Using VIIRS nightlights to estimate the impact of the 2015 Nepal earthquakes

Tveit, Thomas; Skoufias, Emmanuel; Strobl, Eric

doi:10.1186/s40677-021-00204-z

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…While we were assessing the pandemic-induced losses and the time of recovery, we applied an accepted form of analysis using satellite NTL data to study the dimming and recovery of lights after exogenous shocks, such as natural disasters and conflicts (see, for example, [45,46]). Thus, as our analysis indicates, the magnitude of loss due to COVID-19, as well as the speed of recovery, varied widely across the states, with most of the states' ratio of maximum quarterly loss ranging from ~10% to ~15% in the pre-pandemic 4th quarter in 2019, and the total GDP loss ranging from ~5% to ~15% in 2019.…”

Section: Discussionmentioning

confidence: 99%

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Lin¹,

Rybnikova

2023

Geomatics

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Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R2 = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R2 = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R2 = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.

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Section: Discussionmentioning

confidence: 99%

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Lin¹,

Rybnikova

2023

Geomatics

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“…WorldPop is a high-resolution population dataset that is a key component of many studies. WorldPop has been used in resource allocation, disaster management, transport and city planning and environmental impact assessment ( https://www.worldpop.org/about/ ), including for example, estimating the impact of the 2015 Nepal earthquakes 24 , 25 , guiding medical resource allocation of India 26 , assessing global rural accessibility and rural roads investment 27 . Our dataset is designed for fine resolution research and has a broad application prospect for climate change, urban development, public health research and other fields.…”

Section: Background and Summarymentioning

confidence: 99%

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

2022

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Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020–2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways’ (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

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“…They are an important data source for evaluating the losses and impact of earthquakes. Stable monthly and annual composite NTL data, such as DMSP-OLS annual composite data [32] and NPP-VIIRS annual or monthly composite data [28,45], can be used to reliably and effectively assess the long-term impacts of earthquakes. Several earthquake-related indicators, such as peak ground acceleration (PGA) [30,45] and postearthquake nighttime light piecewise (PNLP) [29], have been proposed to describe the long-term changes in NTL intensity and analyse human activity after earthquakes.…”

Section: Introductionmentioning

confidence: 99%

“…Stable monthly and annual composite NTL data, such as DMSP-OLS annual composite data [32] and NPP-VIIRS annual or monthly composite data [28,45], can be used to reliably and effectively assess the long-term impacts of earthquakes. Several earthquake-related indicators, such as peak ground acceleration (PGA) [30,45] and postearthquake nighttime light piecewise (PNLP) [29], have been proposed to describe the long-term changes in NTL intensity and analyse human activity after earthquakes. The relationship between NTL intensity and population or number of buildings was noticed in the early years [30], making it an essential and important analysis angle that could provide a solid and reliable basis for subsequent earthquake monitoring research.…”

Section: Introductionmentioning

confidence: 99%

The Changes in Nighttime Lights Caused by the Turkey–Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data

et al. 2023

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The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.

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Using VIIRS nightlights to estimate the impact of the 2015 Nepal earthquakes

Cited by 8 publications

References 25 publications

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Changes in the Association between GDP and Night-Time Lights during the COVID-19 Pandemic: A Subnational-Level Analysis for the US

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

The Changes in Nighttime Lights Caused by the Turkey–Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data

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