Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data.

IF 11.7 1区 工程技术 Q1 Engineering Sustainable Cities and Society Pub Date : 2021-12-01 Epub Date: 2021-09-25 DOI:10.1016/j.scs.2021.103388
Ya'nan Zhou, Li Feng, Xin Zhang, Yan Wang, Shunying Wang, Tianjun Wu
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Abstract

Understanding the spatiotemporal patterns of the COVID-19 impact on industrial production could improve the estimation of the economic loss and sustainable work resumption policies in cities. In this study, assuming and checking a correlation between the land surface temperature (LST) and industrial production, we applied the BFAST algorithm and linear regression models on multi-temporal MODIS data to derive monthly time-series deviation of LST with a spatial resolution of 1 × 1 km, to quantificationally explore the fine-scale spatiotemporal patterns of the COVID-19 control measures impact on industrial production, within Wuhan city. The results demonstrate that (1) the trend of time-series LST could partly reflect the impact of the COVID-19 pandemic on industrial production, and the year-around industrial production was less than expectations, with a fall of 14.30%; (2) the most serious COVID-19 impact on industrial production appeared in Mar. and Apr., then, after the lifting of lockdown, some regions (approximate 4.90%) firstly returned to expected levels in Jun, and almost all regions (98.49%) have completed the resumption of work and production before Nov.; (3) the southwest and south-central had more serious impact of the COVID-19 pandemic, approximate twice as much as that in the north and suburban, in Wuhan. The results and findings elaborated the spatiotemporal distribution and their changes during 2020 within Wuhan, which could provide a beneficial support for assessment of the COVID-19 pandemic and implementation of resumption plans for sustainable development.

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利用时间序列地球观测数据研究 COVID-19 控制措施对武汉工业生产影响的时空模式。
了解 COVID-19 对工业生产影响的时空模式可以改进对城市经济损失的估算和可持续的复工政策。在本研究中,我们假设并检验了地表温度(LST)与工业生产之间的相关性,在多时相 MODIS 数据上应用 BFAST 算法和线性回归模型,得出空间分辨率为 1 × 1 km 的地表温度月时间序列偏差,以量化探讨 COVID-19 控制措施对武汉市工业生产影响的细尺度时空格局。结果表明:(1) 时序 LST 的变化趋势可以部分反映 COVID-19 对工业生产的影响,全年工业生产低于预期,下降了 14.30%;(2) COVID-19 对工业生产最严重的影响出现在 3 月和 4 月、(3) 西南和中南部受 COVID-19 疫情影响较为严重,约为北部和郊区、武汉的两倍。研究结果和结论阐述了武汉市 2020 年的时空分布及其变化情况,为评估 COVID-19 疫情和实施可持续发展的复产计划提供了有益的支持。
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society CONSTRUCTION & BUILDING TECHNOLOGYGREEN &-GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
CiteScore
18.40
自引率
13.70%
发文量
810
期刊介绍: Sustainable Cities and Society (SCS) is an international journal focusing on fundamental and applied research aimed at designing, understanding, and promoting environmentally sustainable and socially resilient cities.
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