Assess Transportation Impact on the Urban Heat Island Using Thermal Remote Sensing

The goal of this project is to assess the impact of transportation on climate change by using remote sensing technology and statistical analysis. There are two major categories of anthropogenic heat sources that lead to global warming, stationary anthropogenic heat (change of landuse during urbanization and industrialization), and mobile anthropogenic heat (transportation). For a long time, it has been hard to separate and quantify each factor since they work mutually resulting the climate change. This study will take the unique opportunity of COVID-19 lockdown in most major cities in California, US, and propose a new statistical regression model with a dummy variable to access the mobile anthropogenic heat source. Thermal remote sensing will be used to generate land surface temperature (LST) maps over the study region to calculate the urban heat island extend and intensity. I will acquire the MODIS remote sensing imagery along with the local meteorological data to compare temperature dynamics in California cities before and during the COVID lockdown. Then use the statistical model to analyze the data and try to quantify the transportation impact on urban heat in California.