Abstract
Urban growth modifies the physical properties of the Earth's surface and may intensify thermal contrasts between built-up areas and peri-urban zones. This study analyzed the spatio-temporal dynamics of surface urban heat islands in coastal and Andean Ecuadorian cities using Landsat imagery, vegetation and built-up surface indicators, and meteorological reanalysis variables. Landsat products were processed in Google Earth Engine to estimate land surface temperature, calculate spectral indices, and compare urban and peri-urban areas. In addition, fifth-generation terrestrial reanalysis data from the European Centre for Medium-Range Weather Forecasts were incorporated, including air temperature, precipitation, and wind variables. The results show greater urban--peri-urban thermal contrast in Andean cities, whereas the coastal city exhibited higher temporal variability. Vegetation and built-up surface indicators showed differentiated relationships with land surface temperature, and meteorological variables allowed the observed thermal variability to be contextualized.
References
[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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