# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 3 Results  
### Earthquakes  
The first earthquake we analyzed was the Gorkha Nepal Earthquake. Table 2 lists the dates and cloud coverages of pre-and post-earthquake DNB images that were used to compute the average composite DNB images pre- and post-earthquake (Figure 2(a),(b)). Figure 2(c) shows the PNL image. Green indicates areas with a PNL less than one, and these were considered to be zones damaged by the earthquake. Both the extent and intensity of the light in most areas were reduced after the earthquake, with only a few exceptions. The size of the areas that became darker after the earthquake was calculated to be 447.2 km\({}^{2}\) and the size of those that became brighter was calculated to be 145.1 km\({}^{2}\). Using the Mann-Whitney U-test, the DNB changes post-earthquake were statistically significant at \(p<0.001\). \begin{table}
\begin{tabular}{c c c c c c c c|c c c} \hline \hline  & & & \multicolumn{4}{c|}{**Pre-Earthquake**} & \multicolumn{4}{c}{**Post-Earthquake**} \\ \hline Date & 7 April & 8 April & 9 April & 18 April & 21 April & 23 April & 26 April & 2 May & 4 May & 5 May \\ \hline Days Before (After) & & & & & & & & & & \\ the Disaster & 18 & 17 & 16 & 7 & 4 & 2 & 1 & 7 & 9 & 10 \\ Cloud Coverage (\%) & 0.1 & 0.0 & 0.0 & 0.7 & 0.6 & 2.5 & 17.1 & 1.1 & 1.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The dates and cloud coverages of pre- and post-earthquake Day/Night Band (DNB) images of the Gorkha Nepal Earthquake in 2015. We compared the results to DPMs generated using SAR data from CSK. Figure 3d shows the PNL image overlaid by the damaged areas (marked as black pixels) from DPM. The damaged areas in the PNL and DPM images concur with each other in the particular area of central Kathmandu. However, the damaged areas in DPM image are denser in regions A and B where there is no noticeable change in NTL (Figure 3d). By comparing Figure 3d with the Bing satellite map (Figure 3e), we found that region A is a mountainous area and that there is a river in region B. The mountain deformation and river changes created by the earthquake were detected by the SAR data. By comparing the PNL and DPM images with the building map derived from the OSM (Figure 3f), we found that many of the damaged areas detected by radar data were located in areas with no buildings or low building density, whereas damaged areas derived from the NTL were almost all located in central Kathmandu with high building density. To assess the accuracy of using NTL to detect the damage due to earthquakes, we reduced the resolution of the DPM from 30 m to 750 m to match the resolution of the PNL image for comparison. A confusion matrix was constructed (Table 3) that shows an overall accuracy of 75.5%. A True Positive Rate (TPR) of 35.9%, and a False Positive Rate (FPR) of 8.9% indicate that 35.9% of the damaged areas and 8.9% of the undamaged areas were detected as damaged areas. Given that the proportion of the undamaged area was much larger than that of the damaged area, we further calculated the standard Kappa (\(\text{K}_{\text{standard}}\)) as well as Kappa for location (\(\text{K}_{\text{location}}\)) and quantity (\(\text{K}_{\text{quantity}}\)) using [PERSON] approach [73]. \(\text{K}_{\text{location}}\) was calculated by dividing the success due to the model's ability to specify location by the maximum possible success due to a model's ability to specify location perfectly given a fixed quantity of each class, which measures the locational agreement between two maps. \(\text{K}_{\text{quantity}}\) was calculated by dividing the success due to the model's ability to specify quantity by the  
Figure 3: (**a**) The pre- and (**b**) post-earthquake averaging composite DNB images, (**c**) Percent of Normal Light (PNL) image, (**d**) Damage Proxy Map (DPM) image combined with PNL image, (**e**) Bing satellite map, and (**f**) Open Street Map (OSM) building map combined with PNL image of the Gorkha Nepal Earthquake.