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.

中文翻译：

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模拟城市树冠、景观异质性和地表温度之间的关系：一种机器学习方法

美国和全球各地的城市都在将城市绿化作为减轻气温上升对人类健康和生活质量影响的战略。更好地了解树冠的空间配置如何影响地表温度应有助于增加城市绿化的积极影响。本研究采用机器学习方法，利用来自美国九个不同气候区的九个城市的数据，对城市树冠、景观异质性和地表温度 (LST) 之间的关系进行建模。我们从美国地质调查局 (USGS) 分析准备数据系列中收集了夏季 LST 数据，并对这些数据进行处理，得出所考虑城市内每个人口普查区块组的平均、最小和最大 LST（以华氏度为单位）。我们还计算了美国地质调查局 (USGS) 维护的 2016 年或 2019 年国家土地覆盖数据库 (NLCD) 中土地覆盖指定所组成的每个区块组的百分比，具体取决于可用 LST 数据的年份。为所有研究城市购买了高分辨率树冠数据，并使用已建立的景观指标在块组级别测量树冠的空间配置。还计算了水体的景观指标，以纳入水体的冷却效果。我们使用广义提升回归模型 (GBM) 算法根据收集的数据预测 LST。我们的结果表明，树冠对所有研究城市的预测地表温度都有一致且显着的影响，但树冠和水斑的配置在某些地方比在其他地方更重要。研究结果强调了在规划城市绿化时考虑当地气候和现有景观特征的重要性。