Digital soil mapping

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Digital soil mapping

  1. 1. Digital Soil Mapping: New Ideas andTechnologies to Explore Soil-LandscapeRelationshipsPhillip R. OwensSoil Geomorphologist/Pedologist - AssociateProfessor, Purdue University
  2. 2. “Evolution of Phillip Owens”
  3. 3. 1 person per 260 ha10,000 people in2,450 km2
  4. 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. 5. Timeline• Congressional Science Fellow• USDA-ARS Research Scientist – Mississippi State University• Assistant Professor – Purdue University• Associate Professor – Purdue University
  6. 6. Currently….• In-Coming Chair SSSA 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. 7. Congressional Science Fellowship Sponsored by- American Association For the Advancement of Science and the Tri-SocietiesOffice of Senator Blanche L. Lincoln - Arkansas
  8. 8. Senator Lincoln• Chair of the Senate Agriculture Committee• Member - Taxation and IRS oversight and International Trade• Member of Committee on Aging
  9. 9. Issues I Worked On• 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
  10. 10. Back to Science….• Steeplands of Southern Honduras• Structural Heterogeneity verses Functional Homogeniety• Digital soil mapping tools and practices• Applications to solve problems
  11. 11. Characterization of Slumping Potential and Soilsin the Steeplands of Southern Honduras
  12. 12. Mass MovementInfrastructure Damages Domestic Hazards
  13. 13. Namasigue watershed (Honduras) Soils MapSoil Great Groups Fluventic Haplustolls / Pachic Argiustolls Typic Haplustalfs (mod. deep) Typic Haplustalfs (deep) Typic Haplustepts (mod. deep) Typic Ustiorthents (shallow) Typic Ustiorthents & Haplustepts (shallow)
  14. 14. Namasigue watershed soils map 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
  15. 15. Namasigue Watershed: Slope and Landscape PositionClassification Soil Slope Slump Landscape(Great Groups) Depth Range % PositionFluventic Haplustolls >1m 0 - 10% 0 DrainagePachic Argisutolls, deep WaysTypic Haplustalf, deep >1m 10 - 45% <10 BackslopesTypic Haplustalfs, 0.5 - 1m 45 - 60% 20-50 Backslopesmoderately deepTypic Haplustepts, 0.5 - 1m 60 - 90% 10-35 Near Summitmoderately deep Typic Ustiorthents, 0.25 - 1m >90% <10 Summit shallowTypic Ustiorthents, 0.25 - 1m 0 - 20% 0 Ridge TopsTypic Haplustepts, shallow
  16. 16. 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
  17. 17. Evolution of Research Continued• Initial research focus on geostatistics and pedometrics• Example: Potassium availability
  18. 18. Potassium variability across a drainagecatena • No-till past ~10 yr • Soils differ by drainage • No tile drainage in the field
  19. 19. Statistical procedure• Linear model 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).
  20. 20. Topographical wetness index, TWI TWI Low :High : Value sph_anis6.5 - 7 N6.0 - 65.5 - 65.0 - 5 High TWI:4.5 - 54.0 - 4 --flat areas < 4.0 --areas of convergent overland flow Related to potassium availability? PH3 labobs LegendTWI = ln(a/tan(B)) (Quinn et al 1995).
  21. 21. • Surface Exchangeable K runoff • LeachingKmg/kg 250 50 5 cm 30 cm 60 cm
  22. 22. • Differential Nonexchangeable K weathering • Fixation • FerrolysisKmg/kg 2600 800 5 cm 30 cm 60 cm
  23. 23. 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*
  24. 24. Normalized yield maps averaged from all crops(1995 to 2003), greens and blues representareas where the yield was above average,red/yellow below average, blue- highest yields,yellow- lowest yields
  25. 25. Yield Index - Corn: 1995, 1998, 2001 Yield Index - Soybeans: 1996, 1999, 2002 Yield Index - Winter Wheat: 1997, 2000, 2003
  26. 26. What I learned from 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.
  27. 27. What did I want 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.
  28. 28. ClassificationPeople – 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!
  29. 29. Thoughts on Digital Soil 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.
  30. 30. 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 • …
  31. 31. Terrain Attributes Derived From DEMSlope GradientSlope CurvatureAspectHillshadeContourAltitude Above Channel NetworkValley Bottom FlatnessTopographic Wetness Index (TWI)
  32. 32. Slope 760 Km2 Slope in Radians
  33. 33. Altitude above channel network (m) 760 Km2 Altitude above channel network Olaf Conrad 2005 methodology
  34. 34. Multi-resolution index of valley-bottom flatness 760 Km2 Valley Bottom FlattnessGallant, J.C., Dowling, T.I. (2003): A multiresolution index of valley bottom flatnessfor mapping depositional areas, Water Resources Research, 39/12:1347-1359
  35. 35. TWI: 9 760 Km2 Topographic Wetness Index
  36. 36. 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.
  37. 37. 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 Fc0 45 90 180 270 360 Meters
  38. 38. Digital Soil Mapping with 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
  39. 39. 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?
  40. 40. Shaded Relief Elevation Model, Wetness Index, 8 to 20242 to 248 meters Slope, 0 to 4% SSURGO0 0.5 1 2 Miles Brookston 0.80 Km 1.6 Km 3.2 Km Fincastle
  41. 41. Frequency distributions Terrain attribute: Terrain attribute: Altitude above Curvature channel network FrequencyFrequency Fincastle Brookston Fincastle Frequency Brookston ABCN Curvature *Data extracted with Knowledge Miner Software
  42. 42. Frequency, Wetness Index Terrain attribute: Wetness Index Fincastle BrookstonFrequency Wetness index *Data extracted with Knowledge Miner Software
  43. 43. Formalize the RelationshipExample:• 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
  44. 44. Terrain-Soil Matching for Brookston Fuzzy membership values (from 0 to 100%) 2% 100% *Information derived from Soil landscape Interface Model (SoLIM)
  45. 45. Terrain-Soil Matching for Fincastle Fuzzy membership values (from 0 to 100%) 98% 2% *Information derived from Soil landscape Interface Model (SoLIM)
  46. 46. Create Property Map with SoLIMTo 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.
  47. 47. Soil Carbon Content Estimates: 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
  48. 48. Cedar Creek Watershed (700 km2)
  49. 49. 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 0Taxonomic Soil Maps: Available Water Capacity: 125 175 225 275Structural Heterogeneity Functional Homogeneity Day TELM - Threshold-Exceedance-Lagrangian Model (Basu et al., 2009) SWAT – Soil Water Assessment Tool
  50. 50. Legend ZnC3 Zanesville silt loam, 6 to 12 percent slopes, severely erodedSymbolDubois_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 ±
  51. 51. Legend d3_ssur_par3Depth 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 > 500 0.30.6 1.2 1.8 2.4 Kilometers ±
  52. 52. Validation• Dillion Creek Watershed – 127 geo- referenced field observations• Compared SSURGO RV predictions vs. measured: Average difference = 57 cm• Compared TASM predictions vs. measured: Average difference = 22 cm
  53. 53. Location of Marcela Creek and Lavrhina CreekSource: Menezes (2011)
  54. 54. DIFFERENTPHYSIOGRAPHICAL REGIONS • MCW – Campos das Vertentes • Primarily Latosols (Oxisols) in the watershed • LCW – Serra da Mantiqueira • Primarily Cambisols (Inceptisols) •Headwater watershed
  55. 55. Method for estimating hydrologic recharge potential in twowatersheds in Brazil using knowledge-based inferencemapping
  56. 56. Validation with Conditioned Latin Hypercube Sampling Scheme – Lavrhina Creek Watershed
  57. 57. “Pros” to Digital Soil 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)
  58. 58. “Pros” to Digital Soil 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.
  59. 59. “Cons” to Digital Soil 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
  60. 60. Other projects… (Not digital soilmapping)
  61. 61. GlobalSoilMap.net
  62. 62. Saturated hydraulic conductivity (ksat , micrometers per second) from gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution with STATSGO).600 0
  63. 63. Available water capacity is 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
  64. 64. 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
  65. 65. Soil Moisture Regimes…..
  66. 66. The inputs for the GEN MOISTURE REGIME map of soil moisture regimes forthe conterminous U. S. using the java Newhall Simulation Model (jNSM)
  67. 67. The Soil Climate Regimes of theU.S. (USDA-SCS, 1994)
  68. 68. Prism Data Processed Through jNSM• Summer Water Balance• ½ Arcminute ~900 m
  69. 69. Cumulative number of days 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 forConsecutive days per year MCS is moist is dry validation of the model (~ 5000 stations) Key for map e d e f
  70. 70. Geographically Explicit Newhall Simulation Model map of soil moisture regimes made from griddedoutput from PRISM data, STATSGO2 data, and elevation data run through Newhall SimulationModel
  71. 71. 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.
  72. 72. 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.

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