102 normal. To most appropriately characterize the positively skewed data, cubic yards of debris were transformed with the natural log- arithm, and the number of linear street miles was used as an offset in the linear predictor function. As offsets are akin to exposure rates in Poisson regression with unequal time intervals, their use in spatial models is frequently used to adjust the response to vary- ing levels of coverage. Using a log-normal distribution to charac- terize the response variable and taking into account the potential spatial correlation between adjacent cities, a generalized linear mixed model was fit with the SAS procedure PROC GLIMMIX (SAS, 2006), using sustained maximum wind speed, tree density, tree canopy, and the amount of developed urban land as predictor variables. City center location was obtained using Google Earth (2007) (UTM; Universal Transverse Mercator, Northing and Easting) and used to determine spatial context in the analysis. The model results were examined using information cri- teria and P values associated with each independent value. A type I error level of 0.10 was used to eliminate non-signifi- cant effects and their interactions, together with the corrected Akaike’s information criteria (AICC), which is a small sample bias-corrected version of the AIC fit statistic. The AICC quan- tifies the residual variance, with a penalty term for additional independent variables. The final estimated model included only significant effects and also had the lowest AICC, indicat- ing substantial evidence that the data arose from this model. RESULTS Data Collection A random 10% sample of 680 PWs, representing the communities reporting debris data in FEMA category A or G in the 2004 and 2005 hurricane seasons, were reviewed to compile and assess de- bris amounts and cost, as well as tree hazard pruning and removal data. From the 68 PWs, we obtained usable debris amounts and cost data from 43 communities. However, tree pruning and removal data were considerably less available, with only 11 communities specifically referencing tree work in their PWs, and only 5 com- munities providing usable pruning and removal cost estimates. No usable data were obtained from any PW for community for tree removal or pruning rates (removal or pruning quantities/total Escobedo et al.: Hurricane Debris and Damage Assessment tree population). These data were obtained from direct contact with the communities of Orlando, Pensacola, and Winter Park. Averages for the sampled communities are presented in Table 2. Debris Removal Costs and Production Total debris management costs averaged $28.11 per m3 per yd3) in the 43 communities that provided usable data. De- ($21.50 bris production per 30.5 m street segment ranged in values from 0.15 m3 (0.2 yd3 ) in Sanford, to 31 m3 (0.77 yd3 (40.6 yd3 ) and 46.4 m3 (60.7 yd3) in Golf and Gulf Breeze respectively. Thus, rates of debris production were averaged into low, moderate, and high categories. These averages, per street segment, were 0.59 m3 ) for low damage, 3.40 m3 ate damage, and 17.47 m3 (22.85 yd3 (4.45 yd3 ) for moder- ) for high damage levels. Tree density, tree canopy cover, wind speed, and percentage of urban developed land all had an effect on the amount of cu- bic meters of debris. The model which included all significant effects and best supported the data based on the AICC (169) in- cluded a third order interaction of tree canopy cover, wind speed, and percentage of urban developed land (α < 0.10) (Table 3). There was a marginally significant negative correlation be- tween tree density and debris generation (P = 0.08), indicating that as tree density in communities increased there was a slight decrease in debris (Table 3, Figure 1a). Because of the significant interactions among variables, the effects of tree canopy cover, wind speed, and percentage of urban developed land were exam- ined in conjunction with the other variables. Tree canopy cover had a positive relationship with debris when the interactions among variables were taken into account and all other predictor variables were at their averages; as canopy cover increased in a community, debris generation increased (Figure 1b). Sustained wind speed and amount of developed urban land had a more complex effect, and when considered with all other interactions their effect on debris was relatively flat (Figure 1c, Figure 1d). Figure 2a and Figure 2b display the continuous two-way interaction of wind speed when percent developed urban and tree canopy cover were held at constant “low” and “high” val- ues. Debris generation strongly decreased with increasing storm strength when communities had a high tree canopy cover Table 2. Tree cover, developed urban land cover, tree density, wind speed, and debris generation for the communities and hurricanes sampled in the 2004-2005 seasons. Tree Sampled Hurricanes Charleyz Francesy Ivanx Jeannew Dennisv Katrinau Wilmat Study Average ha = hectare; z canopy cover (%) 37 23 26 12 26 12 10 19 Oviedo, De Land; Orange City; Port Orange. y Atlantic Beach; Tampa; Palm Beach Gardens; Palm Springs; Gulf Port; Sanford; Fort Pierce; Daytona Beach; Debary; Deltona; Edgewater. x Destin; Fort Walton Beach; Gulf Breeze. w Oakland Park; Pompano Beach; Palmetto; Belle Isle; Belle Glade; West Palm Beach; Clearwater; Gulf Port. v Destin; Fort Walton Beach; Mary Esther; Gulf Breeze. u Lauderhill; North Lauderdale; Pembroke Pines; Aventura; Surfside; Gulf Breeze. t Lauderdale Lakes; Fellsmere; Opa-locka; Atlantis; Belle Glade; Greenacres; Golf. ©2009 International Society of Arboriculture Developed urban cover (%) 58 68 75 80 77 81 80 75 Average for sampled communities Tree density per ha (acre) 12.7 (31.3) 7.0 (17.3) 13.6 (33.6) 3.7 (9.2) 13.6 (33.6) 4.6 (11.2) 1.4 (3.4) 6.7 (16.6) Sustained wind speed (in knots) 59.3 46.6 43.7 38.8 56.3 45.2 60.9 49.1 m3 (yd3 ) per 30.5 m (100 feet) 9.6 (12.5) 4.0 (5.2) 24.3 (31.8) 1.9 (2.5) 8.0 (10.4) 1.0 (1.4) 13.6 (17.8) 7.1 (9.2)
March 2009
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