# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 4 Discussion  
A natural disaster may cause damage to buildings and infrastructure, power outages, and impact human activities. All of these are expressed as a reduction in light intensity and can be identified in DNB images. Although the causes of light reduction cannot be revealed by DNB, which tend to differ for different types of disasters in different regions, our analyses showed that NPP-VIIRS DNB daily data can be used to assess the impact of various natural disasters, including earthquakes, storms, and floods. However, there are limitations due to the cloud coverage and influences by other human activities. Table 11 summarizes the applications of and major challenges encountered when using this type of data. Earthquakes are one of the most destructive hazards that can cause large-scale building collapse and power outages. The overall accuracy, TRP, FPR and standard Kappa of using NTL to detect damage due to earthquakes were estimated to be 75.5%, 35.9%, 8.9%, and 0.31, respectively, in the Gorkha Nepal Earthquake. Previous research [41] used VIIRS NCC nighttime light to detect damage caused by three earthquakes with estimated overall accuracies of 75.0%, 68.4%, and 14.9%. Our result concurs with the highest accuracy of these three cases. According to the confusion matrices in their study, the standard Kappas would be close to 0, which is lower than our results. The TPRs would be 100%, 76.5%, and 14.9%, two of which would be higher than our results. The accuracy of using NTL to estimate the damage caused by earthquakes is not high due to a number of reasons, including the light from rescue activities that seriously affect the accuracy, and power outages that resulted in darkness from undamaged buildings as well. The situation is different under storm and flood events. For floods, variables that can influence building damage include the nature of the flood (such as water level, flood duration, and flow velocity) and the building properties (such as building structure and building quality) [80]. In urban areas where building structures are generally strong, floods would only destroy individual buildings along rivers. This is similar to storms during which only small numbers of individual buildings of poor quality are affected. Therefore, a decrease in the light in urban areas after floods or storms is likely due to power outages rather than building collapse. In the case of Hurricane Maria, we found a linear relationship between PNL and the power outage rate with an R\({}^{2}\) of 0.94. The linear regression model was further tested by applying it to the example of Cyclone Hudhud. The result showed that the estimated power outage rate was in good agreement with the reported  
Figure 10: The percentage of cloud coverage in the study areas 10 days after each disaster. power outage rate. Previous research [37] used DNB data to detect power outages after Hurricane Sandy and found that the light outage percentages (defined as \(\text{LO}_{\text{DNB}}=(1-\text{Rad}_{\text{post}}/\text{Rad}_{\text{pre}})\)) were in good agreement with the reported power outage rates, which further confirms the linear relationship between PNL and power outage rate. DNB would be more applicable in urban and developed rural areas that have a relatively high normal nighttime light intensity. In mountainous areas or small villages where residential properties are sparsely distributed and infrastructure and business are undeveloped, there is not much light at night even during normal times. The changes in light after disasters would be too small to be detected by satellite. For example, landslides are severe disasters that can cause large areas with buildings to be submerged. The 2017 Mocoa Colombia landslide buried large portions of Mocoa in several feet of mud and caused 329 deaths, 332 injuries, and 70 missing people [81]. However, the average DNB values before and after the landslide were both less than 0.2 nW-cm\({}^{-2}\cdot\)sr\({}^{-1}\), which was almost no difference.