Hidden Markov models applied to vegetation dynamics analysis using satellite remote sensing

Details hidden Markov models (HMM) with respect to their ability to represent time series of remotely sensed data as well as to analyze vegetation dynamics at large scales. The present approach is shown to be a powerful way to classify and extract various dynamics parameters as well as to detect phenological anomalies. The methodology is applied and validated using the Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) time series. The model is then used to determine vegetation active cycle and the length of the growing season in the West African savanna