Spatiotemporal influences of LULC changes on land surface temperature in rapid urbanization area by using Landsat-TM and TIRS images

• Autor(a) principal: Kaiser, Eduardo André
• Data de Publicação: 2022
• Outros Autores: Rolim, Silvia Beatriz Alves, Grondona, Atilio Efrain Bica, Hackmann, Cristiano Lima, Linn, Rodrigo de Marsillac, Käfer, Pâmela Suélen, Rocha, Nájila Souza da, Diaz, Lucas Ribeiro
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/246954
• topic: Modelagem urbana; Processo de urbanização; Meio ambiente urbano; Uso da terra; Temperatura de superfície; Vegetation and urban modeling; Land use and land cover change; Land surface processes; Porto Alegre four districts

The inverse correlation between NDVI and LST is widely known for its long time series. However, when more specific statistical tests were performed, subtle differences in the correlation behavior over time are more clearly observed. In this work, regression analyses were performed between NDVI and LST at intervals of approximately 10 years, quantifying this relationship for an area of transition from vegetation to urban occupation from 1985 to 2018. The removal of vegetation cover (reduction of 51% to 7% in grassland and 14.4% to 0.6% in forest) to occupy impermeable surfaces ( increase of 31% to 91% in urban areas) caused an average LST increase of 4.18 °C when compared to the first and last decades of the historical series. Temporal analysis allowed us to verify the increase in temperature in the four seasons. The largest difference was 6.36 °C between the first and last decade of autumn, 4.40 °C in spring, 4.09 °C in summer, and 2.41 °C in winter. The results also show that LST has a negative correlation with NDVI, especially in urban areas, with an increase in this correlation during the period (1989: R = −0.55; 1999: R = −0.58; 2008: R = −0.59; 2018: R = −0.76). Our study results will help policymakers understand the dynamics of temperature increases by adding scientifically relevant information on the sustainable organization of the urban environment.