Seasonal shifts in satellite time series portend vegetation state change – verification using long-term data in an arid grassland ecosyste

Abstract:

The frequency and severity of drought is forecasted to increase in the 21^st century. The need to understand how managed ecosystems respond to climate is intensified by uncertainty associated with knowing when, where, and how long drought conditions will manifest. Analysis of broad scale patterns in ecosystem productivity can inform our understanding of ecosystem dynamics and improve predictions for responses to climate extremes. We leveraged observations of plant biomass at a long-term ecological research site in southern New Mexico to verify the use of NDVI time-series as a proxy for landscape productivity from 13 years of MODIS data. The period between 2000 and 2013 encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) that yielded decreases followed by increases in biomass with a restructuring of plant communities. We decomposed patterns derived from the 250m MODIS NDVI product over this period into contributions from the long-term trend, seasonal cycle, and unexplained variance using the Breaks For Additive Seasonal and Trend (BFAST) model to identify significant deviations from the modelled trend and seasonal components. Observed breakpoints in NDVI trend and seasonal components were verified with field estimates of species-specific biomass data at 15 sites. We found that breaks in the trend reflected large changes in mean biomass and seasonal breaks reflected changes in dominance of perennial grasses, shrubs, and/or annual grasses. The BFAST method proved useful for detecting observed state changes in this arid ecosystem. The ability to distinguish between long-term phenological change and temporal variability is strongly needed in water-limited ecosystems with high inter-annual variability in primary productivity. We demonstrate that time-series analysis of NDVI data holds potential for monitoring landscape condition at spatial scales needed to generate indicators for ecosystem responses to changing climate.