A Tale of Grass and Trees: Characterizing Vegetation Change in Payne's Creek National Park, Belize from 1975 to 2019  
[PERSON]  
1 Department of Geography, University of Tennessee, 1000 Philip Fulmer Way, Knoxville, TN 37996-0925, USA; [EMAIL_ADDRESS]  
27 May 2020222 June 202022 June 202022 June 202022 June 202022 June 202022 June 20202  
widely classified as savanna (or associated ecoregions) in publicly available datasets, such as the World Wildlife Fund ecoregions [9]. Global studies provide valuable information on the broad distribution, drivers, and health of savanna vegetation on a continental scale. While vast savannas are widely studied and are included in global studies, smaller pockets of savanna have received less attention. There is a lack of understanding of the fine-scale drivers of the heterogeneous nature of savanna vegetation [10]. Vegetation and soil studies in the expansive Brazilian Cerrado have concluded that fine-scale interactions between vegetation and available water [10], and vegetation and soils [11], are key to understanding neotropical savanna vegetation dynamics. Since knowledge of local dynamics is crucial for understanding the larger savanna landscape, investigating vegetation drivers in smaller pockets of savanna can also contribute to the larger understanding of savannas. Smaller savannas are also key components in the preservation of biodiversity and contributions to the economy via tourism [12]. The exclusion of relatively small savannas from global or continental remote sensing studies can be partly attributed to the use of sensors with large spatial resolutions (e.g., Advanced Very High Resolution Radiometer with 4 km pixel size) that contain much intra-pixel variability. For small study areas that focus on localized savanna vegetation dynamics, finer spatial resolution imagery is necessary to sufficiently characterize the area and the heterogeneous vegetation gradients that are characteristic of savanna landscapes. Even when using imagery from sensors with finer spatial resolution, defining the limits of vegetation in savannas is challenging due to the randomness and spatial heterogeneity of the landscape [13]. Another challenge is that savanna vegetation exists in varying compositions and gradients, and one of the most common ways to assess savanna vegetation with remote sensing is to create discrete land cover classes using an unsupervised or supervised technique [14]. These techniques use a set of variables (inputs) to break each pixel in an image into distinct classes (output). Discrete classifications are simple to understand but prevent variation within classes, which, especially for savannas, can result in an inaccurate representation of the continuous and varied transitions between vegetation. The complicated nature of applying classification techniques to remotely sensed imagery in small savanna landscapes suggests that statistically robust methods should be used. Many common supervised classification techniques, such as Maximum Likelihood, function on the assumption that the statistics for each class are normally distributed and then calculate the probability that a pixel belongs to a specific class [15; 16]. Since savanna landscapes often do not fit this definition of normality, such a classification technique is inappropriate. One method to improve supervised classification performance in savanna landscapes is to use non-parametric machine-learning algorithms that do not make assumptions about the distribution of the data, such as Support Vector Machine (SVM) or Random Forest [17; 18]. Both SVM and Random Forest have been used to generate high-performing vegetation classifications in some African savanna landscapes [12; 19; 20]. Savannas in the neotropics cover approximately two million km\({}^{2}\), or 40% of all land area in the ecozone [21; 22]. Of these, the South American savannas (e.g., Brazilian \"Cerrado\" and nearby Bolivian \"Llanos\", Venezuelan-Colombian \"Llanos\", etc.)