Arboriculture & Urban Forestry 37(5): September 2011 ties (15%) were able to report canopy cover and approximately half of these provided an educated estimate. Estimates of can- opy cover used the 2001 NLCD dataset and a mean of 17.4% for communities in this study was found with canopy values between 3.3% and 51.2%. Accounting for debris volume per cm of ice and community infrastructure, 51.8 m3 street distance per cm of ice (276.9 yd3 of debris per km community land area basis, this was 365.4 m3/km2 yd3/mi2 /mi/in) occurred. On a /cm (3143.2 /in). More than 80% (33) of the communities were declared a Federal Emergency Management Agency disaster area. The initial model of hypothesized indicators provided significant (P < 0.001) and strong evidence (R2 predicting debris volume. Street miles and ice thickness were both significant predictors of debris volume (Table 2). Land area, 2001 NLCD total land area, and 2001 NLCD developed land area were also significant predictors, yet not as strong as street distance. Maximum wind speed, canopy cover, and interac- tion among variables showed no relationship to the debris vol- umes. The final estimation model of debris from an ice storm is adj = 0.949) for [1] Debris Volume (m3 ) = -99,136 + 311.2 • Street Distance (km) + 15,031.9 • Ice Thickness (cm) Debris Volume (yds3 ) = -129,677 + 655.1 • Street Distance (mi) + 49,426.5 • Ice Thickness (in) 3). This model location had approximately twice the volume of debris—2.1 million m3 A second model was created that excluded Tulsa, OK (Table —as the next largest site and over a magni- tude greater than the mean debris volumes from these storms. This model was also highly significant (P < 0.001) and provided strong evidence (R2 area and ice thickness to estimate debris volumes in communities after an ice storm. Street distance and other community infrastruc- ture variables were also significant predictors; however, they were not as strong as that of land area. Again, maximum wind speed, canopy cover, or interactions among variables were not signifi- cant. The final estimation model (without Tulsa and the two pre- viously reported outliers) that predicts debris from an ice storm is adj = 0.792) for the predictors total community land [2] 239 Debris Volume (m3) = -63,346 + 1,571.4 • Land Area (km) + 12,832.2 • Ice Thickness (cm) Debris Volume (yds3 + 41,651.5 • Ice Thickness (in) A third debris estimation model was created by normalizing debris on a per unit (street distance and land area) basis. Street distance was a better model predictor than land area (Table 4). This model was significant (P < 0.0.001) with a lower R2 (R2 = 0.439) than the nonnormalized models. No sites were found as outliers with this approach. Ice thickness was again a sig- nificant predictor. Maximum wind speed and canopy were not significant. A significant interaction between ice thickness and wind speed was found. The intercept term was not signif- icant and excluded in the final selected model. Thus, on a de- bris per street distance basis, the final model (R2 adj adj = 0.642) is [3] Debris Volume per street distance (m3 /km) = 100.3 • Ice Thickness (cm) – 1.7 • Ice Thickness (cm) • Maximum Wind Speed (km/h) Debris Volume per Street Distance (yd3 / mi) = 535.9 • Ice Thickness (in) – 14.6 • Ice Thickness (in) • Maximum Wind Speed (mph) Comparison of Model-Predicted Values and Reported Debris Volumes A graphical comparison of all three models was constructed to compare predicted values with actual reported values (Figure 1; Figure 2; Figure 3; Figure 4; Figure 5). Both models one and two had a tendency to under predict for smaller communities, produce estimates that neither consistently over or under predict for medium-sized communities, and more likely to over predict for larger communities. Model two appears to represent actual predictions better for smaller communities with fewer street miles (≤161 km). In contrast, model one appears better suited Table 2. Estimation of tree debris following an ice storm from model one (full and final models without Springfield, MO and St. Louis, MO). Unstandardized Coefficients Model variables Initial model all indicators (R2 (Intercept) Street Distance (km) Ice Thickness (cm) Max. Wind Speed (km/h) Canopy Cover (%) Final a priori model (R2 (Intercept) Street Distance (km) Ice Thickness (cm) B = 0.955, R2 -1,312,401 311.981 16,732.6 -58.31 1603.07 = 0.952, R2 -99,136 311.169 15,031.9 Std. error Standardized Coefficients Beta 12.683 5078.06 1351.81 1278.73 -2.229 0.986 0.136 -0.002 0.053 t-test Statistics t-value adj = 0.949, std. error of est. = 85443, F(4,29) =153.416 p < 0.000) 58,887.6 0.034 24.598 3.295 -0.043 1.254 adj = 0.949, std. error of est. = 82566 F(2,33) = 328.9 p < 0.000) 23,426.8 12.133 4606.00 0.985 0.125 -4.232 25.647 3.264 0.000 0.003 0.966 0.220 0.000 0.000 0.003 0.968 0.002 -0.110 -0.003 0.977 0.522 -0.008 0.227 Sig. Correlations Zero-order Partial ) = -82,529 + 5,314.4 • Land Area (mi) 0.968 -0.007 0.976 0.494 ©2011 International Society of Arboriculture
September 2011
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