## 5 Experiment II--Change Detection
### DSM Based Storm Damage Detection  
To generate the height change maps we used LiDAR2001 and HRSC2003 as pre-change (i.e., before storm event) DSMs, and all DSMs acquired after 2007 as post-change (after storm) DSMs. Figure 8 shows results from using LiDAR2001 as the pre-change DSM, while results based on HRSC2003 are displayed in Figure 9. To allow comparison, both figures use the same color bar as in Figure 7a,b: red represents changes in the forest structure related to the storm event, while green shows positive changes due to tree growth.  
Focusing first on Figure 8, the DSMs show promising results when compared to the change reference mask (Figure 7c), with each map highlighting the forest damages with varying degrees of accuracy. Benefitting from the higher quality of WorldView2012 and Aerial2012 data, the difference maps in Figure 8d,e match better the reference data. Although in Figure 8b,f the residential regions exhibit more noisy structures, the changes highlighted in the forest region show similar results as in Figure 8d,e.  
Figure 8: Height difference maps comparison: (**a**) ALOS2008–LiDAR2001; (**b**) Cartosat2008–LiDAR2001; (**c**) RapidEye2009–LiDAR2001; (**d**) WorldView2012–LiDAR2001; (**e**) Aerial2012–LiDAR2001; and (**f**) TanDEM-X2013–LiDAR2001.  
As the HRSC2003 data were captured two years later than the LiDAR2001 DSM, in Figure 9 the green color, representing positive height differences, is not as dark as in Figure 8. In addition to tree growth, the lighter green may also reflect the lower quality of the HRSC DSM, and the fact that HRSC is smoother than LiDAR2001. In Figure 9, most of the regions which suffered damages from the storm (in red) match the ones in Figure 8, but the size of the regions is different in some cases.  
To further analyze the height difference maps, the forest damage changes are evaluated. For forest damage, change masks are generated by taking the pixels with a height decrease of more than 12 m. The resulting change masks from Figure 8 are displayed in Figure 10a-f. The results from Figure 9 are shown in Figure 10g-l. In each subfigure, the forest mask is shown in light grey and used as background. The extracted change mask is overlaid with the reference change mask. True positives are represented in gray, while red and blue represent false positives and false negatives, respectively. No further refinements were carried out to keep unaltered the original detail of the datasets.  
A visual comparison shows that all change masks in Figure 10 correctly highlight the regions destroyed by the storm as in the reference change mask (Figure 9c), but with different accuracies. By using the LiDAR2001 as pre-change DSM, the results from Cartosat2008 (Figure 9b), WordView2012 (Figure 9d) and Aerial2012 (Figure 9e) are similar and exhibit less false positives than others. Despite of a few false alarms, ALOS2008 (Figure 9a) and even RapidEye2009 (Figure 9c) correctly highlight the changed regions. Using HRSC2003 as pre-change DSM leads instead to more false positives and false negatives than using LiDAR2001. The changed regions are correctly located but with incorrect boundaries and size.  
To quantitatively evaluate the accuracy of these change masks, the user and producer accuracy, together with the overall and Kappa statistic accuracy [46] are reported in Table 7 for the 12 change masks. When the LiDAR2001 data are adopted, all change masks achieve Kappa accuracies higher than 0.5. Based on the HRSC2003, two of the Kappa accuracies are below 0.5. Within both comparisons, WorldView2012 achieves the best producer, overall and Kappa accuracies. Aerial2012 exhibits the best user accuracy across both comparisons.  
Figure 9: Height difference maps comparison: (**a**) ALOS2008–HRSC2003; (**b**) Cartosat2008–HRSC2003; (**c**) RapidEye2009–HRSC2003; (**d**) WorldView2012–HRSC2003; (**e**) Aerial2012–HRSC2003; and (**f**) TanDEM-X2013–HRSC2003.