Digital Soil Mapping by Ronald Vargas Rojas
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The document discusses digital soil mapping and contrasts it with conventional soil mapping, emphasizing the need for accurate, updated, and quantitative soil information for various stakeholders, including scientists, farmers, and decision-makers. It outlines technological advancements in digital soil mapping that leverage continuous models to improve spatial variability representation and data accessibility. Furthermore, the text highlights case studies and methodologies utilized in different regions, showcasing the application of digital mapping techniques to enhance soil data accuracy and utility.
Digital Soil Mapping by Ronald Vargas Rojas
- 1. Digital Soil Mapping Ronald Vargas Rojas 01 April 2012
- 2. • Conventional soil mapping • Technological developments in soil mapping • Case studies Outline
- 4. SOIL INFORMATION IS REQUIRED FOR Bioenergy production Food security Climate change adaptation and mitigation Urban expansion Further ecosystem services Water scarcity- storage Soil biodiversity
- 5. Who are/could be the users of soil information? Decision takers International organizations National governments Extensionists/Farmers Researchers/scientists Modellers from other disciplines requiring soil data Researchers working in the food production field Soil scientists working purely in new approaches Agronomists and extensionists Extensionists that use the data to take decisions and provide advise. At field level. Farmers/Agrodealers In the absence of extension services, farmers require guidance. They could be potential users of soil information. Already happening in precision farming Lecturers and students, etc.
- 6. Soil as body vs. soil as continuum • Soil as soil body (pedon). • Soil unit (mapeable) polipedon. • Discrete Model of Spatial Variation (based on polygons). (Heuvelink, 2005). • Soil is continous in the geographic space. • Continous Model of Spatial Variation (grid-pixel)
- 7. Considerations of conventional soil mapping • Soil information represented by cloropletic (polygon) maps (discrete units). • Discrete model of spatial variation • Maps are static and qualitative (subjectivity when using interpretation). • Soil classes (Typic Ustorthents), difficult to integrate with other themes or sciences (specially with modeling). • Pedology = pure science? Other sciences have made use of the Geo-information tools and methods. • Approach: qualitative or quantitative?
- 8. (V.V. Dokuchaev, Rusia,1883) • Soil as a natural body having its own genesis and history of development. • Conceptual model: the soil genesis and geographic variation of soils could be explained by a combined activity of the 5 factors. • Five soil forming factors: – 1. climate – 2. organisms – 3. parent material – 4. relief – 5. time FIVE SOIL FORMING FACTORS
- 9. SOIL FORMING FACTORS (Hans Jenny, Switzerland/USA, 1941) • Jenny equation, soil forming factors: s = F(cl, o, r, p, t) climate organisms relief parent material time • Generic relation connecting observed soil properties with independent factors that determines the process of soil formation. • A concept, no an equation to be solved
- 10. DMSV, polygon based mapping. Soil classes. Soil survey: + finding soil-landscape units (photointerpretación ). + soil profile description + soil classification + modal profiles and composition of units. Baranja, Croatia (source: Tomislav Hengl, Msc thesis, ITC) CONVENTIONAL SOIL MAPPING
- 12. SOIL-LANDSCAPES
- 13. STUDYING SOILS IN AUSTRALIA
- 14. ¿QUE HA CAMBIADO HASTA NUESTROS DIAS? DID SOIL SURVEY CHANGED ON TIME?
- 15. Available Global and Regional Soil Information
- 16. Harmonized World Soil Database for the region: based on FAO World Soil Map produced on 1978 Source: FAO, presented by E. De Pauw
- 17. Limitations of conventional soil mapping • Pedologist generate a soil-landscape model. The model and knowledge is implicit, not documented and intuitive. Difficult to re- build and reproduce. • Soil class/type cloropletic map. The name comes from a modal profile (most representative of the unit). • Assumptions, limitations and unknown accuracy. • Soils varies along the landscape but still soil. Gradual changes are represented by sharp boundaries. • Variability inside a class is considered non spatially correlated. • Qualitative: there is no quantitative expression of the spatial variability of the soil body and its properties. • Two main types of errors: commission and omission.
- 18. Limitations of conventional soil mapping • Comission errors: when a MLA is established, smaller areas are eliminated. • Omission errors: spatial variability of soil properties inside a polygon is eliminated in the GIS context.
- 19. Commission Errors Zhu, A. 2001
- 20. Omission ErrorsCHLOROPLET MAP ATTRIBUTE MAP P7 P1 P6 P2 P3 P5P4 HOR1 HOR1 HOR1 OBS P1 OBS P4 SOIL TYPE A x weight (%) x weight (%)OBS P5 SOIL TYPE B OM CONTENT IN HOR1 averaging averaging POL1 & 3 0 10 SMU + SDB SOIL TYPESPOLYGON MAP Hengl, T. 2003
- 21. Example
- 22. Example ARE THE SOIL PROPERTIES BETWEEN THESE UNITS DIFFERENT? SOTER map from the Tugen Hills in Kenya overlaid over SRTM DEM and Landsat ETM image (slide credit: Walsh, M., 2009). The boundaries of the polygons representing soil units do not show consistency considering the soil factors.
- 23. Soil Survey diminished significantly
- 24. The different type of users require different type of soil data and information. Usually, soil scientists are used to provide standard type of Soil Survey Report and Data, even if clients are not asking for it . Below some current requisites for soil information as requested by some users of FAO databases: - Soil properties datasets should be available - Data should be quantitative, nor qualitative - Data should be represented in a continuous format (raster) - Accuracy of the products should be included - Apart from global products, more detailed data and information is required. - Global and regional data should be updated - Monitoring of the data is necessary Type of data and information required
- 25. Technological Developments in Soil Mapping
- 26. An alternative approach: digital soil mapping • Based on the Continuous Model of Spatial Variation (CMSV). • Challenge: new methods that represent the spatial variability of soils at known accuracy and that are cheaper and faster. They should take advantage of the available technology. Pedometrics, Digital Soil Mapping, Predictive Soil Mapping.
- 27. PEDOLOGY vs. PEDOMETRICS McBratney et al., (2000) - Geoderma, 97 : 293-327 Qualitative Quantitative Time Pedology Pedometrics 1960
- 28. The creation and population of spatial soil information systems by numerical models inferring the spatial and temporal variations of soil types and soil properties from soil observation and knowledge and from related environmental variables (Lagachiere and McBratney, 2007). Digital Soil Mapping
- 30. Digital Soil Mapping Slide credit: Mendonca, L, 2010
- 32. Digital Soil Mapping Slide credit: McBratney, 2012
- 33. Digital Soil Mapping Slide credit: McBratney, 2012
- 34. Digital Soil Mapping Slide credit: McBratney, 2012
- 35. Many soil points: the easiest approach Slide credit: McBratney, 2012
- 36. Prediction Methods Slide credit: Bob McMillan Which soil data are available? Assign quality of soil data and coverage in the covariate space Homosoil Almost no data Detailed soil maps with legends - Extrapolation from reference areas - Spatially weighted mean Full Cover? No Yes -Spatially weighted mean -Spatial disaggregation Soil Point data scorpan kriging Detailed soil maps with legends and Soil Point data Extrapolation from reference areas: -Soil maps -Soil point data Full Cover? No Yes Soil maps: -Spatially weighted mean -Spatial disaggregation Soil data: - scorpan kriging Time Initially a legacy based approach
- 37. Digital Soil Mapping in Somalia: mapping soil classes (Vargas and Omuto, 2009)
- 38. Case in Somalia: soil data needs Assessment of land degradation Increasing rate of soil loss/gully erosion …what are the figures? Rapid colonization by unpalatable species …which areas? Guidance on crop production and land management A lot of changes/forces putting pressure on traditional land use • Frequent drought • Advice/donor focus on crop production • Loss of palatable vegetation • Changing land tenure • Socio-political upheavals • Population pressure • Reducing herd sizes Moisture & nutrient management issues What to grow and where to grow it?
- 39. Status of legacy data and challenges 1: 1, 000, 000 FAO Soil map • Available datasets have huge gaps • Few soil scientists are available/willing to venture into the field • Limited field validation for most parts of the country • Some data were available • Soil map • Climate data Strategy • Choose area(s) with high demand/less access restrictions • Develop necessary data/re-construct database • Combine tools/methodology for mapping soil • Soil profile descriptions • DSM
- 40. Annual rainfall (mm) High : 750 Low : 10 High Low Focus on high-rainfall areas
- 41. Assessment of available dataCarry out data reconstruction/generation Locate areas for new samples Reclassifying the profiles using WRB method Surface description Pit excavation Profile characterization & soil sampling Laboratory analysis - wet chemistry - Spectral reflectance A few soil profiles data were found At each location
- 42. Digital soil mapping • Soil profile classification - WRB • Input data layers – Landform – Geology – Land cover/use – Climate – Soil attributes • Digital Soil Mapping – – use parametric models : combine regression kriging and Mixed-effects =Fixed and random models – account for variations within and between soil classes – R Computer codes developed • Validation (holdout) and final adjustments Field data Available data Remote sensing Software Existing algorithms Y = a + b1x1 + … + bnxn + η + ε Fixed-effects Random-effects Residuals
- 43. Digital soil map of northwest Somalia Agreement (Kappa) = 63%
- 44. Topsoil loss assessment • Combined RUSLE and field benchmarks for validation • Use soil properties maps
- 45. Soil Mapping with the Soil Inference Model: SoLIM (Axing Xhu, 2012)
- 46. Components The similarity model - overcoming the limitations of “polygon-soil type” model Overview of SoLIM Sij (Sij 1, Sij 2, …, Sij k, …, Sij n) j i Zhu, A.X. 1997. Geoderma, Vol. 77, pp. 217- 242.
- 47. Components Inference - overcoming the limitations of manual delineation and the like Overview Inference (under fuzzy logic) Knowledge on Soil and Environment Relationships Covariates: cl, pm, og, tp, … G.I.S./R.S. Local Experts’ Expertise <= f ( E ) j i S Machine Learning Case-Based Reasoning Spatial Data Mining Zhu, A.X. 1999, IJGIS; Zhu et al., 2001, SSSAJ; Qi and Zhu, 2003, IJGIS; Shi et al., 2004, SSSAJ; Qi et al., 2008, Cartography and GIS; More at solim.geography.wisc.edu
- 48. Overview Raster Soil Type (Series) Map Applications and Assessment Products - Basic output: Fuzzy membership maps Derived products and accuracy: Raster soil type maps
- 49. Accuracy of the Raster Soil Map Sample Size = 99 Overall In Complexes In Single SoLIM Soil Map 83.8% 66.7% 89% 73% 81% 61% SoLIM Soil Map 24 4 30 30 80% 13% Mismatches Correct Total Mismatches Percentage Comparison between SoLIM and Soil Map against field data (Raffelson)
- 50. Digital Soil Mapping in Bolivia for sugarcane suitability assessment (Vargas, 2010)
- 51. CO-KRIGING AND MIXED EFFECTS MODELS 200 soil samples (points) were collected in the field for the topsoil only (30cm) and analyzed at the soil lab. Using Co-Kriging and mixed effect models, properties were predicted and suitability assesment was performed (Omuto and Vargas, 2010). ( ) ( ) ( ) ( ) ( ) 180215210 2 2 1 22110 ,...,,j,...,,i,,~e,,~ b b b eY*bDEM*bb ij i i oi i ijijiijiioijs ==ΝΝ = ++++++= σψ βββθ 0b Predictor: Landsat ETM+ band
- 53. SOIL SUITABILITY ASSESSMENT Elevación Topografía Vegetación Suelo Clima Comparación Algebra de Mapas Requerimientos del TUT Caracteristicas de la Tierra Mapa de Aptitud de Uso para el TUT: CacaoMapa de Aptitud de Uso para el TUT: Cacao
- 55. Digital Soil Mapping in Argentina: disaggregating polygons (Angelini, 2012)
- 56. 121 Soil Profiles sampled 710 Km2. Materials
- 58. Multi-linear Regression Properties to predict Environmental Covariate EC 0-30 cm ASPECT, CN_BL, LS, MRRTF, MRVBF, WTI EC 30-60 cm SLOPE, ASPECT, CN_BL, MBI pH 0-30 cm DEM, SLOPE, ASPECT, CN_BL, CI, MRRTF, WTI pH 30-60 cm ASPECT, CN_BL, LS, MRRTF Clay 0-30 cm SLOPE, ASPECT, Curv1KK, MRVBF, MBI Silt 0-30 cm SLOPE, ASPECT, Curv1KK, MRVBF, MBI Sand 0-30 cm SLOPE, ASPECT, Curv1KK, MRVBF, MBI Clay 30-60 cm CI, MRVBF, MBI Silt 30-60 cm CI, MRVBF, MBI Sand 30-60 cm CI, MRVBF, MBI Depth Prof. SLOPE, CN_BL, MRRTF, MRVBF Covariates selected by stepway Exclusion of outliers 70% of data for make models 30% of data for validation RStudio Modeling
- 59. Sampling of taxonomic soil classes Soil A: 60% Soil B: 40% A1 = Soil B Soil A Cartographic Unit Soil Map A1 • Random sampling 2/5
- 60. Sampling of taxonomic soil classes Merge environmental covariates and soil classes 2/5
- 61. Used Covariates: 100% DEM 100% Potassium_c 98% ChNBL 83% AAChN 69% TWI 41% Landsat 40% Distance 39% V_OFD 32% OFD 28% Slope 25% Environment 23% NDVI01 18% NDVI09 17% Converg_I 13% Stream_P Decision tree model 3/5
- 62. Applying model Map of A Soil Class Map of B soil Class x100 loops 3/5
- 66. Validation 5/5
- 67. Soil Mapping using Area Weighted (USDA, 2012)
- 68. Soil Maps: Weighted mean • Approach 1: Straight areal weighted mean from polygon maps Pre-processing • Approach 2: Harmonising/updating soil polygon maps using environmental covariates • Approach 3: Disaggregating soil polygons -> soil series Deriving soil properties from a soil polygon map
- 69. Soil Maps: Weighted mean Deriving soil properties from a soil polygon map • Approach 1: Areal weighted average 60% Series A 20% Series B 20% Series C Property = 0.6 A + 0.2 B + 0.2 C Property = A (value of dominant soil) OR –> simpler alternative
- 70. USDA-NRCS Approach: Deriving area weighted means of soil properties from a soil polygon ma
- 71. Soil Maps: Weighted mean Deriving continuous depth soil properties from a soil polygon map Depth spline process Soil pH profile data for the test area was extracted from ASRIS. The data contains between 1-5 depth layers of varying thickness and some layers may be missing. A spline is fitted to the data and 1cm interval layers of mean pH is returned. The mean value of pH is derived for the GSM standard depth intervals (i.e. 0-5, 5-15, 15-30, 30-60, 60-100, 100-200). The standard depth mean values are the spline parameters. Only 6 parameters and a smoothing function need to be stored to recreate the spline. The outputs of the splining process are attached to the spatial dataset. In this instance the data not attached to the original conventional vector soil map but to a 90m rasterized version.
- 72. Soil Maps: Weighted mean ASRIS Approach: Deriving continuous depth soil properties from a soil polygon map Conventional Mapping Grid/raster 90m cells Rasterization Soil profile data -variable intervals Fit spline Depth spline function Standard depth mean Standard depths Attach to grid Re-calculate spline David Jacquier et al. Implementing the GlobalSoilMap.net technical specification
- 73. ASRIS Approach: Deriving continuous depth soil properties from a soil polygon map
- 74. Soil Maps: Disaggregation NRCS – Jim Thompson Approach: Disaggregation into component soils or land facets Gilpin Pineville Laidig Guyandotte Dekalb Component Soils Craigsville Meckesville Cateache Shouns Thompson et al. 2010 Each component has a single soil property value Gilpin-Laidig Pineville-Gilpin-Guyandotte Other SSURGO Map Units
- 75. Digital Soil Mapping in Nigeria (Yemefack, 2012)
- 76. 76 FACULTY OF AGRICULTURE, FOOD & NATURAL RESOURCES Digital soil property mapping for Nigeria from legacy soil data: Working towards the first approximation Inakwu O.A. Odeh, with contributions from Hannes Reuter, Johan Leenaars, Alfred Hartemink
- 77. 77 Digital Soil Mapping For Nigeria 77N = 878 Locations of harmonised profiles for pH (1:5 soil-water)
- 78. 78 Digital Soil Mapping For Nigeria 78 Steps in DSM for Nigeria (following Rossiter, 2008): Data archeology- locating and cataloguing of soil legacy data Data capture- scanning and digitisation of analogue soil legacy data Data renewal- transformation of captured legacy data into usable digital database Acquisition and/or transformation of covariate data at fine (100m) grid & sample location ( ) eQfVd += Spline function- fit a spline function to each soil profile to estimate values at GSM standard depths Use data mining tool to interpolate a soil property at each depth onto the fine grid E.g., Digital map (100-m resolution) of a target soil attribute at a given depth predicted from 572 locations and covariates Soil profiles at sample locations
- 79. Spline functions for depth harmonization
- 80. 80 80 pH in 0-5 cm pH in 5-15 cm pH in 15-30 cm pH in 30-60 cm
- 81. 81 81 pH in 60-100 cm pH in 100-200 cm pH in 0-5 cm pH in 5-15 cm
- 82. Global Soil Information Facilities (ISRIC, 2012)
- 83. www.isric.org
- 84. Why do we need SoilProfile.org? • Objective: SoilProfile is a (de)-central repository for collecting, storing, accessing and interacting with soil profile observations • Rationale: SoilProfile is a part of a larger GSIF. It is the physical implementation of ISRICs contribution to fulfil its mandate as World data centre for soil to “Serve the scientific community as custodian of global soil information”
- 86. 2012 AfSP 3rd version : 12500 profiles Malawi Ethiopia Ivory Coast
- 87. Facilitating Soil Production - Visualisations
- 88. SC: Minasny & Malone, 2011) Facilitating Soil Production - Fit the spline
- 89. Facilitating Soil Map Production - DSM any arbitrary soil map production process - which uses auxiliary information SC:Reuter, Lennars, Inakwu 2011 www.globalsoilmap.net
- 90. Using Soil Spectroscopy for Soil Mapping (Viscarra-Rossel, 2012)