Maxime Dekegeleer
Papers
1
Total Citations
4
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1
About
Maxime Dekegeleer is a researcher at the forefront of quantitative wood anatomy, a field that bridges ecology, forestry, and computational imaging. Their work centers on developing high-throughput methods to extract environmental and climatic data from tree rings and wood structures. Dekegeleer’s most notable contribution is the creation of a dedicated pipeline for high-resolution digitization and automated segmentation of wood surfaces, as detailed in their highly cited 2025 paper (4 citations). This innovation dramatically accelerates the traditionally labor-intensive process of analyzing wood anatomy, enabling researchers to unlock centuries of environmental information stored within trees. By streamlining the pathway from raw wood samples to quantitative data, Dekegeleer’s work empowers large-scale studies on forest health, climate change, and tree physiology. Their approach integrates advanced imaging techniques with machine learning, setting a new standard for reproducibility and throughput in the field. With a growing citation impact, Dekegeleer is recognized as a rising leader in computational wood anatomy, making it possible to study tree responses to environmental stress at unprecedented scales. Their research is essential for ecologists, dendrochronologists, and climate scientists seeking to decode the biological archives held within wood.
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Top Papers
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