Uhrin Anton, Onačillová Katarína
Institute of Geography, Faculty of Science, Pavol Jozef Šafárik University in Košice, Šrobárova 2, 04001, Košice, Slovak Republic.
Environ Monit Assess. 2025 Jan 3;197(2):126. doi: 10.1007/s10661-024-13598-8.
In recent decades, global climate change and rapid urbanization have aggravated the urban heat island (UHI) effect, affecting the well-being of urban citizens. Although this significant phenomenon is more pronounced in larger metropolitan areas due to extensive impervious surfaces, small- and medium-sized cities also experience UHI effects, yet research on UHI in these cities is rare, emphasizing the importance of land surface temperature (LST) as a key parameter for studying UHI dynamics. Therefore, this paper focuses on the evaluation of LST and land cover (LC) changes in the city of Prešov, Slovakia, a typical medium-sized European city that has recently undergone significant LC changes. In this study, we use the relationship between Landsat-8/Landsat-9-derived LST and spectral indices Normalized Difference Built-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) derived from Landsat-8/Landsat-9 and Sentinel-2 to downscale LST to 10 m. Two machine learning (ML) algorithms, support vector machine (SVM) and random forest (RF), are used to assess image classification and identify how different types and LC changes in selected years 2017, 2019, and 2023 affect the pattern of LST. The results show that several decisions made during the last decade, such as the construction of new urban fabrics and roads, caused the increase in LST. The LC change evaluation, based on the RF classification algorithm, achieved overall accuracies of 93.2% in 2017, 89.6% in 2019, and 91.5% in 2023, outperforming SVM by 0.8% in 2017 and 4.3% in 2023. This approach identifies UHI-prone areas with higher spatial resolution, helping urban planning mitigate the negative effects of increasing urban LSTs.
近几十年来，全球气候变化和快速城市化加剧了城市热岛（UHI）效应，影响着城市居民的福祉。尽管由于大面积的不透水表面，这一显著现象在较大的都市地区更为明显，但中小城市也存在城市热岛效应，然而对这些城市的城市热岛效应研究却很少，这凸显了地表温度（LST）作为研究城市热岛动态关键参数的重要性。因此，本文重点评估了斯洛伐克普雷绍夫市的地表温度和土地覆盖（LC）变化，该市是欧洲一个典型的中等规模城市，近期经历了显著的土地覆盖变化。在本研究中，我们利用Landsat - 8/Landsat - 9反演的地表温度与从Landsat - 8/Landsat - 9和哨兵 - 2数据中提取的光谱指数归一化差异建筑指数（NDBI）、归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）之间的关系，将地表温度降尺度到10米。使用支持向量机（SVM）和随机森林（RF）两种机器学习（ML）算法来评估图像分类，并确定2017年、2019年和2023年选定年份不同类型和土地覆盖变化如何影响地表温度模式。结果表明，过去十年做出的一些决策，如新城市建筑和道路的建设，导致了地表温度的升高。基于随机森林分类算法的土地覆盖变化评估在2017年、2019年和2023年的总体准确率分别为93.2%、89.6%和91.5%，在2017年比支持向量机高出0.8%，在2023年高出4.3%。这种方法以更高的空间分辨率识别出易出现城市热岛效应的区域，有助于城市规划减轻城市地表温度升高带来的负面影响。
