Characterizing spatial and temporal deforestation and its effects on surface urban heat islands in a tropical city using Landsat time series

With 1.76 million inhabitants, Me ' rida 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 Me ' rida 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 degrees 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.

Automated summary

With the rapid population growth, Me ' rida city has become an attractive city for real-estate investment in southeast Mexico. However, this has led to deforestation and the formation of heat islands. By using Landsat images and the BFAST algorithm, this study analyzed the deforestation trends and identified the occurrence of heat islands in the city. The findings demonstrate the effectiveness of Landsat images as tools for detecting deforestation and estimating land surface temperature.