Postdoc position: R software for robust geostatistical modelling

The Institute of Terrestrial Ecosystems (ITES), Department of Environmental Sciences, ETH Zurich, and the Swiss Soil Monitoring Network (NABO), embark on a new project to develop software for robust geostatistical analyses of soil and other spatial environmental data. The software will be implemented in R, and its development will be complemented by geostatistical case studies that should document and illustrate its use for potential users in government agencies. The project is supported by the Swiss Federal Office for the Environment (FOEN), funding is available for one year.

For this project we seek a postdoctoral fellow. His or her duties will be:
• developing robust methods and algorithms for linear modelling and prediction of spatially correlated data,
• implementing the developed algorithms in R,
• documenting and bundling the software as R add-on package,
• contributing to the illustrating case studies, and
• providing support to NABO staff in geostatistical analyses.

Applicants should have a PhD, preferably in statistics, environmental sciences or engineering or in a related field. Excellent programming skills in R are indispensable. Familiarity with the ESRI GIS tools and experience in GIS analyses is an asset. Good knowledge of English is required; knowledge of German is an advantage. He or she is expected to contribute significantly to methodological research and to design, implement, and document the R software in collaboration with the project leader and NABO staff. Ability and willingness for cooperation with environmental scientists is essential.

Candidates should submit their application (that should include a CV, a statement of research interest and qualifications, and contact details of three academic referees) through the online job portal of ETH Zurich (http://www.pa.ethz.ch/3300_an_offene_stellen/index_EN).

Further information about the position is available from Dr. Andreas Papritz, papritz[at]env.ethz.ch.

Geomorphometry

Geomorphometry, Conceps, Software, Applications

Edited by: Tomi Hengl & Hannes Reuter

Geomorphometry is the science of quantitative land-surface analysis. It draws upon mathematical, statistical, and image-processing techniques to quantify the shape of earth’s topography at various spatial scales. The focus of geomorphometry is the calculation of surface-form measures (land-surface parameters) and features (objects), which may be used to improve the mapping and modelling of landforms to assist in the evaluation of soils, vegetation, land use, natural hazards, and other information. This book provides a practical guide to preparing Digital Elevation Models (DEM) for analysis and extracting land-surface parameters and objects from DEMs through a variety of software. It further offers detailed instructions on applying parameters and objects in soil, agricultural, environmental and earth sciences. This is a manual of state-of-the-art methods to serve the various researchers who use geomorphometry. Soil scientists will use this book to further learn the methods for classifying and measuring the chemical, biological, and fertility properties of soils and gain a further understating of the role of soil as a natural resource.

See the book’s website: http://www.geomorphometry.org/

Contents

I - Concepts
Geomorphometry: a brief guide Mathematical and digital models of the land surface DEM production methods and sources Preparation of DEMs for geomorphometric analysis Geostatistical simulation and error propagation in geomorphometry Basic land-surface parameters Land-surface parameters and objects in hydrology Land-surface parameters specific to topo-climatology Landforms and landform elements in geomorphometry

II - Software
Overview of software packages used in geomorphometry Geomorphometry in ESRI packages Geomorphometry in SAGA Geomorphometry in ILWIS Geomorphometry in LandSerf Geomorphometry in MicroDEM Geomorphometry in TAS GIS Geomorphometry in GRASS GIS Geomorphometry in RiverTools

III - Applications
Geomorphometry – a key to landscape mapping and modelling Soil mapping applications Vegetation mapping applications Geomorphometry and spatial hydrologic modelling Applications in meteorology Applications in geomorphology Applications in Precision Agriculture Modelling mass movements and landslide susceptibility Automated predictive mapping of ecological entities The future of geomorphometry

Global data

Global data

Applied Spatial Data Analysis with R

Applied Spatial Data Analysis with R, by Bivand, Roger S., Pebesma, Edzer J., Gómez-Rubio, Virgilio

The book is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book’s own website.

The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org.

The book is also available in electronic format: http://www.springerlink.com/content/978-0-387-78170-9

The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.

Congratulations to Gerard

Congratulations to Gerard Heuvelink and Ineke

Wishing both of you a happy life together!

Soil Datasets

Spatial data

  • Broom’s barn dataset. Note (pdf), data (txt)
  • Gilgai transect data. From the paper: Webster, R. (1977) Spectral analysis of gilgai soil. Australian Journal of Soil Research 15, 191-204. xls
  • Thickness of soil A horizon from a field near Forbes, New South Wales. From the paper: Pettitt, A.N., McBratney, A.B., 1993. Sampling designs for estimating spatial variance components. Applied Statistics 42, 185–209. xls
  • Soil pH of 1 ha area at a research station in Samford, Queensland. From the paper: Laslett, G.M., McBratney, A.B., Pahl, P.J., Hutchinson, M.F., 1987. Comparison of several spatial prediction methods for soil pH. Journal of Soil Science 38, 325–341. xls
  • The Jura dataset. Appendix C in: Goovaerts, P., 1997, Geostatistics for natural resources evaluation, Oxford University Press. txt More info see Pierre Goovaerts’ webpage
  • The Meuse soil contamination dataset. Burrough, P. A. & McDonnell, R. A., 1998. Principles of geographical information systems. Oxford: Oxford University Press. Provided by David Rositter. zip
  • The Ebergotzen dataset (Tomi Hengl) zip
  • See also AI-Geostats Datasets

Time Series

Infrared spectra

Classical Pedometrics Papers

  • Forbes, J.D., 1846. Account of experiments on the temperature of the earth at different depths, and in different soils, near Edinburgh. Transactions of the Royal Society of Edinburgh 16, 189-236. pdf
  • Bernard A. Keen and William B. Haines (1925) Studies in Soil Cultivation I, The evolution of a reliable dynamometer technique for use in soil cultivation experiments. Journal of Agricultural Science, 15: 375-386. pdf
  • William B. Haines and Bernard A. Keen (1925) Studies in Soil Cultivation II. Test of soil uniformity by means of dynamometer and plough. Journal of Agricultural Science, 15: 387-394. pdf
  • William B. Haines and Bernard A. Keen (1925) Studies in Soil Cultivation III. Measurements on the Rothamsted classical plots by means of dynamometer and plough. Journal of Agricultural Science, 15: 395-406. pdf
  • William B. Haines and Bernard A. Keen (1928) Studies in Soil Cultivation IV. A New form of traction dynamometer. Journal of Agricultural Science, 18: 724-733. pdf
  • W.J. Youden and A. Mehlich, Selection of efficient methods for soil sampling, Contributions of the Boyce Thompson Institute for Plant Research 9 (1937), pp. 59–70. pdf
  • De Gruijter, J.J., McBratney, A.B., 1988. A modified fuzzy k means for predictive classification. In: Bock,H.H.(ed) Classification and Related Methods of Data Analysis. pp. 97-104. pdf
  • Ohashi. Y. Fuzzy clustering and robust estimation. Presentation at the 9th meeting SAS User’s Group International. Hollywood Beach. Florida. 18-21 March 1984. pdf

Soil Atlas of Europe

Alfred Hartemink

SUMMARY: This is an abridged and altered version of a review that appeared in the Journal of Environmental Quality 35: 952-955. “Soil Atlas of Europe”, by European Soil Bureau Network of the European Commission, 2005. Principal editors: A. Jones, L. Montanarella, and R. Jones. Office for Official Publications of the European Communities, Luxembourg. Hardbound, 128 pp. ISBN 92 894 8120. €25. The atlas has 7 sections. In the introduction, some of the main soil properties, processes and land uses are described. Sections are devoted to the soil profile, horizon classification, soil variation, and soil forming processes. Two pages deal with “The soil in your garden” attempting to explain soils to non-soil specialists. There are also paragraphs on soils and agriculture, forestry, soils as a source of raw material, and soils and cultural heritage.

1. History of soil mapping in Europe

The first soil maps in Europe started to appear in the 1800s, and such maps were mostly produced for agricultural purposes or the taxation of rural lands and emphasized surface geology, the degree of weathering of the regolith (Stremme, 1997). The first generation maps produced by Stremme have a strong agrogeological base and were based on limited soil survey work (Table 1). The first soil maps stimulated soil survey and research in most European countries of which the fruits were harvested for the second generation of European soil maps (1965-1985). These developed in the heydays of soil survey and were based on hundreds of detailed national and regional maps. The second generation is now being replaced by a third generation of maps in which full use is made of existing soil information with advancements in GIS, remote sensing and quick and accurate soil observations using a range of sensors. This first Soil Atlas of Europe has interesting sections on those third generation types of soil maps but is largely based on the second generation of maps. The primary aim is to provide comprehensive information about the soils of Europe and raising awareness of issues affecting soils; it is part of the European Soil Thematic Strategy that was adopted by the European Union in 2002. Another goal of this atlas is to educate people about the important role of soils in a non-technical manner.

2. Overview of the book

The atlas has 7 sections. In the introduction, some of the main soil properties, processes and land uses are described. Sections are devoted to the soil profile, horizon classification, soil variation, and soil forming processes. Two pages deal with “The soil in your garden” attempting to explain soils to non-soil specialists. There are also paragraphs on soils and agriculture, forestry, soils as a source of raw material, and soils and cultural heritage. In the second section, the soil types of Europe are described following the World Reference Base (WRB) for soil resources. A brief introduction is given on soil classification and the WRB, followed by descriptions of the major soil types and their distribution across Europe. Seven of the 30 WRB soil types (reference groups) do not occur in Europe, like Ferralsols, Alisols and Lixisols. The soils with the largest extent are Albeluvisols that cover 15% of the European land mass; Podzols cover 14%, and Cambisols cover 12%. There are two pages on soil mapping and there is a paragraph on digital soil mapping. Two overview maps show the availability of soil maps at scales of 1:50,000 or 1:250,000 in Europe. It seems that large countries with large economies and populations (France, Germany, UK) not necessarily have good coverage of detailed soil.

In the second section, the soil types of Europe are described following the World Reference Base (WRB) for soil resources. A brief introduction is given on soil classification and the WRB, followed by descriptions of the major soil types and their distribution across Europe. Seven of the 30 WRB soil types (reference groups) do not occur in Europe, like Ferralsols, Alisols and Lixisols. The soils with the largest extent are Albeluvisols that cover 15% of the European land mass; Podzols cover 14%, and Cambisols cover 12%. There are two pages on soil mapping and there is a paragraph on digital soil mapping. Two overview maps show the availability of soil maps at scales of 1:50,000 or 1:250,000 in Europe. It seems that large countries with large economies and populations (France, Germany, UK) not necessarily have good coverage of detailed soil maps. In fact, smaller and more densely populated countries have more detailed soil maps, or in other words: the smaller the country, the better the availability of detailed soil maps (with the exception of Denmark and Switzerland). There is an array of reasons, but in densely populated places there may have been a historical need to know the land as population pressure was higher. In bigger countries, the need for detailed spatial information about soil resources might have been less pressing as land was amply available.

The third section provides 17 regional maps for the whole of Europe. It starts with an overview map at 1:11.25 million showing the soils in Europe including Turkey and Russia up to the Ural Mountains. The 17 regional maps (Lambert Azimuthal projection) are at scales ranging from 1.175 to 1:6.5 million; most maps are at a scale of 1:2 million. There is a nice text introduction to each regional map but the legend (suborder level) is given only once on pages 40 and 41. Major cities and highways are included which makes orientation easy. The soils and their distribution are based on early work; little new boundaries are present as compared to the 1985 map. Classification has been adjusted from the FAO-Unesco system to WRB.

In the next chapter, the soil types and distribution in Europe are compared to soils in other part of the world. According to this atlas, Europe covers about 5% of the global soils and an overview is given how soil distribution differs between different parts of the world. For example, Leptosols are the most dominant soils in the world, whereas they cover 9% of the European land mass. Ferralsols are dominant in South America whereas Arenosols are the most widespread in Africa. The 1:22 million soil map of Europe and Eurasia shows that the Ural Mountains act as a clear divide in soil distribution. Albeluvisols are dominant on the eastern part, and Histosols, Cryosols and Podzols occur at the same latitude east of the Ural. That is nice about maps - if you look longer you see more. There is a separate section on soils of the Mediterranean regions and soils in the Northern latitudes (with a little bit on global warming).

The next chapter deals with the European soil database and explains what GIS is and how the soil geographical database of Europe is constructed. The database consists of a soil geographical database, soil profile database, hydraulic properties database and the pedotransfer rule knowledge base. These are linked and the first step in the development of an integrated European soil information system. Using this integrated database, small maps are presented showing, for example, clay content in the topsoil, base saturation, or depth to bedrock. Soil erosion and potential N2O maps as well as organic matter maps are shown and these are valuable for formulating policy at the EU level.

After that a section is devoted to the seven key threats to soils in Europe: soil sealing, erosion, loss of organic matter, decline in biodiversity, contamination, hydro-geological risks and salinisation. Except for the decline in biodiversity, contamination and salinisation, the other four threats have been fairly well mapped. The last chapter is called Additional Information and contains maps on rainfall, temperature, land cover, population density and a tiny section on soil education.

The atlas has no index; an atlas without an index is like the internet without a search engine. Somehow this atlas could have resembled the beautiful book “Australian Soils and Landscapes - An Illustrated Compendium” (McKenzie et al., 2004), but it doesn’t. It lacks rigor (too many authors perhaps) and image and map quality are not quite comparable. Some subjects in relation to the soils of Europe are lacking or treated very briefly; for example there is nothing on the manure problem which occurs in some regions, on climate change that will affect the Mediterranean countries and that will also influence change land use in other parts of Europe. There is also nothing on soils and health, or soils and socioeconomics. If this atlas were to live up its promises (raise awareness, didactic etc.) the section on soil education should have been larger.

3. Summary points

In the coming decade, there will be considerable changes in the European landscape. Such changes will perhaps directly result from global warming, but more importantly: many farmers will retire or go out of business due to decreasing farm subsidies and increasing farm output in other parts of the world. Future soil maps of Europe will have to focus on changing land use whereby recreation, nature conservation and urbanization may become more extensive than agricultural land use. Despite some points of critique, I enjoyed reading this atlas and learned much about soil distribution in Europe. There is much information that should be read by pedometricians - the price (€25) is also very affordable, that always helps.

4. How to order

Soil atlas can be ordered from the EUSOILS website at an affordable price. Details for the distribution of the atlas are currently being finalised. Copies should be made available to the public during March 2006. If you wish to reserve a hard copy of the atlas (25 euro), please complete the order form. All registered requests will be forwarded to the Publications Office for processing.

Update: The Soil Atlas of Europe now can be downloaded for free. The user has to download each page separately (128 pages in total). The PDF versions provide a better quality version compared with the JPEG files. There are 20 plates of maps which are included as 2-page PDF files, pages 40-79. User may navigate and select the files to download either by browsing the whole Atlas or by selecting one of the sections in the Contents. Each Page has a Title and belongs to one of the 7 sections of the Atlas (Introduction, The Soil of Europe, Soil Maps of Europe, European Soil: A Global perspective, A Soil Database of Europe, Key threats to soil in Europe. More info here

References:

  1. Commission of the European Communities, 1985. Soil map of the European Communities 1: 1000000. Directorate-General for Agriculture Coordination of Agricultural Research. EEC, Luxembourg.
  2. FAO-Unesco, 1981. Soil map of the world, volume V Europe, 1:5 000 000, Rome.
  3. FAO, 1965. Soil map of Europe, Carte des Sols de L’Europe, Mapa de Suelos de Europa. 6 Map sheets, explanatory text by R. Dudal, R. Tavernier and D. Osmond., Rome.
  4. Jones, A., Montanarella, L. and Jones, R., 2005. Soil atlas of Europe. European Soil Bureau Network. European Commission, Luxembourg, 128 pp.
  5. McKenzie, N., Jacquier, D., Isbell, R. and Brown, K., 2004. Compendium. CSIRO Publishing, Melbourne. Stremme, H., 1928. General map of the soils of Europe (Ogolna Mapa Gleb Europy). International Society of Soil Science, Warszawa.
  6. Stremme, H., 1937. International soil map of Europe, 1:2,500,000. Gea Verlag, Berlin.
  7. Stremme, H.E., 1997. Preparation of the collaborative soil maps of Europe, 1927 and 1937. In: D.H. Yaalon and S. Berkowicz (Editors), History of Soil Science International Perspectives. Advances in Geoecology. Catena Verlag, Armelgasse 11/35447 Reiskirchen/Germany, pp. 145-158.

Hands-on geostatistics, Napoli 2007: five days of geostatistics and jazz

By Tomislav Hengl

SUMMARY: Hands-on geostatistics “Merging GIS and Spatial Statistics” was a training course held at Facolta di Agraria in Napoli in period 29.01-03.02.2007 under auspices of the Commission 1.5 Pedometrics of the IUSS, University of Napoli (SISS, SIPE), and the Institute for Environment (Joint Research Centre). It was an intensive 5-days course with balanced combination of theoretical and practical training, aimed at helping young researcher find their way in the combined used of GIS and geostatistical tools. It gathered 30 PhD students, post-doctoral researchers and specialists from various European universities and research organizations. The course focused on use of remote sensing-based and DEM-based predictors for improving prediction of soil variables. In addition, the lecturers (Hengl T, Pebesma E. and Olaya V.) provided training in five software packages: ILWIS, SAGA, R, GSTAT and GoogleEarth. In this report, you can find more background information about the course, how was it designed and what were its main outputs.

1. Backgrounds:

Facolta di Agraria in Napoli has a long tradition of organizing intensive courses on advanced scientific fields. These courses are intended for members of research groups, PhD students and young researchers. The spiritus movens of these events for last several years was prof. dr. Fabio Terrible. In October 2006, Fabio invited me to organize the next session of the courses, this time in English language and preferably with much more emphasis on practical training. We agreed to prepare an intensive 5-days course on state-of-the-art methods that can be used to integrate GIS and geostatistical tools. We aimed at Master and PhD level students and PostDoc researchers in various fields of environmental and geo-sciences interested in spatial interpolation and analysis of environmental variables. We also decided that it has to be a non-commercial event (the course fees were minimized), which also means that all lecturers would need to volunteer to teach and prepare materials.

The first announcement was launched at the Spatial Data methods meeting in Foggia and via the pedometrics.org website. During the registration, participants were asked to select among 20 topics and between whether they wish to receive more theoretical or more practical training. We finally selected 30 participants based on their (1) academic excellence, (2) research topic and (3) early registration, and prepared a programme that would fit the average profile of the course participant.

Fig.: The computer cluster.

The practical training was designed in such a way that participants were asked to answer and discuss specific research questions. E.g. the first training exercise asked for comparison of spatial prediction models with and without auxiliary maps; the second exercise asked for evaluation of the influence of grid side on the success of prediction models; the final exercises asked for evaluation of complete automation and influence of the sample size on the quality of final predictions. For all exercise we used the Ebergotzen dataset kindly provided by Michael Bock of Scilands GmbH. The complete dataset including the description can be obtained from here.

Group photograph

Fig.: The Hands-on geostatistics training course participants.

Because the course aimed at practical training, we focused much of the course on the use of software. Five packages were used to run the processing and display the results: ILWIS, SAGA, R, GSTAT and GoogleEarth, all available as open source or as freeware, so that no licenses were needed. We wanted to emphasize that open source packages developed jointly by academic groups can have many advantages over commercial software. ILWIS was used to process and prepare vector and raster maps and run simple analysis on multiplayer maps. SAGA, R/GSTAT was used to run predictions and R was used to run statistical analysis and automate data processing. Many operations were available in several packages, which allowed participants to compare them. Once the layers were produced in a GIS or R, they were exported to GoogleEarth to allow visual exploration of data. More detailed instructions on how to install these packages and make first steps in them you can find here.

2. The programme:

The course consisted of five working days. The first day was purely theoretical, second, third and fourth day were a combination of theoretical lectures and practical training and the last day was organized as a workshop where each participant was able to pose technical and theoretical questions to the lecturers and the course participants. We started by introducing each other to course participants. Each participants also presented him/her-self and mentioned his/her backgrounds and expectations from the course. We then inverted the course a bit a distributed a test-your-knowledge-of-geostatistics exercise that consisted of 20 questions. These were all, more all less, simple logical questions that can be solved with some intuition and without big computations. The answers to questions were provided day-by-day, as soon as some topic became actual. In the second part of the first day, key concepts of geostatistics, such as spatial autocorrelation, semi/co-variance, variogram, kriging and kriging variance, were introduced; after that concepts of regression analysis (correlation, GLMs, GLS estimation, prediction error) and, finally, the target technique of the course – regression-kriging – was elaborated in detail.

The second day was dedicated to remote sensing data sources that can be used within the regression-kriging framework. A review of remote sensing system and images was first given including the practical tips on how to browse and obtain remote sensing images. The concept of grid/support size and their connection with scale and complexity of target features was clarified and main applications of geostatistics for remote sensing reviewed. We demonstrated how can geostatistical techniques be combined with remote sensing: to filter the missing pixels, analyze noise in remote sensing images and use them as covariates in the spatial prediction. The objective of the first exercise was to compare ordinary kriging and regression-kriging and evaluate how much do the predictions improve if additional auxiliary information is used (LANDSAT bands and geological map).

On the third day of the course, Victor Olaya provided an extensive overview of the field of geomorphometry including an overview of the techniques that can be used to build or obtain DEMs and extract DEM derivatives in SAGA GIS. Victor specifically suggested which algorithms to choose and how to interpret various land surface parameters and objects derived out of DEMs. The course participants then tested running land surface analysis in SAGA and ILWIS. The objective of the second exercise was to compare the prediction models derived using DEMs of two different sources: 100 m SRTM DEM and the 25 m DEM derived from topo maps.

On the fourth day, Edzer Pebesma made an introduction to the statistical computing environment R and emphasized advantages and disadvantages of using R. Edzer was definitivly the best choice for this task as he was closely involved with the design and development of ‘spatial’ packages in R. He is also the author of the gstat package, probably still the richest geostatistical package in the world. Edzer gave us many tips’n’tricks on how to start working with R, how to create, debug and distribute R scripts and what are the benefits and dangers of data processing automation. We then run an exercise where ordinary kriging with large dataset (2937 observations) was compared with regression-kriging with a much smaller dataset (300 points) but with all possible auxiliary maps including remote sensing bands, DEM derivatives and geological map. The objective of this exercise was to evaluate influence of sample size on the quality of final predictions and discuss dangers of data processing automation. The fifth day of the course was organized as a workshop where each participant got a chance to present his/her work and ask his/her colleagues for help with the data processing. Here many interesting issues were raised, so that also we, the lecturers, got to learn about the field from our colleagues.

3. Outputs

The participants have received basic training in software packages and the most important techniques and applications connected with use of geostatistics jointly with remote sensing and geomorphometry have been explained and elaborated. As an output of the final training day, we managed to produce a R script that automates both fitting of regression models and variograms and spatial predictions and simulations. The final results of predicting sand, silt and clay using regression-kriging can be seen down-below. To produce these maps, we used the regression-kriging framework (more info) that implies principal component transformation on predictors (so-called SPCs), step-wise selection of the most significant predictors and interpolation by both predictions and simulations. The script and input maps for the Ebergotzen dataset are available here (1.4 MB).

Fig.: Final maps of sand, silt and clay for the Ebergotzen area using predictions (above) and simulations (below) with the same regression-kriging model.

Although it was unrealistic to expect that the participants will truly manage to learn to run similar analysis and build R and ILWIS scripts on their own (many participants came to this course without sound backgrounds in geostatistics), we noticed that many are making a serious progress. This is probably because many open source packages are hard to start working with as they are often based on command line interface and the commands follow some particular philosophy. After one learns the basic steps and ways to get support and more explanation of algorithms, it is a steep learning curve. Our intension was similar – we wanted to give an extensive overview of the field (put a bug into ear), warn what might be the bottlenecks and what they should avoid doing and provide the most crucial tricks’n’tips on how start building scripts and how to organize the data processing. We also discovered that many participants are confused with the terminology used and number of options to run analysis with spatial data. We have done our best to try to diminish the terminological confusion (e.g. confusion between universal kriging using coordinates and predictors; confusion between running local and localized predictons) and warn the users which techniques are valid for use and in which situations. We anticipated that, after the course, participants will return to they homes and then have much more time to dig into the data processing steps that are more interesting for their case studies. The rest, we can discuss via the mailing lists.

Fig.: A proof of a significant correlation between geostatistics and music: Victor, Edzer and other course participants doing a jam session during the course dinner. An original jazz piece composed by Victor and dedicated only to this course can be found here.

Finally, I should also mention that it was a great pleasure to work with this group. Self-motivation to master the presented techniques and actively continue using these software packages was overwhelming. I am probably not objective enough to judge about how successful the course was, but I can at least mention some observations on how to improve the course. Number one issue raised was that the it should be longer (e.g. two weeks). The first week would then be organized with a bit less of intensity, while the second week the participants should be able to process (under supervision of the trainers) their own datasets. Many participants had prepared and brought with them their datasets, but there was simply not enough time for course trainers to get deeper into each case study. So now that we know how to improve the course, the only remaining issue is where and when should we put the next one.

Acknowledgments

The author would like to thank Fabio Terribile, Luciana Minieri, Carmelina Pennacchia, and other colleagues from the Facolta di Agraria for organizing this event and University of Napoli for hosting us.

Where to get similar training?

References:

  1. Christensen, R. 2001. Best Linear Unbiased Prediction of Spatial Data: Kriging. In: Cristensen, R. “Advanced Linear Modeling“, Springer, 420 pp.
  2. Curran, P. and P. Atkinson. 1998. Geostatistics and Remote Sensing. Progress in Physical Geography 22:61-78.
  3. Hengl T., Heuvelink G.B.M. and Stein A., 2003. Comparison of kriging with external drift and regression-kriging. Technical report, International Institute for Geo-information Science and Earth Observation (ITC), Enschede, pp. 18.
  4. Hengl T., Heuvelink G.M.B., Stein A. 2004. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 122(1-2): 75-93.
  5. Hengl T., Heuvelink G.B.M., Rossiter D.G., 2007? About regression-kriging: from equations to case studies. Computers and Geosciences, in press.
  6. Pebesma, E., 2001. Gstat user’s manual. University of Utrecht, 108 pp.
  7. Pebesma, E., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30 (2004) 683–691.
  8. Conrad, O. 2007. SAGA - program structure and current state of implementation. In: Böhner, J., Raymond, K., Strobl, J., (eds.) “SAGA - Analysis and modelling applications”, Göttinger Geographische abhandlungen, Göttingen, 39-52.