Developing a composite indicator to prioritize tree planting and protection locations

Abstract

Trees provide numerous ecosystem services to benefit human health, and many cities have launched tree planting and management programs to increase tree populations and optimize tree locations through diverse tree priority schemes. Existing tree priority schemes are typically local-specific, expert-driven, and tree-planting-focused. In this study, a framework that captures interactions among the environment, tree and human demographic information is built. This framework provides a composite indicator, namely a tree priority planting or priority protection index (PPI), that can be integrated within a decision support system such as i-Tree Landscape to provide nationally consistent and locally relevant ways to strategically optimize tree planting and management locations across the entire United States. Three scenarios with the human health concerns are tested in a case study of New York City. The analyses are conducted at the census block group scale that is the finest-level scale available at i-Tree Landscape. The resulting PPI maps are analyzed using spatial statistics and compared against each other to investigate the impacts of alternative investments of limited public resources. The results show that: (1) tree priority patterns change greatly with alternative objectives; (2) adding more indicators to build PPIs lead to more diverse tree priority patterns as high (or low) values of different indicators are often not geographically coincident; (3) incorporating more indicators may not necessarily provide more useful information because the influences of individual indicators may be reduced and diluted by a higher level of aggregation; and (4) disaggregating PPIs may reveal corresponding contributions of individual indicators. Applying the proposed framework to build PPIs has important implications for tree priority effort, scientific exploration, education, and public engagement.

Keywords: Ecosystem services; Environmental inequity; Human health; Scenario analysis; Urban forestry.