Urban landscapes and night time urban heat island

Abstract

The study of urban heat islands (UHI) is of great importance in the context of climate change (CC) and global warming. Cities accumulate heat in urban land covers as well as in built infrastructures, representing true islands of heat in relation to their rural (less artificialized) environment. The densest urban spaces as well as the industrial and commercial areas are characterized by accumulating more heat during the day. On the other hand, the areas of lower density, the Urban Sprawl, tend to have a better climatic behaviour. The lower density as well as the greater amount of vegetation in the Urban Sprawl areas reduce the UHI during daylight hours. Nevertheless, the literature on urban climate has highlighted the singular importance of the nighttime UHI. It is during the night that the effects of UHI become more apparent, due to the low cooling capacity of urban construction materials and is during nighttime that temperatures can cause higher health risks, leading to the aggravation of negative impacts on people’s health and comfort in extreme events such as heat waves becoming more and more frequent and lasting longer. However, the study of nocturnal UHIs is still poorly developed, due to the structural problems regarding the availability of land surface and air temperature data for night time. This paper aims to develop a model for nocturnal UHI using data from Landsat thermal bands (with spatial resolution of 30 square meters per pixel) and contrasting Landsat's very limited nighttime images with daytime ones. The contrast allows the construction of “cooling” models of the LST based on geographical (longitude, latitude, distance to the sea, DTM, slope, orientation, etc.) and land covers characteristics (density, vegetation and building indexes, impervious surface, and others parameters). Said models will allow evaluating the nighttime LST of the different urban landscapes: historical centers, ensanches, discontinuous urban fabrics (of different densities), scattered building areas and industrial and commercial areas, trying to clarify the nightly UHI of Urban Sprawl. The case study is the Metropolitan Area of Barcelona (636 km2, 3.3 million inhabitants).