A temporal segmentation approach for dendrometers signal-to-noise discrimination

Automatic analysis of point dendrometer time series (DTS) registering radial stem variations of trees is of relevant interest to study tree water use and growth. Unfortunately, data from such sensors are often characterized by a large amount of noise that needs to be distinguished by sensors responses induced by biological processes (signal) and irregular fluctuations due to electrical disturbances, malfunctions, or external inputs (noise) such as stem flow, perturbation induced by animals, stem temperature and humidity variations. Although some algorithms have been developed to adjust jumps and correct peaks in DTS, it is nowadays challenging to extract biological signals after a large number of corrections and artifacts introduced during denoising processes. In this study, we present an alternative methodology consisting of the first attempt to automatically identify days in which the dendrometers are registering information related to the activity of the tree and relevant for a specific study (days-of-signal). Through (i) per-day temporal segmentation of different stem behaviors, (ii) daily temporal features extraction, and (iii) automatic days-of-signal and days-of-noise discrimination, we automatically analyzed 19 million DTS records acquired during three years by 12 dendrometers installed on xylem and bark at different stem heights from the collar, at the bottom and top-level, of Pinus sylvestris trees. To train and assess the performance of the model, we constructed a reference dataset by labelling 600 daily DTS into days-of-signal or days-of-noise. As a result of our model application, we detected 3,534 days-of-signal among the altogether 13,152 measurement days with a per sensor overall accuracy, calculated using the reference dataset, ranging between 100% and 82%. Finally, we showed the trend of stem shrinkage and swelling over the three years study period. The large accuracies obtained over the different sensors suggest that our method is versatile and generalizable.