A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study

As a dominant terrestrial ecosystem, forests play a pivotal role, which is substantially challenged by climate extremes. At the same time, the practice of patient science to investigate and understand different intricate climate-driven phenomena is no longer an option. On the other hand, recent technological advancements enable scientists to simultaneously collect and analyse a large volume of complex data. High-resolution tree stem radius measurements and predictive simulation through machine learning algorithms offer powerful opportunities for understanding these dynamics. However, when these machine learning methods are applied without careful consideration of data quality, model biases, and other critical factors, their potential is often compromised. In this study, we aimed to evaluate four Deep Learning algorithms (namely CNN, LSTM, Transformer, and ETSFormer), using automatically measured and high temporal resolution tree stem radius data. We explore the complexities of handling voluminous and authentic datasets required by these algorithms. Initial experiments show that it is possible to achieve an MAE as small as 0.0026 mm on the full data. Furthermore, our study delves into the temporal resolution of data, demonstrating the feasibility of using reduced datasets without compromising accuracy levels. Our best result showed that a reduction of 97 % in collection events increases the MAE by only 6 % with the LSTM model, demonstrating that resource use optimisation can be achieved by slightly reducing the temporal resolution of data collection with marginal error increase. This also shows that LSTM can effectively capture longer-term and complex dependencies, which indicates promising future work with additional environmental data. • Three state-of-the-art Deep learning (DL) algorithms, along with a baseline DL model, were compared for forecasting tree stem radius using point dendrometer data. • Variations in temporal resolution for data acquisition significantly influenced the forecasting performance of DL models. • A notable trade-off in forecasting accuracy was observed when using reduced temporal resolutions.