE – If/Then statements that applies fuzzy logic to apply soil properties
- 41. Soils in HowardCounty • 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?
- 42. Shaded Relief ElevationModel, Wetness Index, 8 to 20 242 to 248 meters Slope, 0 to 4% SSURGO 0 0.5 1 2 Miles Brookston 0.80 Km 1.6 Km 3.2 Km Fincastle
- 43. Frequency distributions Terrain attribute: Terrain attribute: Altitude above Curvature channel network Frequency Frequency Fincastle Brookston Fincastle Frequency Brookston ABCN Curvature *Data extracted with Knowledge Miner Software
- 44. Frequency, Wetness Index Terrain attribute: Wetness Index Fincastle Brookston Frequency Wetness index *Data extracted with Knowledge Miner Software
- 45. 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
- 46. Terrain-Soil Matching forBrookston Fuzzy membership values (from 0 to 100%) 2% 100% *Information derived from Soil landscape Interface Model (SoLIM)
- 47. Terrain-Soil Matching forFincastle Fuzzy membership values (from 0 to 100%) 98% 2% *Information derived from Soil landscape Interface Model (SoLIM)
- 48. Create Property Mapwith SoLIM To estimate the soil property SoLIM/SIE uses: 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.
- 49. Soil Carbon ContentEstimates: Howard County, IN " Carbon Content from Measurements CarbonEstimates Value Kg /m2 High : 7 283 points collected +/- 0.25 % Low : 0 0 1,250 2,500 5,000 7,500 10,000 Meters
- 50. Cedar Creek Watershed(700 km2)
- 51. Structural Heterogeneity vs.Functional Homogeneity at the Cedar Creek Scale Year 2003 12 10 P = 665 mm SF-BF (mm) 8 6 Data 4 TELM SWAT 2 0 Taxonomic Soil Maps: Available Water Capacity: 125 175 225 275 Structural Heterogeneity Functional Homogeneity Day TELM - Threshold-Exceedance-Lagrangian Model (Basu et al., 2009) SWAT – Soil Water Assessment Tool
- 52. Legend ZnC3 Zanesville silt loam, 6 to 12 percent slopes, severely eroded SymbolDubois_Co_Dillon_Cr_Soil_Map_Unit Map Unit Dubois_Co_Dillon_Cr_Soil_Map_Unit Symbol Map Unit Orange_Co_Dillon_Cr_Soil_Map_Units Orange_Co_Dillon_Cr_Soil_Map_Units SdvSoilM_2 MUName MUName SdvSoilM_2 Ba Bartle silt loam AciG Adyeville-Tipsaw complex, 20 to 60 percent slopes Bo Bonnie silt loam, frequently flooded AcmF Adyeville-Wellston silt loams, 18 to 50 percent slopes Bu Burnside silt loam, occasionally flooded AgrA Apalona silt loam, 0 to 2 percent slopes Cu Cuba silt loam, frequently flooded AgrB Apalona silt loam, 2 to 6 percent slopes GlD2 Gilpin silt loam, 12 to 18 percent slopes, eroded AgrC2 Apalona silt loam, 6 to 12 percent slopes, eroded GlD3 Gilpin silt loam, 12 to 18 percent slopes, severely eroded AgrC3 Apalona silt loam, 6 to 12 percent slopes, severely eroded GlE Gilpin silt loam, 18 to 25 percent slopes BbhA Bartle silt loam, 0 to 2 percent slopes GlE3 Gilpin silt loam, 18 to 25 percent slopes, severely eroded CwaAH Cuba silt loam, 0 to 2 percent slopes, frequently flooded, brief duration GoF Gilpin-Berks complex, 20 to 50 percent slopes GacAW Gatchel loam, 1 to 3 percent slopes, occasionally flooded, very brief duration GuD HcgAH Haymond silt loam, 0 to 2 percent slopes, frequently flooded, brief duration Gilpin-Orthents complex, 12 to 25 percent slopes JoA JoaA Johnsburg silt loam, 0 to 2 percent slopes Johnsburg silt loam, 0 to 2 percent slopes PeB PcrB Pekin silt loam, 2 to 6 percent slopes Pekin silt loam, 2 to 6 percent slopes, rarely flooded PeC2 PcrC2 Pekin silt loam, 6 to 12 percent slopes, eroded Pekin silt loam, 6 to 12 percent slopes, eroded, rarely flooded Pg StdAH Stendal silt loam, 0 to 2 percent slopes, frequently flooded, brief duration Peoga silt loam Sf WaaAH Wakeland silt loam, 0 to 2 percent slopes, frequently flooded, brief duration Steff silt loam, frequently flooded WhfC2 Wellston silt loam, 6 to 12 percent slopes, eroded St Stendal silt loam, frequently flooded WhfC3 Wellston silt loam, 6 to 12 percent slopes, severely eroded TlA Tilsit silt loam, 0 to 2 percent slopes WokAH Wellston-Adyeville-Ebal silt loams, 12 to 18 percent slopes, eroded TlB Tilsit silt loam, 2 to 6 percent slopes WpmD3 Wellston-Ebal-Adyeville complex, 12 to 18 percent slopes, severely eroded W Water WppD2 Wilbur silt loam, 0 to 2 percent slopes, frequently flooded, brief duration WeC2 Wellston silt loam, 6 to 12 percent slopes, eroded WeC3 ZnC2 Wellston silt loam, 6 to 12 percent slopes, severely eroded Zanesville silt loam, 6 to 12 percent slopes, eroded Orange County ZnC3 Zanesville silt loam, 6 to 12 percent slopes, severely eroded Orange_Co_Dillon_Cr_Soil_Map_Units Orange_Co_Dillon_Cr_Soil_Map_Units MUName SdvSoilM_2 Dubois County AciG Adyeville-Tipsaw complex, 20 to 60 percent slopes AcmF Adyeville-Wellston silt loams, 18 to 50 percent slopes AgrA Apalona silt loam, 0 to 2 percent slopes AgrB Apalona silt loam, 2 to 6 percent slopes AgrC2 Apalona silt loam, 6 to 12 percent slopes, eroded AgrC3 Apalona silt loam, 6 to 12 percent slopes, severely eroded BbhA Bartle silt loam, 0 to 2 percent slopes CwaAH Cuba silt loam, 0 to 2 percent slopes, frequently flooded, brief duration GacAW Gatchel loam, 1 to 3 percent slopes, occasionally flooded, very brief duration HcgAH Haymond silt loam, 0 to 2 percent slopes, frequently flooded, brief duration JoaA Johnsburg silt loam, 0 to 2 percent slopes PcrB Pekin silt loam, 2 to 6 percent slopes PcrC2 Pekin silt loam, 6 to 12 percent slopes, eroded StdAH Stendal silt loam, 0 to 2 percent slopes, frequently flooded, brief duration WaaAH Wakeland silt loam, 0 to 2 percent slopes, frequently flooded, brief duration WhfC2 Wellston silt loam, 6 to 12 percent slopes, eroded WhfC3 Wellston silt loam, 6 to 12 percent slopes, severely eroded WokAH Wellston-Adyeville-Ebal silt loams, 12 to 18 percent slopes, eroded WpmD3 Wellston-Ebal-Adyeville complex, 12 to 18 percent slopes, severely eroded WppD2 Wilbur silt loam, 0 to 2 percent slopes, frequently flooded, brief duration 0 0.30.6 1.2 1.8 2.4 Kilometers ±
- 53. Legend d3_ssur_par3 Depth of Soil (cm) Valueto Lithic/paralithic (cm) Depth High : 190.873 Low : 20 Value Tilsit_Bedford_Apallona_Johbsburg 0-2 Tilsit_Bedford_Apallona 2-6 Zanesville_Apallona_Wellston 6-12 Gilpin_Wellstone_Adyeville_Ebal 12-18 Gilpin_Ebal_Berks 18-50 Pekin_Bartle 2-12 Cuba 0-2 Steff_Stendal_Burnside_Wakeland 0-2 Rock Outcrop_Steep Slope > 50 0 0.30.6 1.2 1.8 2.4 Kilometers ±
- 54. Validation • Dillion CreekWatershed – 127 geo- referenced field observations • Compared SSURGO RV predictions vs. measured: Average difference = 57 cm • Compared TASM predictions vs. measured: Average difference = 22 cm
- 55. Location of MarcelaCreek and Lavrhina Creek Source: Menezes (2011)
- 56. DIFFERENT PHYSIOGRAPHICAL REGIONS •MCW – Campos das Vertentes • Primarily Latosols (Oxisols) in the watershed • LCW – Serra da Mantiqueira • Primarily Cambisols (Inceptisols) •Headwater watershed
- 57. Method for estimatinghydrologic recharge potential in two watersheds in Brazil using knowledge-based inference mapping
- 58. Validation with ConditionedLatin Hypercube Sampling Scheme – Lavrhina Creek Watershed
- 59. “Pros” to DigitalSoil Mapping • Very consistent product due to the way it is created. • The soil landscape model is explicit. Updates can be completed more efficiently over large areas. • The variability or inclusions can be represented (in some cases)
- 60. “Pros” to DigitalSoil Mapping • End users in the non traditional areas can more easily use some products. • We can use this information to make predictions of soil properties including dynamic soil properties.
- 61. “Cons” to DigitalSoil Mapping • In some locations, the soil-landscape relationship is difficult to determine and represent. Examples are areas with heterogeneous parent materials. • Can be misused (pretty maps not equal to good maps) • Complications with data can stop a project. • Learning new softwares can be very frustrating
- 62. Other projects… (Notdigital soil mapping)
- 66.
- 67. Saturated hydraulic conductivity(ksat , micrometers per second) from gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution with STATSGO). 600 0
- 68. Available water capacityis a measure of how much water the soil can hold and make available to plants. Intuitively, it is the difference between the moisture content at field capacity and the moisture content at the permanent wilting point, which are represented in laboratory measurements as the water contents at 33 kPa and 1,500 kPa, respectively. From gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution). (SSURGO + STATSGO2) 135 0
- 69. Soil carbon content(from soil organic matter content). The carbon content is computed from the organic matter content, accounting for the bulk density, volume of rocks, and a conversion factor (0.58) for the mass of carbon per unit mass of organic matter. From gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution). (SSURGO + STATSGO2) 1194 g C m-2 0
- 70.
- 71. The inputs forthe GEN MOISTURE REGIME map of soil moisture regimes for the conterminous U. S. using the java Newhall Simulation Model (jNSM)
- 72. The Soil ClimateRegimes of the U.S. (USDA-SCS, 1994)
- 73. Prism Data ProcessedThrough jNSM • Summer Water Balance • ½ Arcminute ~900 m
- 74. Cumulative number ofdays per year Cumulative number of days per year Number of days per year MCS is partly MCS is partly moist and partly dry the moisture control section is dry moist partly dry (no matter what temperature Key for maps a - c and above 5 C a b c Consecutive days in the summer MCS Weather station locations used for Consecutive days per year MCS is moist is dry validation of the model (~ 5000 stations) Key for map e d e f
- 75. Geographically Explicit NewhallSimulation Model map of soil moisture regimes made from gridded output from PRISM data, STATSGO2 data, and elevation data run through Newhall Simulation Model
- 76. Projects at CIAT •Linking CIAT to global initiatives, like the GlobalSoilMap.net project in Latin America. • Capacity building of CIAT staff on taxonomic systems for soil classification and digital soil mapping. • Research and capacity-building interactions between CIAT and CORPOICA through CIAT’s agreement with the Colombian Ministry of Agriculture.
- 77. Projects at CIAT •Prepare concept notes and proposals for research projects to be jointly executed by Purdue and CIAT. • Strengthen CIAT’s partnership with Purdue University.