长江三角洲地区城市建筑三维形态对地表热环境的影响

以长江三角洲城市群为例，利用2016~2018年夏季Landsat 8影像数据和三维建筑数据，提取地表温度（LST）并计算三维景观指数，分别在200m、400m、600m、800m、1000m、1200m六个基准空间尺度上，利用空间回归模型探索建筑物三维形态对LST的影响.结果表明：所有城市的内部城区热环境较差，超过50%区域为高温区和次高温区；建筑物三维形态对LST的影响存在尺度效应，随着研究尺度的增大，影响力逐渐减弱，在研究的六个基准尺度中，200m是所有城市的研究适宜尺度；对LST影响最大的指数分别是建筑结构指数（BSI）、建筑平均体积（AV）和分布均匀度（BEI），其中BSI、BEI与LST正相关，除上海、苏州和台州，AV也和LST正相关，表明瘦高、体积小、分布均匀的建筑物有助于降低城市LST，改善城市热环境.

Influences of 3D features of buildings on land surface temperature: A case study in the Yangtze River Delta urban agglomeration

Taking 15 cities in the Yangtze River Delta urban agglomeration as the study area, the LST data were retrieved from Landsat 8 images in the summer (from June to September) from 2016 to 2018 by the radiative transfer equation method, and the three-dimensional (3D) building data in 2018 were derived from the online map service platform Gaode maps through the open API. At six spatial scales of 200m, 400m, 600m, 800m, 1000m, and 1200m, the spatial regression models were used to explore the influence of the 3D building landscape pattern on LST. The results showed that the thermal environment in the 15 cities was relatively harsh, and more than 50% of the areas were high-temperature or sub-high-temperature zones. There was a scale effect on the influence of 3D building landscape pattern on LST. As the scale increased, the influence of the 3D building landscape pattern gradually weakened. Among the six scales, 200m was the appropriate scale for all cities to analyze the influence of 3D landscape pattern of buildings on LST. Among the nine selected 3D landscape metrics, building structure index (BSI), average building volume (AV) and building evenness index (BEI) were the most powerful indicators affecting LST. BSI and BEI were positively correlated with LST for all the cities. Except for Shanghai, Suzhou and Taizhou, AV was also positively correlated with LST, indicating that lean, tall and evenly distributed buildings could help reduce the LST and improve the urban thermal environment.