Arboriculture & Urban Forestry 34(6): November 2008 Table 4. Estimates of total number of trees and standard error (SE), tree density, percent tree cover, leaf area index (LAI), and most common tree species from 14 cities analyzed using the UFORE model.z City Atlanta, GAw Baltimore, MDv Boston, MAw Casper, WYu Freehold, NJt Jersey City, NJt Minneapolis, MNs Moorestown, NJt New York, NYw Philadelphia, PAw San Francisco, CAu Syracuse, NYv Washington, DCr Woodbridge, NJt Total Number of trees SE Tree density (no./ha) 9,415,000 749,000 275.8 2,571,000 494,000 122.9 1,183,000 109,000 123,000 48,000 16,000 6,000 136,000 22,000 979,000 165,000 583,000 5,212,000 719,000 2,113,000 211,000 668,000 98,000 82.9 22.5 94.6 35.5 64.7 53,000 153.4 65.2 61.9 55.7 876,000 119,000 134.7 1,928,000 224,000 121.1 986,000 97,000 164.3 zDivide tree density (no./ha) by 2.471 to convert to no./ac. yTotal tree leaf area divided by total city area. xSR stratified random; RG randomized grid. wData collected by ACRT, Inc. vData collected by U.S. Forest Service. uData collected by city personnel. tData collected by New Jersey Department of Environmental Protection. sData collected by Davey Resource Group. rData collected by Casey Trees Endowment Fund and National Park Service. carbon storage and sequestration (Nowak and Crane 2002), oxy- gen production (Nowak et al. 2007a), structural value (Nowak et al. 2002a), VOC emissions (Nowak et al. 2002b) and building energy conservation, urban forest structure, and potential pest impacts (Nowak et al. 2002b, 2006b, 2006c, 2006d, 2007b, 2007c, 2007d). DISCUSSION The main advantages of the UFORE model are that it uses lo- cally measured field data and the best available peer-reviewed procedures to estimate urban forest functions. Also, it is a pub- licly available model with technical support and training through i-Tree. However, UFORE also has limitations. Functional quan- tifications are estimates based on various algorithms. Many of the functions estimated by the model are difficult to accurately measure in the field; thus, modeling procedures are needed to quantify these effects for urban forests. Because model estimates are only as good as the field data inputs, quality assurance of field data accuracy is important. The model only estimates structure and functions at one point in time but provides a means through permanent recording of plot and tree locations to accurately assess urban forest change through time. The model focuses on estimating structure and ecosystem services. The Urban Forest Effect model uses eco- nomic values from the literature to ascribe a value to these ser- vices. These economic values are straight multipliers (e.g., $/ton) so users can easily substitute their own values if desired. Specific advantage and disadvantages of each module are discussed sub- sequently. Urban Forest Structure This is one of the most accurate modules in the UFORE program because the majority of the estimates are derived directly from the field measurements. If the field variables (e.g., species, dbh, ht) are measured accurately, then the UFORE model can give accurate estimates of structural variables (e.g., number of trees, species, and dbh distribution) with known standard error (uncer- tainty of estimate). The optimal urban forest sample and plot size continue to be investigated, but basic information on this topic is provided in Nowak et al. (2008). Cross-comparisons among cit- ies can be conducted relatively easily with a standardized pro- tocol and approximately 200 0.04 ha (0.1 ac) plots per city. In addition, the model can be easily used in many areas using plot sampling and data collection tools along with model distribution and support through i-Tree (www.itreetools.org). The Urban Forest Effect model offers a means to accurately detect changes in urban forest structure and functions through the use of per- manent plots. However, the field data must be collected during the in-leaf season to measure various required crown parameters needed to estimate leaf area, leaf biomass, and tree health. The structural information provided is designed to aid in manage- ment and to estimate ecosystem functions. Numerous standard tables are produced that display the basic structural data by spe- cies, dbh class, condition class, and/or land use class. Some of the key variables to assess ecosystem functions are leaf area and leaf biomass. These attributes are not directly mea- sured in the field, but rather they are estimated using regression equations. These equations estimate the leaf area or biomass based on species type, crown measures, and tree condition. Other methods can be used to estimate leaf area (e.g., light imaging devices). In tests of various methods against measured tree leaf data, the regression equations used in UFORE were among the best for estimating leaf area of open-grown trees and ease of application (Peper and McPherson 1998). Also, there was no significant difference between the regression equation estimates and the measured tree leaf data (Peper and McPherson 1998). There are also limitations related to the structural value esti- mates. These limitations include limited state costs and species ©2008 International Society of Arboriculture Tree cover (%) 36.7 21.0 22.3 8.9 34.4 11.5 26.4 28.0 20.9 15.7 11.9 23.1 28.6 29.5 LAIy 2.2 1.3 1.0 0.3 1.6 0.4 1.0 1.7 0.9 0.8 0.4 1.2 1.0 1.6 Most common tree species Liquidambar styraciflua Fagus grandifolia Acer platanoides Populus sargentii Acer platanoides Ailanthus altissima Fraxinus pennsylvanica Acer rubrum Ailanthus altissima Prunus serotina Eucalyptus globulus Acer saccharum Fagus grandifolia Liquidambar styraciflua Year Sample typex 1997 SR 2004 SR 1996 SR 2006 RG 1998 SR 1998 SR 2004 RG 2000 SR 1996 SR 1996 SR 2004 RG 2001 SR 2004 RG 2000 SR No. of plots 205 200 217 234 144 220 110 206 206 210 194 197 201 215 353
November 2008
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