Urban Forest Ecosystem Service Optimization, Tradeoffs, and Disparities

Abstract:

Urban land area and the proportion of humanity living in cities is growing, leading to increased urban air pollution, temperature, and stormwater runoff. These changes can exacerbate respiratory and heat-related illnesses and affect ecosystem functioning. Urban trees can help mitigate these threats by removing air pollutants, mitigating urban heat island effects, and infiltrating and filtering stormwater. The urban environment is highly heterogeneous, and there is no tool to determine optimal locations to plant or protect trees. Using spatially explicit land cover, weather, and demographic data within biophysical ecosystem service models, this research expands upon the iTree urban forest tools to produce a new decision support tool (iTree-DST) that will explore the development and impacts of optimal tree planting. It will also heighten awareness of environmental justice by incorporating the Atkinson Index to quantify disparities in health risks and ecosystem services across vulnerable and susceptible populations.

The study area is Baltimore City, a location whose urban forest and environmental justice concerns have been studied extensively. The iTree-DST is run at the US Census block group level and utilizes a local gradient approach to calculate the change in ecosystem services with changing tree cover across the study area. Empirical fits provide ecosystem service gradients for possible tree cover scenarios, greatly increasing the speed and efficiency of the optimization procedure. Initial results include an evaluation of the performance of the gradient method, optimal planting schemes for individual ecosystem services, and an analysis of tradeoffs and synergies between competing objectives.