A machine learning approach to fill gaps in dendrometer data

The machine learning algorithm extreme gradient boosting can be employed to address the issue of long data gaps in individual trees, without the need for additional tree-growth data or climatic variables.

The susceptibility of dendrometer devices to technical failures often makes time-series analyses challenging. Resulting data gaps decrease sample size and complicate time-series comparison and integration. Existing methods either focus on bridging smaller gaps, are dependent on data from other trees or rely on climate parameters. In this study, we test eight machine learning (ML) algorithms to fill gaps in dendrometer data of individual trees in urban and non-urban environments. Among these algorithms, extreme gradient boosting (XGB) demonstrates the best skill to bridge artificially created gaps throughout the growing seasons of individual trees. The individual tree models are suited to fill gaps up to 30 consecutive days and perform particularly well at the start and end of the growing season. The method is independent of climate input variables or dendrometer data from neighbouring trees. The varying limitations among existing approaches call for cross-comparison of multiple methods and visual control. Our findings indicate that ML is a valid approach to fill gaps in individual trees, which can be of particular importance in situations of limited inter-tree co-variance, such as in urban environments.

The online version contains supplementary material available at 10.1007/s00468-024-02573-y.