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Abstract Urban trees are increasingly used by cities for cooling and climate adaptation. However, efforts to increase tree cover across cities have neglected to account for the trees' health and function, which are known to control their associated environmental benefits but have been difficult to assess at scales relevant for management. Here, we use remotely sensed, high resolution canopy temperature as a proxy for tree health and function and evaluate its relation to the built environment across Minneapolis‐St. Paul (MSP) using machine learning analyses. We develop a new index that incorporates information on urban trees' health and function, in addition to their presence. This index, when applied across MSP, suggests that canopy benefits may not be distributed equally even in neighborhoods with similar canopy cover. Furthermore, accounting for tree health and function can yield more effective and equitable benefits by guiding the location and magnitude of intervention for urban tree management.
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Spatial configuration and time of day impact the magnitude of urban tree canopy cooling
Abstract Tree cover is generally associated with cooler air temperatures in urban environments but the roles of canopy configuration, spatial context, and time of day are not well understood. The ability to examine spatiotemporal relationships between trees and urban climate has been hindered by lack of appropriate air temperature data and, perhaps, by overreliance on a single ‘tree canopy’ class, obscuring the mechanisms by which canopy cools. Here, we use >70 000 air temperature measurements collected by car throughout Washington, DC, USA in predawn (pd), afternoon (aft), and evening (eve) campaigns on a hot summer day. We subdivided tree canopy into ‘soft’ (over unpaved surfaces) and ‘hard’ (over paved surfaces) canopy classes and further partitioned soft canopy into distributed (narrow edges) and clumped patches (edges with interior cores). At each level of subdivision, we predicted air temperature anomalies using generalized additive models for each time of day. We found that the all-inclusive ‘tree canopy’ class cooled linearly at every time (pd = 0.5 °C ± 0.3 °C, aft = 1.8 °C ± 0.6 °C, eve = 1.7 °C ± 0.4 °C), but could be explained in the afternoon by aggregate effects of predominant hard and soft canopy cooling at low and high canopy cover, respectively. Soft canopy cooled nonlinearly in the afternoon with minimal effect until ∼40% cover but strongly (and linearly) across all cover fractions in the evening (pd = 0.7 °C ± 1.1 °C, aft = 2.0 °C ± 0.7 °C, eve = 2.9 °C ± 0.6 °C). Patches cooled at all times of day despite uneven allocation throughout the city, whereas more distributed canopy cooled in predawn and evening due to increased shading. This later finding is important for urban heat island mitigation planning since it is easier to find planting spaces for distributed trees rather than forest patches.
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- Award ID(s):
- 1951647
- PAR ID:
- 10325319
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 16
- Issue:
- 8
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 084028
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/ac12f2
