Hands on Digital Soil Mapping 2018

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Sections: Overview | Who is it for? | Software installation | Programme | Materials

Overview

This course introduces methods and software for management, analysis and mapping of soil variables within the R environment for statistical computing. The course alternates between lectures and computer practicals and covers a variety of subjects, such as geostatistics, linear regression and machine learning for soil mapping, quantification of uncertainty and soil map validation. The course aims at soil geographers and environmental scientists who want to learn more about the theory and practice of digital soil mapping. After this course participants will be able to apply the methods learnt to their own datasets. Lecturers are experienced pedometricians and soil data analysis specialists.

Gerard Heuvelink, course coordinator (geostatistics for soil mapping, uncertainty assessment)
Bas Kempen (introduction to R, data preparation, machine learning for soil mapping, validation of soil maps)
Eloi Ribeiro (soil databases)
Madlene Nussbaum, guest lecturer (machine learning and data mining)
Titia Mulder, guest lecturer (soil spectroscopy)

Who is it for?

This course is intended for soil and environmental professionals, researchers and students interested in producing soil maps and/or using local, regional and global soil datasets for digital soil mapping. Participants must have a basic level of statistics, geo-information science and soil/environmental science. Experience with computer programming in R is advantageous but not required. A number of recorded sessions from a previous Spring School can be accessed via the ISRIC YouTube channel at: http://youtube.com/c/ISRICorg.

Software installation

Please note that to participate in the DSM course you have to bring your own laptop computer, with R and RStudio software installed. Also, do not forget a power plug (travel) adapter if you need one. In the Netherlands the power sockets are of type C and F. We will not be able to provide you with these.

Software installation instructions can be found here, and the associated R code to test your installation here. Please read and follow these carefully to ensure you come fully prepared to the spring school.

Programme*

*Working programme subject to changes.

DAY 0 (OPTIONAL, Friday, 25 May 2018):

Block

Topic

Room/ type

Lecturer

9.00 – 17.30

Introduction to the statistical software R

download ithir package

Lumen 1&2

guided self-study

Bas Kempen

DAY 1 (Monday, 28 May 2018):

Time	Topic	Room/ type	Lecturer
8.30 – 9.00	Registration and Coffee	Hall Gaia building
9.00 – 9.45	Official opening of the ISRIC Spring School Group photo	Gaia 1	Rik van den Bosch (ISRIC director), Bas Kempen
9.45 – 10.15	Course introduction and overview	Lumen 1&2	Gerard Heuvelink
10.15 – 10.45	Geostatistics for soil mapping	Lumen 1&2 lecture	Gerard Heuvelink
10.45 – 11.15	Coffee break
11.15 – 12.30	Geostatistics for soil mapping	Lumen 1&2 lecture	Gerard Heuvelink
12.30 – 13.30	Lunch	GAIA first floor
13.30 – 15.00	Geostatistics for soil mapping	Lumen 1&2 computer practical	Gerard Heuvelink Titia Mulder
15.00 – 15.30	Coffee break
15.30 – 17.30	Geostatistics for soil mapping	Lumen 1&2 computer practical	Gerard Heuvelink Titia Mulder

DAY 2 (Tuesday, 29 May 2018):

Block	Topic	Room/ type	Lecturer
9.00 – 10.00	Data preparation for DSM	Lumen 1&2 lecture	Bas Kempen
10.00 – 10.30	Data preparation for DSM	Lumen 1&2 computer practical	Bas Kempen Titia Mulder
10.30 – 11.00	Coffee break
11.00 – 12.30	Data preparation for DSM	Lumen 1&2 computer practical	Bas Kempen Titia Mulder
12.30 – 13.30	Lunch
13.30 – 15.00	Accessing the WOSIS database for soil profile data	Lumen 1&2 lecture and exercise	Eloi Ribeiro (Bas Kempen)
15.00 – 15.30	Coffee break
15.30 – 17.30	Remote and proximal sensing for natural resource inventories	Lumen 1&2 lecture and exercise	Titia Mulder (Bas Kempen)
19.00 – 22.00	Dinner in town (Colours World Food restaurant)

DAY 3 (Wednesday, 30 May 2018):

Block	Topic	Room/ type	Lecturer
9.00 – 10.30	Machine learning 1: Introduction to Random Forest Modelling	Lumen 1&2 lecture	Bas Kempen
10.30 – 11.00	Coffee break
11.00 – 12.30	Machine learning 1: Introduction to Random Forest Modelling	Lumen 1&2 computer practical	Bas Kempen Gerard Heuvelink
12.30 – 13.30	Lunch
13.30 – 15.00	Uncertainty quantification and propagation	Lumen 1&2 lecture	Gerard Heuvelink
15:00 – 15:30	Coffee Break
15.30 – 17:30	Uncertainty quantification and propagation	Lumen 1&2 computer practical	Gerard Heuvelink, Bas Kempen

DAY 4 (Thursday, 31 May 2018):

Block	Topic	Room/ type	Lecturer
9.00 – 10.30	Machine learning 2: Understanding methods, model selection and interpretation	Lumen 1&2 lecture	Madlene Nussbaum
10.30 – 11.00	Coffee break
11.00 – 12.30	Machine learning 2: Understanding methods, model selection and interpretation	Lumen 1&2 computer practical	Madlene Nussbaum Bas Kempen
12.30 – 13.30	Lunch
13.30 – 15.00	Validation of digital soil maps	Lumen 1&2 lecture	Bas Kempen
15.00 – 15.30	Coffee break
15.30 – 17.30	Validation of digital soil maps	Lumen 1&2 computer practical	Bas Kempen Gerard Heuvelink

DAY 5 (Friday, 1 June 2018):

Block	Topic	Room/ type	Lecturer
9.00 – 10.30	Visit to World Soil Museum and assignment	WSM	Stephan Mantel
10.30 – 11.00	Coffee break
11.00 – 12.30	Interactive Workshop opportunity for participants to present their work and receive feedback	Lumen 1+2	Gerard Heuvelink
12.30 – 13.30	Lunch
13.30 – 14.30	Course evaluation, certificates	Lumen 1+2	Gerard Heuvelink
14.30 – 15.30	Measuring the value of the soil resource	Lumen 1+2 guest lecture	David Rossiter
15.30 – 17.00	Closing words and ISRIC borrel (drinks and snacks)	Lumen 1+2	Rik van den Bosch

Materials

Contact:

ISRIC Spring School