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.

研究表明，城市形态可对微气候调节产生影响。相关遥感研究通过分析高分辨率土地覆盖图、景观生态学指标与热红外影像，为上述结论提供了支撑，这类研究统称为土地覆盖格局研究。本研究包含三大目标：其一，评估夜间地表温度（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（OLS）、空间回归、变量选择与多重共线性分析的完整方法论；其三，验证三项关于地表温度与土地覆盖关系的假设，简要概括为：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，土地利用方式的重要性；2）地表温度与道路、建筑及植被之间的关联强度；3）引入景观生态学指标后，地表温度与土地覆盖关系模型的性能提升效果。基于16组不同模型（8组普通最小二乘法模型、8组空间回归模型），本研究证实了上述三项假设，但同时发现建筑、道路与植被的格局与地表温度之间存在复杂关联。结合组成变量的影响强度，我们认为，就目前而言，简约模型对城市规划者更为实用，因其具备更强的可推广性。最后，针对土地覆盖格局与夜间地表温度的空间回归模型，其性能优于非空间线性模型（普通最小二乘法）：空间回归模型可降低异方差性与残差集群现象，并使系数趋于平稳，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。