## 5 Experiment II--Change Detection
### DSM Based Tree Growth Monitoring  
We calculate the PAIs with the described method in each inventory plot, taking the LiDAR2001 as reference pre-change data. The PAIs sets calculated from satellite data and aerial imagery are compared to the PAIs calculated by using two LiDAR datasets. Figure 11 shows the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Pairs** & **User Accuracy** & **Producer Accuracy** & **Overall Accuracy** & **Kappa Accuracy** \\ \hline ALOS2008-LiDAR2001 & 0.8675 & 0.5796 & 0.9372 & 0.6615 \\ Cartosat2008-LiDAR2001 & 0.7457 & 0.7126 & 0.9543 & 0.7038 \\ RapidEye2009-LiDAR2001 & 0.7209 & 0.4862 & 0.9142 & 0.5349 \\ WorldView2012-LiDAR2001 & 0.8816 & **0.7389** & **0.9646** & **0.7847** \\ Aerial2012-LiDAR2001 & **0.9191** & 0.7134 & 0.9629 & 0.7832 \\ TanDEM-X2013-LiDAR2001 & 0.7654 & 0.6955 & 0.9350 & 0.7031 \\ ALOS2008-HRSC2003 & 0.7109 & 0.4131 & 0.8929 & 0.4670 \\ Cartosat2008-HRSC2003 & 0.5959 & 0.5386 & 0.9246 & 0.5246 \\ RapidEye2009-HRSC2003 & 0.6468 & 0.3323 & 0.8638 & 0.3705 \\ WorldView2012-HRSC2003 & 0.7136 & **0.6056** & **0.9881** & **0.6214** \\ Aerial2012-HRSC2003 & **0.7426** & 0.5711 & 0.9328 & 0.6094 \\ TanDEM-X2013-HRSC2003 & 0.6432 & 0.5241 & 0.9225 & 0.5354 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the change masks generated from different combination of DSMs (highest values are marked in bold).  
Figure 10: Forest change masks generated from: (**a**) ALOS2008–LiDAR2001; (**b**) Cartosat2008–LiDAR2001; (**c**) RapidEye2009–LiDAR2001; (**d**) WorldView2012–LiDAR2001; (**e**) Aerial2012–LiDAR2001; (**f**) TanDEM-X2013–LiDAR2001; (**g**) ALOS2008–HRSC2003; (**h**) Cartosat2008–HRSC2003; (**i**) RapidEye2009–HRSC2003; (**j**) WorldView2012–HRSC2003; (**k**) Aerial2012–HRSC2003; and (**l**) TanDEM-X2013–HRSC2003 overlaid with the reference mask. Green: true positives; red: false positives; blue: false negatives; grey: forest mask.  
The scatter plots in Figure 11 show that most of the PAIs are in the range between 0 and 1 m. The results from Aerial2012 and WorldView2012 match well the results derived from LiDAR2010. PAIs detected from Cartosat2008 and TanDEM-X2013 show some differences in comparison to LiDAR2010, but most points are still quite close to the diagonal. The PAIs detected from ALOS2008 and RapidEye2009 are obviously larger than those from LiDAR2010.  
Figure 11: Tree growth detected from: ALOS2008 (**a**); Cartosat2008 (**b**); RapidEye2009 (**c**); WorldView2012 (**d**); Aerial 2012 (**e**); and TanDEM-X2013 (**f**) (\(n=137\)).  
Figure 12: Relationship between tree heights and PAIs for: LiDAR2010 (**a**); Cartosat2008 (**b**); WorldView2012 (**c**); Aerial2012 (**d**); and TanDEM-X2013 (**e**) (\(n=137\); \(x\)-axis is the maximum tree height measured by LiDAR2001 in each inventory plot, while the \(y\)-axis represents the derived PAIs.).  
The five best performing DSMs visually observed from Figure 11 are further used to analyze the relationship between tree height and PAIs (Figure 12). Most plots with lower (younger) trees have larger PAIs with respect to plots with higher trees. In the test region, most trees are 15-30 m high, with PAIs of 0.3-0.5 m per year. A linear least square fitting regression is applied to get the relationship between the tree heights and the PAIs. The obtained regression lines are plotted in Figure 12. All datasets follow similar trends and exhibit decreasing PAIs as the height increase.