Climate change induced by global warming will produce more frequent heatwaves and intensify urban heat islands, thereby increasing heating related risks. Given this scenario, it is important to mitigate heat on urban ecosystems to promote their resilience. This study aims to develop a method to identify heat vulnerable urban areas by considering the land surface temperature (LST) distribution and landscape patterns. Methods included the creation of a cellular space to represent a given urban environment and integrate multidisciplinary databases; the use of Landsat-8 satellite images to estimate fraction vegetation cover, normalized difference moisture indices emissivity, and albedo; and exploratory spatial analyses using Moran's global and local indices to identify landscape patterns associated with Local Climate Zones (LCZ). Analyses revealed that the estimated variables are suitable for explaining LST, which is autocorrelated in space, despite seasonal variations. The methods made it possible to identify heat vulnerable areas, which should be considered when developing adaptation policies, and that heating is associated with areas composed of both compact low rise (LCZ3) and large low rise buildings (LCZ8), whereas cooling is associated with dense trees (LCZA), and when vegetation is associated with both open high rise (LCZ4) and open low rise buildings (LCZ6).

全球变暖引起的气候变化将产生更频繁的热浪并加剧城市热岛，从而增加与供暖相关的风险。鉴于这种情况，减轻城市生态系统的热量以提高其恢复力非常重要。本研究旨在开发一种通过考虑地表温度 (LST) 分布和景观模式来识别热脆弱城市地区的方法。方法包括创建一个细胞空间来代表给定的城市环境并整合多学科数据库；使用 Landsat-8 卫星图像估计植被覆盖率、归一化差异水分指数发射率和反照率；使用 Moran 的全球和本地指数进行探索性空间分析，以确定与本地气候区 (LCZ) 相关的景观模式。分析表明，估计变量适用于解释 LST，尽管存在季节性变化，但它在空间上是自相关的。这些方法使得确定热脆弱区域成为可能，在制定适应政策时应考虑到这一点，并且供暖与由紧凑型低层建筑 (LCZ3) 和大型低层建筑 (LCZ8) 组成的区域相关，而制冷与茂密的树木 (LCZA)，以及当植被与开放式高层建筑 (LCZ4) 和开放式低层建筑 (LCZ6) 相关时。