Project properties
Title Digital soil mapping with uncertain data
Group Soil Geography and Landscape
Project type thesis
Credits 36
Supervisor(s) Gerard Heuvelink en Titia Mulder
Examiner(s) Gerard Heuvelink
Contact info gerard.heuvelink@wur.nl
Begin date 2020/08/01
End date
Description The thesis research begins with getting acquainted with DSM by studying a few key articles and application of DSM to example datasets (using R scripts). Next you will study a known method to incorporate measurement error in regression kriging and apply it to your selected case study. You will compare results with those obtained if measurement error is ignored and explain the differences, both for the resulting soil maps as well as for the associated uncertainty maps (i.e., regression kriging standard deviation maps). Next you will either continue the regression kriging road but work out and test how measurement error influences the calibration of the DSM model (including calibration of variogram parameters) or you will analyse how measurement error can be incorporated in DSM interpolation methods based on machine learning. One possible solution approach might be to use Monte Carlo simulation: sample repeatedly from the probability distribution of the uncertain measurements, run the machine learning method for each of these simulations, and integrate over all simulation results. This will work fine but it is computationally demanding and perhaps there is a faster solution. Current PhD-research in our group is currently developing such methods. The MSc-research must also include a cross-validation of the final maps and their associated uncertainty maps. The results of this MSc research will likely be very useful to the ISRIC SoilGrids project (www.soilgrids.org).
Used skills Geostatistics, GIS, R scripting
Requirements • Solid background in statistical modelling, such as obtained through the Spatial Modelling and Statistics course
• Experience with programming in R
• Affinity with Digital Soil Mapping