2.5.3.2 Tipping points in managed vegetation systems

Remote sensing datasets also have potential applications in the detection of tipping points in managed vegetation systems like pastoral systems (Swingedouw et al., 2020). For instance, (Fernandez-Gimenez et al., 2017) have used Earth observation data to monitor the impact of increased livestock pressure on grazing lands as well as potential shifts in crop density and vegetation types (Figure 2.5.5).

Figure: 2.5.5
Figure: 2.5.5 Trends in five-year moving window autocorrelation. Where original values indicated a significant trend, data were detrended before analysis. Strength of correlations is indicated by Kendall’s Tau where significant. * indicates p < 0.05, ** indicates p < 0.001. Reproduced from Fernandez-Gimenez et al., (2017).

With normalised difference vegetation index (NDVI) data derived from the Advanced Very-High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) missions, the authors detected higher autocorrelation and variance in variability of forage production, which could be interpreted as a potential tipping point in rangeland conditions. Implications for pastoral communities can be significant as grazing lands transition to a more degraded ecosystem that cannot sustain their livelihood. 

In tropical forest settings, too, remote sensing products have been used to identify potential critical transitions. (Verbesselt et al., 2016) used MODIS NDVI and RADAR Vegetation Optical Depth (VOD) monthly data time series of evergreen tropical forests across Africa, South East Asia and South America to detect declining rates of recovery through temporal autocorrelation. The results provide practical thresholds to anticipate collapse of tropical forests facing drought and high temperatures. (See also Chapter 1.6 for further analysis outlining how proximity of landscapes to human activity leads to lower resilience.)

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