## 3 Results
### Land Cover Classification and Change Trajectories
#### 3.1.2 Change Trajectories  
Figure 5 shows the four SVM classification maps after being reclassified. There is an average of 176,765 reclassified pixels in the four maps with approximately 70% being non-woody and 30% woody. The number of pixels in the four classifications are not equal because of the presence of cloud and cloud shadow pixels in the 1993 and 2011 imagery, which were reclassified as no data. Figure 6 shows the overall change trajectory using all SVM classifications in the time-series. Visually, most pixels did not undergo change, as the WWWW (brown) and NNNNN (blue) trajectories represent pixels that, throughout the time-series, remain woody or non-woody, respectively. Table 2 shows that 86.7% of pixels did not undergo conversion throughout the entire time-series, while all other pixels underwent conversion at least once. Of the 13.3% that underwent conversion, 66.3% ended the time-series as non-woody and 33.7% as woody. There are some visually discernible spatial patterns to the conversions that occurred in the overall change trajectory, indicating that some portions of PCNP may be more susceptible to land cover change. Figure 6 shows that changes occurred along the coast in the south and southwest. Conversions also appear to be more frequent on the fringes of the pockets of forest that surround fluvial systems. Substantial change also occurred in the eastern PCNP wetland and within the northern eastern closed forest. It appears that little widespread change occurred in the grasslands, but with pronounced conversions occurring in transition zones between woody and non-woody vegetation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Error Matrix** & **Forest** & **Grass** & **Water** & **Welfand** & **Cloud** & **Cloud** & **Cloud** & **Class Error** & **Class Error** \\  & & & & & & **Shadow** & **Total** & **Commission \%** & **Omission \%** \\ \hline \multicolumn{8}{c}{**1975**} \\ \hline
**Unclassified** & 0 & 0 & 0 & 0 & n/a & n/a & 0 & & \\
**Forest** & 6 & 0 & 0 & 0 & n/a & n/a & 6 & 0.0 & 0.0 \\
**Grass** & 0 & 7 & 2 & 0 & n/a & n/a & 9 & 22.2 & 12.5 \\
**Water** & 0 & 0 & 2 & 0 & n/a & n/a & 2 & 0.0 & 50.0 \\
**Wetland** & 0 & 1 & 0 & 8 & n/a & n/a & 9 & 11.1 & 0.0 \\
**Total** & 6 & 8 & 4 & 8 & n/a & n/a & 26 & & \\ \hline \multicolumn{8}{c}{**1993**} \\ \hline
**Unclassified** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & & \\
**Forest** & 6 & 0 & 1 & 0 & 0 & 0 & 7 & 14.3 & 0.0 \\
**Grass** & 0 & 8 & 0 & 0 & 0 & 0 & 8 & 0.0 & 0.0 \\
**Water** & 0 & 0 & 3 & 0 & 0 & 0 & 3 & 0.0 & 25.0 \\
**Wetland** & 0 & 0 & 0 & 8 & 0 & 0 & 8 & 0.0 & 0.0 \\
**Cloud** & 0 & 0 & 0 & 0 & 3 & 0 & 3 & 0.0 & 0.0 \\
**Cloud Shadow** & 0 & 0 & 0 & 0 & 0 & 3 & 3 & 0.0 & 0.0 \\
**Total** & 6 & 8 & 4 & 8 & 3 & 3 & 32 & & \\ \hline \multicolumn{8}{c}{**2011**} \\ \hline
**Unclassified** & 0 & 0 & 0 & 0 & n/a & n/a & 0 & & \\
**Forest** & 6 & 0 & 1 & 0 & n/a & n/a & 7 & 14.3 & 0.0 \\
**Grass** & 0 & 8 & 0 & 0 & n/a & n/a & 8 & 0.0 & 0.0 \\
**Water** & 0 & 0 & 3 & 0 & n/a & n/a & 3 & 0.0 & 25.0 \\
**Wetland** & 0 & 0 & 0 & 8 & n/a & n/a & 8 & 0.0 & 0.0 \\
**Total** & 6 & 8 & 4 & 8 & n/a & n/a & 26 & & \\ \hline \multicolumn{8}{c}{**2019**} \\ \hline
**Unclassified** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & & \\
**Forest** & 6 & 0 & 0 & 0 & 0 & 0 & 6 & 0.0 & 0.0 \\
**Grass** & 0 & 7 & 2 & 0 & 0 & 0 & 9 & 22.2 & 12.5 \\
**Water** & 0 & 0 & 2 & 0 & 0 & 0 & 2 & 0.0 & 50.0 \\
**Wetland** & 0 & 1 & 0 & 8 & 2 & 0 & 11 & 27.3 & 0.0 \\
**Cloud** & 0 & 0 & 0 & 0 & 4 & 0 & 4 & 0.0 & 33.3 \\
**Cloud Shadow** & 0 & 0 & 0 & 0 & 0 & 4 & 4 & 0.0 & 0.0 \\
**Total** & 6 & 8 & 4 & 8 & 6 & 4 & 36 & & \\ \hline \multicolumn{8}{c}{1975 Kappa Coefficient: 0.8402 Overall Accuracy: 88.5%; 1993 Kappa Coefficient: 0.9612 Overall Accuracy: 96.9%;} \\ \hline \multicolumn{8}{c}{2011 Kappa Coefficient: 0.9474 Overall Accuracy: 96.2%; 2019 Kappa Coefficient: 0.8289 Overall Accuracy: 86.1%} \\ \hline \hline \end{tabular}
\end{table}