348 Nowak et al.: Assessing Urban Forest Structure and Ecosystem Services functions and values can be produced. The model uses a sam- pling procedure to estimate various measured structural attri- butes about the forest (e.g., species composition, number of trees, diameter distribution) within a known sampling error. The model uses the measured structural information to estimate other structural attributes (e.g., leaf area, tree and leaf biomass) and incorporates local environmental data to estimate several func- tional attributes (e.g., air pollution removal, carbon sequestra- tion, building energy effects). Economic data from the literature are used to estimate the value of some of the functions. The model has the following five modules. Urban Forest Structure Urban forest structure is the spatial arrangement and character- istics of vegetation in relation to other objects (e.g., buildings) within urban areas (e.g., Nowak 1994a). This module quantifies urban forest structure (e.g., species composition, tree density, tree health, leaf area, leaf and tree biomass), value, diversity, and potential risk to pests. Sampling Urban Forest Effect model assessments have used two basic types of sampling to quantify urban forest structure: randomized grid and stratified random sampling. With the randomized grid sampling, the study area is divided into equal-area grid cells based on the desired number of plots and then one plot is ran- domly located within each grid cell. The study area can then be subdivided into smaller units of analysis (i.e., strata) after the plots are distributed (poststratification). Plot distribution among the strata will be proportional to the strata area. This random sampling approach allows for relatively easy assessment of changes through future measurements (urban forest monitoring), but likely at the cost of increased variance (uncertainty) of the population estimates. With stratified random sampling, the study area is stratified before distributing the plots and plots are randomly distributed Table 1. General plot information collected for the UFORE Model. Variable Plot IDz Description Unique identifier Plot addressy Date and crew Photo number Measurement unitsz Reference objectsy Distance to reference objecty Direction to objecty Tree measurement point (TMP)y Percent measuredz Land usez Percent inz Tree coverz Shrub coverz Plantable space Required for permanent reference of plot. UFORE Urban Forest Effects. zRequired for UFORE analysis. y ©2008 International Society of Arboriculture Used to help identify plot Units for all measurement in the plot; metric (m/cm) or English (ft/in) At least two objects that will assist in locating plot center for future plot remeasurements Distance from plot center to each reference object (ft or m) Direction from plot center to each reference object (degrees) If plot center falls on a building or other surface (such as a highway) where plot center cannot be accessed, the plot is not moved; all distances and directions to trees are measured and recorded from a recorded fixed point (e.g., building corner) referred to as the TMP Proportion of the plot that is actually measured as portions of plot may be denied access As determined by crew in the field from a standard list of land uses Proportion of the plot in each land use to nearest 1% Percent of plot area covered by tree canopies estimated to nearest 5% Percent of plot area covered by shrub canopies estimated to nearest 5% Percent of plot area that is plantable for trees (i.e., plantable soils space not filled with tree canopies) and tree planting would not be restricted as a result of land use (footpath, baseball field, and so on); to nearest 5% within each stratum (e.g., land use). This process allows the user to distribute the plots among the strata to potentially decrease the overall variance of the population estimate. For example, be- cause tree effects are often the primary focus of sampling, the user can distribute more plots into strata that have more trees. The disadvantage of this approach is that it makes long-term change assessments more difficult as a result of the potential for strata to change through time. There is no significant difference in cost or time to establish plots regardless of sampling methods for a fixed number of plots. However, there are likely differences in estimate precision. Pre- stratification, if done properly, can reduce overall variance be- cause it can focus more plots in areas of higher variability. Any plot size can be used in UFORE, but the typical plot size used is 0.04 ha (0.1 ac). The number and size of plots will affect total cost of the data collection as well as the variance of the estimates (Nowak et al. 2008). Data Collection Variables There are four general types of data collected on a UFORE plot: 1) general plot information (Table 1) used to identify the plot and its general characteristics; 2) shrub information (Table 2) used to estimate shrub leaf area/biomass, pollution removal, and volatile organic compound (VOC) emissions by shrubs; 3) tree informa- tion (Table 3) used to estimate forest structural attributes, pol- lution removal, VOC emissions, carbon storage and sequestra- tion, energy conservation effects, and potential pest impacts of trees; and 4) ground cover data used to estimate the amount and distribution of various ground cover types in the study area. Typically, shrubs are defined as woody material with a diam- eter at breast height (dbh; height at 1.37 m [4.5 ft]) less than 2.54 cm (1 in), whereas trees have a dbh greater than or equal to 2.54 cm (1 in). Trees and shrubs can also be differentiated by species (i.e., certain species are always a tree or always a shrub) or with a different dbh minimum threshold. For example, in densely forested areas, increasing the minimum dbh to 12.7 cm (5 in) can
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