internship and innovation lab presentations

internship and innovation lab presentations

The following students will present next Tuesday (26th) at 2pm in room 0.004 their internships or innovation labs:

Itohan-osa Abu (internship):
Mangrove Mapping with TimeScan Data for Nigeria and an Analysis in Context of Coastal Gas Flaring

Salim Soltani (internship):
Comparative study of Snow Classification Accuracy using optical satellite data with different spatial resolution (MODIS, Landsat, Sentinel, Spot-V)

Sebastian Buchelt (innovation lab):
Processing & Validation of State-of-Art Snow Cover Extent Algorithms using Landsat 5/7/8 Time Series

Internship, Innovation Lab and MSc idea presentations

Internship, Innovation Lab and MSc idea presentations

The following students presented their innovation labs, internships and ideas for MSc. thesis:
Ahmed: ‎
Innovation Lab at DLR (team of Ursula Gessner) and Master Thesis Idea:
Title: Status of Agricultural Lands in Egypt using Earth Observation
Maninder (at DLR, Demmin):
Internship: To find a best fit model by comparing various Evapotranspiration  models using the weather station data for Toitz station, Germany
Thesis idea: comparing the performace of different crop growth models using synthetic remote sensing data at Demmin, Germany
Pilar:
Thesis idea: Time series analysis of flooding and vegetation patterns in wetlands of Colombian Orinoco Basin
Internship and Innovation Lab presentations

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 Imagery in Germany

Marina Reiter: Conservation in Tasmania: Green Leaf Project and World Class Reserve Systems

Sarah Nolting: Regional Scale Statistical Mapping of Snow Avalanche Likelihood and its Combination with an Optical Remote Sensing Based Avalanche Detection Approach – First Attempts for the Province of South Tyrol (Italy)

internship and innovation laboratory presentations

internship and innovation laboratory presentations

The following internship and innovation laboratory projects were presented today:

Karsten Wiertz did his internship at the Białowieza national park on “Spatio-temporal analysis of tree mortality and gaps in the Białowieza Forest using high resolution imagery”.

Jakob Schwalb-Willmann did his internship at the Max-Planck-Institute in Radolfzell on “moveVis GUI: Development of a browser-based web application for animating movement data”.

Marcus Groll worked during his internship at DLR DFD on “Configuration of augmented & virtual Reality content with Unity 3d”.

Pilar Endara analyzed in her internship at DLR DFD the “Spatio-temporal analysis of precipitation and NDVI in Somalia”.

Additionally we heard about the innovation laboratory of Jakob Schwalb-Willmann at EURAC „Sentinel-2 based forest change detection across the Alps“.

 

Explore species-environment interaction

Explore species-environment interaction

Analyzing species-environment interaction is feasible using various data and method. An increasing technology is the tracking of animals and especially its linkage to remote sensing, as covered in AniMove.org. However, with this technology new challenges have to be dealt with, e.g. the decrease in accuracy of tracking devices in dense vegetation. In this innovation laboratory you can explore the error margins of tracking devices in relation to the landcover and explore options to solve it and being able to provide new improved methods to link animal movement data and remotely sensed environmental information.

Deployment of a multi-classifier approach to improve land cover classification accuracy

Deployment of a multi-classifier approach to improve land cover classification accuracy

multi_classifierThis study will examine whether the application of hybrid classifiers increases the classification accuracy in comparison to a single classifier. A combination between parametric and non-parametric classifiers will be applied and their performance will be assessed. The student is expected to gain a deep knowledge of applied Machine Learning algorithms within this innovation laboratory.