The short-term impact of the COVID-19 epidemic on socioeconomic activities in China based on the OMI-NO2 data

Cao, Hui‐Ling; Han, Ling

doi:10.1007/s11356-021-17415-4

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Satellite remote sensing technology has an extensive detection range and synchronous and economical monitoring capabilities. It can realize global observation of the Earth's surface and provides a large amount of useful data and information for detecting smoke released from straw burning [67]. However, individual satellites are limited by their revisit times [68], such that they may not enable real-time monitoring of straw burning events.…”

Section: Real-time Monitoring Issuesmentioning

confidence: 99%

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Liu

et al. 2023

Remote Sensing

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The burning of straw is a very destructive process that threatens people’s livelihoods and property and causes irreparable environmental damage. It is therefore essential to detect and control the burning of straw. In this study, we analyzed Sentinel-2 data to select the best separation bands based on the response characteristics of clouds, smoke, water bodies, and background (vegetation and bare soil) to the different bands. The selected bands were added to the red, green, and blue bands (RGB) as training sample data. The band that featured the highest detection accuracy, RGB_Band6, was finally selected, having an accuracy of 82.90%. The existing object detection model cannot directly handle multi-band images. This study modified the input layer structure based on the YOLOv5s model to build an object detection network suitable for multi-band remote sensing images. The Squeeze-and-Excitation (SE) network attention mechanism was introduced based on the YOLOv5s model so that the delicate features of smoke were enhanced, and the Convolution + Batch normalization + Leaky ReLU (CBL) module was replaced with the Convolution + Batch normalization + Mish (CBM) module. The accuracy of the model was improved to 75.63%, which was 1.81% better than before. We also discussed the effect of spatial resolution on model detection and where accuracies of 84.18%, 73.13%, and 45.05% for images of 60-, 20-, and 10-m resolution, respectively, were realized. The experimental results demonstrated that the accuracy of the model only sometimes improved with increasing spatial resolution. This study provides a technical reference for the monitoring of straw burning, which is vital for both the control of straw burning and ways to improve ambient air quality.

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Section: Real-time Monitoring Issuesmentioning

confidence: 99%

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Liu

et al. 2023

Remote Sensing

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“…Researchers [25][26][27] worldwide observed reductions in NO 2 concentrations due to lockdown and related diminished human activities, notably the reduced industrial and vehicular use. In addition, there are also many studies [28,29] show that a strong correlation between changes in NO 2 concentrations and COVID-19. NO 2 concentrations, as the exhaust gases of vehicle emissions and industrial production, can reflect indirectly the human interventions to restrict the work, travel, and activities of people [8,22].…”

Section: Model Of Non-pharmaceutical Interventions Based On No 2 Conc...mentioning

confidence: 99%

Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations

Dai

Cao

Tong

et al. 2022

BMC Med Res Methodol

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Background With the spread of COVID-19, the time-series prediction of COVID-19 has become a research hotspot. Unlike previous epidemics, COVID-19 has a new pattern of long-time series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with short-time series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms. Methods A dynamical model with only infected humans and removed humans was established. Then the process of COVID-19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a non-linear variable and NO2 concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions. Results The experiments and analysis showed the R2 of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. Conclusion The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID-19 with local fluctuations and multiple peaks, which can provide valuable assistant decision-making information.

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“…During the Covid-19 period, remote sensing has many functions (Louw et al, 2022;Mir et al, 2021), such as analysis of the impact of COVID-19 on the environment and climate change and the environment using sentinel-5P imagery (Ghosh et al, 2022;Madkour, 2022), tracking the spread of covid-19 based on geographic information systems (GIS) (S. K. Das & Bebortta, 2022;Ghasempour et al, 2020;Sarfo & Karuppannan, 2020), mapping traffic density in Wulan during lockdown implementation during covid-19 (Wu et al, 2021), Pre-Lockdown, Lockdown and Post-Lockdown River Water Quality Measurements (N. Das et al, 2022). Remote sensing Utilization Focus was also implemented during covid-19 to analyze surface temperature, Surface Urban Heat Islands Intensity (SUHII), and air pollution such as NO2, CO2 and O3 during Large-Scale Social Restrictions (LSSR) (Cao & Han, 2022;Maithani et al, 2020;Parida et al, 2021;Purwanto et al, 2022), Analysis of air changes in various land cover related to COVID-19 (Matci et al, 2022), River Water Quality Analysis (Chen et al, 2022), transmission rate of covid-19 infection (Jadhav et al, 2021;Kanga et al, 2021).…”

Section: Figure 1 Bibliometric Keyword Analysis Graphmentioning

confidence: 99%

A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database

Azhari,

Iman Hilman,

Siti Fadjarajani

et al. 2022

SPATIAL

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This research using bibliometric analysis aims to identify and visualize research developments in the field of remote sensing studies. The data is obtained from 2021-2022 through dimensions.ai with criteria for articles that have a digital object identifier (DOI). The data generated were 2645 articles which were then analyzed using the VosViewer device. The data analyzed includes the development of popular keywords, affiliations, countries and authors who do a lot of research in the field of remote sensing. The results of the study found 4 clusters with 216 items for keywords, 6 clusters and 36 items for authors, 15 clusters and 323 items for affiliation, 7 clusters and 61 items for country analysis which mostly conducted research on remote sensing.

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The short-term impact of the COVID-19 epidemic on socioeconomic activities in China based on the OMI-NO2 data

Cited by 8 publications

References 33 publications

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations

A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database

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