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当前位置: 首页 > 文章 > 基于VPM模型的长白山自然保护区植被总初级生产力动态变化 应用生态学报 2019 (5) 1589-1598
Position: Home > Articles > 基于VPM模型的长白山自然保护区植被总初级生产力动态变化 Chinese Journal of Applied Ecology 2019 (5) 1589-1598

基于VPM模型的长白山自然保护区植被总初级生产力动态变化

作  者：
平晓莹;马俊;刘淼;常禹;宗敏;熊在平
单  位：
中国科学院沈阳应用生态研究所森林与土壤生态国家重点实验室
关键词：
总初级生产力;长白山自然保护区;VPM模型
摘  要：
总初级生产力(GPP)是碳循环的重要参数,它的准确估算对碳循环及全球气候变化研究有重要作用.利用VPM模型及2000-2015年MOD09A1数据/气候因子的空间数据,对长白山自然保护区的植被GPP进行模拟.结果表明:2000-2015年,保护区GPP年均值为1203 g C·m-2·a-1,GPP呈极显著趋势增长.森林植被GPP年际增长变化在不同植被垂直带下没有显著区别,但从高山苔原带往上,GPP年际增长明显减小.GPP与降水的年际相关性不显著,与温度的正相关关系集中分布在阔叶红松林带和高山苔原带.春季气温对GPP影响最大,有80%像元显示与气温呈正相关.GPP与温度的年际相关性明显高于降水.
作  者：
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;Graduate University of the Chinese Academy of Sciences;Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University;
单  位：
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%Graduate University of the Chinese Academy of Sciences%Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University
关键词：
gross primary productivity;;Changbai Mountain Natural Reserve;;VPM model
摘  要：
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 MOD09 A1 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.

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