Post-Earthquake Night-Time Light Piecewise (PNLP) Pattern Based on NPP/VIIRS Night-Time Light Data: A Case Study of the 2015 Nepal Earthquake

Abstract

Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (kp), recovery rate (kr1), reconstruction rate (kr2), development rate (kd), relative reconstruction rate (krp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction.

Can we rely on VIIRS nightlights to estimate the short-term impacts of natural hazards? Evidence from five South East Asian countries

“…With previous studies showing promising results when using VIIRS data for earthquakes (Zhao et al 2018;Gao et al 2020) and hurricanes (Zhao et al 2020), one would expect that when one tried to correct for noise and seasonality one could see similar statistically significant results also on a more general scale. Also, given the geographical and cultural similarities between the countries in our sample, one could reasonably expect to see consistent and similar results for each of the different types of hazards.…”

“…An extension of this research for other researchers can be to look at specific cases and see if one could identify effects at a more local scale, preferably with high-resolution and good quality underlying data. With the recent proposed correction methods in Gao et al (2020) and Zhao et al (2020) it might also be possible to use STL as a basis to construct a more general methodology for correcting the VIIRS measurements, in particular if it is based on validated GDP data. Additional research efforts could be focussed on different regression methodologies as this type of analysis could lend itself well to other methods.…”

“…There are also problems with seasonality and noise (Levin 2017, Zhao, Hsu, et al 2017. There is no established standard for how to interpret and correct these measurements, albeit Gao et al (2020) and Zhao et al (2020) have tried to use different methodologies related to trend decompositions to get more consistent values. To try to correct for the negative values and the noise, we used different methodologies.…”

“…To try to correct for the negative values and the noise, we used different methodologies. Firstly, we performed a simplified version of the steps described in Gao et al (2020), where STL was used on the radiance values to decompose them into a seasonal, a trend and a remainder component, with the latter two values being used in our regression analysis. Secondly, assuming that negative values are due to errors or faulty measurements, we experimented by setting any negative values to zero.…”

“…More recently one has also seen its use for the Nepal earthquake in 2015 (Gao et al 2020) and hurricanes Irma and Maria in Puerto Rico (Zhao, Liu, et al 2020). These latter articles use different statistical techniques based on seasonal and trend decomposition using Loess (STL) to remove elements related to background noise, seasonality and trends that is present in the VIIRS data (Levin 2017, Zhao, Hsu, et al 2017.…”