{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:25:17Z","timestamp":1772141117613,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities of China","award":["ZY20180101"],"award-info":[{"award-number":["ZY20180101"]}]},{"name":"Fundamental Research Funds for the Central Universities of China","award":["FZ213109"],"award-info":[{"award-number":["FZ213109"]}]},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment","award":["ZY20180101"],"award-info":[{"award-number":["ZY20180101"]}]},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment","award":["FZ213109"],"award-info":[{"award-number":["FZ213109"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic\u2019s impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered; at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government\u2019s strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term.<\/jats:p>","DOI":"10.3390\/rs15010022","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T06:42:57Z","timestamp":1671604977000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Feng","family":"Li","sequence":"first","affiliation":[{"name":"School of Ecological Environment, Institute of Disaster Prevention, Sanhe 065201, China"},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment, Sanhe 065201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Ecological Environment, Institute of Disaster Prevention, Sanhe 065201, China"},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment, Sanhe 065201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Ecological Environment, Institute of Disaster Prevention, Sanhe 065201, China"},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment, Sanhe 065201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1392-2795","authenticated-orcid":false,"given":"Shunbao","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Ecological Environment, Institute of Disaster Prevention, Sanhe 065201, China"},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment, Sanhe 065201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Ecological Environment, Institute of Disaster Prevention, Sanhe 065201, China"},{"name":"Hebei Key Laboratory of Earthquake Disaster Prevention and Risk Assessment, Sanhe 065201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","unstructured":"(2022, August 14). WHO Coronavirus (COVID-19) Dashboard. Available online: https:\/\/covid19.who.int\/."},{"key":"ref_2","unstructured":"International Monetary Fund (2022). World Economic Outlook: War Sets Back the Global Recovery, International Monetary Fund."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, F., Liu, X., Liao, S., and Jia, P. (2021). The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas. Remote Sens., 13.","DOI":"10.3390\/rs13122350"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Luqman, M., Rayner, P.J., and Gurney, K.R. (2019). Combining Measurements of Built-up Area, Nighttime Light, and Travel Time Distance for Detecting Changes in Urban Boundaries: Introducing the BUNTUS Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11242969"},{"key":"ref_5","first-page":"8898468","article-title":"Measurement of Urban Expansion and Spatial Correlation of Central Yunnan Urban Agglomeration Using Nighttime Light Data","volume":"2021","author":"Zhang","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alahmadi, M., Mansour, S., Martin, D.J., and Atkinson, P.M. (2021). An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia. Remote Sens., 13.","DOI":"10.3390\/rs13061171"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, X. (2020). Nighttime Lights and Population Migration: Revisiting Classic Demographic Perspectives with an Analysis of Recent European Data. Remote Sens., 12.","DOI":"10.3390\/rs12010169"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cheng, L., Feng, R., Wang, L., Yan, J., and Liang, D. (2022). An Assessment of Electric Power Consumption Using Random Forest and Transferable Deep Model with Multi-Source Data. Remote Sens., 14.","DOI":"10.3390\/rs14061469"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"120351","DOI":"10.1016\/j.energy.2021.120351","article-title":"Estimating Local-Scale Domestic Electricity Energy Consumption Using Demographic, Nighttime Light Imagery and Twitter Data","volume":"226","author":"Sun","year":"2021","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.energy.2019.04.221","article-title":"Modeling Electricity Consumption Using Nighttime Light Images and Artificial Neural Networks","volume":"179","author":"Jasinski","year":"2019","journal-title":"Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3323","DOI":"10.1038\/s41598-021-81754-y","article-title":"China\u2019s City-Level Carbon Emissions during 1992\u20132017 Based on the Inter-Calibration of Nighttime Light Data","volume":"11","author":"Chen","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1109\/LGRS.2020.3014956","article-title":"NPP-VIIRS Nighttime Light Data Have Different Correlated Relationships with Fossil Fuel Combustion Carbon Emissions from Different Sectors","volume":"18","author":"Shi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1007\/s12524-020-01126-3","article-title":"Detection of Multidimensional Poverty Using Luojia 1-01 Nighttime Light Imagery","volume":"48","author":"Li","year":"2020","journal-title":"J. Indian Soc. Remote"},{"key":"ref_14","first-page":"1217","article-title":"Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China","volume":"8","author":"Yu","year":"2015","journal-title":"IEEE J.-STARS"},{"key":"ref_15","first-page":"157","article-title":"Identification of Poverty Based on Nighttime Light Remote Sensing Data: A Case Study on Contiguous Special Poverty-Stricken Areas in Liupan Mountains","volume":"31","author":"Shen","year":"2019","journal-title":"Remote Sens. Nat. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yong, Z., Li, K., Xiong, J., Cheng, W., Wang, Z., Sun, H., and Ye, C. (2022). Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data to Evaluate Poverty in Southwestern China. Remote Sens., 14.","DOI":"10.3390\/rs14030600"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, X., Zhan, C., Tao, J., and Li, L. (2018). Long-Term Monitoring of the Impacts of Disaster on Human Activity Using DMSP\/OLS Nighttime Light Data: A Case Study of the 2008 Wenchuan, China Earthquake. Remote Sens., 10.","DOI":"10.3390\/rs10040588"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Du, Y., Yi, J., Liang, F., Ma, T., and Pei, T. (2019). Quantitative Association between Nighttime Lights and Geo-Tagged Human Activity Dynamics during Typhoon Mangkhut. Remote Sens., 11.","DOI":"10.3390\/rs11182091"},{"key":"ref_19","first-page":"10854","article-title":"Long-Term Resilience Curve Analysis of Wenchuan Earthquake-Affected Counties Using DMSP-OLS Nighttime Light Images","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J.-STARS"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Elvidge, C.D., Ghosh, T., Hsu, F.-C., Zhizhin, M., and Bazilian, M. (2020). The Dimming of Lights in China during the COVID-19 Pandemic. Remote Sens., 12.","DOI":"10.3390\/rs12193194"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ghosh, T., Elvidge, C.D., Hsu, F.-C., Zhizhin, M., and Bazilian, M. (2020). The Dimming of Lights in India during the COVID-19 Pandemic. Remote Sens., 12.","DOI":"10.3390\/rs12193194"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Q., Sha, D., Liu, W., Houser, P., Zhang, L., Hou, R., Lan, H., Flynn, C., Lu, M., and Hu, T. (2020). Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in Mainland China Using Nighttime Light and Air Quality Data. Remote Sens., 12.","DOI":"10.3390\/rs12101576"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100067","DOI":"10.1016\/j.deveng.2021.100067","article-title":"Tracking Economic Activity in Response to the COVID-19 Crisis Using Nighttime Lights\u2014The Case of Morocco","volume":"6","author":"Roberts","year":"2021","journal-title":"Dev. Eng."},{"key":"ref_24","first-page":"102421","article-title":"Lockdown Induced Night-time Light Dynamics during the COVID-19 Epidemic in Global Megacities","volume":"102","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"197","DOI":"10.14358\/PERS.87.3.197","article-title":"Monitoring Work Resumption of Wuhan in the COVID-19 Epidemic Using Daily Nighttime Light","volume":"87","author":"Shao","year":"2021","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","first-page":"2740","article-title":"Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data","volume":"14","author":"Lan","year":"2021","journal-title":"IEEE J.-STARS"},{"key":"ref_27","first-page":"5111","article-title":"Night-Time Light Imagery Reveals China\u2019s City Activity during the COVID-19 Pandemic Period in Early 2020","volume":"14","author":"Yin","year":"2021","journal-title":"IEEE J.-STARS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105287","DOI":"10.1016\/j.worlddev.2020.105287","article-title":"Examining the Economic Impact of COVID-19 in India through Daily Electricity Consumption and Nighttime Light Intensity","volume":"140","author":"Beyer","year":"2021","journal-title":"World Dev."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Anand, A., and Kim, D.H. (2021). Pandemic Induced Changes in Economic Activity around African Protected Areas Captured through Night-Time Light Data. Remote Sens., 13.","DOI":"10.3390\/rs13020314"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Alahmadi, M., Mansour, S., Dasgupta, N., Abulibdeh, A.O., Atkinson, P.M., and Martin, D. (2021). Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia. Remote Sens., 13.","DOI":"10.3390\/rs13224633"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, M., Ye, H., Niu, Z., Huang, W., Hao, P., Li, W., and Yu, B. (2022). Operation Status Comparison Monitoring of China s Southeast Asian Industrial Parks before and after COVID-19 Using Nighttime Lights Data. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11020122"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tian, S., Feng, R., Zhao, J., and Wang, L. (2021). An Analysis of the Work Resumption in China under the COVID-19 Epidemic Based on Night Time Lights Data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090614"},{"key":"ref_33","unstructured":"(2022, April 25). Overview of Hebei, Available online: http:\/\/www.hebei.gov.cn\/hebei\/14462058\/14462085\/14471224\/index.html."},{"key":"ref_34","unstructured":"(2022, December 06). EOG Nighttime Light. Available online: https:\/\/eogdata.mines.edu\/nighttime_light\/monthly\/v10\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1080\/2150704X.2022.2068987","article-title":"Rapid Assessment of Disaster Damage and Economic Resilience in Relation to the Flooding in Zhengzhou, China in 2021","volume":"13","author":"Li","year":"2022","journal-title":"Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, L., Li, Z., Fu, X., Liu, X., Li, Z., and Zheng, W. (2022). Impact of Power on Uneven Development: Evaluating Built-Up Area Changes in Chengdu Based on NPP-VIIRS Images (2015\u20132019). Land, 11.","DOI":"10.3390\/land11040489"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5860","DOI":"10.1080\/01431161.2017.1342050","article-title":"VIIRS Night-Time Lights","volume":"38","author":"Elvidge","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","unstructured":"(2022, December 06). Level-1 and Atmosphere Archive & Distribution System Distributed Active Archive Center, Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/search\/."},{"key":"ref_39","unstructured":"(2022, December 06). MODIS Vegetation Index User\u2019s Guide. Available online: https:\/\/measures.arizona.edu\/documents\/MODIS\/MODIS_VI_UsersGuide_09_18_2019_C61.pdf."},{"key":"ref_40","unstructured":"(2022, December 06). Quarterly Data for Sub-Provincial Statistics, Available online: https:\/\/data.stats.gov.cn\/easyquery.htm?cn=E0102."},{"key":"ref_41","unstructured":"(2022, December 06). Provincial Dynamics, Available online: http:\/\/wsjkw.hebei.gov.cn\/sjdt\/index.jhtml."},{"key":"ref_42","unstructured":"(2022, December 06). 1:250,000 National Basic Geographic Database. Available online: https:\/\/www.webmap.cn\/commres.do?method=result25W."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, P., Pan, J., Xie, L., Zhou, T., Bai, H., and Zhu, Y. (2019). Spatial-Temporal Evolution and Regional Differentiation Features of Urbanization in China from 2003 to 2013. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010031"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4114","DOI":"10.1080\/01431161.2015.1073861","article-title":"An EVI-Based Method to Reduce Saturation of DMSP\/OLS Nighttime Light Data","volume":"36","author":"Zhuo","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ji, X., Li, X., He, Y., and Liu, X. (2019). A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090419"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhao, M., Cheng, W., Zhou, C., Li, M., Wang, N., and Liu, Q. (2017). GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070673"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s11069-017-3163-1","article-title":"Assessing Macroeconomic Recovery after a Natural Hazard Based on ARIMA\u2014A Case Study of the 2008 Wenchuan Earthquake in China","volume":"91","author":"Zhu","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dai, Z., Hu, Y., and Zhao, G. (2017). The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels. Sustainability, 9.","DOI":"10.3390\/su9020305"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gu, Y., Shao, Z., Huang, X., and Cai, B. (2022). GDP Forecasting Model for China\u2019s Provinces Using Nighttime Light Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14153671"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Meng, Y., Zhu, V., and Zhu, Y. (2021). Co-Distribution of Light At Night (LAN) and COVID-19 Incidence in the United States. BMC Public Health, 21.","DOI":"10.1186\/s12889-021-11500-6"},{"key":"ref_51","first-page":"102855","article-title":"Associations between Nighttime Light and COVID-19 Incidence and Mortality in the United States","volume":"112","author":"Zhang","year":"2022","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_52","first-page":"1","article-title":"Population, GDP, and Carbon Emissions as Revealed by SNPP-VIIRS Nighttime Light Data in China with Different Scales","volume":"19","author":"Shi","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8096","DOI":"10.1038\/s41598-022-12211-7","article-title":"Tracking COVID-19 Urban Activity Changes in the Middle East from Nighttime Lights","volume":"12","author":"Stokes","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101911","DOI":"10.1016\/j.compenvurbsys.2022.101911","article-title":"A Building Volume Adjusted Nighttime Light Index for Characterizing the Relationship between Urban Population and Nighttime Light Intensity","volume":"99","author":"Wu","year":"2023","journal-title":"Comput. Environ. Urban"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, P., Wang, Q., Zhang, D., and Lu, Y. (2020). An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data. Remote Sens., 12.","DOI":"10.3390\/rs12233988"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zheng, Z., Wu, Z., Cao, Z., and Luo, R. (2022). Using Multi-Source Geospatial Information to Reduce the Saturation Problem of DMSP\/OLS Nighttime Light Data. Remote Sens., 14.","DOI":"10.3390\/rs14143264"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:45:24Z","timestamp":1760147124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010022"],"URL":"https:\/\/doi.org\/10.3390\/rs15010022","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]}}}