Aim
The module addresses methods on how Earth Observation and the use of geoinformation can support different fields of land and water management. Focus is set on remote sensing based concepts and methods for monitoring and supporting resource management in agricultural landscapes. Achievements and challenges are elaborated. The students will be guided to gain knowledge in selected practical examples.
Content
Mapping the parameters
- Mapping cropland extent, cropland use, and cropping intensity at different scales
- Rainfed agriculture, irrigated cropland or pasture? Disentangling of complex vegetation classes
- Quantification of cropland production, status quo and developments
- Aproaches to mapping land abandonment
- More crop per drop? Assessing land and water use in irrigated landscapes
Remote sensing contributions to multi-sector assessments
- Population growth and the competition for land in rural areas
- Understanding the role of lakes and wetlands in agricultural landscapes
- Vulnerabillity of agricultural landscapes under changing climate conditions
- Crop yield predictions
Practical part on cropland use intensity in irrigation systems in Central Asia.
Coding
Small classification codes in R will be written (classification and accuracy assessment).
Software
ArcGIS, ENVI / IDL, R
Techniques
Practical work: Mapping cropland extent, cropland vegetation phenology, and crop types at different scales (Landsat / MODIS)
- (Segmentation)
- Classification (random forest, knowledge-based decision trees)
- Accuracy assessment
Content
Seminar plus practical part.
General Course News and Updates
Internship and Innovation Lab presentations
Today some of our EAGLE students presented their internship and innovation laboratory projects. Very interesting topics and they obviously applied and deepened their remote sensing knowledge a lot. Julia Sauerbrey: Prediction of Organic Matter Content from Sentinel-2...
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EAGLE students learn remote sensing field work
The course "from field work to spatial data" by Tobias Ullmann and Martin Wegmann is covering the whole range of field campaign planning and especially training all necessary methods such as GPS handling, coordinate systems, setting waypoints or finding locations. In...
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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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Final presentations of the remote sensing programming course
...
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internship and innovation laboratory presentations
...
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Spatial Python block course
Last week Steven Hill and Thorsten Dahms gave a course that introduced EAGLE students to Python-based spatial data analysis. The advantages and challenges of different python libraries, data sets and methods were covered in hands-on exercises and also discussed...
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Interdisciplinary course in the Bavarian Forest
The Bavarian Forest and the Bohemian Forest together form the largest contiguous forest area in central Europe, which is of an extraordinary importance for the protection and maintenance of biological diversity. Since 1970, a large area of the forest is protected as a...
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One week courses on hyperspectral and time-series analysis
In the past few weeks various block courses by colleagues from DLR have taken place. Divers topics how remote sensing can be used, which methods have to be applied and how to put it into practice were covered by our colleagues Hannes Taubenböck, Martin Bachmann and...
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Urban Geography Course by Hannes Taubenböck
Hannes Taubenböck from DLR discussed with our EAGLE students the application of remote sensing applications within urban research.
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Scientific Presentation of Earth Observation Applications
As every term our students could participated in a scientific presentation course where they learned how to prepare, design and defend a scientific talk. Beside the theoretical part many practical exercises were part of this course and a final presentation in a large...
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