Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model

Abstract:

Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to the six kinds of main land covers’ vegetation index time series data of Northeast China region.

• Periodical: Advanced Materials Research (Volumes 955-959)
• Pages: 863-868
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.955-959.863
• Online since: June 2014
• Authors: Rong Yu*, Bo Feng Cai, Xiang Qin Su, Ya Zi He, Jing Yang
• Keywords: Autoregressive Moving Average Models, Error, Time Series, Vegetation Index

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