Finding and Recruiting Allies
Engaging and Motivating Stakeholders
Show All the Benefits Urban Forestry Can Deliver
Remember in kindergarten, kids were asked to “show and tell” about something they cared about. The same strategy applies here: showing is more powerful than just telling.
The i-Tree suite of user-friendly software tools will enable you to actually calculate the overall value of trees in your community. You can then communicate those values to local stakeholders through presentations or on paper, with easy-to-understand charts and graphs. The complete downloadable suite covers everything from stormwater modeling to carbon sequestration.
Trees work hard – they aren’t just ornaments for your landscape. Around the clock, they help remove atmospheric carbon dioxide and pollution, reduce stormwater flow, save energy, and reduce the dreaded “heat island effect.” i-Tree Landscape allows you to explore tree canopy, land cover, and basic demographic information in a location of your choosing. You’ll have the opportunity to learn about the benefits of trees in your selected location, see how planting trees will increase the benefits provided, and map the areas where you decide to prioritize your tree planting efforts. Try it out here.
i-Tree Design is a free web-based tool that allows anyone to make a simple estimation of the benefits of individual or multiple trees. By inputting location, species, tree size, and condition, users gain an understanding of tree benefits related to greenhouse gas mitigation (carbon sequestration), air quality improvements, and stormwater interception. You can use the tool to draw a building footprint – and virtually “plant” trees around it – to evaluate the impact of trees on building energy use. Try it, then use it in public presentations; it’s a quick attention grabber.
So you’re not a GIS specialist, and you don’t have a good sense of your community’s tree canopy. i-Tree Canopy, while somewhat more time-consuming, allows you to use Google Maps to conduct a virtual, random-sample-based canopy assessment. Define the area you’d like to measure, and Canopy produces randomly-selected aerial images. Users then characterize what cover type they see – using a customized selection of pre-loaded classes. Classify 100 points and the standard error for an area with 30 percent canopy cover will range about 4.6 percent. Classify 1,000 points and you can reduce the standard error to 1.4 percent. It can also measure changes in land cover over time by matching paired images from different dates. Caution from experienced users: To avoid mis-classifications, train your photo-interpreters. Canopy has some limitations, but for most communities willing to spend 10-12 person hours, it’s one of the best bargains out there (and it’s free!).