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

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.

Keywords: COVID-19; Industrial production; Land surface temperature; Spatiotemporal pattern; Time-series analysis.

Fig. 1

Study area and POIs of high energy-consuming companies in Wuhan, China.

Fig. 2

Flowchart for the spatiotemporal patterns of the COVID-19 impact on industrial production, in Wuhan.

Fig. 3

POIs clustering and the pixel-wise ROIs.

Fig. 4

Correlation between LST in daytime and nighttime.

Fig. 5

The trend component of the mean time-series LST data in Wuhan.

Fig. 6

Spatiotemporal distribution of LST deviation of 2020, in Wuhan. The values in the blow text indicated the percentage of minus deviation and the monthly mean deviation.

Fig. 6

Spatiotemporal distribution of LST deviation of 2020, in Wuhan. The values in the blow text indicated the percentage of minus deviation and the monthly mean deviation.

Fig. 7

Hot spot regions and its changes of LST deviation of 2020, in Wuhan (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).

Fig. 8

Pixel-wise maximum LST deviation and their corresponding date.

Fig. 9

Spatiotemporal pattern of the last minus deviation.

Fig. 10

spatiotemporal pattern of the accumulation of deviation.