Establishing the relationship between urban land-cover configuration and night time land-surface temperature using spatial regression

Studies suggest that urban form can influence microclimate regulation. Remote sensing studies have contributed to these findings through analysis of high-resolution land cover maps, landscape ecology metrics, and thermal imagery. Collectively, these have been referred to as land cover configuration studies. There are three objectives to this study. The first is to assess the relationship between nighttime land surface temperatures (LST) and land cover configuration and composition. The second objective is to outline a comprehensive methodology that includes ordinary least squares (OLS), spatial regression, variable selection, and multicollinearity analysis. Our last objective is to test three hypotheses about the relationship between LST and land cover, which can briefly be described as: 1) the importance of land-use regimes in modeling LST from land cover composition and configuration variables; 2) the strength of the correlation between LST and roads, buildings, and vegetation; and 3) the improved quality of models using landscape metrics in modeling the relationship between LST and land cover. Based on 16 different models (8 OLS, 8 spatial regression) we could confirm the above hypotheses, but we found that the configuration of buildings, roads, and vegetation have a complex relationship with LST. Our interpretation of this complexity, combined with the strength of composition variables, is that parsimonious models, for now, are more useful to urban planners because they are more generalizable. Finally, spatial regression models of land cover configuration and LST demonstrated an improvement over non-spatial linear models (OLS). Spatial regression models reduced heteroskedasticity and clusters of residuals, and tempered coefficients, suggesting that the OLS models could be biased. OLS models were still found to be a valuable tool for exploratory analysis.

已有研究证实，城市形态可对微气候调节产生影响。遥感研究通过高分辨率土地覆盖图、景观生态学指标与热红外影像的分析，为该类研究结论提供了实证支撑，此类研究统称为土地覆盖格局研究。本研究设定三项核心研究目标：其一，评估夜间地表温度（nighttime land surface temperatures，LST）与土地覆盖格局及组成之间的关联；其二，构建一套涵盖普通最小二乘法（ordinary least squares，OLS）、空间回归、变量筛选及多重共线性分析的完整研究方法体系；其三，检验三项关于地表温度与土地覆盖关系的假说，简要概括如下：1）在基于土地覆盖组成与格局变量构建地表温度预测模型时，土地利用类型的关键作用；2）夜间地表温度与道路、建筑及植被之间的关联强度；3）引入景观指数（landscape metrics）可提升地表温度与土地覆盖关系模型的建模质量。本研究共构建16组模型（8组普通最小二乘法模型、8组空间回归模型），验证了上述三项假说，但同时发现建筑、道路与植被的格局与夜间地表温度之间存在复杂关联。结合组成变量的解释效力，本研究认为，当前简约模型对城市规划者更为实用，因其泛化能力更强。最后，相较于非空间线性模型（普通最小二乘法模型），基于土地覆盖格局与夜间地表温度的空间回归模型表现更优：空间回归模型可降低异方差性与残差聚类现象，并修正了系数，提示普通最小二乘法模型可能存在偏倚。不过，普通最小二乘法模型仍可作为探索性分析的有效工具。