Understanding the spatial pattern and dynamic processes of vegetation changes and their causes is one of the key topics in research on global change of terrestrial ecosystems. Characterized by vulnerable alpine vegetation, which is sensitive to external disturbance, the Qinghai-Tibet Plateau is one of the ideal areas for studying the response of vegetation to climate change. It is necessary to investigate the impacts of climate change on vegetation in a short synthetic period because of the intense climate variations in the Qinghai-Tibet Plateau. Previous studies have not sufficiently investigated NDVI change comparisons between various periods and the persistence of NDVI trends. In this study, we investigated monthly vegetation dynamics in the Qinghai- Tibet Plateau and their relationships with climatic factors over 15 progressive periods of 18-32 years starting in 1982. This was accomplished by using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data. The NDVI time-synthesis method of each season masks the trends of NDVI variations within the single month. Except for August, vegetation increased in other six months, with a significant increase occurring in April-July and September. The increase rate of NDVI in most months decreased significantly with the extension of the period, indicating that the increasing trend of NDVI slowed down. At pixel scale, the regions with significant changes (including both increase and decrease) in NDVI showed increasing trends in most months, but the range of significant decreases in NDVI expanded faster than that of significant increases. Vegetation activities in the Qinghai- Tibet Plateau are generally controlled by temperature changes, but the dominant climatic factors affecting vegetation are varied in different months and regions. The vegetation activities in April and July were mainly promoted by temperature and sunshine hours, and those in June and September were controlled by temperature, and in August were mainly affected by precipitation. The emergence of long time series NDVI data sets provides a precondition for application of nested time series to study the trend analysis of vegetation growth and change. The persistence of the trend of vegetation activity may help to visualize the process of vegetation change, understand the vegetation response to climate change, and to predict thevegetation growth trend. It is inferred that the increases of NDVI in the future tend to be more moderate in general, but areas with significant pixel- scale changes in NDVI tend to increase in most months.