Research Design and Sampling
A reconnaissance survey was conducted (from October to December, 2018) by a team of 5 people. The site assessment has done to observe the general plot information used to identify the plots and its general characteristics. In this work, trees and shrub information were used to estimate trees and shrubs leaf area/biomass, pollution removal, and volatile organic compound (VOC) emissions. Finally, tree information’s used to estimate forest ecosystem value, carbon storage, carbon sequestration and hydrological functions of Adama city urban forest.
In this study, a total of 214 sample plots have established by using a simple random sampling method. As a general rule, 200 plots (one-tenth acre each) will yield a standard error of about 10% for an estimate of the entire city. As the number of plots increases, the standard error will be decrease; and therefore we were more confident to estimate for the population. With regard to the sample plot size, the standard plot size for an Eco analysis is a 0.1-acre circular plot with a radius of 11.16 m or 0.0407 hectares. The samples of plots were created directly in the Eco application using the random plots generator via the Google Maps function (Fig. 2).
The diameters of all identified trees and shrubs were measured at breast height (1.3 m above ground) using a diameter tape (5 m length). Diameter of individual trees were recorded to calculate basal area and relative basal area of plant species. Height of all sampling trees and shrubs were measured by Silva hypsometer.
The field data collection crews were typically located field plots using maps to indicate plot location. Aerial photographs and digital maps were used in order to locate plots and features. During random plots distribution in the city, the researchers faced a challenge of miss place placement of some plots; for example, some plot center has fallen in buildings, private land and the border of different land ownerships and land-use types; as a result the researcher’s professional skills were used to shift the plot center into appropriate locations.
Data collection and analysis
In this study, the data were collected from sample plots with an area of 0.0407 ha (1/10 ac) that randomly laid in city areas of states and data were analyzed using the i-Tree Eco (formerly Urban Forest Effects (UFORE)) model (David J Nowak et al., 2008). The state plots were based on Forest Inventory Analysis national program plot design and data were collected as part of pilot projects testing FIA data collection in urban areas (Steenberg et al., 2016).
In this work, the i-Tree Eco suite was used to analyze the data. The i-Tree Eco is designed to use standardized field data from randomly located plots, as well as local hourly air pollution and meteorological data, to quantify urban forest structure, ecological function, and the associated value (Nowak, 1993; Soares et al., 2011).
Trees absorb carbon dioxide during photosynthesis, storing carbon and producing oxygen as a byproduct of photosynthesis. Carbon sequestration is the process of removing carbon from the atmosphere and storing it in a physical element (e.g., a tree). i-Tree Eco estimates carbon storage in trees, annual carbon sequestration, and emission of carbon via tree decomposition.
Carbon storage is estimated by multiplying tree biomass by 0.5 (Lamlom & Savidge, 2006). To prevent carbon storage from overestimation for very large trees, total carbon storage is capped 7,500 kg of carbon in i-Tree Eco and forecast. To estimate annual gross carbon sequestration, the tree DBH is incrementally increased in the computer model based on an estimated annual growth rate. The carbon storage in the current year (year 0) is then contrasted with carbon storage in the next year (year 1) to estimate the annual sequestration. The cumulative leaf area in an urban forest canopy is an important variable influencing estimates of biomass, air pollution removal, carbon storage and sequestration, and other ecosystem services.
Leaf area is defined simply as the amount of surface area (one-sided) of leaves on a tree. Leaf area measurements are scaled up to cover an entire urban forest. The cumulative amount of leaf area per unit of projected ground surface area is known as the leaf area index and computed using Eq. (1)
Where LAI = leaf area index; LA = leaf area ((m2); and A = projected ground surface area (m2).
For each tree found in the sample plots carbon storage, annual sequestration, oxygen production, pollutant removal and hydrological functions were estimated using biomass and growth equations. To estimate carbon storage and sequestration, the carbon data were standardized per unit of tree cover. The amount of oxygen produced is estimated from carbon sequestration based on atomic weights using Eq. (2) (Nowak et al., 2007).
The VOC emissions depend on tree species, leaf biomass, air temperature, and other environmental factors. i-Tree Eco estimates the hourly emission of isoprene (C5H8) and monoterpenes (C10 terpenoids), the two dominant VOC categories emitted by trees (Nowak et al., 2007). Standardized emissions are converted to actual emissions based on light and temperature correction factors and local meteorological data (Geron et al., 2006). VOC emission (E) in µg C/tree/h for isoprene and monoterpenes is estimated employing Eq. (3).
E = BE x B x γ 3
Where, E = VOC emission in µg C/tree/h; BE = the base genus emission rate (µg C/g leaf dry weight/h at 30 0C and PAR flux of 1,000 µ mol/m2 /s; and B = species leaf dry weight biomass (g) and Y = γ = exp[β(T-Ts )]
T(°K) is leaf temperature, which is assumed to be air temperature, TS = 303 °K, and β = 0.09.
i-Tree hydro is designed to assess hourly changes in water quantity and quality due to changes in tree and other land cover types within a watershed or nonwatershed area. i-Tree hydro calculates hourly interception, evapotranspiration, runoff, and other hydrologic values based on a semi-distributed, mechanistic rainfall-runoff computer model. Interception is simulated using an improved rutter methodology and evapotranspiration is simulated using improved penman-monteith equation (Yang & Endreny, 2013).