By analyzing the response of vegetation coverage climate change in Beibu Gulf coastal region, this paper aims to provide references for studies on regional vegetation restoration and vegetation productivity. Based on 10- day SPOT-VEGETATION NDVI data and 10- day temperature and precipitation datasets during the period of 2000-2011, this study uses the mathematical-statistic methods, such as dimidiate pixel model, correlation analysis, partial correlation analysis and time-lag partial correlation analysis to explore the quantitative characteristics of temporal- spatial change of vegetation coverage and their correlation with the climatic factors in Beibu Gulf coastal region. The results indicate: 1) In recent 12 years, the vegetation coverage in the Beibu Gulf coastal region increased by 6.79%, from 65.23% in 2000 to 72.02% in 2011.2) The ten-day average NDVI in growing season ranged from 0.21 to 0.67, showing the tendency of a little reduce at the beginning, then going up steadily and finally going down again. 3) The NDVI of all kinds of vegetations had significant relationship with temperature, all passing the test at significant level of 0.01, and the significance level of correlation between NDVI and temperature is higher than correlation between NDVI and precipitation, which showed that the vegetation coverage of NDVI is more sensitive to temperature. 4) The time-lag partial correlation coefficient of NDVI and temperature was significantly higher than that of NDVI and precipitation, the lag time of NDVI and precipitation being 6-9 ten-day periods, that of NDVI and temperature being 0-5 ten-day periods. 5) The growth of different types of vegetations responded to temperature and precipitation differently, and the vegetations whose NDVI have higher time-lag partial correlation coefficient with water and heat conditions have short response time. In conclusion, the vegetation in Beibu Gulf coastal region is in recovery in past 12 years, and its response to precipitation and temperature has obvious threshold and is lag in time.