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 algorithms is still missing. In this study, the seasonal trend loess (STL), breaks for additive season and trend (BFAST) and multi-resolution analysis-wavelet transform (MRA-WT) were explored in order to evaluate their performance in modelling, monitoring and detecting land-cover changes with pronounced seasonal variations from simulated normal difference vegetation index time series. The selected methods have all proven their ability to characterize the non-stationary vegetation dynamics along with different physical processes driving the vegetation dynamics. Our results indicated that BFAST is the most accurate method for the examined simulated dataset in terms of RMSE, whereas MRA-WT showed a great potential for the extraction of multi-level vegetation dynamics. Considering the computational efficiency, both STL and MRA-WT outperformed BFAST.

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<p>Normalized Difference Vegetation Index (NDVI) serves as a significant reference for crop health monitoring. NDVI time series forecasting is a critical issue because of the importance of the involving fields, e.g., food scarcity, climate changes and biodiversity. Therefore, several forecasting models have been suggested and implemented in the literature. Herein, we propose a combination of forecasts using seasonally fitted probability functions changing weights. Contrary to commonly suggested combination models, this one does not rely on overall error measures and/or features, but on time slots similarities between probability density function (PDF) of real observations and forecasts. It is validated with 18 years MOD13Q1 NDVI time series describing a cereal canopy area that belongs to the northwestern of Tunisia. Additionally, the chosen forecasting models are Box Jenkins and Neural Network model. The forecasting accuracy was assessed using the root mean square error (RMSE). According to the results, each season had a different best-fit probability distribution function. Overall, these latter are: Gamma, Beta, Weillbul, and Extreme Generalised Value (EGV). Moreover, the suggested model has shown better forecasting accuracy than individual models, hybrid models and commonly used combining tool (RMSE respectively, 0.003, 0.45, 0.35, 0.38). Interestingly, another seasonally varying weights were determined based on the normal distribution. But, our suggested model showed better forecasting accuracy than this latter (RMSE of normally distributed changing weights= 0.30).</p><div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>

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