SOC Mapping with RK: data preparation andmethod selection
Soil Organic Carbon Seminar
Guillermo Federico Olmedo
Pillar 4 ChairSouth America Soil Partnership
23 November 2016, FAO HQ, Malaysia Room, Rome, Italy
SOC
Outline
1 Conceptual basis
2 Software
3 Data preparationSoil profile data setsCovariate mapping
4 Method selection
5 Uncertainties
6 More information . . .
SOC
Outline
1 Conceptual basis
2 Software
3 Data preparationSoil profile data setsCovariate mapping
4 Method selection
5 Uncertainties
6 More information . . .
SOC
Outline
1 Conceptual basis
2 Software
3 Data preparationSoil profile data setsCovariate mapping
4 Method selection
5 Uncertainties
6 More information . . .
SOC
Outline
1 Conceptual basis
2 Software
3 Data preparationSoil profile data setsCovariate mapping
4 Method selection
5 Uncertainties
6 More information . . .
SOC
Outline
1 Conceptual basis
2 Software
3 Data preparationSoil profile data setsCovariate mapping
4 Method selection
5 Uncertainties
6 More information . . .
SOC
Outline
1 Conceptual basis
2 Software
3 Data preparationSoil profile data setsCovariate mapping
4 Method selection
5 Uncertainties
6 More information . . .
SOC
Conceptual basis
Digital Soil Mapping: Origin
"Soils being a result of a verycomplicated interaction betweenlocal climate, plant and animal
organisms, content and structure ofparent rocks, topography, and,
finally, age of theterrain"(Dokuchaev, 1883)
SOC
Conceptual basis
The quantitative-digital model 2
Sc,p = f (s, c,o, r ,p,a,n) + ε (1)
2[McBratney et al., 2003]
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Software
Why R?
Easy to develop new methodsNot just a statistics package, it’s a (high level) language.Designed to operate the way that problems are thoughtabout.State-of-the-art statistical techniquesState-of-the-art graphic capabilitiesUnderstands spatial (raster, vectorial) dataFree and open source softwareHigly extensible
SOC
Data preparation
Soil profile data sets
Depth modelling 4
To convert soil profile datainto standard depths.Point data: standarddepths used for spatialinterpolation
4[Bishop et al., 1999]
SOC
Data preparation
Soil profile data sets
Depth modelling 4
To convert soil profile datainto standard depths.Point data: standarddepths used for spatialinterpolation
4[Bishop et al., 1999]
SOC
Data preparation
Soil profile data sets
Equal-area spline function5
Consists of a series oflocal quadratic polynomialsthat join at ’knots’ locatedat the horizon boundariesArea to the left of the fittedspline curve is equal to thearea to the right of thecurveMean value of eachhorizon is maintained bythe spline fit
5[Bishop et al., 1999]
SOC
Data preparation
Soil profile data sets
Equal-area spline function5
Consists of a series oflocal quadratic polynomialsthat join at ’knots’ locatedat the horizon boundariesArea to the left of the fittedspline curve is equal to thearea to the right of thecurveMean value of eachhorizon is maintained bythe spline fit
5[Bishop et al., 1999]
SOC
Data preparation
Soil profile data sets
Equal-area spline function5
Consists of a series oflocal quadratic polynomialsthat join at ’knots’ locatedat the horizon boundariesArea to the left of the fittedspline curve is equal to thearea to the right of thecurveMean value of eachhorizon is maintained bythe spline fit
5[Bishop et al., 1999]
SOC
Method selection
Assumptions for regression analysis
The sample is representative of the population for theinference prediction.The error is a random variable with a mean of zeroconditional on the explanatory variables.The independent variables are measured with no error.The independent variables (predictors) are linearlyindependent.The errors are uncorrelated.The variance of the error is constant across observations(homoscedasticity).
SOC
Method selection
Assumptions for regression analysis
The sample is representative of the population for theinference prediction.The error is a random variable with a mean of zeroconditional on the explanatory variables.The independent variables are measured with no error.The independent variables (predictors) are linearlyindependent.The errors are uncorrelated.The variance of the error is constant across observations(homoscedasticity).
SOC
Method selection
Assumptions for regression analysis
The sample is representative of the population for theinference prediction.The error is a random variable with a mean of zeroconditional on the explanatory variables.The independent variables are measured with no error.The independent variables (predictors) are linearlyindependent.The errors are uncorrelated.The variance of the error is constant across observations(homoscedasticity).
SOC
Method selection
Assumptions for regression analysis
The sample is representative of the population for theinference prediction.The error is a random variable with a mean of zeroconditional on the explanatory variables.The independent variables are measured with no error.The independent variables (predictors) are linearlyindependent.The errors are uncorrelated.The variance of the error is constant across observations(homoscedasticity).
SOC
Method selection
Assumptions for regression analysis
The sample is representative of the population for theinference prediction.The error is a random variable with a mean of zeroconditional on the explanatory variables.The independent variables are measured with no error.The independent variables (predictors) are linearlyindependent.The errors are uncorrelated.The variance of the error is constant across observations(homoscedasticity).
SOC
Method selection
Assumptions for regression analysis
The sample is representative of the population for theinference prediction.The error is a random variable with a mean of zeroconditional on the explanatory variables.The independent variables are measured with no error.The independent variables (predictors) are linearlyindependent.The errors are uncorrelated.The variance of the error is constant across observations(homoscedasticity).
SOC
Uncertainties
Model uncertainty
Kriging variance of prediction only speaks about modeluncertaintyMap uncertainty can only be measured using anindependent data set: Independent ValidationIf we can measure te uncertainty, we can compare differentmethods
SOC
Uncertainties
Model uncertainty
Kriging variance of prediction only speaks about modeluncertaintyMap uncertainty can only be measured using anindependent data set: Independent ValidationIf we can measure te uncertainty, we can compare differentmethods
SOC
Uncertainties
Model uncertainty
Kriging variance of prediction only speaks about modeluncertaintyMap uncertainty can only be measured using anindependent data set: Independent ValidationIf we can measure te uncertainty, we can compare differentmethods
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
DSM Capacity Development Program - GSP
e-learning course (9 weeks)Objetives:
Understand the meaning and evolution of the concept ofdigital soil mapping (DSM)Understand soil mapping principlesAppreciate the differences between conventional and digitalsoil mapping approachesIdentify sources of input data for DSMEvaluate input and output data in a DSM processAppreciate the strengths and weakness of legacy data,computing and GIS in DSMProduce a soil map using DSM approachProperly document DSM products
SOC
More information . . .
Hands-on Global Soil Information Facilities (GSIF)
From 15 to 19 May 2017, ISRIC - World Soil Information -Five-day course (+1)Mapping, classification and assessment of soils for soil andenvironmental scientists, students, experts andprofessionals in natural resources management.Wageningen Campus in the Netherlands.The aim of this course is to introduce GSIF and methodsand software for the management, analysis and mappingof global and regional soil dataMore information: ask Bas Kempen!
SOC
More information . . .
Hands-on Global Soil Information Facilities (GSIF)
From 15 to 19 May 2017, ISRIC - World Soil Information -Five-day course (+1)Mapping, classification and assessment of soils for soil andenvironmental scientists, students, experts andprofessionals in natural resources management.Wageningen Campus in the Netherlands.The aim of this course is to introduce GSIF and methodsand software for the management, analysis and mappingof global and regional soil dataMore information: ask Bas Kempen!
SOC
More information . . .
Hands-on Global Soil Information Facilities (GSIF)
From 15 to 19 May 2017, ISRIC - World Soil Information -Five-day course (+1)Mapping, classification and assessment of soils for soil andenvironmental scientists, students, experts andprofessionals in natural resources management.Wageningen Campus in the Netherlands.The aim of this course is to introduce GSIF and methodsand software for the management, analysis and mappingof global and regional soil dataMore information: ask Bas Kempen!
SOC
More information . . .
Hands-on Global Soil Information Facilities (GSIF)
From 15 to 19 May 2017, ISRIC - World Soil Information -Five-day course (+1)Mapping, classification and assessment of soils for soil andenvironmental scientists, students, experts andprofessionals in natural resources management.Wageningen Campus in the Netherlands.The aim of this course is to introduce GSIF and methodsand software for the management, analysis and mappingof global and regional soil dataMore information: ask Bas Kempen!
SOC
More information . . .
Hands-on Global Soil Information Facilities (GSIF)
From 15 to 19 May 2017, ISRIC - World Soil Information -Five-day course (+1)Mapping, classification and assessment of soils for soil andenvironmental scientists, students, experts andprofessionals in natural resources management.Wageningen Campus in the Netherlands.The aim of this course is to introduce GSIF and methodsand software for the management, analysis and mappingof global and regional soil dataMore information: ask Bas Kempen!
SOC
More information . . .
References I
Beaudette, D., Roudier, P., and O’Geen, A. (2013).Algorithms for quantitative pedology: A toolkit for soilscientists.Computers & Geosciences, 52:258–268.
Bishop, T., McBratney, A., and Laslett, G. (1999).Modelling soil attribute depth functions with equal-areaquadratic smoothing splines.Geoderma, 91(1–2):27 – 45.
Hengl, T. (2009).A Practical Guide to Geostatistical Mapping, volume 13.
SOC
More information . . .
References II
Hengl, T. (2016).GSIF: Global Soil Information Facilities.R package version 0.5-3.
McBratney, A., Santos, M. M., and Minasny, B. (2003).On digital soil mapping.Geoderma, 117(1–2):3 – 52.
McBratney, A. B., Odeh, I. O., Bishop, T. F., Dunbar, M. S.,and Shatar, T. M. (2000).An overview of pedometric techniques for use in soilsurvey.Geoderma, 97(3–4):293 – 327.