i-Tree Eco

i-Tree Eco

Application Overview

Complete Inventory

Sample Inventory


System Requirements


i-Tree Eco is a software application designed to use field data from complete inventories or randomly located plots throughout a community along with local hourly air pollution and meteorological data to quantify urban forest structure, environmental effects, and value to communities. Baseline data can be used for making effective resource management decisions, develop policy and set priorities.
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Who is using Eco?

i-Tree Eco has been used by hundreds of users in the United States and internationally. Users interested in using i-Tree Eco abroad should visit the Eco International page to learn more. See the International or United States Eco user maps for a distribution of Eco projects completed around the world.

Initial Decisions

The first steps in creating an Eco project are:

  1. Define the study area for the project. The study area can range from a single tree up to any size tree population. For example, Eco study populations can be a single park, all trees inside a municipal boundary, or all trees inside the county boundary.
  2. Decide whether data will be collected from the complete population or only from random samples of that population. For small populations, complete data collection is possible. More commonly, Eco depends on sampling larger areas of the urban forest such as neighborhoods, sections of a city, an entire city, or urbanized areas around a city.

Complete Inventory Projects

Setting up Eco projects for a complete inventory of trees is relatively straightforward. These projects do not need to establish ground plots as do sample inventories. Projects that are typically suited for the Eco complete inventory option are associated with discrete public or private properties such as corporate campuses, parks, apartment complexes, individual homes or cemeteries.
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Sample Inventory Projects

Eco sampling projects are typically used where the designated study area is too large to cost-effectively inventory the entire tree population. Sampling projects obtain estimates of the characteristics and benefits of a study area from a series of pre-selected sample plots. Such projects usually require project setup that can include characterization of land use and random selection of plot locations in a city using aerial photography.
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Getting Started

Detailed specifications on setting up a project and carrying out field data collection can be found in the Eco Manual. Persons considering their own Eco projects should be aware that the program requires specific types and amounts of data to accurately project the structure and benefits of urban vegetation. The validity of results from Eco, in part, will depend on how closely the user adheres to project setup and sampling protocols.


i-Tree Eco is an adaptation of the Urban Forest Effects (UFORE) model, which was cooperatively developed by US Forest Service Northern Research Station (NRS), the USDA State and Private Forestry's Urban and Community Forestry Program and Northeastern Area, the Davey Tree Expert Company, and SUNY College of Environmental Science and Forestry. The UFORE model was conceived and developed by David J. Nowak and Daniel E. Crane (USFS, NRS), and Patrick McHale (SUNY-ESF). The UFORE software was designed and developed by Daniel E. Crane and its graphical user interface (GUI) by Lianghu Tian and Mike Binkley (The Davey Institute). Many individuals contributed to the design and development process of UFORE application including Mike Binkley (The Davey Institute), Jaewon Choi (SUNY-ESF), Daniel E. Crane (NRS), Greg Ina (The Davey Institute), Robert E. Hoehn (NRS), Jerry Bond and Christopher J. Luley (Urban Forestry LLC), Patrick McHale (SUNY-ESF), David J. Nowak (NRS), Jack C. Stevens (NRS), Lianghu Tian (The Davey Institute), Jeffrey T. Walton (Paul Smiths College), and Robert Sacks (Bluejay Software). Revisions for i-Tree Eco versions were carried out by members of The Davey Institute, including Lianghu Tian, Megan Kerr, Al Zelaya, Scott Maco, and Mike Binkley based on input and newly available research from NRS and feedback from i-Tree users.