SBIR/STTR Research Award

Urban Forestry Modeling and Prioritization Tools

Award identifier 960001aa738929ac2a9f4866a596bfe22ecf7cb3c85b40c80321211476401452Awarding agency Department of Agriculture

Award amount

$450,000

Program and phase

SBIR · Phase II

Award year

2012

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Award details

Awarding agency
Department of Agriculture
Branch
Not reported
Recipient company
Azavea Inc.
Proposal award date
Not reported
Contract end date
Not reported
UEI
KLHHTM7TUWJ3
Solicitation number
USDA-NIFA-SBIR-003621
Topic code
8.1

Research abstract

The Urban Forestry Modeling and Prioritization Tools project addresses the research and tasks necessary for creating cost effective, web-based software that assists urban foresters with strategically selecting planting locations in order to maximize impact on both the environment and local communities. Studies by the US Forest Service and other groups demonstrate the many environmental, health, aesthetic, and economic benefits of a large urban forest canopy. With limited resources to devote to planting and maintaining trees, however, urban foresters, community groups, and other organizations struggle with prioritizing planting locations in order to achieve the greatest economic and ecological return on investment. The difficulty in selecting a planting location is compounded by increasingly limited resources and the different needs and concerns of various communities. The Urban Forestry Modeling and Prioritization Tools were designed to provide assistance in determining effective tree planting locations based on a group's individual needs and priorities. The prototype software application consists of two components - one for the prioritization of tree planting locations and one for future scenario modeling. On the prioritization page, users can more easily identify ideal tree planting locations based on factors such as flood plain location and air quality. Users are able to adjust each factor and, within a second or less, view a personalized heat map identifying key tree planting locations based on the selected criteria. By switching to the scenario modeling page, users can identify an area of interest on the map and select the number, species, and size of trees to plant in that area. The modeling software prototype then applies growth and tree mortality rates to calculate the survival rate of the trees over a thirty year time period. Users can display the financial and ecosystem benefits generated by the trees over several decades to inform their decision-making. The target audiences for the Urban Forest Modeling and Prioritization Tools are municipal governments, regional planning commissions, non-profit organizations including horticultural and urban greening groups, community and neighborhood associations, stormwater and watershed managers, universities, and large parcel owners. Each of these groups could use the web-based tools to more accurately plan tree planting projects and improve the health of the urban forest. The speed and flexibility of the software as well as its usability by non-technical individuals open new possibilities for integrating heat mapping and geographic analysis into web-based modeling activities. Since a large urban forest and urban tree canopy has proven environmental, aesthetic, economic, and health benefits, the entire community benefits from more strategic tree planting.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

28 SBIR/STTR records share this UEI (2006–2021). Reported award amounts total $9,747,804. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Agriculture, Department of Energy, National Aeronautics and Space Administration, National Science Foundation, Department of Commerce, Department of Health and Human Services

Agency intelligence

Department of Agriculture

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