# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 3 Results
### Earthquakes
maximum possible success due to a model's ability to specify quantity perfectly given a fixed \(\text{K}_{\text{location}}\), which measures the agreement in the quantity of the two maps. Kappa values range from \(-1\) (no agreement) to 1 (perfect agreement), where positive values show a greater agreement than usual. A \(\text{K}_{\text{standard}}\) of 0.31 indicates that the damaged areas detected by NTL and DPM have fair agreement overall. A \(\text{K}_{\text{location}}\) of 0.46 and a \(\text{K}_{\text{quantity}}\) of 0.82 indicate that the PNL has a moderate ability to specify the location of damage and a good ability to specify the quantity of damage, respectively. We noticed that a large proportion of the damaged area became brighter after the earthquake and the brighter areas were mostly located in central Kathmandu (Figure 3f). This was probably due to rescue and recovery activities. According to the Ministry of Home Affairs (MoHA) of Nepal, over 130,000 staff from the Nepal Army, Nepal Police, Armed Police Force, as well as 4000 government and private health workers participated in the rescue activities [45]. Rescue and recovery activities from badly damaged buildings, repairing damaged infrastructure, and building temporary houses for displaced people would require a large number of emergency lights [74], resulting in the brighter color on the image. Figure 3f shows that the darker areas were mainly located in suburban or rural areas, whereas brighter areas were mainly located at the urban center, indicating that the urban center may have received more relief than the suburban and rural areas. Assessment of the Central Italy Earthquake in 2016 was based on DNB images listed in Table 4. Figure 4a-c shows the pre- and post-earthquake DNB and PNL images for the four counties in Italy. The difference between the pre- and post-earthquake DNB images was statistically significant (Mann-Whitney U-test, \(p<0.001\)). \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{**Pre-Earthquake**} & & \multicolumn{5}{c}{**Post-Earthquake**} \\ \cline{3-13} \multicolumn{1}{c}{Date} & 4 August & 5 August & 8 August & 9 August & 11 August & 12 August & 13 August & 14 August & 15 August & 26 August & 27 August & 28 August & 30 August \\ \hline \hline Doya & & & & & & & & & & & & & & \\ \(\text{K}_{\text{other}}\) & & & & & &  
Unlike the Gorkha Nepal Earthquake and other previous studies [39; 40], the area of brighter pixels (900.6 km\({}^{2}\)) was far larger than that of darker pixels (183.9 km\({}^{2}\)). The average DNB radiance increased after the earthquake from 0.54 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\) to 0.76 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\), which could be attributed to rescue and recovery activities. Comparing the PNL image with the DPM (Figure 4d,e) produced by ARIA using CSK SAR data, most damaged areas were brighter after the earthquake. By comparing the DPM image with the building map (Figure 4f), we found that most damaged areas detected by DPM were districts where buildings were present, except for the mountainous region A where there were no buildings but land deformation occurred. The overall accuracy was high (90.2%), whereas the TRP was only 12.5% and the standard Kappa was extremely low (0.01) (Table 3, Row 2), indicating that it was not possible to use PNL to detect damaged areas. The overall accuracy was high simply because the undamaged area was much larger than the damaged area. The difference in light change patterns between the Gorkha Nepal Earthquake and the Central Italy earthquake is possibly attributable to their different population, building, and economic conditions. In Italy, these small towns were quiet and peaceful with few people or nighttime activities. When the earthquake occurred, rescue teams entered the affected areas with emergency lights [75; 76], resulting in the town being even brighter than during normal time.