# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 4 Discussion
As the DNB of an area with no light could be larger than 0.5 nW-cm\({}^{-2}\cdot\)sr\({}^{-1}\)[67], these DNB data cannot be used to assess the impact of landslides. When faced with the same types of disasters, the changing pattern of NTL would be different due to the different socioeconomic statuses of the region. Take earthquakes as an example: for densely populated central urban areas, the normal NTL radiance value is very high. When an earthquake causes housing collapses and infrastructure damage, the light loss always becomes obvious. Under these circumstances, the impact of rescue lights is relatively minimal and NTL can be used to detect the affected area [39]. In contrast, the normal NTL for a sparsely populated village is low and rescue lights can be brighter than the normal NTL. Therefore, rescue lights would greatly affect the detection of damage. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Disaster Type** & **Event** & **Application** & **Validation Data** & **Validation Results** & **Challenges** \\ \hline \multirow{4}{*}{Earthquake} & \multirow{4}{*}{Gorkha Nepal Earthquake} & \multirow{4}{*}{Identifify damaged areas} & \multirow{4}{*}{DPM} & \multicolumn{2}{c}{Overall accuracy = 75.5\%} & \\  & & & & TPR = 35.9\% & Rescue and repair activities \\  & & & & K\({}_{\text{standard}}\) = 0.31 & produced extra \\  & & & & K\({}_{\text{quantity}}\) = 0.82 & lights \\  & & & & K\({}_{\text{location}}\) = 0.46 & \\ \hline \multirow{4}{*}{Storm} & \multirow{4}{*}{Tropical Cyclone Hudhud} & \multirow{4}{*}{None} & \multirow{4}{*}{DPM} & \multicolumn{2}{c}{Overall accuracy = 90.2\%} & \\  & & & & TPR = 12.5\% & Rescue and repair activities \\  & & & & K\({}_{\text{standard}}\) = 0.01 & produced extra \\  & & & & K\({}_{\text{quantity}}\) = 0.84 & lights \\  & & & & K\({}_{\text{location}}\) = 0.04 & \\ \hline \multirow{4}{*}{Storm} & \multirow{4}{*}{Hurricane Maria} & \multirow{4}{*}{Detect power outages} & \multirow{4}{*}{DPM, and Published CSER in the U.S.} & \multirow{4}{*}{Strong correlation between the PNL and P\({}_{\text{nopower}}\) (R\({}^{2}\) = 0.94)} & \multirow{4}{*}{Cloud \\  & & & & & \\ \cline{1-1}  & & & & The estimated P\({}_{\text{nopower}}\) of 77.6\% was in good agreement with the report that 80\% of the power supply was not restored. & Cloud \\ \hline \multirow{4}{*}{Flood} & \multirow{4}{*}{None} & \multirow{4}{*}{FPM and news reports} & Mann-Whitney U-test indicates that the difference between pre- and post-flood images is not statistically significant & \\ \cline{1-1}  & & & & Increased rescue vehicles and repair activities \\ \cline{1-1}  & & & & & brought extra lights \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The applications and challenges of using DNB daily observations in disaster assessments. By comparing the differences between NTL changes and the DPMs from the SAR data in the Gorkha Nepal Earthquake, Central Italy Earthquake, and Hurricane Maria, we noticed that the NTL satellite data directly recorded the light created by humans, whereas SAR data only reflect physical damage to the land surface. Therefore, the detected damage using SAR data may also include mountain deformations and other natural landform changes; the data do not directly reflect the impact on humans. NTL can directly reflect how humans are affected by natural disasters. However, the effect detected by NTL is complex and can include building damage, power outages, as well as human activities after the disaster, such as rescue work and changes to the number of vehicles on the road. The light sources cannot be distinguished by NTL itself. Therefore, using NTL to detect damage or power outages would be affected by other human activities like rescue activities and changes in traffic. The major limitation of using DNB to assess the impact by natural disasters is cloud cover. Disasters like floods and storms are always accompanied by heavy rainfall and massive cloud coverage.