deepT

deepT investigates times series of stem radius changes with deep neural network (DNN) algorithms in order to recognize tree species, growth patterns and tree water deficit-induced stem shrinkage characteristics. deepT tests different types of DNN algorithms, develops a Python environment for extensive data sets and quantifies the reliability of different approaches to recognize species-specific patterns and judge their potential for making predictions.

deepT is a collaboration bewtween EMPA (Mirko Lukovic, Cellulose & Wood Materials, Empa Dübendorf) and WSL.

Publications:

Reconstructing radial stem size changes of trees with machine learning
Mirko Luković, Roman Zweifel, Guillaume Thiry, Ce Zhang and Mark Schubert
Journal of the Royal Society Interface, 19, 20220349 (2022).