Forest canopy height is a key indicator of forest growth, disturbance, carbon storage, and ecosystem resilience. However, existing large-scale canopy height products are limited by coarse spatial resolution, restricted temporal coverage, or reduced accuracy in remote regions such as boreal forests. By integrating multi-source satellite observations, photogrammetry, laser altimetry, and advanced machine learning, this project aims to establish an unprecedented multi-decadal record of forest structural change spanning from the Alps to the Arctic.
The project is jointly led by Prof. Livia Piermattei (Remote Sensing of Environmental Changes Group, Department of Geography, University of Zurich) and Prof. Jan Dirk Wegner (EcoVision Lab, Department of Mathematical Modeling and Machine Learning, University of Zurich).
The project is funded by the Swiss National Science Foundation (SNSF) through the project "From Alps to Arctic: Satellite-based Assessment of Forest Canopy Height across Decades", and involves a network of national and international collaborators from Switzerland, Austria, Norway, France, and Canada.
The project team will consist of three PhD students and one postdoctoral researcher.
The announced PhD position is part of the Remote Sensing of Environmental Changes (RSE) group in the Department of Geography at the University of Zurich, Switzerland.
This PhD project will focus on reconstructing forest canopy height models from optical stereo satellite imagery across alpine and boreal forests. The project will exploit archives of stereo satellite imagery (e.g., SPOT-5, Pléiades, WorldView), ArcticDEM, satellite laser altimetry data (ICESat-2, GEDI), and airborne LiDAR data to generate multi-temporal digital surface models (DSMs) and canopy height models (CHMs) spanning more than two decades.
Beyond the methodological developments, the PhD candidate will use the generated canopy height datasets to investigate long-term forest structural dynamics. Research will focus on quantifying spatiotemporal forest change (growth, loss, and regeneration), characterising disturbance processes, and analysing ecosystem responses to natural and anthropogenic drivers across alpine and boreal forests.
The candidate will be based at the Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. The position is primarily office-based, with the flexibility to work remotely one day per week.
You hold a MSc degree in photogrammetry, remote sensing, geomatics, geodesy, physical geography, environmental sciences, computer science, geoinformatics, aero/astro engineering, or a related discipline.
Experience or strong interest in at least one of the following:
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Satellite or airborne remote sensing data processing/analysis
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Stereo photogrammetry and/or SfM software (open-source or commercial)
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Very-high-resolution commercial satellite image processing and/or analysis
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Airborne LiDAR, and/or spaceborne laser altimetry (GEDI, ICESat-2) analysis
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Geospatial data processing
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Scientific programming (Python, R, Julia, or Matlab)
Other relevant, but optional experience (ideally one or more):
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Point cloud processing and/or analysis
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Computer vision and/or machine learning involving geospatial data
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Forest science
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Linux, Git/Github, Jupyter, Cloud computing
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Open-source geospatial stack (e.g., GDAL, PDAL, GeoPandas, xarray)
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Excellent written and oral communication skills (publication or other technical writing, conference poster or talk)
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A fully funded 4-year PhD position.
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Access to unique international remote sensing datasets.
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Project collaboration with leading forest and remote sensing researchers across Europe (such as WSL, TU Wien, NIBIO, and IGE Grenoble) and Canada (Canadian Forest Service).
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Excellent research infrastructure and computational resources.
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A stimulating and supportive research environment at the University of Zurich.
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Opportunity to collaborate with both remote sensing and machine learning research groups at the University of Zurich.
For any questions about the position and the application process, don't hesitate to contact Livia Piermattei.
We look forward to receiving your application with the following documents:
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Cover letter (what excites you about this position and the work you have done) (max two pages)
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CV (max three pages)
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List of publications (if any)
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Master certificate
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Master thesis (or draft if not yet completed)
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Names and contact information (email) of 2 referees.
Applications will be reviewed starting on 17 August 2026 and will continue until the position is filled. The anticipated start date is 1 January 2027.
