Using thermal remote sensing to quantify impact of traffic on urban heat island during COVID-19 lockdown in North California bay area
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Keywords: Remote sensing, MODIS, Urban heat island, Traffic volume, COVID-19
Abstract Type: Paper Abstract
Authors:
My-Thu Tran, San Diego State University
Bo Yang, San José State University
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Abstract
Thermal remote sensing has been widely used in various studies on urban heat island in relation to traffic volume. This research explores the impact of transportation on climate change by using remote sensing technology and statistical analysis during the COVID-19 lockdown in North California. This case study uses morning and afternoon MODIS data in 2019 before the pandemic and 2020 during the pandemic, which provides a wall-to-wall land surface temperature map to precisely measure the impact on the urban heat effect. We derive urban heat budgets in six counties in Northern California. Taking advantage of the twice-daily surface temperature measurements, we retrieve the urban surface energy budgets and construct statistical models between net radiation and both extent and intensity of heat dynamics. We examine the variation of urban heat island intensity and spatial extent under the traffic volume change over the pandemic period. This research quantifies the impact of lockdown policies on heat intensity in urban areas and human mobility in the context of COVID-19 and future pandemics. The quantitative results obtained in this study also provide critical information for analyses of climate change on a global scale.
Using thermal remote sensing to quantify impact of traffic on urban heat island during COVID-19 lockdown in North California bay area
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Paper Abstract