Forest Resources ManagementOpen OpportunitiesForest ecosystems are facing rapid and severe changes due to climate change Increasingly frequent disturbances in Central Europe (Seidl et al., 2014; Senf and Seidl, 2021) affect ecosystem structure with cascading effects on ecosystem functioning and the provision of ecosystem services (Paul et al., 2020).
Diversification strategies are discussed as a predictor for forest resilience (Lloret et al., 2024). Such strategies may be implemented in forest adaptation by diversifying the local tree-species composition (e.g., species mixtures vs. monocultures), the local forest structure (e.g., height distribution), and the management types across a landscape; all of which are discussed as strategies to increase resistance and resilience to disturbances (Elmqvist et al., 2003; Hof et al., 2017; Morin et al., 2011), and to reduce trade-offs between ecosystem functioning and multiple ecosystem services (Knoke et al., 2017; Topanotti, 2024).
Transitions towards new forest management regimes, and in particular changes in tree-species composition, are slow processes due to the long production and planning periods in forest management. This contrasts with recent observations of increasingly severe events, such as the extreme mortality events following the drought in the year 2018 in Central Europe (Buras et al., 2020; Schuldt et al., 2020). Consequently, a new type of thinking developed that interprets disturbances as drivers of forest change rather than disruptions to a status-quo system that needs to be preserved (Buma and Schultz, 2020; Thom et al., 2017). Thom et al. (2017) showed that disturbance characteristics influence this adaptation-fostering effect of disturbances: An increasing frequency and intensity accelerated the adaptation process while an increasing size slowed it down. The study is based on simulation modeling in an unmanaged forest. In managed forests, however, post-disturbance management, such as salvage logging and planting, have a strong potential to alter these dynamics.
- Forestry Sciences, Geomatic Engineering
- Bachelor Thesis, Master Thesis, Semester Project
| Forest ecosystems are experiencing rapid changes due to climate-driven disturbances, which impact their structure, functioning, and ability to provide essential ecosystem services. Diversifying tree-species composition and management strategies is key to enhancing forest resilience, yet the slow pace of these changes contrasts with the urgency created by increasingly severe events like storms and droughts. While disturbances are now seen as opportunities for driving positive forest change, little is known about how historic storms, fires, bark beetle outbreaks and post-disturbance management have influenced biodiversity in managed forests. Addressing this gap is crucial for developing effective management strategies and forest policies to enhance forest resilience and ensure their adaptability to future challenges. - Agricultural, Veterinary and Environmental Sciences, Earth Sciences, Engineering and Technology
- Master Thesis, Semester Project
| Background: Recent years have seen accelerated tree mortality due to heat and drought, with varying responses across tree species. Understanding the physiological mechanisms leading to mortality is critical, particularly in response to drought-induced stress. Tree rings offer historical insights into growth patterns affected by environmental conditions, while remote-sensing-based vegetation indices provide continuous large-scale information about tree health since the mid-1980s. Exploring the correlation between these two data sources may reveal early signs of stress and improve our understanding of mortality mechanisms across key European tree species. This approach will help define species-specific mechanisms of tree mortality and improve our ability to monitor forest health over time.
- Environmental Sciences, Forestry Sciences, Landscape Ecology
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| Forest regeneration is a fundamental process ensuring the sustainability and resilience of ecosystems. Across Europe, tree species regeneration varies due to differences in climate, soil conditions, competition, and forest management practices. Understanding regeneration patterns is crucial for maintaining biodiversity, promoting forest resilience, and ensuring the long-term provisioning of ecosystem services. However, there is limited comprehensive analysis of how tree species regeneration has changed over time at a continental scale. By assessing regeneration trends, dominant regenerating species, and the role of functional groups (deciduous vs. coniferous), we can gain insights into the factors influencing forest regeneration in different regions. - Agricultural, Veterinary and Environmental Sciences, Engineering and Technology
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis, Semester Project
| Understanding forest structure, including species-specific density, is essential for sustainable forest management and climate resilience. Spruce (Picea abies) a dominant tree species in Swiss forests, plays a critical ecological and economic role, yet quantifying its density over large areas remains challenging. While traditional methods rely on labor-intensive field surveys, advancements in high-resolution remote sensing and deep learning enable the extraction of tree-level information across large spatial scales. True-color near-infrared (RGBI) aerial imagery, combined with deep learning, provides a promising approach for accurate, scalable mapping of spruce density. - Environmental Sciences, Geomatic Engineering, Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Tree species maps are essential for better forest management, forest cover, biomass, and biodiversity assessment. The temporal and spatial location and identification of tree species is extremely important and necessary for forest management and conservation. The use of remote sensing products in forestry allows for time flexible and cost-effective assessment of forest characteristics. Deep learning methods enable high predictive accuracy and have the potential to revolutionize forestry understanding, data collection and enable the development of numerous applications. Tree species identification is essential for assessing biodiversity, understanding forest resilience to climate change, and developing forest management strategies. However, identifying tree species is challenging, and further research needs to focus on developing new models to address this issue.
- Artificial Intelligence and Signal and Image Processing, Forestry Sciences, Geomatic Engineering
- ETH Zurich (ETHZ), Master Thesis
| Tree species identification is essential for biodiversity monitoring, forest management, and understanding ecological processes. Advances in computer vision and deep learning have enabled the use of multi-view convolutional neural networks (CNNs) to classify species by integrating complementary information from different views. While such techniques hold promise, the variability in resolution, size, and perspective of the data presents challenges that must be addressed for robust identification. Citizen science data provide a wealth of georeferenced plant images captured by volunteers. These datasets, which include diverse environments, seasonal conditions, and perspectives, can enhance the training and generalization of multi-view CNN models. This thesis explores the integration of multi-view data and citizen science images to develop a scalable, high-accuracy tree species identification framework. - Computer Vision, Forestry Sciences, Photogrammetry and Remote Sensing
- Master Thesis
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