Course detail

LCE5735 - Applications in Geostatistics in Agrarian Sciences


Credit hours

In-class work
per week
Practice
per week
Credits
Duration
Total
6
3
6
5 weeks
90 hours

Instructor
Gener Tadeu Pereira
José Silvio Govone
Sonia Maria de Stefano Piedade

Objective
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.

Content
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

Bibliography
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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
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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: http://cran.r-project.org/doc/Rnews).
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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
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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.