Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics

Abstract

The availability of long-term time series (TS) derived from remote sensing (RS) images is favorable for the analysis of vegetation variation and dynamics. However, the choice of appropriate methods is a challenging task. This article presented an experimental comparison of four methods widely used for the detection of long-term trend and seasonal changes of TS, with a case study in north-western Tunisia. The four methods are the Ensemble Empirical Mode Decomposition (EEMD), Multi-Resolution Analysis-Wavelet transform (MRA-WT), Breaks for Additive Season and Trend (BFAST), and Detecting Breakpoints and Estimating Segments in Trend (DBEST). Their efficiencies were compared by analysing Normalized Difference Vegetation Index (NDVI) TS from 2001 to 2017 in the study area, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The variations of long-term NDVI trends were analysed using non-parametric statistical tests. Results indicated that MRA-WT gave efficient results for both trend and seasonal changes, especially in forest area. Moreover, it exhibited the fastest efficiency in terms of time of execution and thus recommended for detecting detailed features (such as forest fire detection). DBEST also showed a good performance for trend detection in forest area as MRA-WT, however, it was more constrained to a longer computational time of execution. BFAST and EEMD exhibited a better performance in bare soil and cropland areas, and the latter can be taken as an appropriate and fast alternative for a general long-term trend overview with long TS.