Trees feel the environment. Trees know the environment. Trees sleep and trees are active. In the figure, we see the dendromenter data (named “original”) of a tree with some models trying to predict the tree growth (based on different time bins). We additionally see a rough […]
Explainable Artificial Intelligence (XAI) is gaining importance in various fields, including forestry and tree-growth modelling. However, challenges such as evaluating model interpretability, lack of transparency in some XAI methods, inconsistent terminology, and bias towards specific data types hinder its integration.In their article our colleagues Anahid Jalali, […]
Presentation given by our ecologist Anita Zolles (BWF) on the topic of AI for climate sensitive tree growth modeling. This scientific meeting was held in the week of April 17-21, 2023 in Palencia (Spain) to address Artificial Intelligence in the field of Ecosystem Management in its […]
(by Albert Villalobos Gasca, Umweltdata GmbH) Being able to describe with exactitude the shape of the tree crowns has always been a complex task. Nowadays, Terrestrial Laser Scanners (TLS) can play a big difference in the creation of twin models. Applied to the forests, the result […]
(by Anita Zolles, BFW) Zooming in on our dendrometer data! Did you know that tree circumference can change from one hour to the next? Aaaaaand… are they only increasing, or can tree trunk sizes also decrease? Actually both, as we can see in the three graphs […]
The goal of the work package 6 is to develop machine learning prediction model for tree growth using a data centric paradigm to achieve a high performance AI-model on high-frequency tree growth data. Furthremore, we aim to develop a framework focusing on data quality improvement by […]
