Course detail

LCE5735 - Applications in Geostatistics in Agrarian Sciences

Credit hours

In-class work
per week
per week
10 weeks
60 hours

José Silvio Govone
Sônia Maria De Stefano Piedade

Train students in the basic and fundamental concepts of geostatistical techniques for their correct
application in Earth Sciences. Conduct field experiments to, through geostatistics, analyze the spatial
variability of elements of interest, such as soil, plant and climate attributes. Students will learn the basic
theoretical concepts related to spatial interpolation using geostatistical methods with an emphasis on
using computer programs to practice the learned concepts. The course will be developed through
theoretical and practical classes with exercises that students must perform. In the development of
theoretical and practical classes, students will have to develop several proposed exercises and present
the results to the teacher in the form of reports. During the course, students will also have to work on
geostatistical concepts and techniques using a set of data of interest , and present, in the form of
seminars, the results of this project. Train students in the analysis of point data and analysis of data
distributed in areas. Finally, students will have to submit a written assignment about these results.

1. Introduction: a) Spatial and geostatistical statistics; b) Notation; c) Examples of motivation. 2. Exploratory Data Analysis: a) Univariate Description; b) Bivariate Description; c) Spatial Description; d) Spatial Continuity Analysis. 3. Deterministic Procedures: a) Thiessen Polygons; b) Inverse of the Weighted Distance; c) Application Examples. 4. Random function models: a) Domain; b) 2nd Order Stationary Hypothesis; c) Intrinsic Hypothesis. 5. Geostatistics: a) The Kriging Paradigm; b) Simple Kriging; c) Ordinary Kriging; d) Kriging with Trend; e) Estimate variance; f) Indicator Kriging; g) External Validation and Cross Validation. 6. Co-Kriging: a) The Co-Kriging Paradigm; b) Definition of the Estimator; c) Required Conditions; d) Application Examples. 7. Stochastic Simulation: a) Geostatistical formalism; b) Local Uncertainty and Spatial Uncertainty; c) Simulation and Estimation; d) Sequential Gaussian Simulation. 8. Computational programs aimed at Geostatistics: a) Demo of the Surfer current program; b) Demo of the GeoR package. 9. Point Data and Distributed Data in Areas: Kernel Estimator, Ripley and the nearest neighborhood methods, correlation, and spatial regression.

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