## 4 Discussion  
This study demonstrates a novel application of a non-parametric supervised classification technique in a heterogeneous neotropical savanna landscape. SVM classifiers are especially useful when training data is limited and boundaries between classes are not abrupt, which are both true for this study. The SVM classification of the heterogenous landscape in PCNP is high-performing and performs similarly to other studies that use SVM to classify savanna vegetation [19; 45; 46]. This research adds to the growing recognition of the robustness of SVM classifiers in savannas and other heterogenous landscapes. The use of SVM needs to be explored further in savanna landscapes, especially in other small Central American savannas that are underrepresented in regional and global studies. Understanding conversions to and from woody and non-woody vegetation is a simple, but important way for researchers and land managers to compartmentalize and interpret change occurring on a landscape. This streamlined view of vegetation change is especially useful in savanna landscapes, which contain grass and trees in alternative stable states that are constantly experiencing change driven \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trajectory** & \multicolumn{3}{c}{**SVM Classifier \% Change**} \\ \hline  & **1975–1993** & **1993–2011** & **2011–2019** \\ \hline
**Non-Woody to Woody** & 6.6 & 1.3 & 1.9 \\
**No Change** & 91.2 & 95.7 & 93.3 \\
**Woody to Non-Woody** & 2.2 & 3.0 & 4.8 \\ \hline
**Total** & 100.0 & 100.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Change trajectory percent changes between successive imagery dates. Figure 7: Change trajectory maps for successive imagery dates: (**a**) 1975 and 1993; (**b**) 1993 and 2011; (**c**) 2011 and 2019. White pixels represent no data. by disturbances and feedbacks [6]. Understanding where land conversions are occurring in savanna and adjacent landscapes is crucial so that managers can investigate the source of change and determine if planning and intervention is needed or if the change is part of the overall function of the ecosystem. Our analysis reveals a relatively stable landscape in PCNP, but with spatially concentrated areas that are prone to vegetation conversion. The overall change trajectory (Figure 6, Table 2) does not reveal evidence of widespread woody or non-woody conversion in PCNP from 1975 to 2019. This finding is notable because it differs from studies of savanna vegetation dynamics on other continents. In sub-Saharan Africa, remote sensing studies have found evidence of woody encroachment in savannas [12; 20]. The authors of [47] reviewed 16 studies that present evidence of woody encroachment and supplement those findings with continental-scale remote sensing analysis. Moreover, at a continental scale, a connection between mean annual precipitation and woody encroachment was discovered by the authors of [48], who also suggested that future increases of water availability in African Savannas could lead to enhanced woody conversion. In a protected area in northern Australia, researchers found forest expansion and grassland contraction in a lowland Eucalyptus savanna [49]. Further evidence of tropical rain forest expansion into savanna was found across multiple sites in northeast Australia, most likely attributed to increased atmospheric CO\({}_{2}\)[50]. In South America, it has been suggested that the expansion of riparian forests within the Brazilian Cerrado savannas has been occurring for thousands of years and will continue to do so with modern climate change [51]. Based on an extensive review covering 112 savanna sites, the authors of [5] conclude that widespread woody encroachment is occurring in savannas across Africa, Australia, and South America; however, regionally specific characteristics of plant biology, land use, disturbance, moisture availability, soil nutrients, etc. determine encroachment susceptibility for the landscape.