A Spatially Explicit Approach for Optimizing Urban Afforestation to Achieve Project Goals

Urban forests provide essential ecosystem services, support native biodiversity, and advance to sustainable development goals. The benefits provided are often influenced by the forest's configuration within the urban landscape matrix, emphasizing the importance of increasing tree cover in strategic locations. Despite increasing investments in urban tree planting initiatives, many projects lack a systematic, data-driven approach for prioritizing planting locations and assessing their ecological and socioeconomic impacts. This gap limits practitioners' ability to develop urban afforestation plans that balance spatial availability, resource limitations, and competing project goals. In this study we introduce a spatially explicit tool designed to identify optimal tree planting sites within urban environments based on specific goals or desired outcomes. The tool's algorithm analyzes high-resolution geospatial data to identify and rank viable planting locations, simulate afforestation scenarios, and integrate external programs to quantify potential outcomes. The tool produces a detailed inventory of the number of trees that can be planted in any given city, along with actionable information including the geographic coordinates of each site and the maximum size tree that can be accommodated in each. By using this information to quantify specific outcomes, such as ecosystem service provisioning or impacts to native biodiversity, users can evaluate whether the project's goals are achievable and which planting strategy is most effective. Furthermore, the precise number and location of planting sites can be incorporated to strengthen long-term management plans and resiliency efforts. This presentation will feature case studies from the US and Europe to assess the impacts of different urban afforestation strategies, and demonstrate the tool's potential to strengthen urban tree planting efforts across the world.