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Department of Environmental Systems Science

AcronymD-USYS
Homepagehttp://www.usys.ethz.ch/
CountrySwitzerland
ZIP, City 
Address
Phone
TypeAcademy
Parent organizationETH Zurich
Current organizationDepartment of Environmental Systems Science
Child organizations
  • Institute for Atmospheric and Climate Science
  • Institute for Environmental Decisions D-USYS
  • Institute of Agricultural Sciences
  • Institute of Animal Science
  • Institute of Biogeochemistry and Pollutant Dynamics
  • Institute of Integrative Biology
  • Institute of Terrestrial Ecosystems
  • USYS Transdisciplinarity Lab


Open Opportunities

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Betriebliche Optimierungsmodelle in der Schweizer Landwirtschaft

  • ETH Zurich
  • Chair of Agricultural Economics and Policy D-USYS

Basierend auf Ihren Kenntnissen aus der Vorlesung «Optimierung landwirtschaftlicher Produktionssysteme» erstellen Sie ein Optimierungsmodell in Excel oder R und beantworten damit eine von Ihnen erarbeitete Forschungsfrage.

  • Agricultural Economics, Environmental Sciences, Operations Research
  • Bachelor Thesis

Designing an easy-to-apply Swiss wood valuation framework for scientific applications and decision support systems

  • ETH Zurich
  • Forest Resources Management

Forests provide essential ecosystem services, with wood production being a key source of income for forest management. However, wood is a heterogeneous good, and deriving accurate revenues and costs from forest growth simulations is complex. This project aims to develop a sophisticated wood valuation framework for Switzerland, addressing the limitations of current models. The framework will enhance decision support systems, aiding in efficient forest management, economic forecasting, and policy design

  • Environmental Sciences
  • ETH Zurich (ETHZ), Master Thesis

Tree species identification using deep learning

  • ETH Zurich
  • Forest Resources Management Other organizations: Photogrammetry and Remote Sensing (Prof. Schindler)

Tree species maps are crucial for effective forest management, biomass assessment, and biodiversity monitoring. Remote sensing products offer flexible and cost-effective ways to assess forest characteristics, while deep learning methods promise high predictive accuracy and transformative applications in forestry. This study aims to apply novel deep learning approaches to detect and identify individual trees and tree species in mixed forests. By addressing the challenges of tree species identification, this research will enhance biodiversity assessment, forest resilience understanding, and management strategies.

  • Artificial Intelligence and Signal and Image Processing, Forestry Sciences, Geomatic Engineering
  • ETH Zurich (ETHZ), Master Thesis, Semester Project

Evaluating the Potential of Any-Aged Forestry Compared to Regular and Close-to-Nature Management: A Forest Modeling Study

  • ETH Zurich
  • Forest Resources Management

This Master’s thesis explores whether Any-Aged Forestry (AAF), could offer a more optimal management strategy compared to Regular Forest Management (RFM) and Close-to-Nature management in a Swiss forest context. Using Forest Studio, a forest modeling platform under development, the project will implement and test the AAF approach alongside conventional management systems in a case study area in Canton Zurich. The goal is to evaluate performance across ecological and economic indicators.

  • Forestry Sciences not elsewhere classified
  • ETH Zurich (ETHZ), Master Thesis

Making aviation's climate claims credible

  • ETH Zurich
  • Climate Policy Group

Can we keep flying while still meeting climate goals? This Master thesis dives into a bold idea: balancing fossil jet fuel emissions by locking away an equivalent amount of CO₂ underground. But how can we be sure it actually works — and that the public trusts it?With this work, you will explore the rules, standards, and safeguards needed to make this vision credible. You’ll help shape the future of climate accountability in aviation as part of a broader project on geologically-balanced fuels. The thesis will be supervised by researchers from ETH Zurich and the University of Oxford, with the possibility to conduct the research at either locations. 👉 Curious? Find out more in the full project description in the leaflet attached.

  • Accounting, Auditing and Accountability, Business and Management, Environmental Sciences, Justice and Legal Studies, Policy and Administration, Tourism, Transportation
  • ETH Zurich (ETHZ), Master Thesis, Other specific labels

Does Management Unit Definition Matter? Impacts on Ecosystem Services in Contrasting Landscapes

  • ETH Zurich
  • Forest Resources Management

Management units (MUs) are the core spatial entities where forest management strategies are applied. Their delineation is typically based on stand characteristics (e.g., tree species, age structure), ownership boundaries, or legacy units established decades ago. Before a new forest management plan is developed—typically every ten years—foresters revise these MU boundaries using aerial imagery and ground-based assessments. However, the implications of how these boundaries are defined are rarely questioned. This Master’s thesis investigates how different approaches to MU definition, and how frequently they are revised, may alter the expected provisioning of ecosystem services (ES) such as timber and biodiversity. The study compares these impacts across two contrasting landscapes: one topographically complex and one relatively homogeneous.

  • Forestry Sciences not elsewhere classified
  • ETH Zurich (ETHZ), Master Thesis

Exploring the Impact of Process Scaling in Forest Dynamics Models: Tree-Level vs. Stand-Level Definitions

  • ETH Zurich
  • Forest Resources Management

Forest dynamics models often define ecological processes such as growth, mortality, and regeneration at a fixed spatial scale, typically the tree or stand level. This thesis explores how different spatial definitions of these core processes affect long-term projections of forest development. Using an existing forest dynamics model with both tree-level and stand-level formulations, the study will compare simulation outcomes and assess computational performance for applications at larger landscape scales.

  • Forestry Sciences not elsewhere classified
  • ETH Zurich (ETHZ), Master Thesis

What does just mean in climate mitigation?

  • ETH Zurich
  • Climate Policy Group

One of the biggest hurdles to climate policy action is low public acceptance. Justice is a key driver of policy acceptance, but it remains unclear why and how people make judgments on this. We are developing a tool to measure justice perceptions using distributive justice principles, and are looking for a Master's student to do a project using a mix of qualitative interviews and quantitative analysis of multi-country survey data. 

  • Political Science
  • Master Thesis

Mapping spruce density using aerial imagery and deep learning

  • ETH Zurich
  • Forest Resources Management Other organizations: EcoVision Lab

Climate change is increasing tree mortality due to drought and biotic infestations, but current detection methods are limited by data availability and low transferability. This study aims to use deep learning with true color near-infrared RGBI aerial imagery to detect spruce mortality in mixed forests. By integrating field inventories and RGB imagery, the method will be analyzed using R or ArcGIS Pro to accurately assess vegetation conditions.

  • Environmental Sciences, Geomatic Engineering, Information, Computing and Communication Sciences
  • Master Thesis, Semester Project

Bottlenecks in Climate Change Adaptation for Arable Crop Farming

  • ETH Zurich
  • Chair of Agricultural Economics and Policy D-MTEC

This master thesis examines the bottlenecks in climate change adaptation for arable crop farming, with a focus on the behavioral and socioeconomic factors. By analyzing survey data from 700 farms in Germany, it aims to identify the key barriers to effective uptake of adaptation measures and provide insights for developing more efficient and targeted strategies to enhance agricultural resilience to climate change.

  • Agricultural Economics
  • Master Thesis
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