References

i-Tree County offers a quick and easy way to estimate the benefits provided by trees in a county, or a specific area within a county.

Tree Benefits¶

Based on the tree and impervious cover data, along with other local data, the following ecosystem services for trees are assessed for the year 2010:

Carbon¶

Carbon storage and annual sequestration values are calculated from two separate sources depending upon location in non-forest or forest land cover. Land cover classification was determined using the National Land Cover Database (NLCD).

• Non-forest carbon: For non-forest NLCD classes, total carbon storage and net annual sequestration were estimated using values from urban forests (Nowak et al., 2013). Net annual sequestration is estimates of carbon accumulation from tree growth minus estimated carbon lost through decomposition due to tree mortality. Carbon storage was estimated based on the national average storage value of 7.69 kgC/m² tree cover (standard error (SE) = 1.36 kgC/m²). Net sequestration was based on state estimates that varied based on length of growing season and averaged 0.226 kgC/m² tree cover/yr (SE = 0.045 kgC/m² tree cover/yr). State values varied from 0.430 kgC/m² tree cover/yr (Hawaii) to 0.135 kgC/m² tree cover/yr (Wyoming) (Nowak and Greenfield 2010). These estimates per unit of tree cover are essential as these values were applied to the tree cover estimates (m²) from the tree cover map to estimate total carbon (kg).

• Forest carbon: For forested regions, total carbon storage and net annual sequestration were derived from U.S. Forest Service Forest Inventory and Analysis (FIA) data for each county (Special thanks to Jim Smith for extracting these county FIA data). Net annual sequestration was carbon accumulated annually between FIA re-measurements based on accumulation from tree growth and new trees minus carbon lost through tree mortality.

  Note: sequestration in forests is based on field measurements of change including the influx of new trees and loss of existing trees; in non-forest areas, net sequestration is modeled based on tree growth of existing trees and estimated mortality based on tree condition over a one-year period; this estimate does not include new tree influx and only includes a partial loss of carbon from mortality due to decomposition (entire carbon from trees is not removed, only part of carbon lost to decomposition is removed).

  Total carbon storage and net sequestration per hectare of land was converted to total carbon storage and net sequestration per hectare of tree cover by dividing the carbon per hectare by percent tree cover in the forest land in the county. As tree cover on FIA land was not known, tree cover estimates from NLCD forest classes were used. In counties where tree cover in forest land was less than 10 percent (19 counties), tree cover was set to 10 percent to avoid inflating carbon density values per unit of cover due to low tree cover estimates. If a county had no FIA carbon storage data, but had tree cover estimates, storage density values (kgC/m² tree cover) from the closest county were used. FIA carbon storage densities per m² of land area averaged 6.3 kgC/m²; carbon storage density adjusted for tree cover equaled 9.8 kgC/m² tree cover.

  Net sequestration per m² of tree cover was calculated in the same manner as for carbon storage. For net carbon sequestration, values for some counties are missing. If a county had a missing value, sequestration density values (kgC/m² tree cover/yr) from nearby counties in the same state were used. If the entire state had missing values, the county sequestration value was estimated based on converting the national FIA sequestration density value from all known counties to state values based on the ratio of state sequestration densities to national sequestration density for non-forest areas:

  Forest sequestration density for state = national average forest density x (state non-forest sequestration density / national average non-forest density).

  This procedure was used for net forest sequestration in many western states (AZ, CA, ID, MT, NM, NV, OR, UT, WA, WY). The average net sequestration value for forests was 0.14 kgC/m² tree cover/yr (average SE = 0.10 kgC/m² tree cover/yr)(see "i-Tree Landscape Carbon Storage and Sequestration for US Counties"). This value is about 60 percent of the non-forest sequestration value. This difference is likely due to increased growth rates in urban areas (due to more open-grown nature of trees) and differences in means of calculating net sequestration (forest estimates remove all carbon from trees that die, but in urban estimates only a small portion are removed).

The 2017 value of carbon storage and sequestration is estimated at $143 / metric ton of carbon (Interagency Working Group, 2016).

Air Pollution¶

Air pollution removal and value estimates are based on procedures detailed in Nowak et al. (2014). This process used local tree cover, leaf area index, percent evergreen, weather, pollution, and population data to estimate pollution removal (g/m² tree cover) and values ($/m² tree cover) in urban and rural areas for each county. These values are applied to the m² of tree cover to determine total removal and values related to carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), particulate matter less than 2.5 microns (PM_2.5), particulate matter between 2.5 and 10 microns (PM_10*), and sulfur dioxide (SO₂). Value estimates are based on local health impacts estimated using the U.S. EPA BenMAP model for each county (based on local population data) for all pollutants except for CO and PM_10*, which use externality values ($/t) to estimate pollutant removal value.

Estimates of pollution removal varied by county. Average county removal rates are used, but have a potential maximum and minimum value (see i-Tree Landscape Pollutant Ranges) that illustrates a potential range. The minimum and maximum values on average are about 57 percent of the mean value. Average differences from the mean varied from a low of 30 percent for NO₂ to a high of 106 percent for PM_2.5. The maximum and minimum values are likely unreasonable values as they assume a maximum or minimum removal rate for every hour of the year. No maximum or minimum values are estimated for CO.

Hydrology¶

Estimates of transpiration, precipitation interception, and avoided runoff for each county in the conterminous United States in 2010 were developed using the i-Tree Eco model and local leaf area indices and weather data. Methods are detailed in Hirabayashi (2015), Hirabayashi and Endreny (2015) and Hirabayashi and Nowak (2015). The margin of error on these estimates is unknown.

Publications¶

• Hirabayashi, S., 2015. i-Tree Eco Precipitation Interception Model Descriptions.
  http://www.itreetools.org/eco/resources/iTree_Eco_Precipitation_Interception_Model_Descriptions.pdf [accessed Apr. 2015].
• Hirabayashi, S., Endreny, T.A., 2015. Surface and Upper Weather Pre-processor for i-Tree Eco and Hydro.
  http://www.itreetools.org/eco/resources/Surface_weather_and_upper_air_preprocessor_description.pdf [accessed Apr. 2015].
• Hirabayashi, S., D.J. Nowak. 2015. i-Tree Eco United States County-Based Hydrologic Estimates and Estimates of Species Differentiation.