Using several experimental approaches, we have demonstrated that electrical resistivity tomography (ERT) is a reliable nondestructive tool for estimating the moisture content of heartwood in situ. ERT measurements show that water pockets in heartwood (wetwood) are present in a large majority (90%) of silver fir ( Abies alba Mill.) trunks.
Context For wood professionals, the presence of wetwood in wood logs leads to an increase in costs, especially during the drying process. Assessing these internal properties in situ with a nondestructive method will provide reliable information for improved management of respective forests.
Aims The objective of this study was to evaluate the efficiency of the electrical resistivity tomography (ERT) tool to detect wetwood in standing trees and to estimate the mean moisture content (MC) of silver fir trunks.
Methods The study was carried out in 3 forests located in the region “Massif Central” in France. We selected 58 silver fir trees, visually healthy and without visible default. Each tree has been subject to regular ERT measurements for more than a year. At the same time, one to three cores were taken from each tree in order to measure the actual MC of the wood.
Results 90% of the silver fir trees showed the presence of wetwood in their heartwood. Our results showed a significant correlation between the mean heartwood MC measured on cores and the mean electrical resistivity (ER) obtained with ERT.
Conclusion (i) The presence of wetwood occurs in a high proportion of the silver fir trees studied, and (ii) ERT can be used to estimate the average MC of the heartwood of standing trees. However, the data provided by ERT vary seasonally and do not allow the precise location of wetwood.
Electrical resistivity tomography; Wetwood; Abies alba; Moisture content; Nondestructive testing
Martin, L., Cochard, H., Mayr, S. et al. Using electrical resistivity tomography to detect wetwood and estimate moisture content in silver fir (Abies alba Mill.). Annals of Forest Science 78, 65 (2021). https://doi.org/10.1007/s13595-021-01078-9
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The datasets generated and/or analyzed during the current study are available in the Data INRAE repository, https://doi.org/10.15454/VT1CSA