Surface urban heat island and geospatial indicators: comparative decadal assessment through remote sensing
Surface Urban Heat Islands (SUHI) are formed through processes that include decreasing vegetation and increasing materials that are more conductive to heat. To investigate this phenomenon, the objective of this work is to comparatively verify the surface temperature and geospatial indicators to anal...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Brasil |
| Institución: | Associação Nacional de Tecnologia do Ambiente Construído (ANTAC) |
| Repositorio: | Ambiente construído (Online) |
| Idioma: | inglés |
| OAI Identifier: | oai:seer.ufrgs.br:article/138042 |
| Acceso en línea: | https://seer.ufrgs.br/index.php/ambienteconstruido/article/view/138042 |
| Access Level: | acceso abierto |
| Palabra clave: | Urban Climate Remote Sensing Landsat Surface Urban Heat Island Land Use and Land Cover Land Surface Temperature Clima Urbano Sensoriamento Remoto Ilha de Calor de Superfície Landsat-7 Uso e Ocupação do Solo Temperatura de Superfície Terrestre |
| Sumario: | Surface Urban Heat Islands (SUHI) are formed through processes that include decreasing vegetation and increasing materials that are more conductive to heat. To investigate this phenomenon, the objective of this work is to comparatively verify the surface temperature and geospatial indicators to analyze environmental variation in the city of São Paulo, Brazil. The study method is remote sensing, with data from the Landsat 8 satellite over a ten-year timeframe. The variables include land cover, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and land surface temperature (LST), thereby obtaining the Urban Thermal Field Variation Index (UTFVI) and estimating SUHI formation. The results allowed for the presentation of a progression of variables, highlighting the recurrent formation of SUHI in the central and eastern regions of the city of São Paulo, Brazil. Conversely, the southern and extreme northern districts exhibited the best NDVI indices and low LST values. |
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