Figure 4 - uploaded by Xiwei Fan
Content may be subject to copyright.
Source publication
To rapidly identify earthquake damage areas after earthquakes occur, a method based on Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) images with nearly constant contrast (NCC) is proposed. The differences between post-earthquake and mean image of 3 months VIIRS NCC NTL before an earthquake are taken to reflect the effects...
Context in source publication
Citations
... Zhao et al. conducted a study on selected disaster cases to assess the impact on urban of natural disasters [4], and the best result was 78%. Fan et al. proposed a method to measure the impact of earthquakes on urban brightness loss [38], which was applied to three earthquakes with magnitude 8.0 in Chile. The best result is 68.4% accuracy. ...
The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities’ energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts.
... The illumination before and after the event is used to detect the affected areas. In a similar manner, the impact of the earthquakes via night-lights has also been analyzed for Nepal in 2015 and for Chile in 2014 and 2015 (Fan et al., 2019). Light pollution has also been studied with VIIRS data. ...
Currently, the global urban environment is constantly monitored by a plethora of earth observation sensors. In this chapter, we focus specifically on nocturnal optical data a.k.a. night-lights. The nightlights have come into play since the beginning of the 1990s. The main advantage of the data is the very strong correlation between socioeconomic phenomena, with population and the gross domestic product (GDP) in specific. Night-lights are deployed to monitor urban areas through time, utilizing a multiannual time-series, comprising 20 years or more. The analysis was originally based on the defense meteorological satellite program (DMSP) operational linescan system (OLS) sensor. The OLS provides a valuable and unique dataset that can acquire relatively constant characteristics, after intercalibration, and thus become suitable for time-series comparisons. Unfortunately, OLS data are limited by the coarse spatial and very coarse spectral resolutions. But recently, the temporal resolution of the night-lights has been improved, to include monthly composites at first and then daily rasters. This phenomenal increase in temporal resolution has paved the way for very novel applications to monitor seasonal variation in urban areas. The visible infrared imaging radiometer suite (VIIRS) and the day/night band (DNB) sensor onboard the SUOMI satellites were used as the data source for this analysis. Based on the VIIRS data, applications emerged permitting the exploration of season variation, at monthly or even shorter intervals. Nevertheless, the newer high temporal resolution poses technical challenges in order to remove noise and form a meaningful time-series.
... Nighttime light data has been used in many studies related to disaster monitoring, urban sprawl, and human activity [24][25][26][27][28]. For instance, Fan et al. [29] used NPP-VIIRS nighttime light data to monitor recovery after earthquakes and quickly assess earthquake damage. Li et al. [30] researched the variation of nighttime illumination in different seismic regions and the influence of human activities on nighttime illumination. ...
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.
... Gillespie et al. (2014) used nighttime lights to analyze the dynamic recovery process after the 2004 Indonesian tsunami. Fan et al. (2019) used NPP-VIIRS nighttime light data to monitor recovery following earthquakes and to conduct rapid assessments of earthquake losses. For economic and population development monitoring, Lo (2002) extracted surface area and volume variables from the TIN model. ...
The outbreak of the coronavirus disease 2019 (COVID-19) epidemic has resulted in large threats and damage to society and the economy. In this study, we evaluate and verify the comprehensive resilience and spatiotemporal impact of the COVID-19 epidemic from January to June 2022 in mainland China based on multisource data. First, we adopt a combination of the mandatory determination method and the coefficient of variation method to determine the weight of the urban resilience assessment index. Furthermore, Beijing, Shanghai, and Tianjin were selected to verify the feasibility and accuracy of the resilience assessment results based on the nighttime light data. Finally, the epidemic situation was dynamically monitored and verified with population migration data. The results show that urban comprehensive resilience of mainland China is shown in the distribution pattern of higher resilience in the middle east and south and lower resilience in the northwest and northeast. Moreover, the average light intensity index is inversely proportional to the number of newly confirmed and treated cases of COVID-19 in the local area. This study provides a scientific reference to improve the comprehensive resilience of cities to achieve the goals of sustainable development (SDGs 11): make cities and human settlements resilient and sustainable.
... Previous studies that used the monthly product of VIIRS/DNB to assess the impact of earthquakes on NTL (e.g., the 2015 Nepal earthquake) were less successful in mapping these NTL changes [29][30][31], which may be due to the temporal averaging and smoothing of nightly data into a single monthly product, and the fact that the recovery of electricity may happen within a week or two, as we found in this case. Studies using nightly VIIRS/DNB to detect the impact of earthquakes on NTL may better identify such changes; however, nightly images are more prone to be affected by cloud cover [32]. To overcome cloud cover problems, we examined changes in NTL between pre-earthquake and post-earthquake periods, enabling us to also test for statistically significant changes in NTL. ...
The 6 February 2023 earthquakes that hit south-eastern Turkey were amongst the deadliest in the past century. Here, we report the ability to map and quantify areas impacted by these earthquakes using changes in nighttime lights, as mapped by NASA’s VIIRS/DNB sensor. We show the correspondence between the 7.8 magnitude earthquake and impacted areas, located in cities and towns, mostly along the fault line, in areas where macroseismic intensity values were higher than 7. We verified the darkening of night lights as recorded by VIIRS using the new SDGSAT-1 Glimmer multispectral nighttime sensor, as well as by comparing changes in nighttime lights with reports on damaged buildings. The ability to rapidly map impacted areas from space using nighttime lights is of key importance for prioritizing and directing emergency and rescue services globally.
... Gillespie et al. [45] used nighttime lights to analyze the dynamic recovery process after the 2004 Indonesian tsunami. Fan et al. [46] used National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime lighting data to monitor recovery following earthquakes and to conduct rapid assessments of earthquake losses. ...
In recent years, with the acceleration of urbanization and abnormal changes in the overall climate, cities have been increasingly threatened and affected by disasters. Assessing and improving urban resilience, as well as postdisaster recovery monitoring, are of great significance for relevant municipal departments. Taking the heavy rainstorm event that occurred on July 20, 2021, in Zhengzhou City as an example, this study explored the resilience and postdisaster recovery of Zhengzhou City based on remote sensing data and other multisource data. First, we calculated the resilience assessment index and built an assessment model. Then, we analyzed and evaluated the resilience. Finally, based on NPP-VIIRS nighttime light data, we verified the accuracy of the resilience results and dynamically monitored the postdisaster recovery process. The results show that the overall resilience of Zhengzhou City to waterlogging disasters is low in the southwest and high in the northeast. The changing trend of the nighttime light brightness following the disaster was consistent with the resilience assessment results. Then, due to rescue work, the light index increased briefly in July; Due to the serious impact of the disaster, the facilities were damaged, and the light index was reduced in August. With the development of recovery work, the disaster-influenced and light index areas gradually recovered and exceeded the predisaster level. Corresponding urban resilience strategies were proposed based on the assessment results. This study can provide a scientific basis and reference for relevant aspects such as disaster prevention, recovery, and reconstruction in Zhengzhou City and other cities.
... A neural network algorithm was also applied to evaluate power outages' temporal and spatial distribution quantitatively. In 2018, Fan analysed the VIIRS nearly constant contrast (NCC) environmental data record (EDR) data of the target area around the earthquake [36]. After eliminating the influence of clouds, moonlight, solar irradiance and low-quality pixels, the standard deviation change before and after the disaster was calculated to detect the affected area of disasters. ...
Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method.
... The NTL data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) NTL data and Suomi National Polarorbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data are stable and persistent data sources [21,22]. The advantages of NTL data in terms of a large detection range and high temporal resolution have been widely used in the fields of economic growth [23][24][25], urbanization [26,27], poverty [28,29], carbon emissions [30][31][32], electricity consumption [33,34], natural disasters [35][36][37] and air quality assessment [38][39][40]. However, there are differences in the relationship between the NTL brightness of different land use types and the spatiotemporal distribution of the population. ...
Accurately and precisely grasping the spatial distribution and changing trends of China’s regional population is of great significance in new urbanization, economic development, public health, disaster assessment, and ecological environmental protection. To monitor and evaluate the long-term spatiotemporal characteristics of the population distribution in China, a population monitoring estimation model was proposed. Based on remote sensing data such as nighttime light (NTL) images, land use data, and data from the fifth, sixth, and seventh censuses of China, the population spatiotemporal distribution in China from 2000 to 2020 was analyzed with a random forest algorithm. This study obtained spatial distribution maps of population density at a 1 km x 1 km resolution in 2000, 2010, and 2020. The results revealed the trend of the spatiotemporal pattern of population change from 2000 to 2020. It shows that: the accuracy assessment using the 2020 census population of townships/streets as a reference shows an R2 of 0.67 and a mean relative error (MRE) of 0.44. The spatial pattern of the population in 2000 and 2010 is generally unchanged. In 2020, population agglomeration is evident in the east, with a slight increase in the proportion of the population in the west. The patterns of population agglomeration and urbanization also change over time. The population spatiotemporal distribution obtained in this study can provide a scientific reference for urban sustainable development and promote the rational allocation of urban resources.
... Cole et al. (2017) exploited the neural network model and NPP-VIIRS day-night band (DNB) data to monitor power outages following Hurricane Sandy in 2012. Fan et al. (2018) analysed three major earthquakes using NPP-VIIRS/DNB NTL data and found that the accuracy of earthquake loss assessment increased with the increase in earthquake intensity. Zhao et al. (2018) utilized pre-and post-disaster NPP-VIIRS/DNB NTL data to evaluate disaster losses in earthquake, storm and flood areas. ...
The timely and accurate assessment of flooding disasters and economic resilience is significant for post-disaster reconstruction and recovery. In July 2021, the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data were explored as a proxy to assess the flooding damage caused by heavy rainfall in Zhengzhou City,China. A combination of the night-time light (NTL) changes and the radiation normalization method was used to rapidly identify affected areas and extract populations following the flooding disaster. A daily gross domestic product (GDP) prediction model was developed to evaluate the economic resilience of Zhengzhou City using multi-temporal DNB daily and monthly NTL data. The severity of the disaster was estimated by the extent of power outages, flooding crisis regions, and affected populations. It has been predicted that the Zhengzhou economy is unlikely to be restored to its normal level before the end of 2021 owing to the dual impact of the coronavirus outbreak and flooding disaster; the revised recovery time prediction is late April 2022. We concluded that our NTL data provided new, simple, and effective insights into the post-flooding assessment of the affected areas, populations, GDP forecast, and economic recovery.
... Related papers on hurricanes are Bertinelli and Strobl (2013) on the local economic impact of hurricanes in the Caribbean, Mohan and Strobl (2017) on the short-term impact of cyclone Pam in the South Pacific, Del Valle et al. (2018) on cyclone impacts in Guangdong, China, Ishizawa et al. (2019) on hurricane impacts in the Dominican Republic, and Miranda et al. (2020) on windstorm impacts in Central America more generally. Night lights have also been used to study earthquake impacts (Kohiyama et al., 2004;Fan et al., 2019;Nguyen and Noy, 2020), and a combination of disaster types globally Chapter 3 and for Indonesia and Southeast Asia respectively (Skoufias et al., 2020(Skoufias et al., , 2021. contributions by Henderson et al. (2012) and Chen and Nordhaus (2011). ...
... The authors design an automated program that detects anomalies in light intensity in potential earthquake zones to identify damaged areas, and show that day-to-day DMSP night light images do a fairly good job at identifying damaged areas even distant from the epicenter. 10 Making use of the newer VIIRS night light data, Fan et al. (2019) compare day-to-day night light changes after earthquake impacts to pre-earthquake mean radiance to rapidly predict damaged areas after impact. The authors report reasonable success in predicting damage from earthquakes by cross-referencing pixels with light change to USGS's Shakemaps for a number of major earthquakes. ...
... The estimated reduction in night light thus captures more than power outage alone, giving further support for the use of night lights to estimate post-disaster impacts. For a discussion on disaster-related power outages, see appendix section 4.B.1 11 In their study (Fan et al., 2019), light reduction correlates strongly with ground shaking above MMM 5 and especially above MMi 6. Accuracy is in the order of 85% for the two studied 124 Chapter 4 correlates well with high shaking intensities, as modelled in the Shakemaps by USGS. The approach of the authors, however, is very case-specific, and they consider only the first days after impact to identify damaged areas. ...
How do weather anomalies affect the economy at the local level? This paper presents a new data set that links weather data to annual average night-light emission data for 24.000 0.5°× 0.5° grid-cells around the globe for the period 1992–2013. Interpreting night-light emission as a proxy for economic activity, these data allow one to investigate how weather anomalies affect economic activity. Global coverage avoids selection bias, while high spatial resolution avoids averaging out heterogeneity in local impacts at higher aggregation levels. Our data show significant effects on the local growth of night-light for storms, excessive precipitation, droughts, and cold spells. Moreover, we find evidence for significant spatial spillovers to neighboring areas. Our results suggest that these offsetting spillovers are typically local. As positive and negative effects average out in larger areas, our results call for the analysis of economic effects of weather anomalies at a high geographical resolution. Finally, our results are driven by events in lower income regions. As climate change is expected to make weather patterns more erratic, our new data can inform emerging debates on how this will affect the economy in both science and politics.
