## 2 Materials and Methods
### Data and Image Analysis  
#### 2.3.1 Remotely Sensed Data  
This study used remotely sensed Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery to create land cover classification and change trajectories and carry out Normalized Difference Vegetation Index (NDVI) analysis on freely available imagery from four years in the dry season: 1975, 1993, 2011 and 2019. Landsat imagery was chosen for this analysis for its relatively fine spatial resolution and its long-term database of publicly available satellite imagery. All Landsat imagery was obtained from the United States Geological Survey Earth Explorer and came atmospherically and geometrically corrected [39]. The images from March 1975 and March 2011 are completely cloud-free, while those from March 1993 and January 2019 have some clouds and cloud shadows, which were masked out. The MSS data (bands 1-4), obtained by Landsat-2 in 1975 at 80 m spatial resolution, were resampled to 30 m pixel size to align with the resolution of the TM (bands 1-7) and OLI/TIRS (bands 1-8, 10, 11) imagery from Landsat 4, 5 and 8 (1993, 2011 and 2019). All raw image bands (except OLI band 9) were subset to a shapefile of PCNP obtained from Protected Planet and were used in the classification analysis. Additionally, the three other layers outlined in Section 2.3.2 below were created or obtained to be used as variables in the SVM classification.  
3.2 Other Data: Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), and Soil Type  
_NDVI_ is a commonly used vegetation index that has a variety of applications, but its inherent use is to quantify and discriminate vegetation characteristics [40]. The calculation for _NDVI_ is:  
\[NDVI=\frac{NIR\ -\ Red}{NIR+Red}\]  
where _NIR_ is near infrared reflectance and _Red_ is red reflectance in the electromagnetic spectrum. _NDVI_ is an indicator of vegetation productivity and degradation [40; 41]. Using band math tools in the Environment for Visualizing Images (ENVI) 5.5 software, four NDVI maps, one for each image in the time series, was created from the _NIR_ and red bands to be used as an input variable (Figure 3).  
As was previously stated, edaphic factors are key to understanding the distribution of woody and non-woody vegetation in the lowland savannas of Belize. Nutrient-poor soils in the Belize lowlands stem from inundations in the wet season due to poor drainage from subsurface clay and intense desiccation in the dry season [35]. Higher elevations and variable slopes can result in locally improved drainage and the opportunity for soil development and woody vegetation establishment. To encompass the importance of topography in the classification, a 30 m digital elevation model (DEM) from the USGS Shuttle Radar Topography Mission (SRTM) was included (Figure 3). The DEM was acquired from the USGS Earth Explorer website and the study area was subset in ArcMap 10.7.1 using the PCNP shapefile.  
While the subsurface clay in PCNP affects vegetation through seasonal inundations, the soil types that overlay the clay also affect vegetation distribution. Alluvial deposits along rivers and streams, for example, are nutrient rich and promote woody growth compared to the Maya Mountain outwash that underly the grasslands [28]. To incorporate the importance of soil type in the classification, a Belize soils map created by the Selva Maya consortium (Figure 3) was included as a variable [42].