Resumen
El crecimiento urbano modifica las propiedades físicas de la superficie terrestre y puede intensificar los contrastes térmicos entre áreas construidas y zonas periurbanas. Este estudio analizó la dinámica espacio-temporal de las islas de calor superficial en ciudades ecuatorianas costeras y andinas mediante imágenes Landsat, indicadores de vegetación y superficie construida, y variables meteorológicas de reanálisis. Se procesaron productos Landsat en Google Earth Engine para estimar temperatura superficial terrestre, calcular índices espectrales y comparar áreas urbanas y periurbanas. Además, se incorporaron datos de reanálisis terrestre de quinta generación del Centro Europeo de Predicción Meteorológica a Medio Plazo de temperatura del aire, precipitación y viento. Los resultados muestran mayor contraste térmico urbano-periurbano en las ciudades andinas, mientras que la ciudad costera presentó mayor variabilidad temporal. La vegetación y la superficie construida mostraron relaciones diferenciadas con la temperatura superficial, y las variables meteorológicas permitieron contextualizar la variabilidad térmica observada.
Citas
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[2] J. A. Voogt and T. R. Oke, “Thermal remote sensing of urban climates,” Remote Sens. Environ., vol. 86, no. 3, pp. 370–384, Aug. 2003, doi: 10.1016/S0034-4257(03)00079-8.
[3] D. Zhou et al., “Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives,” Remote Sensing 2019, vol. 11, no. 1, Dec. 2018, doi: 10.3390/RS11010048.
[4] S. Peng et al., “Surface Urban Heat Island Across 419 Global Big Cities,” Environ. Sci. Technol., vol. 46, no. 2, pp. 696–703, Jan. 2011, doi: 10.1021/ES2030438.
[5] L. Zhao, X. Lee, R. B. Smith, and K. Oleson, “Strong contributions of local background climate to urban heat islands,” Nature, vol. 511, no. 7508, pp. 216–219, Jul. 2014, doi: 10.1038/nature13462.
[6] G. Manoli et al., “Magnitude of urban heat islands largely explained by climate and population,” Nature, vol. 573, no. 7772, pp. 55–60, Sep. 2019, doi: 10.1038/s41586-019-1512-9.
[7] Q. Weng, D. Lu, and J. Schubring, “Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies,” Remote Sens. Environ., vol. 89, no. 4, pp. 467–483, Feb. 2004, doi: 10.1016/J.RSE.2003.11.005.
[8] F. Yuan and M. E. Bauer, “Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery,” Remote Sens. Environ., vol. 106, no. 3, pp. 375–386, Feb. 2007, doi: 10.1016/J.RSE.2006.09.003.
[9] J. C. Jimenez-Munoz, J. A. Sobrino, D. Skokovic, C. Mattar, and J. Cristobal, “Land surface temperature retrieval methods from landsat-8 thermal infrared sensor data,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 10, pp. 1840–1843, 2014, doi: 10.1109/LGRS.2014.2312032.
[10] D. Baño, J. Salazar, and M. Delgado, “Remote Sensing in the Design of Urban Planning Strategies, Case Study Urban Heat Island of the Metropolitan District of Quito, Ecuador,” Latin-American Journal of Computing, vol. 5, no. 1, pp. 17–26, Aug. 2018. Disponible: https://doaj.org/article/2f70efe3c73243178289ed26349795d9
[11] J. Garzón, I. Molina, J. Velasco, and A. Calabia, “A Remote Sensing Approach for Surface Urban Heat Island Modeling in a Tropical Colombian City Using Regression Analysis and Machine Learning Algorithms,” Remote Sensing 2021, Vol. 13, vol. 13, no. 21, Oct. 2021, doi: 10.3390/RS13214256.
[12] T. Chakraborty and X. Lee, “A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability,” International Journal of Applied Earth Observation and Geoinformation, vol. 74, pp. 269–280, Feb. 2019, doi: 10.1016/J.JAG.2018.09.015.
[13] J. Schwaab, R. Meier, G. Mussetti, S. Seneviratne, C. Bürgi, and E. L. Davin, “The role of urban trees in reducing land surface temperatures in European cities,” Nat. Commun., vol. 12, no. 1, Nov. 2021, doi: 10.1038/s41467-021-26768-w.
[14] I. D. Stewart and T. R. Oke, “Local Climate Zones for Urban Temperature Studies,” Bull. Am. Meteorol. Soc., vol. 93, no. 12, pp. 1879–1900, Dec. 2012, doi: 10.1175/BAMS-D-11-00019.1.
[15] J. Muñoz-Sabater et al., “ERA5-Land: A state-of-the-art global reanalysis dataset for land applications,” Earth Syst. Sci. Data, vol. 13, no. 9, pp. 4349–4383, Sep. 2021, doi: 10.5194/ESSD-13-4349-2021.
[16] N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sens. Environ., vol. 202, pp. 18–27, Dec. 2017, doi: 10.1016/J.RSE.2017.06.031.
[17] X. Yu, X. Guo, and Z. Wu, “Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method,” Remote Sens. (Basel)., vol. 6, no. 10, pp. 9829–9852, Oct. 2014, doi: 10.3390/RS6109829.
[18] S. B. Duan et al., “Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD, ARM, and NDBC sites,” Int. J. Digit. Earth, vol. 14, no. 5, pp. 640–660, May 2021, doi: 10.1080/17538947.2020.1862319.
[19] E. Massaro et al., “Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes,” Nat. Commun., vol. 14, no. 1, May 2023, doi: 10.1038/s41467-023-38596-1.
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