Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia
The main goal of this paper is to analyze the performance of two deep learning models Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) network for non-stationary Normalized Difference Vegetation Index (NDVI) time-series prediction. Both methods have provided good performances in the different time series. The BiLSTM has shown the best agreement with the lowest root mean square error (RMSE) and the highest Pearson correlation coefficient (R) of 0.034 and 0.93, respectively.
An Improved Forecasting Model from Satellite Imagery Based on Optimum Wavelet Bases and Adam Optimized LSTM Methods
This paper proposes a new hybrid approach I-WT-LSTM (i.e., Improved Wavelet Long Short-Term Memory (LSTM) Model) for forecasting non-stationary time series (TS) from satellite imagery. The proposed approach consists of two steps: The first step aims at decomposing TS using Multi-Resolution Analysis wavelet (MRA-WT) into inter-and intra-annual components using 18 different mother wavelets (MW). Then, the energy to Shannon entropy ratio criterion is calculated to select the best MW. The second step is based on the LSTM model using Adam optimizer to predict the future. The proposed approach is tested using TS derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2001 t…
Trend Analysis Using Discrete Wavelet Transform (DWT) for Non-stationary NDVI Time Series in Tunisia
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 f…
Comparative study of three satellite image time-series decomposition methods for vegetation change detection
International audience; Satellite image time-series (SITS) methods have contributed notably to detection of global change over the last decades, for instance by tracking vegetation changes. Compared with multi-temporal change detection methods, temporally highly resolved SITS methods provide more information in a single analysis, for instance on the type and consistency of change. In particular, SITS decomposition methods show a great potential in extracting various components from non-stationary time series, which allows for an improved interpretation of the temporal variability. Even though many case studies have applied SITS decomposition methods, a systematic comparison of common algori…