Journal of Arboriculture 31(2): March 2005 61 Table 3. Target ranges for structures, pedestrians, and vehicles. Vehicular, pedestrian, and structural targets are categorized by their frequency or monetary value. For example, the probability of a vehicle or pedestrian occupying a target area in target range 4 is between the lower and upper limits of 1/10,000 and 1/500. Using the value of a “hypothetical life” of £1,000,000 the structure value within the target range 4 is £101–2,000. Target range 1 Structure (repair value)* (a) Very high value (b )Habitable 2 3 4 5 6 * High value Moderate–high value Moderate value Low value Very low value 10–36 per hour 1–9 per hour < 1 per hour ≤ 1 per day ≤ 1 per week is used to produce a data set (Table 4) of comparative dry weight estimates of trees and branches ranging from 10 to 600 mm (0.4 to 24 in.) diameter. An upper limit of 600 mm has been selected to represent a 1/1 impact potential on the premise that impact from a tree with a stem diameter of 600 mm (24 in.) has a 1/1 probability of causing maximum possible damage to most frequently encountered targets. From this point, the impact potential reduces to 1/23,500 for a 10 mm (0.4 in.) branch or tree. For initial assessments, the probabilities are grouped into ranges 1 through 5 in Table 5. Impact potential range 1 represents a range of diameter greater than 450 mm (18 in.) and is calculated from the estimated dry weight of the 600 mm (24 in.) diameter tree. Range 1 has a 1/1 probability of causing significant harm upon impact with a target. Range 5 repre- sents 10 to 25 mm (0.4 to 1 in.) diameter and has a prob- ability 1/2,500 of causing significant harm upon impact with a target. If, in exceptional circumstances, the failure of a branch of less than 10 mm (0.4 in.) diameter is considered significant, it has a probability of 1/23,500. Probability of Failure Accurately assessing the probability that a tree or branch will fail is highly dependent on the skill and experience of the assessor. This component of the system provides five ranges, each range representing a range of probability of failure within a year, expressed as both a percentage and a ratio calculated from the upper value of that range. Having assessed the tree, the assessor should visualize 100 similar trees in a similar condition in the same environment and estimate how many would be likely to fail during the coming year. If the answer to this question is none, then consider 1,000 or 10,000 trees. A probability of failure range 1 to 5 (Table 6) is then selected. Employing this method of assessing probability, inspectors become increasingly aware both of features and conditions that lead to tree failure and of the probability of tree failure. Observing the patterns and frequency of tree failure within this structured framework and applying scientific knowledge to these observations can significantly increase the consistency with which tree inspectors assess the probability of tree failure. ©2005 International Society of Arboriculture Pedestrian frequency > 36 per hour–constant Vehicular frequency (a) Motorway (b) Trunk road, built-up and non-built-up areas (c) Principal road, built-up area Principal roads, non-built up-area Minor roads, moderate use or poor visibility Minor roads, low use and good visibility Minor private roads and tracks (no data available) None 1/500 1/10,000 1/120,000 Structure values represent the likely cost of repair or replacement. Very high = £50,001–1,000,000; high = £10,001–50,000; moderate–high = £2,001–10,000; moderate = £101–2000; low = £11–100: very low = ≤£10. Table 4. Biomass dry weight estimates (Tritton and Hornbeck [1982]). Fraction of dry Dbh* (mm) 10 25 50 100 150 200 250 300 350 400 450 500 550 600 Dry weight (kg) y = axb ** 0.11263 1.0713 5.8876 32.357 87.67 177.82 307.77 481.81 703.8 977.26 1305.5 1691.4 2138 2647 weight (600 mm) as a ratio 1/23,505.722 1/2,471.6699 1/449.74 1/81.834 1/30.203 1/14.891 1/8.604 1/5.496 1/3.762 1/2.71 1/2.03 1/1.566 1/1.24 1/1 *Diameter at breast height, 1.37 m (4.5 ft). **x = dbh (mm); y = dry weight estimate; a = allometric coefficient 0.1126294414; b = allometric coefficient 2.458309949. 1/20 1/100 Probability ratioz 1/1
March 2005
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