Lecturer Spotlight: Dr. Julia Rieder

Lecturer Spotlight: Dr. Julia Rieder

November 9, 2026

Why do some trees survive extreme drought while neighbouring trees of the same species and age suffer severe damage or even die? For Julia Rieder, answering this question means looking beyond individual trees and investigating the complex interactions between forest structure, soil conditions, competition and environmental stress. As a researcher at the Earth Observation Research Cluster (EORC), she combines forest ecology, field measurements and advanced LiDAR technology to understand how forests respond to a changing climate.
Julia studied Geography at the University of Würzburg, where she subsequently completed her Master’s degree in Applied Physical Geography, focusing on environmental analysis and geospatial methods. Her early research explored soil moisture dynamics in forest ecosystems, providing the foundation for her later work on drought responses and forest resilience. She successfully defended her PhD in June 2026, investigating the abiotic and biotic drivers of drought responses in European beech (Fagus sylvatica).
Her doctoral research addressed one of the pressing questions in forest ecology: why do individual trees respond so differently to the same extreme drought conditions? Following the exceptional drought years of 2018 and 2019, Julia investigated more than 500 mature European beech trees across 20 forest sites in northern Bavaria. By combining detailed field observations, soil analyses and three-dimensional forest structure information derived from LiDAR, she identified how tree characteristics, neighbourhood competition and local environmental conditions influence drought-induced defoliation and mortality.
A particularly interesting aspect of her work is the use of three-dimensional information to understand forest structure at the individual-tree level. Airborne laser scanning and mobile LiDAR provide detailed information about canopy architecture, tree dimensions and the spatial relationships between neighbouring trees. These observations help reveal how competition for resources and the surrounding forest structure influence the ability of individual trees to cope with environmental stress.
Julia also develops tools that make these advanced analyses more accessible. She created TreeCompR, an open-source R package for calculating tree competition indices using conventional forest inventory data as well as three-dimensional point clouds. The package bridges forestry, ecological research and remote sensing, allowing researchers to quantify competitive interactions between individual trees using different types of spatial information.
Today, her research at EORC also contributes to the EO4CAM project, where she works on the application of Earth observation for environmental monitoring and climate adaptation. Her broader interests include LiDAR-based 3D modelling, forest and tree structures, tree vitality and mortality, and the integration of remote sensing with ecological field observations.
In EAGLE, Julia teaches LiDAR Remote Sensing, introducing students to the possibilities of three-dimensional Earth observation. Students learn how laser scanning works, how point clouds can be processed and analysed, and how 3D information can be used to investigate vegetation structure and environmental processes. Her teaching connects technical methods with ecological applications, demonstrating how detailed spatial information can help answer questions that matter for forest ecosystems and their management.
For Julia, LiDAR is much more than a tool for measuring tree height. It provides a way to understand the complex three-dimensional relationships within forests and how these relationships influence ecosystem resilience. Her work shows how combining field ecology, remote sensing and programming can help us understand which forests are most vulnerable to climate change and how they might be managed to better withstand future droughts.

follow us and share it on:

you may also like:

Lecturer Spotlight: Thilo Erbertseder

Lecturer Spotlight: Thilo Erbertseder

Thilo is the person at EORC who can tell you exactly how much air pollution was hanging over a given megacity in any given year since the 1990s, and what that actually meant for the people living there. He teaches atmospheric composition and urban remote sensing at...

Lecturer Spotlight: Mirjana Bevanda

Lecturer Spotlight: Mirjana Bevanda

There's a particular moment in the EAGLE program that shows that EAGLE is different, it is the moment the students realize their thesis isn't just another task to receive a M.Sc., their internship or InnoLab isn't just ECTS points, but it's a real world application....

Lecturer Spotlight: Julian Faeth

Lecturer Spotlight: Julian Faeth

Dr. Julian Fäth is a postdoctoral researcher at our EORC. His teaching combines remote sensing, forest ecology and geospatial data science with a strong focus on operational applications and climate adaptation. Since 2018, Julian has been actively involved in...

Lecturer Spotlight: Martin Bachmann

Lecturer Spotlight: Martin Bachmann

Martin's the lecturer who actually built the systems behind the hyperspectral data EAGLE students learn to analyze. He's a research scientist with the Land Surface Dynamics Department at DLR's German Remote Sensing Data Center in Wessling, and his over twenty years of...

Lecturer Spotlight: Tanja Kraus

Lecturer Spotlight: Tanja Kraus

Tanja is the lecturer whose work keeps landing directly on the desks of the people who actually have to act on it: Bavarian ministries, municipalities, foresters, farmers and planners. She joined DLR's German Remote Sensing Data Center (DFD) in 2002 and has been...

Lecturer Spotlight: Michael Wurm

Lecturer Spotlight: Michael Wurm

Michael's the guy you want in front of the class when it's time to actually open up an image and start segmenting it. At EAGLE he teaches the urban side of things: environmental exposure, pollutants, green space, noise, traffic, air pollution, urban heat islands. And...

bzgl. Art. 50 Verordnung (EU) 2024/1689 (AI Act):

Überwiegend werden eigene originäre Texte und Bilder des EORC genutzt, jedoch sind einige Inhalte von blog post Texten teilweise mit Hilfe von KI überarbeitet worden und einige Bilder sind ganz mit KI erstellt, die jedoch deutlich keine photorealistische Darstellungen abbilden. Aussnahmen sind KI generierte fernerkundliche Datensätze, die explizit für Forschungszwecke mit KI erstellt wurden, hier wird aber durch den assoziierten Text der wissenschaftliche Grund der KI generierten Bilder erläutert.

Share This