## 3 Results
### Land Cover Classification and Change Trajectories
#### 3.1.2 Change Trajectories

Table 1: Error matrices for Support Vector Machine classification results based on Landsat imagery and other data for 1975, 1993, 2011 and 2019. The 1975 and 2011 images were completely cloud-free. Figure 5: Maps showing the SVM land cover classification after reclassifying pixels as “Woody” or “Non-Woody”: (**a**) 1975; (**b**) 1993; (**c**) 2011; (**d**) 2019. White pixels represent cloud and cloud shadow, which were reclassified as no data. Figure 6: Overall change trajectory map using all four (1975, 1993, 2011, 2019) land cover classifications. Woody is “W” and non-woody is “N.” White pixels represent no data. While the overall change trajectory map shows a more holistic view of change in the time-series, showing only the conversions between successive imagery dates allows for highlighting the timing of certain changes. Figure 7 shows three change trajectories which highlight woody and non-woody conversions between adjacent imagery dates. These maps make the timing of certain conversions much clearer. The conversions between 1975 and 1993 are perhaps the most distinct, with the south and southwest portions of PCNP undergoing widespread woody conversion (colored in yellow) along the coast. Non-woody conversion (green) occurred in the east in the transition from forest to wetland, indicating that these pixels may have converted from forest to wetland. Table 3 shows that 6.6% of all pixels converted to woody vegetation between 1975 and 1993, which is the highest rate of woody conversion between all four imagery dates. The 2.2% conversion to non-woody land cover between 1975 and 1993 is the lowest of the three change trajectories. Between 1993 and 2011, more than 95% of all pixels underwent no change and 3% converted to non-woody. Spatially, most of this change occurred along the coast, but there are also non-woody conversions within the grassland, wetland, and closed forest, especially in transitional areas. The conversions that occurred between 2011 and 2019 were mostly non-woody and appear to be in areas that had already undergone conversion at other points in the time-series. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Trajectory** & **1975** & **1993** & **2011** & **2019** & **Pixel Count** & **\% Change** \\ \hline
**WWWW** & W & W & W & W & 41296 & 24.4 \\
**WWWN** & N & W & W & W & 3546 & 2.1 \\
**WWNW** & W & N & W & W & 398 & 0.2 \\
**WWNN** & N & N & W & W & 423 & 0.2 \\
**WNWW** & W & W & N & W & 398 & 0.2 \\
**WNWN** & N & W & N & W & 418 & 0.2 \\
**WNNW** & W & N & N & W & 258 & 0.2 \\
**WNNN** & N & N & N & W & 2134 & 1.3 \\
**NWWW** & W & W & W & N & 2060 & 1.2 \\
**NWWN** & N & W & W & N & 4231 & 2.5 \\
**NWNW** & W & N & W & N & 181 & 0.1 \\
**NWNN** & N & N & W & N & 1181 & 0.7 \\
**NNWW** & W & W & N & N & 1172 & 0.7 \\
**NNWN** & N & W & N & N & 3091 & 1.8 \\
**NNNN** & W & N & N & N & 2988 & 1.8 \\
**NNNN** & N & N & N & N & 105445 & 62.3 \\ \hline
**Total** & & & & & 169220 & 100.0 \\ \hline \end{tabular}
\end{table} 
Table 2: SVM classifier change trajectory pixel counts and percent change with explanation of each change trajectory. Woody is “W” and non-woody is “N”.