Improved Model Estimates of Tree Debris Following Ice Storms
Richard J. Hauer and Brandon B. Schulz
Abstract: Planning to prepare for storms should involve the estimation of tree debris. This paper tested an improvement of a rapid estimation model of tree debris following ice storms. An initial model found using 30-m resolution National Land Cover Database (NLCD) tree canopy cover (TCC) data did not significantly (P ˜ 0.20) improve estimation of tree debris within a community right-of-way (ROW) following an ice storm. We tested if finer resolution National Agriculture Imagery Program (NAIP) TCC imagery (2-m resolution or better) could more accurately predict tree debris after an ice storm. Tree canopy cover was estimated with NAIP across the entire community (TCCCITY) and also the area that only covered the ROW plus a 15.24-m (50-foot) buffer on each side (TCCROW). The TCCCITY (P = 0.08) estimate marginally improved tree debris prediction in the overall multiple regression model (R2 adj = 0.917; F = 133.8; df = 3,33), but this was not the case with the TCCROW (P = 0.66) estimate. The TCCCITY estimate was 34.7% (SEM = 2.0) and significantly (P < 0.001) 2 times greater than in the 16.2% (SEM = 2.2) TCC estimate from NLCD imagery. We found the TCCROW was 32.6% (SEM = 1.6) and significantly lower (P = 0.003) than in TCCCITY. Results from this study may improve the overall ability to predict tree debris following ice storms from the regional models currently used to a more local estimate for a city. Future investigations are needed to determine if this is the case.
Keywords: Decision Making; Green Spaces; Planning and Management; Tree Canopy; Urban Forestry