| Global change has become a hot issue in today’s scientific community, involving many disciplines and worldwide government organizations’cooperation. The IPCC fifth report shows that since 1950s the earth’s temperature has increased by 0.6～0.7℃. As one of the core research program in the International Geosphere-Biosphere Program, Global Change and Terrestrial Ecosystems (GCTE) is to study the complex effects of global change on terrestrial ecosystems. Forest ecosystem is the main terrestrial ecosystems. In the natural state, forest ecosystems have automatic adjustment function and it can keep carbon balance. But climate change and human activity can disrupt this balance and bring a lot of incalculable loss to human society. Forests absorb carbon fixation, mainly through leaves, stem and roots of trees. Photosynthesis shows that the leaves can directly absorb carbon dioxide of atmospheric. And the carbon dioxide of tree trunk is derived from various pathways’assimilation and conversion of carbon dioxide. The carbon of leaves and trunk do not exist in the same way, and their formation progress is out of sync, which may result in different carbon sequestration between trunk and canopy leaves. So when we understand clearly the difference of carbon sequestration between tree trunk and canopy that is synchronized or not. Then we can estimate the amount of fixed carbon in certain time, and it is important to carbon cycle in forest ecosystems. It helps us to understand the relationship between climate and vegetation and provide basic data for further study of terrestrial ecosystems’response to global change. In addition, for the forest, the interpretation of aerial photographs computed NDVI, NPP and so on, and most of the changes represent the canopy only. Since the canopy of trees and trunk has different release and decomposition of fixed carbon cycle, there is a change in status of the canopy and the trunk in growing differences, or how the contact has yet to be studied. Researching relationship between vegetation canopy and trunk has referential meaning for using remote sensing data to study global change.We use tree ring width and normalized difference vegetation index (NDVI) as surrogate indexes, representing the tree trunk and canopy to research the different of trunk and canopy. The research carried out earlier abroad, and domestic research of the relationship of vegetation indices and tree-ring is rare, and most scholars separate tree-ring or vegetation indices to study. In order to study this problem, we choose two different climatic backgrounds locations for the study area. They are Xinglong Mountain in Yuzhong County, Gansu Province and Qinghua forestry in Youyang County, Chongqing. Xinglong Mountain located in Yuzhong County of Lanzhou City, Gansu Province, and it is a famous national natural forest protection area. It is a temperate semi-humid climate, but also has significant continental climate, with four distinct seasons. Qinghua Forestry located in Youyang Tujia and Miao Autonomous County, Chongqing, and it is humid subtropical climate with abundant precipitation, annual accumulated more than 4500℃. So it has good heat and water resources. Selected two sites in the study area are far apart and have different climatic characteristics. While Xinglong Mountain and Qinghua Forestry both have large tracts of forest, away from the city, less affected by human activities, and reducing the trees due to the impact of human activities generate uncertainty.The data that we used include tree-ring width data, MODIS-NDVI data, and temperature and precipitation data. We collected 140 picea wilsonii tree-ring samples from Xinglong Mountain Yuzhong County, Gansu Province 50 fir from Qinghua Forestry Youyang Tujia and Miao Autonomous County of southeast Chongqing. After the basic program of dendrochronology, finally we use 132 tree-ring samples from Xinglong Mountain, and 21 tree-ring samples from Qinghua Forestry. MODIS-NDVI data use MOD13Q1, with 16 days’time resolution, and spatial resolution of 250m. After pretreatment, we use maximum synthesis method to obtain annual maximum NDVI, namely NDVImax, and the calculated value of monthly NDVI. Temperature and precipitation data are come from weather stations that Yuzhong County and Youyang County, and temperature and precipitation data calculated by the growing season. Using a linear regression, correlation analysis and multivariate linear regression, analysis the relationship between tree-ring width of the standard sequence and NDVI under two different temperature and precipitation, and to explore the relationship reveal trunk and canopy of trees.The results showed that:2001-2012 the NDVI maximum in Xinglong Mountain had a downward trend overall in interannual variability and standardized tree-ring width showed an upward trend overall. But the standardization of tree-ring width and NDVI maximum had no significant correlation. From 2000 to 2010 NDVI maximum in Qinghua Forestry had an upward trend overall in interannual variability and standardized tree-ring width showed an upward trend overall. The entire wheel width and earlywood width both have significantly positive correlation with the NDVI of the last year and the NDVI from July to September of the last year, while latewood width and the entire wheel width both have significantly negative correlation with NDVI of April and May in the same year.Xinglong Mountain is warm semi-humid climate, and Qinghua Forestry is subtropical humid climate. In two temperature and precipitation conditions, the relationship between Qinghua Forestry tree-ring width and NDVI showed that trunk and canopy of trees exhibited one year lag synchrony, but the changes of trunk and canopy of trees in Xinglong Mountain are not synchronized. Therefore, in the use of remote sensing data of forest vegetation changes, Xinglong Mountain can’t fully represent the whole area of the forest biomass, productivity, timber volume and so on. But in Qinghua Forestry trunk and canopy of trees had one year lag synchrony, and remote sensing data can be applied to represent vegetation changes throughout the forest. In addition, in the future to study the use of forest vegetation remote sensing data, they should first consider that the trunk and canopy of trees’change is synchronization or not in study area. |
