Land and Water Management

04-GEO-APP2

Lecturer

Michael Thiel

ECTS

5 ECTS

 

Aim

The module addresses methods on how Earth Observation and the use of geoinformation can support different fields of land and water management. The students will be guided to gain knowledge in selected practical examples.

 

Content

A general introduction on the subject, which strongly integrates large fields of environmental sciences and studies, is given. The students select topics in which remote sensing and geoinformation can significantly contribute parameters for answering relevant management questions. The topics include the derivation and use of parameters for monitoring land and/or water resources and examples how they can actually implemented in analytical or predictive models, or in indicator systems. The examples may include the management of the resources in rangelands, croplands, irrigation and drainage systems, river catchments, urban areas, or others. Focus may be set on special geographical settings. Depending on the selected topics and scale relevant Earth Observation parameters can include land cover and land use mapping, biophysical variables (LAI/FPAR/Chlorophyll, evapotranspiration , etc.), biomass or crop yields, soil moisture, phenological metrics and other dynamic parameters.

 

Coding

Coding examples and individual work will be covered

Software

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

Techniques

Different techniques will be introduced and practically applied.
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Content

The content of scientific with regard to the audience will be discussed.

General Course News and Updates

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On Monday, 24th of September, at 1pm the following internship reports will be presented: Bharath: "Installation and Characterization of an imaging Spectrometer for the UAV-based remote sensing" Johannes: "Crop classification based on S1/S2 in...

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EAGLE students coding with sweets

EAGLE students coding with sweets

Today our EAGLE students applied data munging, pipes, plotting and statistics using colour distribution of sweets. They specifically used the dplyr, ggplot, kableExtra and others to compute derivatives, rearrange the data, plot it and run statistics on colour...

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