Point dendrometers have been widely used to monitor stem growth and phenology as well as tree drought response in forestry and to drive irrigation scheduling in woody crop species.
If the technical improvements of these tools has allowed to reduce costs and to manage them by remote control, the meaning of the signal (increase/decrease of the stem radius in the short-medium and long time) remains nowdays very difficult to interprete. For this reason a few algorithms were developed in recent years (Giovannelli et al., 2007, Cocozza et al., 2018; Zweifel et al., 2016; Deslauriers et al., 2010) allowing more reliable reading of the physiological meaning of the dendrometer signals. Thus stem shrinking, swelling and seasonal trends were associated to precise phenological phases, tree water status, cold acclimation, carbon storage, wood quality.
Although the dendrometer signals are commonly used to monitor stem increase, carbon accumulation and physiological status of entire forest stands, they could be ideally used as a proxy of the health/vitality of single trees. For this purpose, there is a need to set new algorithms to analyse the dendrometer signals able to match with the particular growth conditions of trees in urban contexts. As point dendrometers can be easily integrated in wireless system and remote control networks, they can ideally be used in complex system of environmental monitoring of the urban and periurban area in a view of smart cities development.
In this context, we installed point dendrometers on Pinus radiata trees growing in a semi-urban garden near Rome with the aim to verify if the dendrometer signals could be used as a proxy of tree vitality. We verify the hypothesis that through the decomposition of dendrometer signals in phase cycles (amplitude and duration of shrinkage and swelling, ratio between amplitude of cycles and duration) is possible to extrapolate reliable index to associate to tree vitality. These results could be used as basis to develop species related databases and new algorithms to analyse dendrodata of multiple woody species growing in urban areas.
Evenaged fifty years-old Pinus radiata individuals were selected and, based on their vitality score, assigned into three classes: alive, compromised and dead (five trees for each class respectively). In order to avoid artifact signals due to the internal stem wood degradation or knots, tomographic scanning were performed around each stem by an impulse tomography unit (Arbotom technology). On mid-June, point dendrometers were installed on the stem at breast height and data were recorded every 15 min with a CR 1000 data logger (Campbell Scientific, Inc. Logan, UT, USA). The stem radius increment, ∆R (mm) and amplitude and duration of stem shrinkage (∆w, mm and h respecitvely), were calculated following the stem cycle analyses approach (Giovannelli et al., 2007).
During July 2020, alive trees showed a positive trend of the stem radius. On the contrary compromise and dead trees had a constant decrease of the stem radius. Thus alive trees had higher positive growth cycle (when the maximum stem radius value of a cycle exceed the previous ones) than compromise and dead trees. The duration of the stem shrinkage were significantly lower in alive trees than in compromise and dead ones. Moreover in alive trees, the beginning of stem shrinkage was delayed with respect to the dead trees showing that in the latter, the stem radial variations were due mainly to the temperature effect rather than to the radial and longitudinal water fluxes adjustments.
Our preliminary data showed that point dendrometers signals could be used to define ripetible and valuable proxies of the tree vitality and health in Pinus radiata growing in the periurban areas. An increase in the number of trees monitored is however necessary to better understand the relationship between the change in the rhythm of stem water and the decline in tree health.
Giovannelli et al., (2007). Journal of experimental botany 58, No. 10, pp. 2673–2683
Cocozza et al., (2018). Forests 9, 134
Zweifel et al., (2016). New Phytologist 211, 839–849
Deslauriers et al., (2011). Dendrochronologia 29:151–161