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.

Maps Metadata

Map Layers

US Census Block Groups
Administrative boundary data displaying the 2010 block group boundaries. Block groups are clusters of blocks within the same census tract. Block groups generally contain between 600 and 3,000 people.
US Census Places
Administrative boundary data displaying the 2010 place boundaries. Places include both incorporated places and census designated places. Incorporated places are established for the provision of services for a concentration of places, whereas census designated places are not legally incorporated under state laws.
US 111th Congressional Districts
Administrative boundary data displaying the 2010 congressional district boundaries for the 111th United State's Congress. Congressional districts are the areas from which people are elected to the U.S. House of Representatives. Each area is roughly equal in population to the other congressional districts in the same state.
US Counties
Administrative boundary data displaying the 2010 county boundaries. Counties and county equivalents are the primary legal divisions of most U.S. states.
US States
Administrative boundary data displaying the 2010 state boundaries.
US National Forests
A depiction of the boundary that encompasses a National Forest.
US Ranger Districts
A depiction of the boundary that encompasses a Ranger District.
CFLR Boundaries
Depicts the boundaries for the Collaborative-Forest Landscape Restoration (CFLR) and High Priority Restoration (HPR) projects.
Watershed (HUC12)
Depicts the geographic division of the United States into hydrologic units based on watershed boundaries. These divisions are sixth-level classifications identified by a 12-digit unique hydrologic unit code (HUC).

Canopy & Land Layers

Tree Canopy
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 (www.mrlc.gov). For areas where high resolution land cover data are available, that dataset will be displayed by default.
Impervious
Impervious cover estimates are derived directly from 2011 National Land Cover Data (NLCD) or 2001 NLCD data in Hawaii and Puerto Rico (as 2011 data are not available). These data estimate percent impervious cover using satellite data with a 30 meter resolution (www.mrlc.gov). For areas where high resolution land cover data are available, that dataset will be displayed by default.
Plantable Space
Available planting space estimates are derived from National Land Cover Data (NLCD) where plantable space = land area - (canopy + impervious).
Land Cover
2011 and 2001 National Land Cover Database (NLCD) provides a synoptic nationwide classification of land cover into 16 classes at a spatial resolution of 30 meters.

Homer, C.G.; Dewitz, J.A.; Yang, L.; Jin, S.; Danielson, P.; Xian, G.; Coulston, J.; Herold, N.D.; Wickham, J.D.; Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing. 81 (5): 345-354.


Forest Risk

Wildfire Potential
A map classified by the relative potential for wildfire that would be difficult for suppression resources to contain. The intended use of this layer is to help inform evaluations of wildfire risk or prioritization of fuels management needs across large spatial scales. This map layer was derived by the U.S. Forest Service by classifying the Wildfire Hazard Potential (WHP) continuous dataset values. The process entailed converting the WHP values to integers, evaluating the statistical distribution of WHP values and then classifying them into fire classes: very high, high, moderate, low, very low. WHP ranges for each class are unknown. The non-burnable and water values were incorporated from the LANDFIRE FBFM40 layer to produce the final classified WFP. (source)

Health Risk

Air Quality
Pollution concentrations are estimated for ozone (O3) and particulate matter less than 2.5 microns (PM2.5) and derived from the U.S. Environmental Protection Agency’s (EPA) Downscaler Model. Average and maximum values are estimated from the pollution concentration for all days in 2008. (source)
UV Index
Ultraviolet (UV) index values displayed as the average UV index at solar noon for all days between 2008 and 2012 and the maximum UV index at solar noon for all days between 2008 and 2012. The UV index scale was developed by the World Health Organization to more easily communicate daily levels of UV radiation and alert people to when protection from overexposure is needed most. (source)
Wildfire Potential
A map classified by the relative potential for wildfire that would be difficult for suppression resources to contain. The intended use of this layer is to help inform evaluations of wildfire risk or prioritization of fuels management needs across large spatial scales. This map layer was derived by the U.S. Forest Service by classifying the Wildfire Hazard Potential (WHP) continuous dataset values. The process entailed converting the WHP values to integers, evaluating the statistical distribution of WHP values and then classifying them into fire classes: very high, high, moderate, low, very low. WHP ranges for each class are unknown. The non-burnable and water values were incorporated from the LANDFIRE FBFM40 layer to produce the final classified WFP. (source)

Land Surface Temperature Methods

Land surface temperatures were estimated for the United States based on Landsat 8 data and standard procedures from the literature as described below.

Landsat Scene Selection
Landsat scenes were downloaded for the U.S. and U.S. territories (https://lta.cr.usgs.gov/L8) that were as cloud-free as possible (June, July, August) for the years 2013, 2014 and 2015. Scenes were selected to provide total U.S. coverage for each year. As scenes overlapped, many areas had two or more scenes covering a location.
Converting Scene Data to Surface Temperatures
Landsat 8 Thermal Infrared Sensor (TIR) band values were converted to land surface temperature by:
  • Converting satellite data to At-Satellite Brightness temperature based on equations detailed in USGS (2017).
  • Calculating emissivity values from NDVI using the NDVI Thresholds method (Sobrino et al. 2001).
  • Converting At-Satellite Brightness temperature and emissivity information for each 30 m pixel to Land Surface temperature based on equations given in section 3.4 in Weng et al. (2004).
Cloud and Snow Removal
To remove the existing clouds for the images, Fmask software was used (https://github.com/prs021/fmask). The software called Fmask (Function of mask) is used for automated masking of clouds, cloud shadows, and snow for Landsat TM/ETM+ images. Pixels where clouds were removed were converted to “no data”.
Calculating Land Surface Temperature Differences
As Landsat scenes had various dates across the United States, each Landsat scene was processed independently to minimizing the effect of different scene dates on surface temperatures. To standardize the land surface temperature estimates, the Landsat scene’s median land surface temperature was subtracted for each pixel land surface temperature value to create a relative surface temperature difference estimate. With this approach, about one-half of scene pixels will have above median temperatures and one-half will have below median temperatures, but the amount they differ from the mean will vary based on local land surface temperatures.
Within each year’s data (e.g., 2015) all scenes were mosaicked and where scenes overlapped, an average of surface temperature differences among the multiple scenes was used to calculate the surface temperature difference at each pixel with multiple values.
To help fill in for “no data” pixels due to clouds or snow, data from the various years (2013-2015) were used. The first priority was given to data from 2015. If “no data” pixels existed in 2015, data from 2014 were used to fill in these missing pixels. If “no data” pixels existed in 2015 and 2014, data from 2013 were used to fill in these missing pixels. If “no data” pixels existed in 2015. 2014 and 2013, these pixels were labeled as “no data”.
Land surface temperature difference are projected at 90 meter resolution in i-Tree Landscape.

Land Surface Temperature Hotspots Methods

To illustrate areas with relatively warm land surface temperatures (LST) and large amounts of growing space (to potentially cool the environment) or high human populations (areas with greatest human health risks to warm temperatures), two temperature indices were produced. In each index, the LST value used is the calculated LST difference from the scene mean as described above.

LST_Growing Space Index
To create this index, the LST values were scaled on a score of 0 – 100 nationally by subtracting the minimum LST value from each pixel’s value and then dividing the value by the range of LST values (national maximum value minus national minimum value):
    LST Index = (pixel LST – minimum LST) / LST range
With this index, 0 represents the coolest pixel (lowest LST) and 100 represents the warmest pixel (highest LST).
A growing space index of 0 – 100 was also created for each pixel based on percent of the pixel that is potentially readily plantable – that is, not covered by impervious, water or tree cover
The average of the LST Index and Growing Space Index was calculated to develop the LST_Growing Space Index. This index was then restandardized on to a scale of 0 – 100, with low values representing areas with lower surface temperatures and/or low percent green space and high values representing areas with relatively large amounts of growing space that are relatively warm.
LST_People Index
This index averaged the LST index (above) with a population density index. This population density index (0-100) was developed nationally by subtracting the minimum population density (PD) based on US Census block group data from each pixel’s PD value and then dividing the value by the range of PD values (national maximum PD value minus national minimum PD value):
    PD Index = (pixel PD – minimum PD) / PD range
This index was then restandardized on to a scale of 0 – 100, with with low values representing areas with lower surface temperatures and/or low population density and high values representing areas with relatively high population density that are relatively warm.

Base Maps

Google Streets
Street map provided by Google Maps.
Google Aerial
Aerial imagery provided by Google Maps.
Bing Streets
Street map provided by Microsoft Bing.
Bing Aerial
Aerial imagery provided by Microsoft Bing.
Open Street Map
Street map provided by Open Street Map.
Blank Canvas
White base layer with no map data.

Methodology

Location Information

Canopy

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 (www.mrlc.gov). 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

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 (www.mrlc.gov). 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 (www.mrlc.gov; US EPA 2015). In Hawaii and Puerto Rico the 2001 land cover data is the only available year of data.

Census Data

U.S. population statistics are derived directly from the U.S. Census Bureau data (www.census.gov) 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

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).

Value of carbon storage and sequestration is estimated at $139.33 / metric ton of carbon (Interagency Working Group, 2013).

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.

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.


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.

Publications