Prepared by Ian Seiferling

Scope of Analysis

Urban trees and green infrastructure (GI) can play a role in urban heat island (UHI) mitigation: reducing building energy requirements via surface shading and evapotranspiration and, likewise, creating more comfortable outdoor microenvironments. They also function to sequester atmospheric carbon, improve air quality and reduce stormwater and pollutant runoff. What’s more, these multiple services are in addition to the ecological (e.g., biodiversity support), aesthetic, psychological, and direct health benefits trees provide to urban citizens (1, 2, 3).

Though urban tree benefits are evident, the proper tools and understanding to efficiently increase urban forest cover, manage urban trees and GI and balance their cost-benefit trade-offs are lacking. Considering the fine-grain scale of cities, baseline data on urban heat islands and trees have been prohibitively rare. Moreover, contention between the emerging benefits of urban tree populations and their costs bring to light the need for high quality data and new tools that integrate the complexity of urban landscapes while also engaging citizens.

We propose to develop and implement the concept of networked, smart urban forests and use the tools we develop to quantify the role of trees in urban climate adaptation, human health, and planning. To do so, we propose to couple an array of low-cost urban environmental sensors with a citizen science initiative. The project will address the current void in fine-grain urban temperature data at the city-scale. A primary objective will be to establish networked temperature sensors that operate at multiple scales within the city, with the resulting data integrated and standardized through state-of-the-art computational modelling and data management technologies. The result will be a multi-layered (temperature, tree distribution, urban topographies), high-resolution data set that can be used to establish UHI mitigation standards, temperature reduction targets, and tree planting sites.

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