Dynamics of gross primary productivity with VPM model in Changbai Mountain Natural Reserve, Northeast China.

doi:10.13287/j.1001-9332.201905.029

Abstract

Abstract: Precise estimation of gross primary productivity (GPP), the key parameter in carbon cycle analysis, plays an important role in the research of carbon cycle and global climate change. Vegetation GPP was simulated by VPM model based on MOD09A1 and climate data in Changbai Mountain Natural Reserve from 2000 to 2015. The results showed that mean GPP was 1203 g C·m^-2·a^-1. The annual vegetation GPP significantly increased from 2000 to 2015. There was no significant difference in the temporal trends of forest GPP at different vertical vegetation zones. However, GPP of the alpine tundra decreased remarkably. The correlation between GPP and precipitation was not significant. The positive correlation of GPP and temperature was mainly distributed in broad-leaved Korean pine forests and alpine tundra. Spring temperature had the strongest influence on GPP, with 80% pixels had a positive correlation with temperature. The GPP had a stronger correlation with temperature compared with precipitation.

PING Xiao-ying, MA Jun, LIU Miao, CHANG Yu, ZONG Min, XIONG Zai-ping. Dynamics of gross primary productivity with VPM model in Changbai Mountain Natural Reserve, Northeast China.[J]. Chinese Journal of Applied Ecology, 2019, 30(5): 1589-1598.

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