Data - References

i-Tree Landscape offers users a wide variety of data and map layers.

More information can be found in the Landscape section of the i-Tree Tools Archives.

The references are grouped in separate pages.


Location Information


Tree cover estimates are derived directly from 2011 National Land Cover Data (NLCD) or 2001 NLCD data in Alaska, Hawaii and Puerto Rico (as 2011 data are not available). These data estimate percent tree cover using satellite data with a 30 meter resolution ( The 2001 tree cover estimates are known to underestimate tree cover by an average of 9.7 percent, but the range of underestimation varies by region and land cover class (Nowak and Greenfield 2010). It is believed, based on preliminary tests, that the 2011 tree cover maps also underestimate tree cover. Therefore, the tree cover maps are likely conservative in estimating tree cover as well as ecosystem services, which are derived from tree cover. To help overcome this presumed underestimate of tree cover, high resolution tree cover maps are used where available.


Impervious cover estimates are derived directly from 2011 National Land Cover Data (NLCD) or 2001 NLCD data in Puerto Rico and Hawaii. These data estimate percent impervious cover using satellite data with a 30-meter resolution ( The 2001 impervious cover estimates are known to underestimate tree cover by an average of 1.4 percent (Nowak and Greenfield 2010). It is believed that the 2011 NLCD impervious data provide a reasonable estimate of impervious cover.

Land Cover

Land cover data are derived from the 2011 and 2001 NLCD which provides a synoptic nationwide classification of land cover into 16 classes at a spatial resolution of 30 meters (; US EPA 2015). In Hawaii and Puerto Rico the 2001 land cover data is the only available year of data.


U.S. population statistics are derived directly from the U.S. Census Bureau data ( and are believed to be without error. Census data are provided for each geographic unit.

Forest Risk

Health Risk

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 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 2018 value of carbon storage and sequestration is estimated at $188 per 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 ($/m2 tree cover) in urban and rural areas for each county. These values are applied to the m2 of tree cover to determine total removal and values related to carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter less than 2.5 microns (PM2.5), particulate matter between 2.5 and 10 microns (PM10*), and sulfur dioxide (SO2). Value estimates are based on local health impacts estimated using the U.S. EPA BenMAP model for each county (based on local population data) for all pollutants except for CO and PM10*, which use externality values ($/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 PM2.5. 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.


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.

Tree Planting Prioritization

To determine the best locations to plant or protect trees, tree and impervious cover data in conjunction with U.S. Census data can be used to create an index that highlights priority areas among the selected geographic units. With these index values, the higher the index value, the higher the priority of the area for tree planting or protection. The index is developed by weighting the criteria that are selected by the user, along with the associated weights, assigned by the user. The sum of the criteria weights must equal 100.

As geographic areas differ in size, all index inputs are either in percentages or standardized per unit area or person. Each non-percentage layer was standardized on a scale of 0 to 1, with 1 representing the geographic area with the highest value in relation to priority (e.g., areas with highest population density, lowest stocking density, or lowest tree cover per capita were standardized to a rating of 1).

Standardized values for population density (PD) are calculated as:

PD = (n - m) / r

Where PD is the value (0 - 1), n is the value for the geographic area (population / km2), m is the minimum value for all geographic areas, and r is the range of values among all selected areas (maximum value - minimum value).

Standardized value for percent population below poverty line (BPL) was calculated as:

BPL = percent_population_below_poverty_line / 100

Standardized value for tree cover per capita (TPC) is calculated as:

TPC = 1 - [(n - m) / r]

Where TPC is the value (0 - 1), n is the value for the census block (m2 / capita), m is the minimum value for all census blocks, and r is the range of values among all census blocks (maximum value - minimum value).

Standardized value for tree stocking (TS) is calculated as:

TS = [1 - (t / (t + g)]

Where TS is the value (0 - 1), t is percent tree cover, and g is percent grass cover.

Individual scores were combined based on the following formula to produce an overall priority index (PI) value, where the user selects the index layer and its weight:

PI = (index_1 * weight_1) + (index_2 * weight_2) + (index_3 * weight_3)

The final index was standardized to yield values between 0 (lowest priority) and 100 (highest priority).

A default index is given based on PD, TS, and TPC, where the default index = (PD * 40) + (TS * 30) + (TPC * 30). This index is a type of "environmental equity" with areas of higher human population density and lower tree cover tending to get a higher index value.


"Understanding i-Tree: 2021 Summary of Programs and Methods" (self-hosted copy).