Thesis subject

MSc thesis topic: Can we detect small temporal differences in spring/fall phenology using high spatial and temporal resolution remote sensing data?

As a consequence of climate change, trees have to adapt to new conditions. To study which beech tree from which location will be best adapted to future climate, (beech) trees from all over Europe were planted in a randomized block experiment called a provenance trial. These trees are regularly measured.

Background

One of the observed differences between the different provenances is the moment they start to grow leaves in spring, and shed leaves in autumn. In the experimental plot at the Oostereng (close to Wageningen), 33 different beech provenances were planted 25 years ago, in subplots of 10x10m, each containing 50 trees. The small size of the experiment, combined with the short time period in which differences between the provenances can be observed, asks for high spatial and temporal resolution remote sensing data.

In 2024 a large number of UAV flights (RGB, multispectral and LiDAR) were done, which are continued in 2025. Last year a student mainly looked into the multispectral data, so for this year we plan to investigate if UAV-LiDAR and potentially RGB data are suitable to capture the differences in leaf development in spring and fall for the different provenances.

Objectives and Research questions

  • Investigate the possibilities of the different remote sensing data sources to capture variation in spring and fall phenology on a subplot level for the Oostereng Beech provenance trial.

Requirements

  • Affinity with forest ecology
  • Followed the Advanced Earth Observation course (GRS32306), since drone data processing will be part of the methodology

Literature and information

Theme(s): Sensing & measuring; Integrated Land Monitoring