Urban areas are characterised by the dominance of impervious surfaces and decreased presence of vegetation compared to their rural surroundings. The resultant increase in temperature is known to amplify global warming, with negative impacts on health and increased energy requirements for cooling. Intra-urban variations in temperature have received less attention than urban–rural variations, although the former can be even larger than the latter. Land cover composition is known to influence surface temperature, while the influence of heights, of buildings and vegetation, is less explored. There are also fewer studies in high-latitude cities although extreme heat events are increasing in frequency and severity in these cities, and high-resolution geospatial datasets are often available for detailed analysis. The aim of this study is therefore to assess the influence of selected land cover variables on the estimated surface temperature in the four largest cities in Denmark—Copenhagen, Aarhus, Odense and Aalborg.

Land surface temperatures (LST) of the four cities were estimated using Band 10 (10.60–11.19 μm) from Landsat 8 imagery. Vegetation cover, building cover, vegetation height and building height were estimated using 4-band aerial imagery, building footprints and LiDAR-based elevation models, and their correlations with LST were estimated. Moving average filters, with window sizes from 3 × 3 (90 m × 90 m) to 11 × 11 (330 m × 330 m), were used to understand the area of influence of surrounding land cover on the LST within 30-m cells. When vegetation cover and building cover increased from 0–5% to 95–100%, median values of LST decreased by 4.16 ± 0.76 °C and increased by 4.31 ± 0.69 °C, respectively. Land cover variables within 7 × 7 windows (210 m × 210 m) are shown to have strong correlations with the LST of 30-m cells. The area of influence of building heights on the LST of 30-m cells was the largest in Copenhagen, which also has the tallest buildings among the cities. LST reduced by 4.10 °C when the mean vegetation height within a 30-m cell increased from 0–2 m to 20–22 m, and by 5.75 °C for 210 m × 210 m patches with the same height range. A combination of increased vegetation cover and height could therefore be used to regulate temperature in or close to hot spots in cities depending on the availability of space.

与农村地区相比，城市地区的特点是不透水的表面占主导地位，植被减少。众所周知，由此导致的温度升高会加剧全球变暖，对健康产生负面影响，并增加冷却所需的能源。尽管城市内部的温度变化甚至可能比城市内部的变化大，但城市内部的温度变化受到的关注要少于城市-农村的变化。众所周知，土地覆盖物的成分会影响地表温度，而建筑物和植被的高度影响则较少。尽管在这些城市中极端高温事件的频率和严重性正在增加，但在高纬度城市中的研究也较少，而且高分辨率地理空间数据集通常可用于详细分析。

使用Landsat 8影像的波段10（10.60-11.19μm）估算了四个城市的地表温度（LST）。使用4波段航拍图像，建筑物覆盖区和基于LiDAR的高程模型估算了植被覆盖率，建筑物覆盖率，植被高度和建筑物高度，并估算了它们与LST的相关性。使用窗口大小从3×3（90 m×90 m）到11×11（330 m×330 m）的移动平均滤波器来了解30 m小区内周围土地覆盖对LST的影响区域。当植被覆盖率和建筑物覆盖率从0–5％增加到95–100％时，LST的中值分别降低了4.16±0.76°C和4.31±0.69°C。结果表明，7×7窗口（210 m×210 m）内的土地覆盖变量与30 m小区的LST有很强的相关性。建筑物高度对30米牢房LST的影响面积是哥本哈根最大的，哥本哈根也是城市中最高的建筑物。当30 m单元内的平均植被高度从0–2 m增加到20–22 m时，LST降低4.10°C；对于具有相同高度范围的210 m×210 m斑块，LST降低5.75°C。因此，根据空间的可用性，可以结合使用增加的植被覆盖度和高度来调节城市热点或附近热点的温度。