# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 5 Conclusions  
The availability of the NPP-VIIRS DNB daily data enabled the use nighttime light data for short-term disaster assessments. This study explored the ability to use DNB daily data by assessing three types of natural disasters including earthquakes, storms, and floods. A series of preprocessing procedures were used to ensure the high data quality and that the PNL images were generated to reflect DNB changes following six typical disaster events. The results showed that the overall accuracy and standard Kappa of using PNL to detect the damage after the Gorkha Nepal Earthquake were 75.5% and 0.31, respectively. However, it was not possible to use PNL to detect the damage after the Central Italy Earthquake because many areas became brighter. This differs from previous research [39; 40]. During the power restoration period after Hurricane Maria, the PNL value was significantly correlated with the power outage rate (R\({}^{2}\) = 0.94), indicating that NTL can be used to track power recovery trends. In the Cyclone Hudhud event, the P\({}_{\text{nopower}}\) estimated by PNL was in good agreement with the reported power outage, which is consistent with the work by [PERSON] et al. [37]. In the flood events, power outages were detected by NTL after the Yulin Flood, but were not be detected after the Louisiana Flood because of the long cloudy days. The usefulness of NTL and SAR data in analyzing the impact of earthquakes and storms was compared. NTL can directly reflect human activities but cannot distinguish the causes of light reduction. SAR can detect building damage but also reflects natural land deformation. The major limitation of using DNB to assess damage after earthquakes is the presence of rescue activities, which are also related to the socioeconomic situation of the region. For the assessment of disasters accompanied by precipitation such as storms and floods, the presence of cloud is the major challenge that needs to be overcome.  
This study has some limitations. In the absence of accurate survey data for building damage and power outages, we used DMPs from SAR data and power outage rates from local news reports as alternatives to validate the results. However, there are limitations and noise in these data, which lead to errors in the validation accuracy. Further validation of the assessments can be achieved as more accurate data on building damage and power outages become available. Given that the NTL and SAR data reflect different aspects of the impacts of natural disasters on humans, the combined use of these two data sources may improve the accuracy of damage detection and the assessment after natural disasters, which can be explored in future work. NASA has published the Black MarbleNTL daily data that offer cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected VIIRS DNB radiance [83]. Future work can evaluate the usefulness of this product to assess different disasters.  
[PERSON], [PERSON] and [PERSON] conceived and supervised this study. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] proposed the method. [PERSON], [PERSON] and [PERSON] processed the data. [PERSON], [PERSON] and [PERSON] analyzed the results and wrote the paper.  
This research was funded by the National Key Research and Development Program of China (No. 2017 YFE0100700), the National Natural Science Foundation of China (No. 41871331, 41801343, 41471449 and 41701462), and the China Scholarship Council for Xizhi Zhao to conduct the research on which this paper is based at the University of Queensland (No. 201706140143).  
The authors would like to thank the anonymous reviewers for their constructive comments on an earlier version of this paper.  
The authors declare no conflict of interest.