Spatial Modeling and Prediction

04-GEO-MET1

Lecturer

Martin Wegmann

ECTS

5 ECTS  

Aim:

Within this course different methods to analyse point pattern statistically and conduct a spatial prediction are covered. Students will learn how to design such analysis, how to avoid caveats, troubleshoot errors and interpret the results.

 

Content

Different statistical methods will be applied for analysing spatial point patterns, such as vegetation samples or biodiversity related information. These results will be statistically predicted using methods such as GLM, GAM, Random Forest or MaxEnt. Implications of spatial point patterns as well as chosen environmental parameters will be discussed. All methods will be practically applied during the course using the programming language R. The needed pre-requisites are covered in the course “Applied Programming for Remote Sensing and GIS“.

 

Coding

Coding examples and individual project work

Software

Various software programs will be used, but mainly OpenSource software such as R and GRASS.

Techniques

Different techniques will be introduced and practically applied such as randomForest, GAM or MaxEnt
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Content

The theory and practice of spatial modeling with a focus on ecology and conservation

General Course News and Updates

Summer School of Alpine Research

Summer School of Alpine Research

Last week, Laura, an 8th gen EAGLE Student, participated in the Summer School of Alpine Research, conducted by the University of Innsbruck, in the beautiful location of the Austrian Oetztal in Obergurgl. The focus of the Summer School was on Close Range Sensing...

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EAGLE M.Sc. thesis in the Arctic

EAGLE M.Sc. thesis in the Arctic

Our EAGLE student Ronja Seitz is conducting her field work for her Master thesis in the Arctic, on Svalbard. She started collecting her data in June to build up a timeseries with UAS multispectral data to investigate disturbances like rain on snow (ROS) events and...

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Master Defense: Comparing the suitability of remote sensing and wildlife camera time series for deriving phenological metrics of understory vegetation in temperate forests of Upper Franconia, Bavaria

Master Defense: Comparing the suitability of remote sensing and wildlife camera time series for deriving phenological metrics of understory vegetation in temperate forests of Upper Franconia, Bavaria

On September 18, Sarah Schneider will present her master thesis "Comparing the suitability of remote sensing and wildlife camera time series for deriving phenological metrics of understory vegetation in temperate forests of Upper Franconia, Bavaria" at 14:00 in...

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