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GEOSTAT software

Software iconList of FOSS software used in this course and installation instructions. Follow these instructions to prepare and customize the software before the beginning of the course.

Literature used

ASDAR bookList of books / lecture notes used in this course. See also: CRAN Task View: Analysis of Spatial Data.

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Automated geostatistics using multisource data

Objective: Test various automated spatial prediction frameworks using 2D, 3D and 2D+T data.

General description: This lecture extends 2D geostatistics (Meuse case study) to 3D soil data (Ebergotzen) and 2D+T time series of meteo measurements (HRtemp). In the first examle (2D) organic carbon is soil is mapped in horizontal space only using GLMs, randomForest and regression trees. In the second example soil sand content is mapped using 3D regression-kriging combined with splines, and in the 3rd example mean daily temperatures are predicted using time-series of MODIS LST images together with some static predictors.

Required back-ground knowledge: solid knowledge of geostatistics and handling spatial data in R;
Software / R packages required: sp, raster, rgdal, spacetime, GSIF, plotKML;

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Literature:

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