Abstract: Vegetation plays an important role in the global cycle of geobiochemistry, significantly affects the climate and sensitively responds to the climate change on the contrary. Consequently, studying the spatio-temporal features of the vegetation is vital for climate monitoring. The ecosystem of vegetation in the Eurasia is severely affected by climate change. As the border of the Indian subcontinent and Tibet Plateau, Himalayas alike makes intensivelyresponse to the climate change. It is an all-but uninhabited part in the world, holds abundant biodiversity, and has the most integrated vertical distribution of the vegetation.The vegetation in Himalayas Mountain has been well studied on the climate patterns, vegetation forms, land use and climate factors. However, the vegetation on the whole south mountain and the conditions of growth rate haven't been well studied. Besides, the primary climate factors affecting the vegetation change are still unknown. Based on remote sensing and grid data from MODIS NDVI in the mountainous areas of south Himalayas during 2001-2015, trend curvetheory and multiple regression method were applied toanalyze the spatio-temporal features of south Himalayas, and Pearson correlation analysis was utilized to study the correlations between climate factors and the NDVI_max values in this area during the past 15 years. The results show as follows:(1)during 2001-2015, the average value of NDVI_max decreased from January to March and slowly increased from April to June, then reached the peak period from June to September and began to decrease gradually from October to December. On the whole, the average value of NDVI_max was high(0.59), decreased from South-East to North-West, and furthermore, varied over elevation changes regularly, and 80% of the vegetation was below 4 050 meters; (2)the NDVI_max value in this area changed by timephase, of which the annual average varied in 3 phases, the change rates were 0.003 9·a^-1 and 0.005 3·a^-1 in 2001-2006 and 2010-2015, respectively, while-0.007 0·a^-1 in 2006-2010; during the growing season, the NDVI_max varied in 4 phases, the rates of change were-0.001 8·a^-1 and-0.010 6·a^-1 in 2001-2004 and 2007-2010, respectively, 0.014 8·a^-1 in 2005-2006 when the NDVI_max value reached the maximum and 0.006 3·a^-1 in 2010-2015 when the increase of NDVI_max value fluctuated. The vegetation was degrading insignificantly in most areas, while improving insignificantly(0.05< p<0.01)in a few areas and improving most significantly at the low altitudes in the west; (3)the vegetation in the area was affected by temperature and precipitation at the same time. Meanwhile, the vegetation could be improved by glacial melt run-off caused by temperature rising at high altitudes and affected by human activities at low altitudes in the middle of south Himalayas to some extent.

Key words: south Himalayas Mountain, vegetation change, remote sensing, climate change