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

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.

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