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

Author

Viovy, Nicolas ; Saint, Gilbert

Author_Institution

Lab. d´´Etudes et de Recherche en Teledet., Toulouse, France

Volume

32

Issue

4

fYear

1994

fDate

7/1/1994 12:00:00 AM

Firstpage

906

Lastpage

917

Abstract

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

Keywords

geophysical techniques; hidden Markov models; image sequences; optical information processing; remote sensing; AVHRR; Advanced Very High Resolution Radiometer; NDVI; geophysical measurement technique; hidden Markov model; image processing; image sequences; normalized difference vegetation index; optical IR infrared visible; phenological anomalies; remote sensing; season; temporal change variation; time series; vegetation dynamics; vegetation mapping; Atmosphere; Biosphere; Hidden Markov models; Radiometry; Remote monitoring; Remote sensing; Satellite broadcasting; Surface resistance; Time series analysis; Vegetation;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.298019

Filename

298019