Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

By M. Forkel, N. Carvalhais, Jan Verbesselt, Miguel D. Mahecha, C. Neigh, Markus Reichstein. Remote Sensing, 2013

502 citations recorded by the source corpus.

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends

Subfields: Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/46749581923691b0119a66e99f0a67ce2de17f71

Citations in the corpus