A comparison of tree planting prioritization frameworks: i-Tree Landscape versus spatial decision support tool

Different models as well as planting prioritization and optimization schemes based upon diverse ecological, social, and economic goals and preferences have been used to develop more efficient and effective tree planting schemes. We compare tree planting prioritization scenarios identified from i-Tree Landscape’s priority planting index to optimal scenarios identified from a spatially explicit multi-objective decision support framework at the census block group level in the Bronx, NY. We explore four scenarios with varying objectives considering populations below the poverty line, avoided runoff, and PM2.5 air pollutant removal monetary benefits. Results show that when prioritizing single objectives (e.g., PM2.5 air pollutant removal) using the same per area of tree canopy benefits from the spatially distributed modeling of ecosystem services, the two approaches recommend similar block groups for additional tree cover. Scenarios considering multiple objectives, however, result in different optimal solutions, with the decision support framework generally recommending more block groups for increased tree cover than i-Tree Landscape’s methodology. When the per area of tree canopy benefits from i-Tree Landscape are used as input in the i-Tree Landscape prioritization scenarios and the spatially distributed benefits used in the decision support framework scenarios, different optimal solutions are identified between the two approaches across all four scenarios, with i-Tree Landscape recommending fewer block groups for increased tree cover. Such a comparison will help inform the development of flexible multi-objective decision support tools to guide future greening initiatives towards prioritizing planting locations that maximize multiple objectives, as well as areas to preserve urban forests.