Detection of unfavourable urban areas with higher temperatures and lack of green spaces using satellite imagery in sixteen Spanish cities

• Published in: Urban forestry & urban greening Vol. 78; p. 127783
• Main Authors: Rodríguez-Gómez, Francisco, Fernández-Cañero, Rafael, Pérez, Gabriel, del Campo-Ávila, José, López-Rodríguez, Domingo, Pérez-Urrestarazu, Luis
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.12.2022
• Subjects: Heat island; Landsat-8; Normalized difference vegetation index; Remote sensing; Urban greening; Heat island; Landsat-8; Normalized difference vegetation index; Urban greening; Remote sensing
• ISSN: 1618-8667; 1610-8167
• DOI: 10.1016/j.ufug.2022.127783

This paper seeks to identify the most unfavourable areas of a city in terms of high temperatures and the absence of green infrastructure. An automatic methodology based on remote sensing and data analysis has been developed and applied in sixteen Spanish cities with different characteristics. Landsat-8 satellite images were selected for each city from the July-August period of 2019 and 2020 to calculate the spatial variation of land surface temperature (LST). The Normalized Difference Vegetation Index (NDVI) was used to determine the abundance of vegetation across the city. Based on the NDVI and LST maps created, a k-means unsupervised classification clustering was performed to automatically identify the different clusters according to how favourable these areas were in terms of temperature and presence of vegetation. A Disadvantaged Area Index (DAI), combining both variables, was developed to produce a map showing the most unfavourable areas for each city. Overall, the percentage of the area susceptible to improvement with more vegetation in the cities studied ranged from 13 % in Huesca to 64–65 % in Bilbao and Valencia. The influence of several factors, such as the presence of water bodies or large buildings, is discussed. Detecting unfavourable areas is a very interesting tool for defining future planning strategy for green spaces. •The most unfavourable areas with excess of temperature and lack of green areas were determined.•Landsat-8 satellite images used to get data on temperature and quantity of vegetation.•A Disadvantaged Area Index was combined with a k-means classification clustering.•Interesting tool for prioritizing future green spaces’ locations within a city.