Authors: Ali Ben Abbes; Mohamed Farah; Imed Riadh Farah; Vincent Barra
Addresses: SIIVT-RIADI Laboratory, National School of Computer Science, University of Manouba, Tunisia ' SIIVT-RIADI Laboratory, National School of Computer Science, University of Manouba, Tunisia ' SIIVT-RIADI Laboratory, National School of Computer Science, University of Manouba, Tunisia ' LIMOS Laboratory, University of Blaise Pascal, France
Abstract: Nowadays, vegetation monitoring using remotely sensed data is an important far-reaching real-world issue. The main purpose of this study is to build a triplet Markov chain (TMC) to model and analyse vegetation dynamics on large-scales using non-stationary normalised difference vegetation index (NDVI) time series. TMC is a generalisation of hidden Markov models (HMMs), which have been widely used to represent satellite time series images but which they proved to be inefficient for non-stationary data. The TMC model proposed in this paper overcomes this limit by adding an auxiliary process which allows modelling non-stationarity. In order to assess the performance of the proposed model, experimentation is carried out using moderate resolution imaging spectroradiometer (MODIS) NDVI time series of the north-western region of Tunisia. The TMC model is compared to standard HMM and seasonal auto regressive integrated moving average model (SARIMA) and proved to achieve the best performance with an overall accuracy prediction rate of 92.8% and a kappa coefficient of 0.885.
Keywords: NDVI time series; vegetation dynamics; triplet Markov chain; TMC; hidden Markov model; HMM; non-stationarity; remote sensing.
International Journal of Information and Decision Sciences, 2019 Vol.11 No.2, pp.163 - 179
Received: 24 Apr 2017
Accepted: 23 Nov 2017
Published online: 22 Jul 2019 *