Spatiotemporal Analysis of COVID-19 Lockdown Impact on the Land Surface Temperatures of Different Land Cover Types in Louisiana  

Spatiotemporal Analysis of COVID-19 Lockdown Impact on the Land Surface Temperatures of Different Land Cover Types in Louisiana

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作  者:Priscilla M. Loh Yaw A. Twumasi Zhu H. Ning Matilda Anokye Diana B. Frimpong Judith Oppong Abena B. Asare-Ansah Recheal N. D. Armah Caroline Y. Apraku Priscilla M. Loh;Yaw A. Twumasi;Zhu H. Ning;Matilda Anokye;Diana B. Frimpong;Judith Oppong;Abena B. Asare-Ansah;Recheal N. D. Armah;Caroline Y. Apraku(Department of Urban Forestry, Environment and Natural Resources, Southern University and A&M College, Baton Rouge, USA)

机构地区:[1]Department of Urban Forestry, Environment and Natural Resources, Southern University and A&M College, Baton Rouge, USA

出  处:《Journal of Geographic Information System》2023年第5期458-481,共24页地理信息系统(英文)

摘  要:The COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experienced a decline in air pollution emissions and some environmental changes, offering a rare opportunity to understand the effects of fewer human activities on the earth’s temperature. Hence, this study compares the changes in Land Surface Temperature (LST) that were observed prior to the pandemic (March & April 2019) and during the pandemic lockdown (March & April 2020) of three parishes in Louisiana. The data for this study was acquired using Landsat 8 Thermal Infrared Sensor (TIRS) Level 2, Collection 2, Tier 2 from the Google Earth Engine Catalog. For better visualization, the images that were derived had a cloud cover of less than 10%. Also, images for the three study areas were processed and categorized into four main classes: water, vegetation, built-up areas, and bare lands using a Random Forest Supervised Classification Algorithm. To improve the accuracy of the image classifications, three Normalized Difference Indices namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-Up Index (NDBI) were employed using the Near Infrared (NIR), Red, Green and SWIR bands for the calculations. After, these images were processed in Google Earth Engine to generate the LST products gridded at 30 m with a higher spatial resolution of 100 m according to the pre-pandemic (2019) and lockdown (2020) periods for the three study areas. Results of this study showed a decrease in LST values of the land cover classes from 2019 to 2020, with LST values in East Baton Parish decreasing from 44°C to 38°C, 42°C to 38°C in Lafayette Parish, and 43°C to 38°C in Orleans Parish. The variations in the LST values therefore indicate the impact of fewer anthropogenic factors on the earth’s temperature which requires regulatory and mitigative measuresThe COVID-19 pandemic posed a serious threat to life on the entire planet, necessitating the imposition of a lockdown mechanism that restricted people’s movements to stop the disease’s spread. This period experienced a decline in air pollution emissions and some environmental changes, offering a rare opportunity to understand the effects of fewer human activities on the earth’s temperature. Hence, this study compares the changes in Land Surface Temperature (LST) that were observed prior to the pandemic (March & April 2019) and during the pandemic lockdown (March & April 2020) of three parishes in Louisiana. The data for this study was acquired using Landsat 8 Thermal Infrared Sensor (TIRS) Level 2, Collection 2, Tier 2 from the Google Earth Engine Catalog. For better visualization, the images that were derived had a cloud cover of less than 10%. Also, images for the three study areas were processed and categorized into four main classes: water, vegetation, built-up areas, and bare lands using a Random Forest Supervised Classification Algorithm. To improve the accuracy of the image classifications, three Normalized Difference Indices namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-Up Index (NDBI) were employed using the Near Infrared (NIR), Red, Green and SWIR bands for the calculations. After, these images were processed in Google Earth Engine to generate the LST products gridded at 30 m with a higher spatial resolution of 100 m according to the pre-pandemic (2019) and lockdown (2020) periods for the three study areas. Results of this study showed a decrease in LST values of the land cover classes from 2019 to 2020, with LST values in East Baton Parish decreasing from 44°C to 38°C, 42°C to 38°C in Lafayette Parish, and 43°C to 38°C in Orleans Parish. The variations in the LST values therefore indicate the impact of fewer anthropogenic factors on the earth’s temperature which requires regulatory and mitigative measures

关 键 词:Urban Heat Island Anthropogenic Activities Greenhouse Gas Greenspace WETLANDS 

分 类 号:TP3[自动化与计算机技术—计算机科学与技术]

 

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