dc.contributor.advisor | | |
dc.creator | Rodríguez-Gómez, F. | es |
dc.creator | Fernández Cañero, Rafael | es |
dc.creator | Pérez Urrestarazu, Luis | es |
dc.creator | Pérez, Gabriel. | es |
dc.creator | del Campo-Ávila, José. | es |
dc.creator | López-Rodríguez, Domingo. | es |
dc.date.accessioned | 2023-11-23T18:07:36Z | |
dc.date.available | 2023-11-23T18:07:36Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | Rodríguez-Gómez, F., Fernández Cañero, R., Pérez Urrestarazu, L., Pérez, G., del Campo-Ávila, J. y López-Rodríguez, D. (2022). Detection of unfavourable urban areas with higher temperatures and lack of green spaces using satellite imagery in sixteen Spanish cities.. Urban Forestry & Urban Greening, 78 (2022 (78)). https://doi.org/10.1016/j.ufug.2022.127783. | |
dc.identifier.issn | 1618-8667 | es |
dc.identifier.uri | https://hdl.handle.net/11441/151454 | |
dc.description.abstract | 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 devel oped 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 9 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Urban Forestry & Urban Greening, 78 (2022) | |
dc.relation.isreferencedby | Rodríguez Gómez, F., Fernández Cañero, R.,...,Pérez Urrestarazu, L. (2023). Temp_distribution_green_areas [dataset]. idUS (Depósito de Investigación de la Universidad de Sevilla). https://doi.org/10.12795/11441/152674 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Urban greening | es |
dc.subject | Remote sensing | es |
dc.subject | Heat island | es |
dc.subject | Normalized difference vegetation index | es |
dc.subject | Landsat-8 | es |
dc.title | Detection of unfavourable urban areas with higher temperatures and lack of green spaces using satellite imagery in sixteen Spanish cities. | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos. | es |
dc.relation.publisherversion | https://doi.org/10.1016/j.ufug.2022.127783 | es |
dc.identifier.doi | 10.1016/j.ufug.2022.127783 | es |
dc.contributor.group | Universidad de Sevilla. AGR-268: Naturación Urbana e Ingeniería de Biosistemas. | es |
dc.journaltitle | Urban Forestry & Urban Greening | es |
dc.publication.volumen | 78 | es |
dc.publication.issue | 2022 (78) | es |