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遥感技术与应用  2020, Vol. 35 Issue (5): 1037-1046    DOI: 10.11873/j.issn.1004-0323.2020.5.1037
1.北京大学 城市规划与设计学院,深圳 518055
2.北京大学 城市与环境学院,北京 100871
Uncertainty of the Ecosystem Models in Simulating the Trend and Drivers of Leaf Area Index in China
Gang Liu1(),Yuxing Sang1,Qian Zhao1,Cong Jiang1,2,Zaichun Zhu1()
1.School of Urban Planning and Design,Peking University,Shenzhen 518055,China
2.College of Urban and Environmental Sciences,Peking University,Beijing 100871,China
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为了厘清中国近30 a来植被生长趋势及其对不同环境变化的响应,使用了3套长时间序列遥感叶面积指数(Leaf area index, LAI)数据集以及8套生态系统模型,对LAI变化趋势从总量、空间分布以及不同植被类型进行了分析与归因。总量上,1982~2015年遥感观测的LAI趋势(9.8×10-3m2/m2·a)高于生态系统模型模拟的趋势(4.2×10-3m2/m2·a),大气二氧化碳浓度上升是主要驱动因素((3.5×10-3m2/m2·a);遥感观测到全国79.5%的区域LAI都呈现显著增长的趋势,而生态系统模型模拟LAI的增长面积占比为33.1%;除草地外,生态系统模型低估了其他植被类型的LAI变化趋势。模型对降雨变化的响应过于敏感以及对人为活动模拟能力不足是模型模拟中国LAI变化趋势不确定性的重要来源。本研究定量分析了近30 a中国各种植被变化情况及其驱动因子,并对模型低估中国植被生长进行了解释,为后续中国地区植被相关研究提供了参考。

关键词: 叶面积指数中国遥感生态系统模型    

In order to clarify the trend of vegetation growth and its response to the changing environment factors in China during the past 30 years, three sets of long-term satellite-based LAI (Leaf Area Index) datasets and eight process-based ecosystem models are used to analyze the trend of LAI and its attribution. In the total, the trend of satellite-based LAI datasets (9.8×10-3m2/(m2·year1)) during 1982~2015 is much higher than ecosystem process-based models LAI datasets (4.2×10-3m2/(m2·year1)), in which CO2 is the dominant contributor (3.5×10-3m2/(m2·year1)). In the spatial pattern, the satellite-based LAI datasets show that about 79.5% of area in which LAI has a significant increasing trend, while about 33.1% of the area in which LAI simulated by process-based ecosystem models shows a growth trend. Except for grassland, the other vegetation types all shows that the LAI from models underestimates the growth of vegetation. The response to the changes of precipitation is too sensitive in models and models’ insufficient ability to simulate human activities are important sources of uncertainty in the model’s simulating the trend of LAI in China. The study quantitatively analyzes the change of LAI and its drivers of various vegetation types in China in the past 30 years, and conducted attribution analysis. This study also explains the underestimation of vegetation growth in process-based ecosystem models, which provides a reference for subsequent research on vegetation in China.

Key words: Leaf Area Index(LAI)    China    Remote sensing    Ecosystem model
收稿日期: 2020-08-17 出版日期: 2020-11-26
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目“全球植被叶面积指数未来变化趋势研究”(41901122)
通讯作者: 朱再春     E-mail:;
作者简介: 刘刚(1998-),男,湖北武汉人,硕士研究生,主要从事全球变化生态学研究。E?
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刘刚,桑宇星,赵茜,江聪,朱再春. 生态系统模型模拟中国叶面积指数变化趋势及驱动因子的不确定性[J]. 遥感技术与应用, 2020, 35(5): 1037-1046.

Gang Liu,Yuxing Sang,Qian Zhao,Cong Jiang,Zaichun Zhu. Uncertainty of the Ecosystem Models in Simulating the Trend and Drivers of Leaf Area Index in China. Remote Sensing Technology and Application, 2020, 35(5): 1037-1046.


图1  1982~2015年中国生长季LAI距平(m2/m2)
图2  1982~2015年中国生长季LAI趋势空间格局(10-2m2/m2·a)
图3  中国生长季LAI趋势各驱动因子的贡献及主导因子
图4  不同植被类型生长季LAI趋势及其驱动力(10-2m2/(m2·a))
图5  1982~2015年环境因子趋势及遥感观测的 LAI趋势与模型模拟的 LAI趋势差值
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