Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data

Abstract

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns.

Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

“…We assume that the peak of greenness at stand basal area 10 m 2 ha −1 is explained by the dense shrub cover in this regeneration phase. Our results imply that browning trends (de Jong et al, 2013; Higginbottom & Symeonakis, 2020), are not necessarily only a sign of human disturbance (Murillo‐Sandoval et al, 2017), or drought (Anyamba & Tucker, 2005), but in some cases could also be a sign of increasing forest biomass and ecosystem recovery. Furthermore, all greening trends may not be indicative of an increase in biomass.…”

Valuation of Ecosystem Services in South Africa, 2001–2019

“…As a result, different provinces show different trajectories of change and with respect to specific land-cover types (Table 3). Values of NPP for the different years under analysis in kg C m −2 are presented in Figure 6 as an aggregated indicator for overall land degradation, as has been used in previous studies (e.g., [28,39,45,48,62]). It is notable that, at this national scale, similar patterns are seen in each time slice, reflecting the influence of summer rainfall (eastern side of South Africa), winter rainfall (western fringe of South Africa) and proximity to oceanic moisture sources [63,64].…”

“…Remote sensing methods of mapping regional vegetation change and land degradation have been widely used across southern Africa for examining spatial and temporal variations in semi-natural ecosystems that are important for biodiversity and ecosystem services (e.g., [22,[26][27][28]44,45]). Specifically, NDVI values from time series of remote sensing data can be used to identify which regions are undergoing a decrease in NPP values [46,47] and are thus experiencing degradation, compared to those regions where NPP values are increasing (e.g., [48]). This in turn has implications for identifying sustainable development strategies for regions already experiencing climate stresses that impact on ecosystem vigor and ecosystem service provision [49][50][51][52].…”

Place-Based Analysis of Satellite Time Series Shows Opposing Land Change Patterns in the Copperbelt Region of Zambia

“…The authors in [33] used MODIS and Landsat both to analyze degradation processes in South West Niger. The authors in [34] identified shifts in vegetation productivity in African savannahs by applying BFAST on GIMMS NDVI. The authors in [6] used BFAST on the Landsat-derived moisture index to detect vegetation degradation in the Kavango-Zambezi Transfrontier Conservation Area (KAZA).…”