Internship and M.Sc. idea presentations

Internship and M.Sc. idea presentations

on Thursday, December 13th, at 12:30 we will have the following presentations in the student working room (Josef Martin Weg 52, 3rd floor):

internship presentations:

Johni Miah
“Remote Sensing and Geographic Information System for Decision Making”
Benjamin Lee
“Quantifying intertidal areas in the East Asian-Australasian Flyway”
Marina Reiter
“Computation of sewer system in Ulm”

M.Sc. idea presentations:

Marina Reiter
“Analysis of urban green areas in German cities”
Fowad Ahmed
“Drought Monitoring in the University Forest Sailershausen”
M.Sc. graduation by Jakob Schwalb-Willmann

M.Sc. graduation by Jakob Schwalb-Willmann

Congratulation to Jakob Schwalb-Willmann who successfully graduated today! His M.Sc. topic was “A deep learning movement prediction framework for identifying anomalies in animal-environment interactions” aiming to explore the potential of animal movement analysis for informing remote sensing data analysis. 

The M.Sc. was conducted in collaboration with the Max-Planck-Institute of Ornithology and supervised by Martin Wegmann and Kamran Safi.

From the abstract: “The environmental conditions that animals are exposed to infuence their movement throughout the landscape. A variety of modeling approaches aim to quantify the relationships between animal movement trajectories and environmental variables, e.g. acquired through satellite remote sensing. However, many of such approaches are designed for species-specifc movement modeling and often rely on a priori assumptions, knowledge-based parameterization or the selection of features. In this study, the use of representation learning for predicting animal-environment interactions in a non-parametric, species-independent framework is investigated to enable the unsupervised detection of anomalies. It is shown that a Long Short-Term Memory (LSTM) network is capable of learning generalized representations of the interactions between location and raw environmental features in movement sequences which had been simulated under an agent-based resource selection regime unknown to the network. Thus, the network is able to reconstruct such movement sequences that show similar feature interaction characteristics to the ones the network had learned. On the contrary, the reconstruction of sequences containing patterns that are novel to the network results in a reconstruction error response that was utilized to detect anomalies in movement sequences. It is discussed, how the proposed framework may be applicable for the analysis of animal movement, e.g. for outlier detection, behavioral segmentation or movement simulation. In addition, its potential for supporting environmental research on an inter-species scale, e.g. by detecting phenological indicators from animal movement, is examined.

EAGLE M.Sc. idea presentations

EAGLE M.Sc. idea presentations

On Monday, 24th of September from 1:30 onwards the following EAGLE students will present their M.Sc. idea. Everybody is welcome to join their presentations and to provide feedback:

“Time-Series Analysis of Sentinel-1 and Sentinel-2 Imagery for Detection of active Morphodynamics in the Atacama Desert, Chile”

Risk assessment for flood events based on remote sensing data – a case study for North-Rhine Westfalia, Germany

“Remote sensing of water quality using Sentinel-2 towards a potential separation of harmful algal blooms from other Algae”

“Assessing the development of circular irrigation in South Africa since 19*”

“Tracking crop penology based on S1-Time series”

“Solar power potential in Portugal”

“An approach to optimize object-based classification: Mapping dead trees and gaps in Białowieża Forest”

“Deep learning for Instance Segmentation and a comparison to a Conventional Segmentation on historical aerial images of the Second World War”

“Drought Monitoring in University Forest with Hyper-Spectral And In-Situ Data”

New MSc thesis: Time series analysis in Colombian Orinoco Basin

New MSc thesis: Time series analysis in Colombian Orinoco Basin

Pilar Endara started her M.Sc. thesis on “Time series analysis of flooding and vegetation patterns in wetlands of the Colombian Orinoco Basin”

The ecosystems that are present within Colombian Orinoquia flooded savannas are currently being threatened by conversion of natural systems into intensive rangelands with introduced pastures, croplands and palm oil plantations. The loss of natural floodplain ecosystems has serious negative impacts on several important ecosystem services such as habitat quality for biodiversity, long-term carbon sequestration and water regulation.

In addition, this region’s natural vegetation endures strong intra-annual hydrological regimes, which in turn may affect the productivity, and consequently, the region’s carbon dynamics. Therefore, the objective of this thesis is to understand how, and if, the vegetation productivity in this area varies along the year, taking into account the strong hydrological variability.

The results of this thesis are going to be integrated with information from WWF Colombia to provide stakeholders and decisions-makers a tool that will allow them to assess for best practices of land use planning and land management.

First supervisor is Claudia Künzer, second supervisor is Martin Wegmann

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
Thesis idea: Time series analysis of flooding and vegetation patterns in wetlands of Colombian Orinoco Basin
MSc thesis started by Jakob Schwalb-Willmann

MSc thesis started by Jakob Schwalb-Willmann

Jakob Schwalb-Willmann just started his M.Sc. thesis titled “A deep learning movement prediction model using environmental data to identify movement anomalies”. He will combine animal movement and remote sensing data in order to develop a generic, data-driven DL-based model that predicts movements from movement history alongside environmental covariates in order to detect movement anomalies. He will establish simulated, controlled environments that allow precise adjustments of the model inputs to test the model’s feedbacks and its variability. It can be considered as a precursor study for the model’s deployment on real data and to only experimentally apply it on such due to the given constraints (time and content) of his M.Sc. thesis. The first supervisor is Dr. Martin Wegmann.