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A Decision Support System to Develop, Analyze, and Optimize Urban & Community Forests

Grant Number
16-DG-11132544-034

A decision support system (DSS) for i-Tree Landscape that strategically manages the uncertainty in i-Tree predictions to increase the chance of managers achieving desired benefits and services from urban and community forests.

We create a decision support system (DSS) for i-Tree Landscape that strategically manages the uncertainty in i-Tree Eco, Hydro, and Forecast predictions to increase the chance of managers achieving desired benefits and services from urban and community forests. Our challenge includes: increasing predictive accuracy of these i‐Tree models while not creating undue data requirement burdens; and reporting predictive accuracy to inform and not confuse users. Our methods include identifying: a) drivers of model uncertainty; b) methods to estimate model uncertainty; and c) ways to view and reduce model uncertainty. Our expected outcomes are: uncertainty estimators for i-Tree Eco, Hydro, and Forecast; integration of this uncertainty in our DSS; case studies demonstrating the use of the DSS to minimize the impacts of development and redevelopment on urban and community forests; and dissemination of results.

Contact
Charles Kroll
cnkroll@esf.edu
315-470-6699
Organization
The Research Foundation for SUNY
1 Forestry Dr
Syracuse, NY 13210
Total Project Cost
$ 572,724
=
Federal Share
$ 285,340
+
Grantee Share
$ 287,384
Year of Award
2016
Year of Expiration
2019
Sub-Topics
Urban Forest Management, Sustainable Development
State(s)/Region(s)
New York
Keywords
decision support
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