4 Brazee and Marra: Nondestructive Detection of Internal Decay in American Elms (Ulmus americana) bark so that the nail point contacted the sapwood, spaced 15 to 25 cm apart. These constituted measuring points (MPs) from which sonic and electrical-resistance data were collected. The MPs were sequentially num- bered with MP-1 placed at magnetic north. For all cross sections, every attempt was made to use as many MPs as possible, to a maximum of 24, proportionally to the circumference of the cross section. Diameter at breast height (dbh) was also collected for each tree. Sonic Tomography (SoT) Sensors are magnetically attached at each SoT MP and connected via cable to a detection module that is wirelessly connected to the PiCUS software on a lap- top. At each MP, sound waves are initiated with sequential taps from the “sonic hammer” connected wirelessly to the detection module. The software then uses these data along with the inter-MP distances to calculate sonic velocities. The software then pro- duces an image with a colorimetric scale depicting wood densities at that cross section. The colorimetric scale designates intact, non-decayed wood as brown (higher relative velocities) while decaying wood is designated by green, magenta, and blue (lower rela- tive velocities, in decreasing order). Electrical-Resistance Tomography (ERT) Positive and negative leads, attached to each pair of SoT-ERT MP nails, connect via cable to a detection module connected wirelessly to the PiCUS software. The detection module automates a process whereby, starting with one pair of leads and proceeding sequen- tially around the tree through each subsequent pair of leads, an electrical pulse is generated and detected by the other electrode pairs. Deviations from homogene- ity in the wood result in a map of relative electrical resistivity, correlating principally with water content but also changes in ion concentration and/or cell structure. The ERT map uses red to portray areas of highest electrical resistivity (low conductivity), pro- gressing through orange, yellow, green, and blue with decreasing resistivity (high conductivity). Data from SoT and ERT must be interpreted jointly to accurately predict the internal condition at each cross section of a tree, based on the following criteria (slightly modified from Marra et al. 2018): A) Maximum wood density and the absence of moisture represent sound (nondecayed) wood, which appears brown in the SoT and yellow, orange, and red in the ERT; ©2020 International Society of Arboriculture B) Maximum wood density and the presence of moisture represents incipient decay or bacterial wetwood, in that reductions in wood density are not detectable, which appears brown in the SoT and blue in the ERT; C) Reduced wood density and the presence of moisture represent active decay, which appears green, magenta, and blue in the SoT and blue in the ERT; D) Reduced wood density and the absence of moisture represent a cavity, which appears non- brown in the SoT and non-blue in the ERT. This hypothesis is based on destructive samples collected from 48 hardwood trees (Acer saccharum, A. rubrum, Betula alleghaniensis, B. lenta, and Fagus grandifolia) during two previous studies (Brazee et al. 2011; Marra et al. 2018), as well as guidelines pro- vided by the manufacturer. Statistical Analyses Chi-square goodness of fit was used to determine if there were significant differences in the frequency of decay incidence by injection history (injected vs. non-injected) using expected values (Zar 1999). Chi- square was also used to test for significant differences in the frequency of elms within each decay class. Decay classes (I = < 25%; II = 25 to 50%; III = 50 to 75%; and IV = > 75%) were established based on per- cent decay values generated from the SoT results. Analysis of variance (ANOVA) was used to test for significant differences among elms by decay inci- dence and injection history using the following vari- ables: (1) sampling height at MP-1 (distance from soil); (2) DSH; and (3) dbh. For elms with internal decay, linear regression was used to determine if there were significant differences in mean percent decay by DSH and dbh. Further, ANOVA was used to test for significant differences in percent decay by injection history, DSH class (I = < 100 cm; II = 100 to 125 cm; III = 125 to 150 cm; and IV = > 150 cm) and dbh class (I = < 75 cm; II = 75 to 100 cm; III = 100 to 125 cm; and IV = > 125 cm), excluding both DSH and dbh class I due to low sample size (n < 5). Post hoc analyses were per- formed on DSH and dbh class using Tukey’s HSD test. Percent decay values were arcsine-transformed prior to analysis (Zar 1999) while untransformed val- ues are presented in all graphs. Differences were determined to be significant at P ≤ 0.05 for all tests.
January 2020
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