Submitted to: Ecological Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/8/2017
Publication Date: 7/1/2017
Publication URL: http://handle.nal.usda.gov/10113/5863766
Citation: Browning, D.M., Maynard, J.J., Karl, J.W., Peters, D.C. 2017. Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem. Ecological Applications. 27:1677-1693.
Interpretive Summary: The need for reliable tools capable of identifying vegetation transitions that influence ecosystem function has never been greater. We assessed long-term and seasonal vegetation change metrics derived from satellite imagery using the BFAST algorithm with independent field-measured biomass and found success in 93% of cases relative to the long-term trend and 84% of cases relative to the seasonal cycle reflecting changes in plant community phenology. Time series remote sensing methods like BFAST have potential to greatly benefit landscape research and management by providing spatially-explicit indicators of change across broad extents at time steps commensurate with the ecological processes driving them. In addition this method can put traditionally infrequent field measurements in the context of long-term patterns and seasonal cycles. For example, livestock seasons of use and stocking rates on U.S. federal lands are typically determined based on field observations of rangeland condition and available forage assessed at 5 to 10 year intervals. Implementing open-source code and publicly available satellite data, federal, state, and academic researchers as well as federal and state land managers have the ability to implement the principles, methods and findings of this study for an array of management programs and monitoring efforts.
Technical Abstract: Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of the extreme events will be manifest in the ecosystem. In water-limited ecosystems with high inter-annual variability in rainfall, it is important to be able to distinguish responses that result from seasonal fluctuations from long-term increases or decreases in rainfall. A tool that successfully distinguishes seasonal from directional biomass responses in the long-term trend would allow land managers to make informed decisions during extreme climatic events. We leveraged long-term observations (2000-2013) of quadrat-level plant biomass at multiple locations across a semi-arid landscape in southern New Mexico to verify the use of NDVI time-series derived from 250-m MODIS data as a proxy for changes in above-ground productivity. This time period encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) followed by subsequent drought years (2011 through 2013) that resulted in a restructuring of plant community composition in some locations. Our objective was to decompose vegetation patterns derived from MODIS NDVI over this period into contributions from: (1) the long-term trend, (2) seasonal cycle, and (3) unexplained variance using the Breaks For Additive Season and Trend (BFAST) model. BFAST breakpoints in NDVI trend and seasonal components were verified with field-estimated biomass at 15 sites ranging in species diversity, vegetation cover, and soil properties. We found that 42 of 45 breaks in NDVI trend reflected large changes in mean biomass and 16 of 19 seasonal breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time-series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability.