Estimating The Forest Vegetation Carbon Sequestration In South China And Southeast Asia

Focusing on Interaction between climate change and terrestrial ecosystems, Global Change and Terrestrial Ecosystems (GCTE) is being an important field in global change research.. Forest ecosystems, as the main terrestrial ecosystems, has an irreplaceable role in the global carbon sequestration and the global climate change. In recent decades, the Southeast Asia has reached a high population density and rapid economic growth period, its sustainable development and influence on global change is becoming the global concerns.In this paper, we focus on forest carbon sequestration and climate change of the typical area in Southern China and Southeast Asia, and we also combined with multi-source data to verify the accuracy of the simulation results. Based on remote sensing data, applied GLOPEM-CEVSA model, a remote sensing model (GloPEM)coupled with ecosystem process model(CEVSA) to simulate the forest carbon sequestration.(1) Forest interannual change analysis based on GIMMS NDVIthe mean annual NDVI of forest area is 0.58 from 1984 to 2006, and it showed a distruibution that tropical regions and coastal areas were higher than subtropical and inland respectively. The vegetation NDVI can better reflect the forest seasonal growth. Under the monsoon climate, the mean forest NDVI of Southeast China show a more distinctly seasonal changes. Philippines and Malaysia, due to monsoon and ocean climate, the seasonal fluctuations of NDVI was quite small.The NDVI in Philippine Islands were largely maintained at 0.5 to 0.7 or so, and autumn and winter NDVI was significantly lower than spring and summer. Because of the continental climate and Himalayas moutain, the mean annual NDVI in northern of Indo-China Peninsula was lowest in summer but had a large increase (0.7 to 0.9) in autumn and winter. In India Peninsula, the mean annual NDVI in winter and spring higher than other seasons, primarily because of the southwest monsoon brought abundant rainfall to promote vegation growth.(2) Forest carbon sequestration based on modellingComparing with GPPDI 572 forest sites, the modeled NPP is relatively well consisted with the observed data. The modeled NPP is higher about 18%(R2=0.827), and the MODIS NPP is lower about 38%(R2=0.759). The modeled NPP is better related with GPPDI than MODIS. However the simulated NPP are both slightly higher than MODIS and GPPDI. Due to the scaling, using the ground observations to evaluate the modeled results may still have some problems. From the growing season fluctuations curves of the model simulated NPP,we can see that the model can better response of the forest seasonal variationThe multiyear mean NPP showed a decreasing trend from coastal areas to inland, and south area were higher than north. The mean NPP was 1762.47gC/m2/a of the whole region, for Philippine and Malaysia Archipelago sub-regions, the mean NPP were highest(2300.22gC/m2/a and 2259.36gC/m2/a), followed by Indo-China Peninsula (1698.52gC/m2/a), the India Peninsula (1438.15gC/m2/a), Southeast China (1088.44gC/m2/a), the lowest is Southwest China (1000.36gC/m2/a). The mean NPP of the evergreen broadleaf forest up to 2038.945gC/m2/a, followed by deciduous broadleaf forest 1388.796gC/m2/a, mixed forests 1109.116gC/m2/a, deciduous needleleaf forest and evergreen needleleaf forest were the lowest, followed by 970.8gC/m2/a and 810.97gC/m2/a.(3) The assess of climate change influence on forest carbon budgetThe distribution and evolution of forest is strongly influenced by human activity and climate change. Particularly in developing countries, where people living near the forest area, need to rely on exploiting forest resources to escape poverty. Economic production lead to forest over-exploitation, which led to decrease of the forest area and quality. According to China and Southeast Asia, we use the climate data to analysis the characteristic of climate changes, and its effect on carbon sink of forest resources . From 1984 to 2006 seasonal variation curve of 8 days average NPP, we can know that the model simulate the vegetation NPP can better response to temperature and precipitation. The different vegetation type NPP and its impact of temperature and precipitation in different regions were different.