280 Burcham et al.: Internal Defects and Their Effect on Trunk SurfaceTemperature of the stem. The height of each transect was adjusted to equal twice the stem’s diameter, measured 1 m above the ground (Fig- ure 4). Statistical representation of transect surface tempera- ture was derived using a measure of central tendency, the mean [1] variability (viz. dispersion from the mean), the standard deviation [2] and distribution symmetry, Pearson’s skewness coefficient [3] within each transect. In these equations, ti sents a temperature value at position i, the mean temperature, and n represents the repre- represents total num- ber of temperature measurements within a transect. Second, temperature data were extracted from the trunk along five parallel, vertical lines positioned equidistantly across the stem breadth (Figure 4). Surface temperature mea- surements extracted along each line were combined by av- eraging the measurements at each vertical position into a single column; this produced an approximation of the verti- cal temperature change along the 3 m trunk section. Tem- peratures extracted from individual stems were randomly checked for dissimilarities in trending imparted from emissiv- ity measurement errors caused by the camera’s viewing angle. With these values, trunk surface temperature and relative de- fect CSA (%) were plotted together in a dual y-axis coordinate plane as a function of stem height for all trees in the experi- ment, allowing an evaluation of vertical changes in temperature alongside corresponding changes in internal condition (Figure 5). For each tree, a trend line was fitted to the trunk surface temperature using simple linear regression. At the trunk posi- tion nearest the maximum extent of internal defects, the differ- ence between the surface temperature (point “A”) and its trend line (point “B”) was determined and recorded (Figure 5). This value (“deviation from linear trend”) represented the magnitude of localized temperature anomalies near the position of inter- nal defects relative to the general vertical temperature trend. The linear relationship between relative defect CSA (%) and the statistical descriptions of surface temperature, includ- ing those extracted from rectangular transects (mean, stan- dard deviation, skewness) and vertical linear plots (deviation from linear trend), were separately evaluated in scatterplots containing all trees and defect categories. Bivariate correla- tion analysis was subsequently used to assess the linear de- pendence of these variables using the Pearson correlation coefficient (r) and coefficient of determination (r2 ). Multiple tests were conducted after separating the sampled stem sec- tions into three representative groups according to their defect category, including 1) undamaged and discolored, 2) decayed and cavitated, and 3) decayed + discolored and cavitated + discolored. Finally, the suitability of simple, polynomial, or logarithmic linear regression models for this dataset was as- sessed based on the degree of dependence displayed in scat- terplots and correlation statistics. All statistical tests were con- ducted in SPSS Statistics 19.0 for Windows (IBM Corp. 2010). ©2012 International Society of Arboriculture Figure 4. As shown in this diagram, temperature data was extract- ed from each tree using two separate methods. First, tempera- tures within vertically adjacent rectangular transects (a) were ex- tracted for analysis. Second, temperature values were extracted along five lines equidistantly spaced across the stem breadth (b) and averaged into one linear trend line. Temperature values were then compared with the quality of internal stem tissue immedi- ately beneath the area from which they were extracted. Figure 5. For each specimen, the relationship between vertical temperature changes and internal stem condition was summa- rized in a dual y-axis coordinate plane containing the trunk sur- face temperature (°C), extracted from five parallel vertical lines, and relative defect CSA (%) as a function of stem height (cm). As seen in the example from specimen J, the difference between the surface temperature (point “A”) and its trend line (point “B”) was assessed at the position containing the largest internal defect.
November 2012
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