Arboriculture & Urban Forestry 34(6): November 2008 to illustrate how plot size affects the total tree and standard error estimate. Average plot time for field plot setup, cover estimates, and measurements per tree were used to estimate how average field measurement time would likely vary as tree cover changes. In a separate analysis, an additional test of plot size and plot design was conducted using GIS tree cover, land use, and parcel data for the city of Syracuse. Five hundred points were randomly distributed throughout the city. At each point, the following seven different plot sizes or designs were constructed around the point using GIS: 1) one-twenty-fourth acre (0.017 ha) circular plot; 2) one-twelfth acre (0.034 ha) circular plot; 3) one-tenth (0.04 ha) circular plot; 4) one-eight acre (0.05 ha) circular plot; 5) one-sixth acre (0.067 ha) circular plot; 6) one-fourth acre (0.1 ha) circular plot; and 7) four one-twenty-fourth acre (0.017 ha) circular plots (cluster plot) using the USDA Forest Service For- est Inventory and Analysis (FIA) plot design (USDA Forest Service 2000). With this cluster plot design, three subplots were established 120 ft (36.6 m) from the center subplot at 120°, 240°, and 360° azimuths. For each of the plot sizes and designs, total amount of tree cover within the plot was assessed usinga2ft (0.61 m) resolu- tion tree cover map (Myeong et al. 2003), and the number of parcels and associated number and area of land uses in each parcel within the plot design was recorded using a digital land use parcel map. The average amount of permissions required for each plot design was categorized among three classes: 1) per- mission required (residential land use parcels); 2) permission questionable—uncertain if crew would need to obtain permis- sion (commercial/industrial, institutional, utility/transportation parcels); and 3) no permission needed (greenspace, street right- of-ways, and vacant parcels) to assess how permissions would vary based on plot size and design. The average percent of plot area within the parcel that contained the plot center was also calculated. This calculation was done to help determine how Number of trees 387 much of the plot area would require the crew to move to an additional parcel and how much of that extra plot space would require additional permissions. Mean tree cover and standard error for each plot design were calculated and compared with the actual tree cover as classified by the tree cover map. Effect of Sample Size on Total Population Estimate Precision To determine the effect of sample size on the standard error estimate for the total tree population, sample data from 14 cities were analyzed using the UFORE model (Nowak and Crane 2000; Nowak et al. 2002) (Table 1). For each city, population total, standard error (SE), and relative SE were calculated. The relative SE is a measure of estimated reliability and is the ratio of SE to the estimate, in this case, population total (SE/total × 100) (US Department of Health and Human Services, Centers for Disease Control and Prevention 2007). Eleven of the cities were sampled using a stratified random sampling approach, and three using a randomized grid approach, which was used to facilitate long-term monitoring of urban forest change. Standard error for each city was standardized to a population size of 200 plots using the formula: SEstandard deviation/√n. The average SE using 200 plots was calculated for the 14 cities and used to illustrate how SE of the total tree population estimate will vary as sample size varies between 10 and 500 plots. RESULTS Effect of Plot Size on Data Collection Time and Total Population Estimate Precision Increasing plot size from a one-twenty-fourth acre (0.017 ha) plot to a one-sixth acre (0.067 ha) plot nearly doubled the amount of time needed to measure the plot variables, but also nearly cut in half the relative standard error for the total popu- Table 1. Estimates of total number of trees and standard errors from 14 cities analyzed using the UFORE model.z City 200 ploty Total Atlanta, GAw Baltimore, MDv Boston, MAw Freehold, NJu Jersey City, NJu Minneapolis, MNt Moorestown, NJu Morgantown, WVs New York, NYw Philadelphia, PAw San Francisco, CAr Syracuse, NYv Washington DCq Woodbridge, NJu 9,415,000 2,571,000 1,183,000 48,000 136,000 979,000 583,000 658,000 5,212,000 2,113,000 668,000 876,000 1,928,000 986,000 zAverage relative standard error 12.1%. yEstimated standard error (SE) and relative standard error (SE/total × 100; RSE) using a sample of 200 one-tenth acre (0.04 ha) plots. xStr. random stratified random sample; random grid randomized grid sample. wData collection by ACRT, Inc. vData collection by U.S. Forest Service. uData collection by New Jersey Department of Environmental Protection. tData collection by Davey Resource Group. sData collection by West Virginia University. rData collection by city personnel. qData collection by Casey Trees Endowment Fund. ©2008 International Society of Arboriculture SE 749,000 494,000 109,000 6,000 22,000 165,000 53,000 79,000 719,000 211,000 98,000 119,000 224,000 97,000 Year 1997 2004 1996 1998 1998 2004 2000 2004 1996 1996 2004 2001 2004 2000 No. plots 205 200 217 144 220 110 206 136 206 210 194 197 201 215 SE 758,000 494,000 114,000 5,000 23,000 122,000 54,000 65,000 729,000 216,000 97,000 119,000 224,000 100,000 RSE 8.1 19.2 9.6 10.1 16.7 12.5 9.3 9.9 14.0 10.2 14.5 13.5 11.6 10.2 Samplex Str. random Str. random Str. random Str. random Str. random Random grid Str. random Str. random Str. random Str. random Random grid Str. random Random grid Str. random
November 2008
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