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Information Technology and Management Science
Article . 2015 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions

Authors: Stepchenko, Arthur; Chizhov, Jurij;

Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions

Abstract

Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.

Country
Latvia
Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Earth observation Index (economics) Markov chain Climate change Normalized Difference Vegetation Index Variable (computer science) Statistics medicine medicine.symptom Time series Vegetation (pathology) Remote sensing

Library of Congress Subject Headings: lcsh:T58.5-58.64 lcsh:Information technology

Keywords

Markov chains, NDVI, Forecasting; Markov chains; NDVI, Forecasting

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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