Contact person: Golozubov Oleg
e-mail: omgolozubov@sfedu.ru
Digital Soil mapping (6 ECTS)
Aim of the course
The discipline “Digital Soil mapping” is based on comparatively young
branch of soil science mixed with mathematical modeling, computer science and
last achievements in geographical informational systems (GIS). According to that
the knowledge of bases of the listed above disciplines is required from students at
the entrance level. Nevertheless, complexity of studying Digital Soil mapping is
compensated by a wide range of opportunities of application of the acquired
knowledge. Soil maps in various scales and attributes must be taken into account in
almost all environmental investigations, land assessment and food security
planning.
The aim of this course is to provide students with theoretical background and
practical skills in developing of wide range of complex environmental-oriented
projects with certain soil information involved. The discipline “Digital Soil
mapping” gives a clear understanding of the subject for those involved in soil, soilecological, soil-agrochemical, landscape mapping and closely related disciplines.
Teaching
The following methods and forms of study are used in the course:
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Lectures
Individual GIS-Project development
Self-study
Use of different reference books and Internet resources
At the end of the course the students are supposed to present finished GIS-project, make an
oral presentation of it and participate in discussion. Upon the successful completion, the students
will gain credits.
Course content
Detailed Course Description
A table of the detailed course description for each day of the course follows below. There may
be some changes to the contents of the lectures during the course. Possible changes appear on
this page so please keep updated with this course plan.
MODULE 1. Introduction to Geostatistics and some Preliminaries
1 lecture
 What is geostatistics? An introduction to and the history of geostatistics.
 Basic statistics. A short overview of discrete and continuous random variables and
distributions. Mean, median, mode, variance, standard deviation, coefficient of variation
skewness and curtosis of random variables. Multivariate distributions. The covariance
and correlation between random variables.

The statistical programming environment R. Introduction to R and the geostatistical
packages.
Exploratory data analysis. Summaries of data, boxplots, histograms, empirical cumulative
distributions, qqplots, plot functions from R geostatistical packages. Transformations of data.
Exercise
2nd lecture
Interpolation methods. Linear, polynomial, inverse distance weighted, weigthed moving
average. Radial basis functions: Inverse multiquadratic, multilog, multiquadratic, generalised
multiquadratic, natural cubic spline, thin plate spline. Modified Shepard’s method,
Exercise
3rd lecture
Interpolation methods. Thiessen polygons, triangulation (with linear interpolation), nearest
neighbour interpolation, natural neighbour, kriging.
Exercise
4th lecture
Spatial processes. Definitions. Random functions. Spatial distributions. Stationarity: Strict,
second-order, and intrinsic stationarity. Ergodicity.
Spatial correlation. Covariance functions. Variograms, second-order and intrinsic stationary.
Covariance function vs. variogram.
MODULE 2. Estimation and Modeling of Variograms
1st lecture
Estimation of variograms. Sampling designs. Estimation of covariance functions and
variograms. Variogram clouds and sample variograms. Robust variograms. Presence of a drift.
Anisotropy.
Reliability. Variability of the empirical variograms and covariance functions.
Exercise Exercises 1
Exercises 2
2nd lecture
Modeling the variogram. The class of positive definite functions. Behaviour of the variogram
model. Unbounded and bounded models. Combining models (nested models). Modelling
anisotropy.
3rd lecture
Fitting variogram models. Manual fitting (fitting by eye). Automatic fitting by least squares.
Exercise
4th lecture
Validation of fitted variogram models. Statistical tests and cross-validation. Modelling in case
of unknown drift. Variogram of raw vs. residual values.
MODULE 3. Kriging
1st lecture
Kriging. Theory of kriging: ordinary, and simpel.
Exercise
2nd lecture
Examples of kriging. The effect of changing variogram, target, point and sampling intensities to
kriging.
3rd lecture
Kriging. Theory of kriging: universal, robust, block, median, lognormal.
Exercise
4th lecture
Examples of kriging. Block kriging, effect of anisotropy, irregularly spaced data, mapping
using kriging.
MODULE 4. Cokriging and Non-Linear Kriging
1st lecture
Multivariate processes. Definition of such processes.
Multivariate spatial correlation. Cross-covariance functions and cross-variograms. Estimation
of cross-covariance functions and cross-variograms. Pseudo-crossvariogram.
Exercise
2nd lecture
Cokriging. Linear model of coregionalization. The theory of cokriging. Principal components
analysis in cokrigíng.
3rd lecture
Non-linear kriging. Indicator kriging
Exercise
4th lecture
Continuing the exercises.
MODULE 5. Kriging and Model-Based Geostatistics
1st lecture
Categorical kriging. Using indicator kriging on categorical data.
Exercise
2nd lecture
Multivariate Normal Distribution.
3rd lecture
Model-based geostatistics for Gaussian fields.
Exercise
4th lecture
Continuing the exercises
MODULE 6. Spatial modeling
1st lecture
Sampling designs. Pure random sampling. Stratified random sampling. Systematic sampling.
Comparison of sampling methods. Optimal sampling for mapping. Theory of nested sampling.
Exercise
2nd lecture
Conditional simulation. Definition and use of conditional simulations. Classification of
methods. Direct conditional simulation of a continous variable: sequential simulation and
covariance matrix decomposition. Fast and exact simulation algorithm for general Gaussian
(Markov) random fields.
3rd lecture
Conditional simulation. Simulation of a categorical variable: sequential indicator simulation
and truncated Gaussian simulation.
Exercise
4th lecture
Mini project. Discussion of mini project.
MODULE 7. GIS: Large-scale digital soil mapping
1st lecture
GIS. Large-scaled agroeсological maps .
Exercise Exercises.
Data set.
Answers to exercises (to appear).
2nd lecture
GIS. Morpho-metrical relief characteristics from vector legasy maps and DEM.
Exercise Exercises.
3rd lecture
Data set.
Answers to exercises (to appear).
GIS. Factor-index approach to defining elementary soil areals.
Exercise Exercises.
4th lecture
Data set.
Answers to exercises (to appear).
GIS. Spatial data interpolation in soil studies. Uncertainties evaluation of thematic soil maps: case study.
Estimation by root-mean-square error – RMSE. Mapping uncertainties.
Exercise Exercises.
№
1.
2.
3.
4.
5.
6.
7.
8.
Data set.
Answers to exercises (to appear).
Subject
Introduction to Geostatistics
Variogram
Kriging
Multivariate Kriging
Model-based statistics
Spatial modelling
GIS and soil mapping
GIS-project presentation
Form of Lesson
Lecture, Exercises.
Lecture, Exercises.
Lecture, Exercises.
Lecture, Exercises.
Lecture, Exercises.
Lecture, Exercises.
GIS-project,
Lecture, Exercises.
Seminar
Duration
16 hrs
16 hrs
16 hrs
16 hrs
16 hrs
16 hrs
32 hrs
Date
20 hrs
Requirements
During the session students are required to
 attend class lectures;
 attend computer class practical workshops;
 Implement calculations according to Exercises data set;
 develop GIS-project according to individual variant;
 represent the main ideas of GIS-project in oral presentation at the seminar;
 be prepared to participate in final course discussion.
Exercise data set consists of various maps (raster and vector) presented in the form of
GIS-project under QGIS 2.8. There are more than 40 variants of data sets for OOPT of
Rostov region. Installation set and manual for QGIS2 available. Home works can be fulfilled
off-line.
Model exercises should be implemented under AdvancedGrapher (free soft).
Spatial statistics exercises samples written on R-language and works under SAGA-GIS (the
part of QGIS2)
Grade determination
 Class participation - 20%
 Exercise data set - 30%
 GIS-project presentation – 20%
 Participation in discussion – 20%
Reading
1. Digital soil mapping: theoretical and experimental studies. – M.: Dokuchaev Soil Science
Institute, 2012. – 350 p. Editor-in-chief: Academician A.L. Ivanov, Russian Academy of
Agricultural Sciences
2. Geo statistics and soil geography–М.: Nauka, 2007. -175 p
3. ISO 5725-1, “Accuracy (trueness and precision) of measurement methods and results –
Part 1: General principles and definitions”.
4. D.G.Rossiter. Accessing the thematic accuracy of area-class soil maps. Preprint.2001
5. Kryshhenko V.S., Golozubov O.M., Kolesov V.V. – Mathematical modeling in soil
science. Rostov, 2012.
6. Samsonova V.P. Spatial variability of soil properties. – М.: LKI, 2008. 160 p.
7. http://www.gisa.ru
8. http://gis-lab.info
9. http://www.geospatialworld.net
10. http://www.directionsmag.com
11. http://www.gislounge.com
All references are available at computer class.
Presentations of all lectures along with other graphical materials are located on GIS-server at
computer class and available via local network.