Relationships between land surface temperature (LST) and spatial configuration of urban form described by landscape metrics so far have been investigated with coarse resolution LST imagery within artificially superimposed land divisions. Citywide micro-scale observations are needed to better inform urban design and help mitigate urban heat island effects in warming climates. The primary objective was to sub-divide an existing high-resolution land cover (LC) map into groups of patches with distinct spatial and thermal properties suitable for urban LST studies relevant to micro-scales. The secondary objective was to provide insights into the optimal analytical unit size to calculate class-level landscape metrics strongly correlated with LST at 2 m spatial resolution. A two-tiered unsupervised k-means clustering analysis was deployed to derive spatially distinct groups of patches of each major LC class followed by further subdivisions into hottest, coldest and intermediary sub-classes, making use of high resolution class-level landscape metrics strongly correlated with LST. Aggregation class-level landscape metrics were consistently correlated with LST for green and grey LC classes and the optimal search window size for their calculations was 100 m for LST at 2 m resolution. ANOVA indicated that all Tier 1 and most of Tier 2 subdivisions were thermally and spatially different. The two-tiered k-means clustering approach was successful at depicting subdivisions of major LC classes with distinct spatial configuration and thermal properties, especially at a broader Tier 1 level. Further research into spatial configuration of LC patches with similar spatial but different thermal properties is required.

中文翻译：

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一种使用类级景观指标确定具有不同热特性的高分辨率城市形态模式的简单方法

迄今为止，地表温度 (LST) 与由景观指标描述的城市形态空间配置之间的关系已使用人工叠加的土地分区内的粗分辨率 LST 图像进行了研究。需要全市微尺度观测来更好地为城市设计提供信息，并帮助减轻气候变暖中的城市热岛效应。主要目标是将现有的高分辨率土地覆盖 (LC) 地图细分为具有不同空间和热特性的斑块组，适用于与微尺度相关的城市 LST 研究。次要目标是深入了解最佳分析单元大小，以计算与 2 m 空间分辨率下的 LST 密切相关的类级景观指标。部署了两层无监督 k 均值聚类分析，以导出每个主要 LC 类在空间上不同的斑块组，然后进一步细分为最热、最冷和中间子类，利用高度相关的高分辨率类级景观指标与 LST。聚合类级别的景观指标始终与绿色和灰色 LC 类的 LST 相关，并且其计算的最佳搜索窗口大小为 2 m 分辨率的 LST 的 100 m。方差分析表明，所有第 1 层和大多数第 2 层细分在热量和空间上都不同。两层 k 均值聚类方法成功地描绘了具有不同空间配置和热特性的主要 LC 类别的细分，尤其是在更广泛的第 1 层级别。