How the Model Works

In order for UFORE to calculate anything about a city, neighborhood or region, data must be collected on the trees in the area of interest. Previous UFORE analyses have focused on cities or street tree inventories. However, UFORE can handle inventories or sampling of any area, no matter how large or small.

If an inventory is conducted (i.e., all trees are measured; a 100% sample), then UFORE calculates values for each tree and for the total population. If only a sample is examined (i.e., plots are randomly located within the area of analysis), then UFORE calculates estimates for the total population along with estimates of the variability or certainty of the estimates.

Structural Calculations

Structural data provide information on the physical characteristics of the vegetation (e.g., species composition, tree sizes, tree health, leaf area). Structure is what a person sees and what urban foresters and homeowners manage. Many of the structural estimates are based directly on the field measurements (e.g., species, DBH, tree height, crown width, etc.). Some variables are estimated based on equations that use the measured data (e.g., leaf area and leaf biomass are estimated using tree species and crown measures). Statistical formulas are then used to calculate totals, means, and standard errors. Estimates are calculated for each stratum (e.g., land use) and many of the structural estimates are used to calculate forest functions.

Carbon Calculations

Carbon storage and sequestration by trees reduces carbon dioxide, the main greenhouse gas that contributes to global warming To estimate the amount of carbon stored and annually sequestered or removed by trees (whole tree: from roots to leaves), tree biomass equations are used in conjunction with urban tree growth estimates from various data in the literature. Growth estimates by species, condition, and/or land use are applied to each tree sampled. Methods

Building Energy Use Calculations

Information on tree sizes, types, and distance and direction from two-story building is used to estimate tree effects on building energy use. The UFORE model uses published methods to estimate existing tree effects in summer and winter space conditioning energy use and associated carbon dioxide emissions from power plants. Methods

Air Pollution Effects Calculations

Urban vegetation removes a number of air pollutants including nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and particulate matter. Gaseous pollutants (e.g., ozone) tend to be removed within the stomates of the leaves, while particles are mostly captured on the plant surface. An overview of these processes can be found in "The Effects Of Urban Trees On Air Quality." Hourly air pollution removal by the urban forest and the associated improvements in air quality are calculated using a hybrid multi-layer / big-leaf modeling approach. UFORE uses local hourly weather and air pollution concentration data along with leaf area data to estimate hourly pollution removal of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, and particulate matter less than 10 microns in size. Boundary layer height measurements are also used to help estimate percent air quality improvement due to pollution removal by trees. In addition, UFORE also calculates hourly volatile organic compound (VOC) emissions by trees. VOCs can contribute to ozone formation, and are important to help understand the net effect of trees on ozone and to provide inputs into ozone photochemical models in order to determine the best strategies to improve air quality. Methods

Pollen Calculations

To determine the effects of tree pollen on urban residents, UFORE incorporates a pollen rating scale called the Ogren Plant-Allergy Scale or OPALS (Ogren 2000). Each species is assigned a pollen score of 1 (most allergy free) to 10 (most allergenic). The scale takes into account inhalant pollen allergy, odor allergy, skin reactions to contact, poisonous properties and many other plant characteristics. UFORE calculates an overall allergy rating by weighting the species scores by leaf area of the species in the strata or study area. Methods

Carbon

Carbon sequestration (annual removal from the air) and total cumulative storage (in woody stems and roots) by urban vegetation is important because the source of the carbon is carbon dioxide, the main greenhouse gas that contributes to global warming. To estimate monetary value associated with urban tree carbon storage and sequestration, carbon values were multiplied by the estimated marginal social costs of carbon dioxide emissions. Methods

Air Pollutants

Air quality improvement from trees is important and valuable because the EPA classifies many larger cities and urbanized areas as being in 'non-attainment' for ozone or other air pollutants (see the EPA's Green Book). A number of factors influence the valuation of the removal of these air pollutants. For example, air pollution effects human health and poor air quality increases health care costs for individuals and society. Reduction in air pollutants has a value in terms of lower medical and hospitalization costs as well as health insurance premiums. Methods

Compensatory Value

The compensatory value of the trees was calculated based on procedures prescribed by the Council of Tree and Landscape Appraisers (CTLA) (8^th ed., 1992). Compensatory value derived using these procedures is regularly used to determine monetary settlement for damage or death of plants through litigation, insurance claims, loss of property value for income tax deductions, and real estate assessments. It is based, in part, on the replacement cost of a similar tree, and is an estimate of the amount of money the tree owner should be compensated for tree loss.

Compensatory value is based on four factors:

• trunk area (cross sectional area at 1.37 meters)
• tree species
• condition
• location

Trunk area and species are used to determine the basic value, which is then multiplied by condition and location rating of each tree (0-1) to arrive at the final compensatory value.

For trees that could be transplanted, the value of the largest transplantable tree is used based on values set by local chapters of the International Society of Arboriculture. Methods

Measurement error

UFORE assumes that the measured field data is unbiased and measured without error. If the plots are randomly located and no selection bias is used in establishing the plot (e.g., moving the plot to change the number of trees in the sample), then the plots selection should be unbiased. Also if all the measurements are made and entered in the database accurately (e.g., species identification, DBH and height measurements), then there should be no measurement errors. UFORE has internal quality controls to check for various errors, but quality controls measures should also be taken in the field to minimize or eliminate measurement errors.

Model uncertainty

All models have an inherent uncertainty associated with them as they are based on mathematical equations. The absolute amount of uncertainty is unknown, but model results have been checked against various measured data sets to make sure the model estimates and patterns are realistic and reasonable, and are within known measurement ranges. As new data and research become available, model updates will be made to reduce uncertainty and continue to provide the best results possible.

Sampling uncertainty

Unless all trees are measured, there will be a certain amount of uncertainty as to what the total estimate is for the population. The sampling results from UFORE should be accurate and unbiased, but will have a certain amount of variability associated with estimating population totals from a sample. In general, the more plots that are established, the lower the variability and higher the certainty of the estimate. Standard errors for estimates are given to report on the amount of this sampling uncertainty.