## 1 Introduction  
Globally, drylands cover around 50% of the earth's terrestrial surface [1]. Of these drylands, savannas cover extensive portions of Africa, South America, and Australia. In southern Africa, over 55% of the landscape is classified as savanna [1, 2]. Savannas are a type of dryland, and therefore are water limited systems. They are traditionally defined as grasslands with scattered trees, but practically are defined as a heterogeneous landscape composed of a mix of grasses, shrubs, and trees [1, 3, 4]. Savannas play a key role in the global carbon cycle and global net primary production [5, 6, 7, 8, 9, 10]. Due to their importance and high levels of utilization, it is critical to understand savanna landscape patterns over time for management of these systems, especially into the future under climate change. The major drivers of savanna systems are precipitation, fire, herbivory, and humans [11; 12; 13; 14]. Parks and protected areas are used extensively across landscapes to conserve and protect the natural vegetation and wildlife populations. Countries across southern Africa employ varied management approaches within such protected areas, as regards fire, herbivory, and human usage. These management practices range from no fire management to regular prescribed burns, no animal population control to culling of herds to maintain ideal population sizes, and allowing extractive use of resources to not permitting any human use of natural resources [15]. As such, understanding the success of such different and varied protection regimes is key. Using remote sensing to monitor vegetation in savannas, while important, is notoriously difficult, as these landscapes are transitional with variable vegetation cover. Because a savanna can be any structure of vegetation in the gradient of grassland, shrubland, or woodland, certain continuous techniques have been developed to best study these systems over time. A significant study in the literature concerning drylands has developed a novel approach, utilizing continuous time series analyses, to show change in these systems over time [16]. Time series analysis of the normalized difference vegetation index (NDVI) is often used as a proxy for vegetation health and abundance [16; 17; 18; 19; 20; 21; 22]. Using such time series approaches, we can draw information about vegetation properties and ecological responses to change at a regional scale [23]. One result that has been produced in the literature is that of \"greening\". Using satellite remote sensing, global greening has been observed during the 21 st century by the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) [24]. Several studies have concluded that CO\({}_{2}\) fertilization is the largest contributing factor. Other factors include nitrogen levels, land use, temperature, and climate [25; 26; 27]. Precipitation is known as the predominant factor in shaping landscape pattern and productivity across southern Africa [9]. In areas with up to mean annual precipitation of 750 mm, grassland dominated systems are expected [28]. More mixed systems are expected for areas with precipitation between 750 and 950 mm. For areas with precipitation above 950 mm, trees become the more common vegetation type [9; 14; 28; 29]. Further, [PERSON] et al. (2013) and others found that the boundary between a mixed system and tree-dominated system is influenced by temperature [28]. Specifically, temperature and resulting decreases in soil moisture can adversely impact vegetation health and productivity in savanna systems [30]. Access to deeper ground water can also play a role in a tree's ability to exist on the landscape [31]. Population pressure and human utilization of the landscape are increasing, putting greater pressure on protected areas through land conversion (positive impact) and extractive use of resources (negative impact) [2; 32; 33].