Arboriculture & Urban Forestry 37(5): September 2011 age requires trained people to evaluate tree damage, determine corrective actions, and estimate woody debris volumes. Existing debris estimation models and approaches vary in complexity, the time needed to implement, and the required skill level of the evaluator (King et al. 2007; Escobedo et al. 2009). On-the- ground approaches using sample plots or ocular estimates and remote sensing techniques using aerial sketchmapping, airborne videography, aerial photography, and satellite images are sev- eral ways to estimate damage following ice storms (Bruederle and Stearns 1985; De Steven et al. 1991; Hauer et al. 1993; Jacobs 2000; Bloniarz et al. 2001; Cielsa et al. 2001; Rhoads et al. 2002; Scarr et al. 2003; King et al. 2007; Stueve et al. 2007). These approaches have been used to estimate damage at regional, stand, and individual tree levels. Estimates are based on the percent reduction in canopy cover, change in stand com- position, difference in vegetation vigor, basal area change, and comparison of pre- and post-storm inventories, and formulas. A rapid estimate within one day and ideally within 12 hours of the storms’ end is important for emergency planners to rate recovery needs and mobilize fiscal, human, and equipment re- sources where necessary (Bloniarz et al. 2001; FEMA 2007). Rapid assessment approaches support quick evaluation to deter- mine if state and federal disaster declarations are appropriate. Estimates of debris volumes following ice storms can be generated using a street-segment based approach (Blo- niarz et al. 2001). This approach can be performed either by collecting a statistical sample in the field or estimating with i-Tree, using the Storm Damage Assessment Proto- col (Bloniarz et al. 2001). Ideally, the i-Tree approach uses pre-storm sample plots to predict tree debris. The United States Army Corps of Engineers has also generated ice storm tree debris estimates using methods adapted from hurri- cane tree debris estimation models (Escobedo et al. 2009). Ice Storm Frequency and Severity Ice storms are common to the eastern region of the United States. Other locations (e.g., inland parts of the U.S. Pacific Northwest and parts of Europe) also experience these storms (Sanzen-Baker and Nimmo 1941; Irland 2000; Hauer et al. 2006; Kenderes et al. 2007). The U.S. National Weather Service defines an ice storm as 6.35 mm or more of ice thickness on sur- faces from freezing rain (Irland 2000). The geographical extent of ice storms varies from a localized to widespread area (Hauer et al. 2006). The severity of extreme widespread ice storms, such as the 1998 North American ice storm, can exceed several billion U.S. dollars in losses. Annual losses in the U.S. from ice storms are estimated at USD $226 million (Changnon and Changnon 2002; Changnon 2003). Depending on the location, the mean time for major ice storms to reoccur is between 20 and 100 years (Melancon and Lechowicz 1987; DeGaetano 2000; Pasher and King 2006). Extreme ice storms, such as the 1998 North American ice storm in the northeastern United States, are estimated to return once every 220 to 290 years (Proulx and Greene 2001). Whether localized or widespread, damage to electric distribution systems, blocked roadways, and prop- erty damage from fallen trees and limbs pose significant safety concerns and disrupt normal community functions. The time to recover from ice storms may take longer in more rural areas, especially the repair of downed electrical systems (Call 2010). MATERIALS AND METHODS Data Sources A list of communities from 15 eastern U.S. states that experienced ice storms was developed based on the experience of the authors and the respective state Urban & Community Forestry (U&CF) coordinators. Federal Emergency Management Agency (FEMA) declarations of winter storms were viewed online (www.fema.gov/ news/disasters.fema) and ice storm disaster locations recorded to further determine potential study sites. Targeted communities were sent a questionnaire and asked to supply information about ice storms since 2000; severity of the ice storm based on ice thick- ness and maximum wind speed during the ice storm; how much private land debris, public land debris, and total tree debris was collected; and a description of their urban forest structure includ- ing percent canopy cover and urban forest species composition. The questionnaire served as an initial source for collecting community data using a repeated contact approach (Dillman 2007). Each community was contacted up to four times to elic- it a response, by regular mail, e-mail, and phone. A letter and questionnaire were sent by e-mail in early March 2010 to com- munities with known e-mail addresses, or regular mail to those with no known e-mail address. A reminder postcard was sent two weeks later to all communities with a state U&CF coordina- tor supplied postal street address or an e-mail reminder to those without a street address. A third contact was made at the end of March by phone call to all nonrespondents, followed by a final fourth contact and questionnaire in April to those that had still not responded. A second wave of questionnaires was sent start- ing in early April to more communities whose contact informa- tion was later received. Questionnaire delivery used the previ- ously described methods and commenced in early May 2010. Once surveys were received, any missing information was sought. Cities were first contacted for clarification of data or asked directly for missing data. Further, missing city data was located online through the official city website or other online means (i.e., www.city-data.com/city). Missing weather information (ice thickness and wind speed) was located principally through col- lected data of the National Oceanic and Atmospheric Adminis- tration (U.S.) (www.weatherpages.com/wxhistory.html). Various other websites were searched for data and clarification on specific ice storms. Additional data developed from the 30 m resolution 2001 National Land Cover Database (NLCD) for tree canopy, population, and land area were collected for responding commu- nities for the urban forest data interface, from the USDA Northern Research Station (http://nrs.fs.fed.us/data/urban) (Nowak 2010). Descriptive statistics and multiple regression modeling used SPSS version 18.0. A multiple regression model was cre- ated to test the a priori hypothesized relationship between the dependent variable (tree debris volume, m3 ) and four inde- pendent variables (ice thickness, cm; maximum wind speed, km/h; percent tree canopy, and community infrastructure). The community infrastructure variables (public linear street distance, km; total community land area, km2 km2; and developed land area, km2 ) were each tested individu- ally in preliminary separate regression models (Appendix). Significance for all tests used an a ≤ 0.05 significance level. Potential outliers within the multiple regression model were dis- cerned using the Mahalanobis distance procedure at the < 0.001 ©2011 International Society of Arboriculture ; total land area, 237
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