Abstract
In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI) is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.
Highlights
Remote sensing has shown great opportunities in vegetation mapping and monitoring over the past decades
The normalised difference vegetation index (NDVI) is an indicator that describes the amount of chlorophyll and shows the relative density and health of vegetation; it is an important variable for vegetation forecasting
The aim of the experiment described in this paper is to examine accuracy of the discrete time, continuous state m-th order Markov chains combined with feature selection method – stepwise regression and feature extraction method – principal component analysis as data pre-processing methods in the NDVI time series forecasting problem
Summary
Remote sensing has shown great opportunities in vegetation mapping and monitoring over the past decades. Markov chains are often used for capturing dynamic behaviour with a large stochastic component [5] They are effective in modelling time series. If a Markov chain can model the time series accurately, good predictions and optimal planning in a decision process can be made [6]. In their previous research [3], the authors used discrete time, discrete state first order Markov chains in order to obtain shortterm forecasts of the NDVI time series. The aim of the experiment described in this paper is to examine accuracy of the discrete time, continuous state m-th order Markov chains combined with feature selection method – stepwise regression and feature extraction method – principal component analysis as data pre-processing methods in the NDVI time series forecasting problem
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
