Course detail

LCE5735 - Applications in Geostatistics in Agrarian Sciences

Credit hours

In-class work
per week
per week
5 weeks
90 hours

Gener Tadeu Pereira
José Silvio Govone
Sonia Maria de Stefano Piedade

Empower students in basic concepts and principles of geostatistical techniques for its correct application in earth sciences. Perform field experiments for through geostatistics, analyze the spatial variability of elements of interest, such as soil, plant and atmosphere . Students will learn the theoretical concepts basics related to spatial interpolation using geostatistical methods with an emphasis on the use of computer programs to practice the concepts learned. The development of the course will be through theoretical and practical lessons with exercises that students should perform. In the development of theoretical and practical classes, students will have to develop various exercises proposed and submit the results to the teacher in the form of reports.During the course, students will also have to work the concepts and geostatistical techniques using a data set of interest, and present, in the form of workshops, the results of this project. Finally, students will have to present a work written about these results.

1. Introduction: a) spatial statistics and geostatistics; b) Notation; c) Examples of motivation. 2. Exploratory data analysis: (a) description univariate analysis; (b) description bivariate analysis; (c) Description Characteristics; (d) analysis of the spatial continuity. 3. Deterministic procedures: a) polygons of Thiessen; b) Inverse Distance Weighted Average; c) Application Examples. 4. Models of random function: (a) Field; b) hypothesis of stationarity of 2nd Order; c) intrinsic hypothesis. 5. Geostatistics: (a) the paradigm of kriging; b) Simple Kriging; c) Ordinary Kriging; d) kriging with a tendency; e) variance of the estimate; f) Kriging; g) External validation and cross-validation. 6. Co-Krigagem: (a) the paradigm of Co-Krigagem; b) Definition of the estimator; c) Conditions required; d) Application Examples. 7. Stochastic simulation: a) Geostatistical formalism; b) uncertainty and Spatial Uncertainty; c) Simulation and pets; d) sequential Gaussian simulation. 8. Computer programs directed to Geostatistics: the demo of the program Surfer 9; b) Demo of the program GS 9; c) Demo of the program Vesper; d) Demo of the packageJ GeoR

Goovaerts, P.Geostatistics for Natural Resources Evaluation. Oxford University Press, Inc, New York, USA, 1997.
Isaaks, E. H.; Srivastava, R. M. An Introduction to Applied Geostatistics. Oxford University Press, Inc, New York, USA, 1989.
Deutsch, C. V.; Journel, A. G. Geostatistical Software Library and User’s Guide. Oxford University Press, New York, USA, 1998.
Soares, A. Geoestatística para as Ciências da Terra e do Ambiente. Instituto Superior de Técnico, IST Press. Lisboa, Portugal, 2000.
Journel, A. G. Fundamentals of Geostatistics in Five Lessons.American Geograph Union. p. 40, 1989.
Bivand, R.S., Pebesma, E.J., Gómez-Rubio, V. Applied Spatial Data Analysis with R. Series: Use R. 378 p. Springer, 2008.
VIEIRA, S. R. Tópicos em Ciência do Solo: Geoestatística em Estudos de variabilidade Espacial do Solo. Sociedade Brasileira de Ciência do Solo. Vol. 1, Viçosa. 54 p., 2000
Gamma Design Software. Gs+: Geostatistics for the Environmental Sciences: V.9.0. Gamma Design Software, Plainwell, Michigan, USA, 2004.
Whelan, B.M., McBratney, A.B. & Minasny, B. Vesper 1.5 – spatial prediction software for precision agriculture. In P.C. Robert, R.H. Rust & W.E. Larson (eds) Precision Agriculture, Proceedings of the 6th International Conference on Precision Agriculture, ASA/CSSA/SSSA, Madison, Wisconsin, 14p., 2002
Trangmar, B.B., Yost, R.S., Uehara, G. Aplication of geostatistics to spatial studies of soil properties. Adv Agron, Madison, v38, p. 45-94, 1985.
Burrough, P.A. Spatial aspects of ecological data, In: Data Analysis in Communit and Landscape Ecology, Jongman, R.H.G. et al. Cambridge University Press, 1995.
Rossi, E. A. et al. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol. Monogr., v62, p. 277-314, 1992.
Ribeiro JR., P.J. and Diggle, P.J. geoR: A package for geostatistical analysis. R-NEWS Vol 1, No 2. ISSN 1609-3631, 2001 (Available for download at:
Chilès,Jean-Paul & Delfiner,Pierre.Geostatistics: Modeling Spatial Uncertainty. Wiley, p. 734,Hardcover, 2012
Minasny, B.; Vrugt, Jasper A.;McBratney, Alex B.Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation, Geoderma, 2011. doi:10.1016/j.geoderma.2011.03.011
Li,Jin; Heap, Andrew D.A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors.Ecological Informatics v.6, p.228–241, 2011.
Lu, Anxiang;Wang, Jihua; Qin, Xiangyang;Wang, Kaiyi;Han, Ping; Zhang,Shuzhen. Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China.Science of The Total Environment,v.425, p.66–74,2012.