Modeling the urban heat island at a winter event in Três Lagoas, Brazil
The urban heat island is one of the most investigated environmental problems at the local climate scale. It is a thermal anomaly resulting from the difference in temperature between urban areas and the surrounding rural areas that add heat to the atmosphere and lead to thermal discomfort for part of...
| Autores: | , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2021 |
| País: | Brasil |
| Recursos: | Universidade Estadual Paulista (UNESP) |
| Repositório: | Repositório Institucional da UNESP |
| Idioma: | inglês |
| OAI Identifier: | oai:repositorio.unesp.br:11449/206228 |
| Acesso em linha: | http://dx.doi.org/10.1016/j.uclim.2021.100853 http://hdl.handle.net/11449/206228 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Multicriteria modeling NDVI Surface heat island Urban heat island |
| Resumo: | The urban heat island is one of the most investigated environmental problems at the local climate scale. It is a thermal anomaly resulting from the difference in temperature between urban areas and the surrounding rural areas that add heat to the atmosphere and lead to thermal discomfort for part of the population. This study aims to identify and analyze the urban heat island in the city of Três Lagoas, in the state of Mato Grosso do Sul, Brazil, and represent it by multicriterial modeling considering air temperature and surface characteristics as parameters. For this purpose, air temperature was recorded using mobile transects and images from the Landsat 8 satellite for an unsupervised automatic classification of the visible and near infrared bands, in addition to NDVI, which was grouped into classes. Statistical relations were determined between the intensity of the urban heat island in places where temperatures were recorded and urban parameters, such as vegetation, buildings, and exposed soil. The resulting model for a winter event identifies a heat island of strong magnitude associated with densely built areas and exposed soil. We verified through descriptive statistics that the generated model approached the reality with a 95% confidence. |
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