Vegetation NDVI And Phenology Change In Northern China Based On Remote Sensing

Vegetation is an important part of the global terrestrial ecosystems. The relationship between vegetation and climate factors has become the hot issue of global change research. A variety of linear trend analysis methods have been used by many scholars to study the trend of vegetation change. The performance of nonlinear changes for vegetation respond to climate factors, so the use of piecewise linear regression method to study the vegetation at different stages could be well explain the vegetation response to climate factors. Therefore, it is necessary to study vegetation change for different seasons and its nonlinear response to climate factors. The Northern China was selected, using GIMMS NDVI data from1982to2006, to study vegetation change and its response to climate factors in Northern China from two aspects of NDVI and vegetation phenology. The main conclusions are as follows:1. GIMMS NDVI and MODIS NDVI data had a high correlation coefficient (0.9-1.0) and the mean slope of the two data around1.0in most areas, indicating that GIMMS NDVI data with better data quality, and then GIMMS NDVI data could be used for subsequent NDVI and phenological change research.2. Growing season precipitation was the main driving force of the growing season NDVI change. Growing season NDVI was in general with an upward trend; growing season NDVI was rapidly upward trend before the turning point of growing season precipitation, and then significantly decreased after the turning point of growing season precipitation. When the growing season temperature and precipitation experienced an increasing trend in the growing season, the growing season NDVI rose significantly; however, when the growing season temperature increased with growing season precipitation reduced will occurred drought, resulting decline in growing season NDVI.3. Spring temperature was the main driving force of the spring NDVI change, while spring NDVI decreased could be attributed to drought stress strengthened by increased spring warming and less spring precipitation in some areas. Summer precipitation was the main driving force of the summer NDVI change. Summer NDVI rose significantly with summer temperature and summer precipitation increased. But summer NDVI decreased could be attributed to drought stress strengthened by increased summer temperature and less summer precipitation. Autumn NDVI was in general with an upward trend; Autumn NDVI rose rapidly before the turning point of autumn temperature, while rose slowly after the turning point of autumn temperature. Autumn NDVI decreased after the turning point of autumn temperature could be attributed to drought stress strengthened by autumn warming and less autumn precipitation after the turning point of autumn temperature.4. The start of the growing season (SOS) was in general delayed trend; the entire study area was in general advanced trend before the turning point of SOS; and the entire study area was delayed trend after the turning point of SOS. The SOS and temperature had a good negative correlation trend and better consistency. The end of the growing season (EOS) was in general delayed trend; the entire study area was in general delayed trend before the turning point of EOS; and the entire study area was weak delayed trend after the turning point of EOS. The EOS trend could be attributed to drought stress strengthened by combined effect of temperature and precipitation. The length of the growing season (LOS) overall shortened; the entire study area was extended trend before the turning point of LOS; and the entire study area was shortened trend after the turning point of LOS.The main innovation of this study is as follows:1. Compared PDSI drought index calculation results of a linear regression method with different time periods, PDSI drought index was used with a combination of temperature and precipitation data from a comprehensive analysis of nonlinear changes for growing season and seasonal NDVI in northern China for better research results. 2. Compared with the results of a linear regression method with different time periods, the piecewise linear regression method was applied to the end of growing season and the length of growing season in northern China for better research results.