Within the biosphere

vegetation is critical as it supports the bulk of human and animal life and largely controls the exchanges of water and carbon between the land and the atmosphere.Monitoring vegetation dynamics gives valuable information on the condition of an ecosystem.Human or climatic induced changes can be detected and consequences like overuse can be avoided.Remote sensing can provide the necessary spatial and temporal explicit data for a continuous monitoring.The Medium Resolution Imaging Spectrometer（MERIS）

on board of ENVISAT-1 fulfils the information gap between the current high and low spatial resolution sensors.Natural resources can also be monitored at global and regional scales every few days using the MERIS data.In this respect

the use of MERIS full resolution data（300m pixel size） has a great potential for regional and global land cover mapping.MERIS is a fully programmable imaging spectrometer covering visible and near-infrared wavelengths.It is a multidisciplinary instrument operating in the 390 nm to 1040 nm spectral range.Often vegetation indices are used in order to assess information on vegetation dynamics and conditions.This paper presents a first analysis of MERIS-Full Resolution（FR） data to observe seasonal variations of Normalized Difference Vegetation Index（NDVI） in Fujian province

South China.Because of too heavy cloud contamination only ten dates within the time interval from June 2004 to August 2005 were available:28/6/2004

11/7/2004

17/7/2004

18/8/2004

6/3/2005

6/6/2005

24/7/2005

27/7/2005

3/8/2005

9/8/2005.First

Process method for monitoring seasonal variety in region vegetation is discussed;Second

A few kinds of NDVI calculated by near-infrared bands and red band is compared with one another;Third

NDVI in summer 2004 and summer 2005 were acquired by maximum value composites（MVC）.The results of our investigation based on above show:（1）The image geometric precision derived from the MERIS tie point grid is good for monitoring large scale land cover

with good alignment with Fujian province boundary vector data;（2）Flags files in MERIS data can not only supply the satisfying product which distinguish ocean from land

but also cloud detection product;（3）NDVI got by combining band 10（near-infrared）and band 8（red）better than that got by combining band 10 and band 6;（4）Condition vegetation growing in summer in 2004 is evidently inferior to that in 2005 according to combined NDVI got by combining band 10 and band 8;（5）Temporal and spatial change of MERIS-NDVI preferably responses to climate change in the study area.These meteorological variables influence NDVI variability at different seasons and time lags.Vegetation responds to short-term climate variability in complex ways and serves as a source of memory for the climate system.There are a few of problem in the study because of the limitation of data source in amount and quality.（1）In order to decrease error in vegetation change result

the effect to NDVI by the insufficiency resulted from cloud detect should be investigated;（2）Only three seasonal vegetation change in one year were analyzed.For get more reliable conclusion

more years time serial MERIS data should be gathered and investigated;（3）Both MERIS and MODIS are Medium Resolution Imaging Spectrometer.They have like spatial resolution.If advantage of MERIS and MODIS vegetation indices could be integrated.Vegetation dynamics would be more effectively monitored.So

advantage and disadvantage with MERIS and MODIS data vegetation indices should be discovered by comparing MERIS data vegetation indices with that of MODIS data in-depth.