Ping Xiao Ying, Ma Jun, Liu Miao, Chang Yu, Zong Min, Xiong Zai Ping
Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
Graduate University of the Chinese Academy of Sciences, Beijing 100049, China.
Ying Yong Sheng Tai Xue Bao. 2019 May;30(5):1589-1598. doi: 10.13287/j.1001-9332.201905.029.
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·a. 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.
精确估算总初级生产力（GPP）这一碳循环分析中的关键参数，在碳循环和全球气候变化研究中具有重要作用。基于MOD09A1和长白山自然保护区2000 - 2015年的气候数据，利用VPM模型模拟了植被GPP。结果表明，平均GPP为1203 g C·m·a。2000年至2015年期间，年植被GPP显著增加。不同垂直植被带森林GPP的时间趋势无显著差异。然而，高山冻原的GPP显著下降。GPP与降水量之间的相关性不显著。GPP与温度的正相关主要分布在阔叶红松林和高山冻原。春季温度对GPP的影响最强，80%的像元与温度呈正相关。与降水量相比，GPP与温度的相关性更强。
