New PhD student at the Dept. of Remote Sensing

New PhD student at the Dept. of Remote Sensing

m

News

November 2, 2015

Steven Hill

Steven Hill

We wellcome Steven Hill who has just started working as a PhD candidate at the Dept. of Remote Sensing since October 2015. After the successful completion of his M.Sc thesis on “Predicting the Forest Development after Natural Disturbance using Airborne LiDAR”, he will  will now more profoundly deal with topics such as multi-temporal 3D data processing,forest structure modeling and spatial characterization of regeneration dynamics following natural disturbances. The tentative title of his upcoming PhD thesis will be “Monitoring the structural parameters of forest habitats by multi-source/multi-temporal remote sensing data”. Steven Hill has also been recently accepted as a fellow of the Helmholz Research School on Mechanisms and Interactions of Climate Change in Mountain Regions (MICMoR).

 

you may also like:

A Glimpse into Our Research: Data on Display in the Foyer

A Glimpse into Our Research: Data on Display in the Foyer

Stepping into the foyer, visitors are now greeted by large, striking images that tell the story of our research through data. Each visual represents a unique scientific perspective – from the Arctic to the cultivated landscapes of Bavaria, and from forest canopies to...

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand Supervised by Dr. Julian Fäth and...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.