Nighttime light perspective in urban resilience assessment and spatiotemporal impact of COVID-19 from January to June 2022 in mainland China

Liu, Yaohui; Liu, Wenyi; Zhang, Xinyu; Lin, Yu; Zheng, Guoqiang; Zhao, Zhan; Cheng, Hao; Gross, Lutz; Li, Xiaoli; Wei, Benyong; Su, Fei

doi:10.1016/j.uclim.2023.101591

Cited by 7 publications

(3 citation statements)

References 46 publications

(44 reference statements)

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“…In this study, nighttime light indices were extracted using the National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data [37,38]. The TNLI was selected as the primary index for computation and analysis [39,40]. The TNLI was derived by summing the digital number (DN) values of light within administrative units, formulated as follows:…”

Section: Nighttime Light Index Calculationmentioning

confidence: 99%

The Evaluation and Comparison of Resilience for Shelters in Old and New Urban Districts: A Case Study in Kunming City, China

Zhou,

Liu,

Lin

et al. 2024

Sustainability

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As a critical resource in emergency response and a pivotal element in disaster prevention and risk reduction, shelters play a central role in the holistic continuum of rescue and relief efforts. However, existing research often overlooks the comprehensive assessment and enhancement of shelter resilience. This study proposes a novel safety–robustness–accessibility (SRA) model aimed at evaluating and enhancing the overall resilience of shelters in the face of disasters. Firstly, a resilience assessment system for shelters was established, leveraging multi-source data and encompassing diverse dimensions, including safety, robustness, and accessibility. Subsequently, the entropy weight method was utilized to determine the weights of the assessment indicators. The case study and comparative analysis were conducted on shelters situated in two urban areas, old and new, in Kunming City, China, namely Wuhua District and Chenggong District. The findings reveal a higher quantity of shelters in Wuhua District compared to Chenggong District; however, the overall resilience level is relatively low, predominantly categorized as “Mid-Low” grade, constituting a substantial 57.94%. Conversely, shelters in Chenggong District exhibit a relatively high resilience level, predominantly classified as “Medium” grade, accounting for 33.77%. This study furnishes valuable data references and specific strategies aimed at enhancing the resilience of urban shelters against disasters. It offers crucial insights for urban planning and management to strengthen shelter resilience, thereby contributing to the development of a more resilient and sustainable urban future.

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Section: Nighttime Light Index Calculationmentioning

confidence: 99%

The Evaluation and Comparison of Resilience for Shelters in Old and New Urban Districts: A Case Study in Kunming City, China

Zhou,

Liu,

Lin

et al. 2024

Sustainability

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“…Nighttime light remote sensing is an optical remote sensing technology that detects and obtains information on nighttime lights, providing a quick, accurate, and objective view of the surface and human activities [14][15][16]. Unlike daytime remote sensing, nighttime light remote sensing can reveal information that is not visible during the day [17,18].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Liu

Qiu

et al. 2023

Land

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Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China.

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“…To be specific, Ye et al applied nighttime light data to analyze the spatiotemporal characteristics of urbanization in the Taiwan Strait; they found that nighttime light data increased from 729, 863 in 1992 to 2, 729, 0552 in 2020, with the percentage of high-value light changing from 2.59% to 12.22% [20]. Liu et al also utilized nighttime light data to evaluate the characteristic of change before and after COVID-19 in mainland China [21]. Generally, nighttime light from different regions is positively correlated with the level of regional economy [22][23][24], and the total amount of nighttime light can be used to reflect the level of regional economic development.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Coupling Coordination Degree between Economy and Eco-Environment Systems in the Yangtze River Delta from 2000 to 2020

Ji,

Wang,

Xie

et al. 2023

Systems

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The quantitative evaluation of the coupling coordination degree (CCD) between the regional economy and eco-environment systems is of great importance for the realization of sustainable development goals, which could identify economic or eco-environmental cold areas. To date, traditional evaluation frameworks mainly include the indicator system construction based on statistical data, which seldom utilize the geo-spatiotemporal datasets. Hence, this study aimed to evaluate the CCD change trend of the Yangtze River Delta (YRD) and explore the relationship between the CCD, economy, and eco-environment on the county scale. In this study, YRD was selected as the study area to evaluate its level of CCD at different periods, and then the nighttime difference index (NTDI) and eco-environmental comprehensive evaluation index (ECEI) were calculated to represent the difference in the development of the regional economy and the eco-environmental quality (EEQ). The CCD between the two systems was then calculated and analyzed using global, local, and Geary’s C spatial autocorrelation indicators, in addition to change trend methods. The main findings showed that: (1) During the period 2000–2020, the economic system in YRD showed a continuously upward trend (0.0487 a−1), with average NTDI values of 0.2308, 0.2964, 0.3223, 0.3971, and 0.4239, respectively. In spatial terms, the economy system showed a distribution of “high in the east and low in the west”. (2) YRD’s EEQ indicated a gradual upward trend (from 0.3590 in 2000 to 0.3970 in 2020), with a change trend value of 0.0020 a−1. Spatially, the regions with high ECEI were mainly located in southwestern counties. (3) In the past 20 years, the CCD between economic and eco-environment systems showed an increased change trend, with a change trend value of 0.0302 a−1. The average CCD values for the five periods were 0.3992, 0.4745, 0.4633, 0.5012, and 0.5369. The overall level of CCD improved from “moderate incoordination” to “low coordination”. (4) Both NTDI and ECEI indexes have a positive effect on the improvement of regional CCD. However, the contribution of NTDI is a little higher than that of ECEI.

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Nighttime light perspective in urban resilience assessment and spatiotemporal impact of COVID-19 from January to June 2022 in mainland China

Cited by 7 publications

References 46 publications

The Evaluation and Comparison of Resilience for Shelters in Old and New Urban Districts: A Case Study in Kunming City, China

The Evaluation and Comparison of Resilience for Shelters in Old and New Urban Districts: A Case Study in Kunming City, China

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Evaluation of Coupling Coordination Degree between Economy and Eco-Environment Systems in the Yangtze River Delta from 2000 to 2020

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