Optimal selection of wavelet transform parameters for spatio-temporal analysis based on non-stationary NDVI MODIS time series in Mediterranean region

Abstract

The multi-resolution analysis based on wavelet transform (WT) has widely proved its effectiveness for the analysis of non-stationary time series (TS) in the remote sensing field. However, there are three essential parameters affecting wavelet-aided data in vegetation dynamics detection. This study proposes a new methodology that serves for the proper selection of the most critical parameters to carry out an optimal analysis for vegetation seasonal and long-term dynamics, specifically adapted to NDVI TS. The specific objectives are: (1) to simulate a TS dataset to understand the best WT parameter performance for trend and seasonal changes detection, and (2) to assess the impact of those WT parameters using NDVI MODIS TS in the Mediterranean area along the period 2001–2019. Different types of trends and WT were considered (i.e., Discrete WT (DWT) and Maximal Overlap DWT (MODWT)) in the simulated dataset. The best WT parameters were calculated based on an iterative algorithm that computes the trend and seasonal components using the optimal level of decomposition for each TS and mother wavelet (MW). Then, the findings obtained from the simulated data were exploited using Earth observation data. In the step, the Theil–Sen’s slope (Q) derived for the best MWs was calculated to provide the trend magnitude and direction. The z-score and energy to Shannon entropy ratio (R(S)) were computed to select the most adequate MW based on pixel characterization. On one hand, the simulated results proved the Multi-resolution analysis DWT (MRA-DWT) as the most optimal wavelet type. Level 5 of decomposition was obtained in 99% and 72% of simulated TS according to the trend and seasonal analysis, respectively. Symlet family gave the optimal results in 92% (trend) and 64% (seasonal) of simulated TS. On the other hand, based on R(S), sym12 MW from Symlet family showed the most dominant wavelet for forest, savanna and mosaic land covers, followed by db14 MW from Daubechies family which was dominated in cropland. From more practical adaptation, sym12 was revealed as an effective solution for all the pixels in the Mediterranean area with a relative error lower than 15%. The vegetation change analysis in the Mediterranean area showed a relationship between QNDVI and precipitation in almost 37% of the area, being Turkey, Greece and North Africa the regions with a high correspondence with precipitation. Around 5% of the study area showed to be possibly affected by land degradation processes.