Abstract
Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)- vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001–2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982–2010) data found that for MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.
Highlights
In southern Africa, long-term changes in the savanna ecosystem productivity and structure are believed to be driven by a combination of biotic and abiotic drivers [1,2,3,4], potentially representing irreversible landscape degradation [5]
While the correlation and trends (Figure 2) are similar, they exhibit differences during the peaks resulting from differences in sensors, time points within the month and the frequency of measures (MODIS being more frequent, with often daily observations being integrated into the monthly measures)
The Dynamic Factor Analysis (DFA) model performance for Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Advanced Very High Resolution Radiometer (AVHRR) NDVI3g was compared for the same suite of candidate explanatory (independent) variables (CEVs) and the same model type (multi-linear regression used in a previous study [61]: precipitation (P), mean temperature (T), maximum temperature (M), fire (F), soil moisture (S) and potential evapotranspiration (E)
Summary
In southern Africa, long-term changes in the savanna ecosystem productivity and structure are believed to be driven by a combination of biotic (including human) and abiotic drivers [1,2,3,4], potentially representing irreversible landscape degradation [5]. Changes labeled “degradation” in the southern African savanna [6] are usually referring to the process of shrub encroachment (a shift from grass- and tree-dominated landscapes to less biologically productive landscapes dominated by scrub), and has been well documented throughout southern Africa [7,8,9] Given this trend of potential degradation, understanding the temporal and spatial changes in vegetation dynamics and identifying the dominant drivers responsible for these documented vegetation transitions is critically important for management, in the context of increasing current climate variability and the increased future variability and climate change predicted for this region [10]. Simultaneously identifying these spatial and temporal patterns of multiple, dynamic explanatory variables of importance, while accounting for unexplained, but shared, temporally varying trends on this longer time scale, is necessary to help improve our understanding of the controlling factors of vegetation response
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