A Study on the Scaling-Up of CO2 Fluxes at Haibei Station Based on GIS and RS Technologies

Continuous long-term measurements of carbon fluxes have been obtained by improved technology at the ecosystem scale and under natural conditions. Nevertheless, these are still point measurements with limited geographic coverage Quantitative extrapolations and scaling-up of tower station measurements are now urgently needed for carbon cycle research. This paper attempts to solve this problem by combining the technologies of CO2 flux measurements, mathematic modeling, Geographic Information System (GIS) and Remote Sensing (RS). It is well known that the light-response function allows partitioning net ecosystem exchange (NEE) into gross primary production (GPP) and total ecosystem respiration (Re). The GPP and Re can be estimated from continuous CO2 flux measurements using non-linear identification of the non-rectangular hyperbolic model of ecosystem-scale, light response curves. Patterns of the relationships of GPP and Re with the Normalized Difference Vegetation Index (NDVI), and other factors, indicate possibilities to establish multivariate functions, allowing scaling-up of local fluxes to larger areas with GIS technology, temporal NDVI inversed from RS images, and other tools. Our study is based on continuous CO2 flux data, which were measured at the Haibei Alpine Meadow Ecosystem Research Station (Haibei Research Station) on the Qinghai-Tibet Plateau, the Chinese Academy of Science. Using the above methods, we achieved good flux-NDVI relationships at the site and applied these relationships in the scaling-up of site-specific CO2 flux measurements to regional levels. Consequently the annual spatial and temporal variation of CO2 flux was obtained for this plateau and eco-region. Characterizing spatial and temporal dynamics of CO2 flux will facilitate investigations of the carbon source or sink status of grassland ecosystems on the plateau.