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Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach

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  • Bev Wilson
  • Shakil Bin Kashem
  • Lily Slonim

Abstract

Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening. This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others. The findings underscore the importance of considering the local climate and existing landscape features when planning for urban greening.

Suggested Citation

  • Bev Wilson & Shakil Bin Kashem & Lily Slonim, 2024. "Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach," Environment and Planning B, , vol. 51(8), pages 1895-1912, October.
    • Handle: RePEc:sae:envirb:v:51:y:2024:i:8:p:1895-1912
    DOI: 10.1177/23998083241226848
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    References listed on IDEAS

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    1. Bev Wilson, 2020. "Urban Heat Management and the Legacy of Redlining," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(4), pages 443-457, October.
    2. Dakota McCarty & Jaekyung Lee & Hyun Woo Kim, 2021. "Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    3. Xing Zhang & Tianjun Zhou & Wenxia Zhang & Liwen Ren & Jie Jiang & Shuai Hu & Meng Zuo & Lixia Zhang & Wenmin Man, 2023. "Increased impact of heat domes on 2021-like heat extremes in North America under global warming," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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