Prioritizing Preferable Locations for Increasing Urban Tree Canopy in New York City

This paper presents a set of Geographic Information System (GIS) methods for identifying and prioritizing tree planting sites in urban environments. It uses an analytical approach created by a University of Vermont service-learning class called “GIS Analysis of New York City's Ecology” that was designed to provide research support to the MillionTreesNYC tree planting campaign. These methods prioritize tree planting sites based on need (whether or not trees can help address specific issues in the community) and suitability (biophysical constraints and planting partners’ existing programmatic goals). Criteria for suitability and need were based on input from three New York City tree-planting organizations. Customized spatial analysis tools and maps were created to show where each organization may contribute to increasing urban tree canopy (UTC) while also achieving their own programmatic goals. These methods and associated custom tools can help decision-makers optimize urban forestry investments with respect to biophysical and socioeconomic outcomes in a clear and accountable manner. Additionally, the framework described here may be used in other cities, can track spatial characteristics of urban ecosystems over time, and may enable further tool development for collaborative decision-making in urban natural resource management

Variation the in relationship between urban tree canopy and air temperature reduction under a range of daily weather conditions

Mitigating heat is a vital ecosystem service of trees, particularly with climate change. Land surface temperature measures captured at a single time of day (in the morning) dominate the urban heat island literature. Less is known about how local tree canopy and impervious surface regulate air temperature throughout the day, and/or across many days with varied weather conditions, including cloud cover. We use bike-mounted air temperature sensors throughout the day in New Haven, Connecticut, USA, from 2019 to 2021 and generalized additive mixed models across 156 rides to estimate the daily variation in cooling benefits associated with tree canopy cover, and warming from impervious surface cover in 90 m buffers surrounding bike observations. Cooling is inferred by subtracting the bicycle-observed temperature from a reference station. The cooling benefits from tree canopy cover were strongest in the midday (11:00–14:00, −1.62 °C), afternoon (14:00–17:00, −1.19 °C), and morning (8:00–11:00, −1.15 °C) on clear days. The cooling effect was comparatively smaller on cloudy mornings −0.92 °C and afternoons −0.51 °C. Warming from impervious surfaces was most pronounced in the evening (17:00–20:00, 1.11 °C) irrespective of clouds, and during cloudy nights (20:00–23:00) and cloudy mornings 1.03 °C 95 % CI [1.03, 1.04]. Among the hottest observed days (top 25th percentile of reference station daily maxima), tree canopy was associated with lower temperatures on clear afternoons −1.78 °C [-1.78, −1.78], cloudy midday −1.17 °C [-1.19, −1.15], clear midday −1.12 °C [-1.12, −1.11]. We add a broader spectrum of weather conditions by explicitly including clouds, and greater temporal resolution by measuring throughout the day to bike-based urban heat research. Future mobile sampling campaigns may broaden the spatial extent with more environmental variation, representing an opportunity for public science and engagement