Pre-conference workshops, Monday 26 June 

These workshops are offered by researchers from around the world in the lead up to Pedometrics 2017. They cover topics ranging from those specifically aligned to Pedometrics to more general soil science, software, programming, decision making, scientific publishing and statistical topics. Workshops are open to all, also those not participating in the conference.

  • Full-day workshops (100 euro pp. including lunch)
  • Half-day workshops (60 euro pp. including lunch)
  • Two half-day workshops (100 euro pp. including lunch)

Please register for the workshop(s) you wish to attend. Morning tea and/or afternoon tea as well as lunch is included for all workshops. Please note that each workshop may have specific requirements listed (such as own laptop, pre-installed software, readings, etc) which we strongly encourage you to follow to get the most from your participation.   

Each workshop requires a minimum of 15 participants to be held. If a workshop does not proceed, participants will be offered the option to transfer to another workshop or receive a full refund. There is also a maximum number of places per workshop and a place can only be assured once payment has been made. We advise you to book early to avoid disappointment.  

 

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Note: when registering, you will be redirected to a Wageningen University & Research page in a new window to process your registration and payment. For PE&RC and WIMEK PhD candidates: to receive the PE&RC or WIMEK subsidy for workshop participation, please inform the PE & RC Office (office.pe@wur.nl ) or WIMEK office (WIMEK@wur.nl) about your participation in one or more workshops, so that we can arrange the subsidy with your group. You will still need to register and pay for the workshop(s) using this system.

 

Full-day Workshops 9:00-17:00

Morning Workshop 9:00-12:30

Afternoon Workshop 13:30-17:00

USE R! PEDOMETRICS TUTORIALS

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The statistical programming environment R has been of continuous interest for development of applications and tools implementing state-of-the-art knowledge of pedometrics. Under R umbrella already many Special Interest Groups (SIGs) exist that cover e.g. spatial data, environmental problems and similar.

This workshop aims at bringing together experienced R package developers that have, over the years, produced some frequently used tools for importing, organizing and processing soil data. We will try to educate and inform Pedometrics conference participants on possibilities of analyzing and visualizing their data using Open Source software tools, especially for the purpose of making reproducible research and for data processing automation. The workshop will also be a chance for Pedometrics conference participants to meet the original package developers and get an insight into development trends and opportunities.

Specific tutorials will cover:

➢ Soil data classes in R and conversion and harmonization of data (GSIF package)
➢ Plotting soil-depth relationships (aqp package)
➢ Soil spectroscopy packages (prospectr, inspectr, asdreader, soil.spec packages) 
➢ Using Machine Learning Algorithms for predictive modelling and soil data analytics (caret, ranger, randomForestSRC, xgboost, h2o packages)
➢ Using R for 2D, 3D and 3D+T visualization of soil maps and soil processes
➢ Using R for the production of web maps and visualisations (mapview, shiny, leaflet packages)
➢ Exporting soil data to standard formats (OGC standards and similar)

Preliminary timetable

  •   9:00–10:30 Programme overview, software installation and first steps
  • 10:30–11:00 Coffee break
  • 11:00–13:00 R tutorials: importing and organizing soil data
  • 13:00–14:00 Lunch break 
  • 14:00–15:30 R tutorials: predictive modelling using Machine Learning algorithms
  • 15:30–16:00 Coffee break
  • 16:00–17:00 R tutorials: visualizing soil data and models (webinar by Dylan Beaudette from California)

REQUIREMENTS

  • Laptop computer (preferably with Linux OS and/or Windows 7+ OS) with at least 4GB RAM and wifi
  • Preinstalled software following the installation instructions
  • Bringing your own data sets is highly recommended but not required

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lecturers

T. (Tom) Hengl is a senior researcher at ISRIC — World Soil Information with core speciality in big data analytics and automated soil mapping. Tom has backgrounds in soil mapping and geo-information science. He is currently the project leader of the Global Soil Information Facilities — a suite of software tools, web-facilities and data sets (SoilGrids, soil profiles) for automated global soil mapping. ORCID ID: http://orcid.org/0000-0002-9921-5129

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P. (Pierre) Roudier is a soil scientists at Landcare Research National Institute in New Zealand. He has over 10 years of experience with developing tools and software solutions for soil sampling, soil spectroscopy and soil data analytics. Pierre is author of multiple R package including aqp, inspectr, clhs and plotKML

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D. (Dylan) Beaudette is a soil scientist working for the NRCS on soil survey in the Sierra Foothill Region of California. He was formerly a graduate student in the Soils and Biogeochemistry group at UC Davis. Dylan leads development of the R package aqp and soildb. He is also the leading author of the SoilWeb app and web interface for accessing soil data in USA.

Soil profile image analysis: estimating soil properties from digital photography

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The workshop will bring together researchers with practical experience of soil profile characterisation, and researchers with image analysis experience. The goals of the workshop are (1) to identify what is possible in terms of using automated image analysis to characterise soil profiles, and (2) to demonstrate the potential of this research area in a practical setting. Image analysis approaches will be described and then tested on images of soil profiles, and the practicalities of field-based soil profile image analysis discussed. Future work will also be discussed and linkages formed.

A worked example session will be used to demonstrate image analysis approaches applied to sample soil profile images. Software for analysing the sample images will be developed by Matt Aitkenhead prior to the workshop, and will include a range of image analysis methods/metrics that can be applied in real-time. The worked examples will be informed by discussion during the earlier session, in which the sample images are discussed and visible features identified and described. This will allow us to demonstrate the detection/measurement of profile features from automated image analysis.

PRELIMINARY TIMETABLE

  • Introductory presentation (Matt Aitkenhead)
  • Image analysis metrics
  • What can the eye do – and how could we use this?
  • Session on naked-eye observation of soil profile images (led by Alfred Hartemink)
  • Practicalities and protocols
  • Worked example session, with sample images analysed in real-time (led by Matt Aitkenhead)
  • Potential linkages to soil survey datasets
  • Closing discussion session (Matt Aitkenhead & Alfred Hartemink)

Each presentation will be 20 minutes, with 10 minutes for questions/discussion. The ‘naked eye observation’ and ‘worked examples’ sessions will last for ~2 hours each. 

REQUIREMENTS

There are no special requirements for this workshop. Participants may bring their own laptops. 

 

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Lecturers

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Alfred Hartemink is Chair and Professor of Soil Science at the University of Wisconsin – Madison. He teaches Pedology and Introduction Soil Science. His research focusses on digital soil morphometrics, soil mapping, and soil C.

 

 

Dr Matthew Aitkenhead is a soil scientist who currently works for the James Hutton Institute. Much of his work has involved linking remote sensing and field-based information with existing maps, to produce models of soil character in a rapid and cost-effective manner. Recent work has involved linking these models to mobile phone technology, to produce apps that enable researchers and land managers to monitor the environment.  The growth of national and international legislation requiring large-scale and repeatable environmental monitoring is driving the development of rapid assessment tools for soils, and he is working heavily in this area. He is currently an Associate Editor for three journals: Remote Sensing Letters, Advances in Artificial Neural Systems and Soil Use and Management. He also leads the Scottish Regional Group of the British Society of Soil Science.

Spatial sampling for mapping

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Sound sampling design is essential for the collection of data to support reliable scientific inference and decision making for management and policy.  What counts as a sound design depends on the problem of interest, and the nature of the inference that is required.  Many statistical analyses of spatial data, particularly for the prediction of local values as in mapping, are done in a model-based context in which data are treated as realizations of an underlying random process.  In this setting it is not necessary to select sampling locations by probability sampling, and there is scope to optimize sampling patterns computationally.
In this course I shall introduce some of the concepts that underlay the optimization of spatial sampling.  These include methods to ensure good spatial coverage by a sample, experimental designs adapted for spatial surveys, model-based optimization of the grid-spacing and model-based optimization of the coordinates of sampling locations for ordinary kriging and kriging with an external drift. 

TIMETABLE

  • 9.00 - 9.30 Introduction sampling for mapping
  • 9.30 - 10.15 Spatial coverage sampling
  • 10.15 - 10.45 Break
  • 10.45 - 11.30  Fuzzy k-means sampling
  • 11.30 - 12.30  Response surface sampling and latin hypercube sampling
  • 12.30 - 13.30 Lunch
  • 13.30 - 14.30  Model-based optimization of grid-spacing
  • 14.30 - 15.00 Break
  • 15.00 - 16.00 Model-based optimization of coordinates of sampling locations
  • 16.00 - 17.00 Sampling for validation

requirements

Participants in the course will be provided with scripts for the free R platform which will allow them to use the  methods that are described to solve sampling optimization problems. Basic knowledge of R is required. 

Participants are encouraged to bring their own laptops with pre-installed R and if preferred, a GUI for R such as R Studio or Tinn-R.

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Lecturer

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Dr Dick J. Brus is senior scientist at Wageningen University and Research in the Netherlands and adjunct Professor at the School of Geography of Nanjing Normal University. He has rich experience in geostatistics and statistical sampling in space and time. He applies these statistical methodsin mapping and monitoring of natural resources such as soil, water and vegetation. He published numerous papers in international journals and is second co-author of the book Sampling for Natural Resource Monitoring (J.J. de Gruijter, D.J. Brus et al., 2006, Springer Verlag). In 2014 he was awarded the Visiting Professorship for Senior International Scientists by the Chinese Academy of Science.

Using R for infrared spectroscopy research

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Collecting and analysing large number of samples may often be expensive, although necessary for several studies. Infrared spectroscopy is a high-throughput, non-destructive, and cheap sensing method that has a large range of applications in agricultural, plant and environmental sciences.  The inherent complexity of Infrared data makes necessary the use of advanced statistical tools to extract relevant information from a sample of a given material (e.g. soil).

This half-day workshop intends to present a tutorial on multivariate analysis using the R statistical Software, with focus on chemometric methods for:

  • Pre-processing and outlier detection
  • Measuring the similarity/dissimilarity between spectra
  • Calibration sampling (what and how many samples to choose to build accurate multivariate models)
  • Multivariate calibration (global models, local models for large and complex datasets)
  • Transferring NIR data across different instruments

During the course, we will show how to efficiently integrate different R tools for advanced soil spectroscopy research. We will provide the attendees with several R code examples as well as soil datasets. Important links:

prospectr: https://cran.r-project.org/web/packages/prospectr/vignettes/prospectr-intro.pdf

resemble: http://l-ramirez-lopez.github.io/resemble/

 

PRELIMINARY TIMETABLE

  • 09:15– 09:30 Introduction 
  • 09:30–10:45 Pre-processing and sampling spectral information (with computer practical)
  • 10:45–11:00 Coffee break
  • 11:00–12:30 Multivariate calibration (with computer practical)
  • 12:30–13:30 Lunch

requirements

All participants must bring their own laptops with R > 3.3.1 and R Studio Desktop > 0.99.903 installed. The computer practicals are done in the R language. Prior experience with R is not a prerequisite but at least some experience is recommended. 

 

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lecturers

Leonardo Ramirez-Lopez – NIR Data Analytics, BUCHI Labortechnik. Switzerland.

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Leonardo holds a PhD in Soil Science and 10 years of experience on infrared spectroscopy. He has worked at several research groups, in Brazil (University of São Paulo), Germany (University of Tübingen), Belgium (Université catholique de Louvain) and Switzerland (ETH Zurich). He currently works as NIR Data Analytics Manager at BUCHI Labortechnik AG.  He has developed two (publicly available) R packages for the analysis of soil infrared spectral data: prospectr and resemble. The first one includes functions for spectral pre-processing and calibration sampling, while the second one includes functions for modeling complex spectral data.

 

Alexandre Wadoux – Soil Geography and Landscape group, Wageningen University, Netherlands

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Alexandre is PhD candidate at Wageningen University. He completed a M.Sc. in Physical Geography at the University of Tübingen (Germany) where he conducted research on soil spectroscopy and soil-landscape analysis. His work focuses on statistical analysis of environmental variables and sampling design optimization mostly using the R software. He is part since 2015 of the EU funded FP7 Marie Curie Initial Training Network (ITN) ‘Quantifying Uncertainty in Integrated Catchment Studies (QUICS)’.

Interpretation of Mid Infrared (MIR) Spectra of Soils 

Image illustrating a soil profile, FTIR spectrometer and FTIR spectrum of the top horizon of the soil in the MIR range

Image illustrating a soil profile, FTIR spectrometer and FTIR spectrum of the top horizon of the soil in the MIR range

he infrared region of the spectrum  is widely  used  for soil sensing and monitoring.  To date, the  near  infrared  (NIR)  region  has  been  far more  extensively  used  than  the  mid infrared  (MIR) region. However, MIR  spectra  can  provide  more information, particularly in  relation  to mineralogy,  than  NIR  spectra.    Hence MIR  spectra  give  more  complete chemical  profiles or “fingerprints” of soil  and,  in addition,  are more readily interpreted. An  MIR spectrum  is  generated  by plotting  absorption  against  frequency, which  to  the untrained  eye  can  appear meaningless.  Though much of the  information  concealed  in these  absorption  bands  can ultimately  be  extracted  via  chemometrics  and statistical modelling, the ability to  interpret the MIR spectra  is  fundamental  to obtain preliminary information that will help us understand the nature of the soil sample(s).  In  a  typical  MIR  spectrum,  different  absorption bands  are  observable  along  the frequency  range  (4000-400 cm-1) and  are  related  to  the  type  of  chemical  bonds  and functional groups present in the substrate. These bands occur at specific frequencies for particular  chemical  components,  thus  making  it possible  to  infer  the  organic  and inorganic  constituents  of  a  sample.  The  MIR spectra  can  therefore  be  invaluable  in providing a rapid insight into,  and a means of visualising,  the differences between soils. Although interpreting  MIR  spectra  of  soil  can  be  difficult  and complex,  with  some training  in  spectral  interpretation,  aspects  of  the  soil  such  as the  nature  and  relative proportions  of  organic  matter, minerals  and  clay  minerals  can  all  be  readily  assessed. Training  in  MIR  spectral interpretation  of  soil  is  not  readily  available,  and  so this workshop aims to try and address this by providing  the attendees with  an overview and basic  guidance  on  the  fundamental  steps  required for  an  accurate  diagnostic assessment of soil MIR spectra. 

PRELIMINARY TIMETABLE

  1. Introduction to MIR spectra of soil (presentation/lecture) – 30 min
  2. Spectral features of organic soils (presentation/lecture) - 30 min + practical exercise -15 min
  3. Spectral features of mineral soils - (presentation/lecture) - 30 min + practical exercise -15 min
  4. Practical exercise – interpretation of soil spectra – 1 hour
  5. Summary/ discussion – 30 min
     

REQUIREMENTS

Participants may either use their own laptops with software pre-installed or use one of the computers which will be made available. Spectral files and other materials will be made available during the workshop.

 

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lecturers

Jean Robertson
Jean has been head of the IR section at The James Hutton Institute for over 10 years, working with both Fourier Transform Infrared spectroscopy (FTIR) and Near Infrared (NIR) spectroscopy. She trained as a chemist, and her expertise in FTIR spectroscopy was first developed through her PhD, awarded in 1990, in which she studied structures of organometallic compounds. Through her work at the Institute she now applies this expertise in IR spectroscopy to a wide range of naturally occurring samples. She has developed the specialist knowledge necessary for interpreting the complex FTIR spectra of minerals, soils, sediments, fungal species and vegetation. In addition, from the samples generated for the National Soil Inventory of Scotland, she has been responsible for the creation of high quality FTIR and NIR national spectral datasets for Scotland. Much of her research relates to analysis of relationships between this spectral data and the other data held for the soils. She is also responsible for providing commercial FTIR analysis for a wide range of industrial clients.


Jean.robertson@hutton.ac.uk
The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, Scotland (UK)

 

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Estefanía Pérez-Fernández
Estefanía is a biologist by training and gained her PhD with the University of Seville (Spain), in collaboration with The James Hutton Institute (Scotland), where she specialized in the use of near infrared spectroscopy for the study of animal ecology (herbivores). She has ample expertise in the application of chemometrics to develop predictive calibrations that allow estimation of a range of chemical properties of different types of natural samples, mainly soil, vegetation and animal faeces. Currently undertaking post doc research within the IR Section at The James Hutton Institute, Estefanía is in charge of developing new applications for crop, plant and soil research using both FTIR (Fourier Transform Infrared) spectroscopy and NIR (Near Infrared) spectroscopy analytical techniques. In addition to this, she also collaborates with the commercial analysis of samples from the oil, gas, engineering and food industries. 


estefania.perez-fernandez@hutton.ac.uk
The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, Scotland (UK)