Abstract
Over the last decades, Earth’s surface has suffered an intense urbanisation process that has impacted Land Use/Land Cover (LULC) and Earth’s surface energy balance. Such a rapid and unexpected phenomenon was not carried out in a sustainable way compromising Earth’s existence in the long term. Therefore, the United States identified 17 Sustainable Development Goals (SDGs) to meet within 2030. To make the world more resilient and sustainable and combat climate changes, information concerning LULC conversion trends and land surface albedo is essential. Such variables are directly responsible for the increment and decrement of air and surface temperature and, consequently, to the Urban Heat Island (UHI) phenomenon. The present paper explores Google Earth Engine (GEE) platform potentialities in investigating the relationship between LULC transformation and land surface albedo extracted from medium-resolution satellite data. The present analysis was performed on the study area of Berlin for 8 years, from 2011 to 2019. Two radiometrically and atmospherically corrected Landsat images were gathered from Landsat 5 and Landsat 8 missions, respectively. Once clouds have been masked, SwirTirRed (STRed) and Normalized Difference Bareness (version 1) (NDBaI1) indices were implemented to distinguish LULC types. Subsequently, LULC changes were assessed, and land surface albedo was estimated by programming a proper code. Thus, the relationship among those features was investigated in such areas in the considered period. GEE appears as the optimal solution to meet research goals and to extend the analysis at a global scale.
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This research is partially funded under the project “AIM1871082-1” of the AIM (Attraction and International Mobility) Program, financed by the Italian Ministry of Education, University and Research (MIUR).
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Capolupo, A., Monterisi, C., Sonnessa, A., Caporusso, G., Tarantino, E. (2021). Modeling Land Cover Impact on Albedo Changes in Google Earth Engine Environment. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_7
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