6533b7dafe1ef96bd126f492

RESEARCH PRODUCT

Trend Analysis Using Discrete Wavelet Transform (DWT) for Non-stationary NDVI Time Series in Tunisia

Manel RhifImed Riadh FarahAli Ben AbbesBeatriz Martínez

subject

Discrete wavelet transformTrend analysisWaveletSeries (mathematics)StatisticsWavelet transformNormalized Difference Vegetation IndexEnergy (signal processing)MathematicsStatistical hypothesis testing

description

In this paper, the trends in non-stationary Normalized Difference Vegetation Index (NDVI) Time Series (TS) over different areas in Tunisia are analyzed by applying wavelet transform and statistical tests. In the first step, the Discrete Wavelet Transform (DWT) was applied on three different time series in order to detect changes. Therefore, the different parameters of DWT were tested. In fact, the level of decomposition was calculated. The Maximum Energy to Shannon Entropy Ratio Criterion (MEER) was then investigated to choose the more suitable mother wavelet. Finally, the Mann-Kendall test (MK) was calculated for the last approximation of components to identify the variation in trend. In fact, the Daubechies 7 gives the best MEER for two case studies (6.0218, 2.5892) and Daubechies has better results for the other region (0.2226).

yearjournalcountryeditionlanguage
2021-01-01
https://doi.org/10.1007/978-3-030-51210-1_294