Abstract
BackgroundUnderstanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients.Methodology/Principal FindingsWe applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique to ten years of monthly remote sensing data (MODIS-derived normalized difference vegetation index, NDVI) and a suite of environmental covariates: precipitation, mean and maximum temperature, soil moisture, relative humidity, fire and potential evapotranspiration. Monthly NDVI was described by cyclic seasonal variation with distinct spatiotemporal patterns in different physiographic regions. Results support existing work emphasizing the importance of precipitation, soil moisture and fire on NDVI, but also reveal overlooked effects of temperature and evapotranspiration, particularly in regions with higher mean annual precipitation. Critically, spatial distributions of the weights of environmental covariates point to a transition in the importance of precipitation and soil moisture (strongest in grass-dominated regions with precipitation<750 mm) to fire, potential evapotranspiration, and temperature (strongest in tree-dominated regions with precipitation>950 mm).Conclusions/SignificanceWe quantified the combined spatiotemporal effects of an available suite of environmental drivers on NDVI across a large and diverse savanna region. The analysis supports known drivers of savanna vegetation but also uncovers important roles of temperature and evapotranspiration. Results highlight the utility of applying the DFA approach to remote sensing products for regional analyses of landscape change in the context of global environmental change. With the dramatic increase in global change research, this methodology augurs well for further development and application of spatially explicit time series modeling to studies at the intersection of ecology and remote sensing.
Highlights
Understanding the drivers of large-scale vegetation change is critical to managing landscapes for the mutual benefit of human and natural systems
Degradation of southern African savanna [9] is usually represented on the landscape as a shift from grass- and tree-dominated landscapes to less biologically productive ones dominated by scrub [10,11,12]
While rising and declining phases of normalized difference vegetation index (NDVI) are generally parallel across different precipitation polygons, vegetation dynamics differ spatially and temporally during periods of NDVI minima and maxima (Fig. 2)
Summary
Understanding the drivers of large-scale vegetation change is critical to managing landscapes for the mutual benefit of human and natural systems. Degradation of southern African savanna [9] is usually represented on the landscape as a shift from grass- and tree-dominated landscapes to less biologically productive ones dominated by scrub [10,11,12]. Given this trend, understanding the spatial and temporal dynamics of vegetation change and identifying the main drivers of vegetation transition are critically important for land management, in light of significant climate variability and possible directional climate change expected in the region [13,14]. This study aimed to improve our understanding of the role, magnitude and spatial distribution of the key environmental factors driving vegetation change in southern African savanna, and how they vary across physiographic gradients
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