新冠肺炎防控对路易斯安那州不同土地覆盖类型地表温度影响的时空分析

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
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引用次数: 0

摘要

COVID-19大流行对整个地球的生命构成了严重威胁,有必要实施封锁机制,限制人们的行动,以阻止疾病的传播。这一时期经历了空气污染排放的下降和一些环境变化,为了解人类活动减少对地球温度的影响提供了难得的机会。因此,本研究比较了路易斯安那州三个教区在大流行之前(2019年3月和4月)和大流行封锁期间(2020年3月和4月)观察到的地表温度(LST)变化。本研究的数据是使用来自谷歌地球引擎目录的Landsat 8热红外传感器(TIRS) Level 2, Collection 2, Tier 2获取的。为了更好地可视化,导出的图像的云量小于10%。此外,对三个研究区域的图像进行处理,并使用随机森林监督分类算法将其分为四大类:水域、植被、建成区和裸地。为了提高图像分类的准确性,采用归一化植被指数(NDVI)、归一化水体指数(NDWI)和归一化建筑指数(NDBI) 3个归一化差异指数,分别在近红外(NIR)、红、绿和SWIR波段进行计算。然后,在谷歌Earth Engine中对这些图像进行处理,根据三个研究区域的大流行前(2019年)和封锁(2020年)时期,生成30 m网格化的地表温度产品,空间分辨率更高,为100 m。研究结果表明,2019 - 2020年,各土地覆盖类别的LST值呈下降趋势,东巴吞教区LST值从44°C降至38°C,拉斐特教区LST值从42°C降至38°C,奥尔良教区LST值从43°C降至38°C。因此,地表温度的变化表明人为因素对地球温度的影响减少,这需要采取调节和缓解措施,以持续降低地表温度并控制小气候,特别是在城市地区。
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Spatiotemporal Analysis of COVID-19 Lockdown Impact on the Land Surface Temperatures of Different Land Cover Types in Louisiana
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 measures to continually reduce LST and control microclimate, especially in urban areas.
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