This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns.

Blue steel roof is advantageous for its low cost, durability, and ease of installation. It is generally used by industrial areas. The accurate and rapid mapping of blue steel roof is important for the preliminary assessment of inefficient industrial areas and is one of the key elements for quantifying environmental issues like urban heat islands. Here, the DeeplabV3+ semantic segmentation neural network based on GaoFen-2 images was used to analyze the quantity and spatial distribution of blue steel roofs in the Nanhai district, Foshan (including the towns of Shishan, Guicheng, Dali, and Lishui), which is the important manufacturing industry base of China. We found that: (1) the DeeplabV3+ performs well with an overall accuracy of 92%, higher than the maximum likelihood classification; (2) the distribution of blue steel roofs was not even across the whole study area, but they were evenly distributed within the town scale; and (3) strong positive correlation was observed between blue steel roofs area and industrial gross output. These results not only can be used to detect the inefficient industrial areas for regional planning but also provide fundamental data for studies of urban environmental issues.

Impervious surfaces (IS), the most common land cover in urban areas, not only provide convenience to the city, but also exert significant negative environmental impacts, thereby affecting the ecological environment carrying capacity of urban agglomerations. Most of the current research considers IS as a single land-cover type, yet this does not fully reflect the complex physical characteristics of various IS types. Therefore, limited information for urban micro-ecology and urban fine management can be provided through one IS land-cover type. This study proposed a finer IS classification scheme and mapped the detailed IS fraction in Guangzhou City, China using Landsat imagery. The IS type was divided into seven finer classes, including blue steel, cement, asphalt, other impervious surface, and other metal, brick, and plastic. Classification results demonstrate that finer IS can be well extracted from the Landsat imagery as all root mean square errors (RMSE) are less than 15%. Specially, the accuracies of asphalt, plastic, and cement are better than other finer IS types with the RMSEs of 7.99%, 8.48%, and 9.92%, respectively. Quantitative analyses illustrate that asphalt, other impervious surface, and brick are the dominant IS types in the study area with the percentages of 9.68%, 6.27%, and 4.45%, respectively, and they are mainly located in Yuexiu, Liwan, Haizhu, and Panyu districts. These results are valuable for research into urban fine management and can support the detailed analysis of urban micro-ecology.