Forest Resources Management

Open Opportunities

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

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

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

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

Assessing spruce mortality in mixed forests using RGBI aerial imagery and deep learning ETH Zurich Forest Resources Management Climate change is causing increased tree mortality due to drought and biotic infestations, but current methods for automatic detection are limited by data availability and low transferability. This study aims to develop a deep learning approach using 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. The latest advancements in remote sensing data offer a promising solution to accurately assess vegetation conditions at various scales. Environmental Sciences ETH Zurich (ETHZ), Master 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

The need for criteria and indicators to qualitatively assess forest development ETH Zurich Forest Resources Management Climate change is altering forest composition and structure globally, necessitating forecasts of management approaches to sustain forest ecosystem services (ESS). Models of forest development (MFD) and decision support systems (DSS) are key tools, but linking their outputs to specific ESS indicators remains challenging. This project aims to identify and assess the use of criteria and indicators in forestry, evaluate modelling results from MFD and DSS, and match these outputs with relevant forest characteristics such as biodiversity and carbon storage. The goal is to develop proxies for assessing the potential diversity of future forests. Environmental Sciences Bachelor Thesis

Economic perspectives on landscape connectivity and collaboration in bark beetle management ETH Zurich Forest Resources Management Climate change is accelerating the impact of spruce bark beetle (Ips typographus) calamities on Norway spruce forests, particularly in Central Europe. Proactive, long-term adaptation of tree-species composition is more promising than reactive outbreak control for managing these beetle outbreaks. However, studies suggest that the spatial configuration of tree species may have an even greater effect on reducing pest spread. This project aims to evaluate the effectiveness of different management strategies, their interactions, and the importance of cooperation among forest owners in mitigating economic losses and enhancing ecosystem services. Agricultural, Veterinary and Environmental Sciences, Environment and Resource Economics ETH Zurich (ETHZ), Master Thesis

How does tree growth respond to release events? ETH Zurich Forest Resources Management Forest stands are frequently affected by natural and manmade disturbances, such as windthrow, insect calamities, or tree harvests. Small-scale disturbances, which cause partial stand removal, are more common and lead to changes in light, microclimate, and wind forces for remaining trees. The response of trees to these disturbances varies based on species, morphology, and life history, influencing forest resilience. This project aims to develop and test hypotheses on factors determining tree response to disturbances, using long-term data from research plots in Switzerland. Environmental Sciences, Forestry Sciences Master Thesis