With 1.76 million inhabitants, Mérida city has recently become the most populated city in southeast Mexico, being one of the most attractive cities for investment in real-estate projects. This has increased deforestation to the periphery, resulting in heat islands. We used time series of Landsat images and the BFAST algorithm to analyze annual deforestation around Mérida city over the 2000–2018 period. Land surface temperature was also estimated using Landsat images to compare temperatures before and after deforestation and examine heat islands in the city. The deforestation maps had a 96.82% overall accuracy on average, user’s and producer’s accuracy values of 91.62% and 95.46%, respectively, and an estimated total deforested area of 5413 ha over the study period. Land surface temperature increased in 2.36–3.94 °C after deforestation, and heat islands of varying intensity were detected in 80% of the urban territory, mainly where deforestation occurred. Our results demonstrate the effectiveness of Landsat images and the BFAST algorithm for detecting deforestation in peri-urban spaces, as well as the usability of Landsat images for estimating land surface temperature. These images are effective tools for urban land-use planning and for examining the formation of heat islands in tropical cities.

梅里达市拥有 176 万居民，最近成为墨西哥东南部人口最多的城市，成为房地产项目投资最具吸引力的城市之一。这增加了周边地区的森林砍伐，导致了热岛。我们使用 Landsat 图像的时间序列和 BFAST 算法来分析 2000 年至 2018 年期间梅里达市周围的年度森林砍伐情况。还使用 Landsat 图像估算了地表温度，以比较森林砍伐前后的温度并检查城市中的热岛。森林砍伐地图的平均总体准确度为 96.82%，用户和生产者的准确度值分别为 91.62% 和 95.46%，研究期间估计的森林砍伐总面积为 5413 公顷。森林砍伐后地表温度升高了 2.36–3.94 °C，80% 的城市领土发现了不同强度的热岛和热岛，主要发生在森林砍伐的地方。我们的结果证明了 Landsat 图像和 BFAST 算法在检测森林砍伐方面的有效性城市周边空间，以及 Landsat 图像用于估算地表温度的可用性。这些图像是城市土地利用规划和检查热带城市热岛形成的有效工具。