| 表征亚热带常绿林光合作用季节变化特征的多种植被指数 |
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| 引用本文： | 钱钊晖,王绍强,陈敬华,周国逸,张雷明,李焱沐,孟泽,陈蝶聪.表征亚热带常绿林光合作用季节变化特征的多种植被指数[J].生态学报,2018,38(16):5771-5781. |
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| 作者姓名： | 钱钊晖 王绍强 陈敬华 周国逸 张雷明 李焱沐 孟泽 陈蝶聪 |
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| 作者单位： | 中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室;中国科学院大学资源与环境学院;中国科学院华南植物园;广东省生态气象中心 |
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| 基金项目： | 国家重点研发计划（2017YFC0503803）；中国科学院重点部署项目（KFZD-SW-310-01）；国家自然科学基金（41571192） |
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| 摘 要： | 利用遥感方法可以在区域尺度反演地表植被的光合生理状况和生产力变化,但亚热带常绿林冠层结构季节变化较小,传统的光谱植被指数对植被光合作用难以准确捕捉。利用2014—2015年中国科学院广东省鼎湖山森林生态试验站多角度自动光谱观测系统的光谱反射数据,分别反演传统冠层结构型植被指数(NDVI)、光合生理生化型植被指数(CCI)和叶绿素荧光型植被指数(NDFI_(685)和NDFI_(760)),并利用不同类型植被指数的组合,构建多元线性回归模型。结果表明:亚热带常绿针阔混交林三种类型植被指数均与GPP的动态变化有显著的相关性,其中,NDVI是表征GPP较优的植被指数(R~2=0.60,P0.01),其次为CCI(R~2=0.55,P0.01),而NDFI能够作为辅助指数,有效提高NDVI(R~2=0.68,P0.001)和CCI(R~2=0.67,P0.001)表征GPP的程度。多个植被指数参与构建的多元回归模型能够有效提高亚热带地区常绿林GPP季节动态变化的拟合精度,提升遥感精确评估亚热带森林生产力的能力。
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| 关 键 词： | 亚热带 常绿林 光合作用季节变化 植被指数 |
| 收稿时间： | 2017/7/21 0:00:00 |
| 修稿时间： | 2018/4/2 0:00:00 |
Study of multiple vegetation indices reveals photosynthetic phenology in a subtropical evergreen forest |
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| QIAN Zhaohui,WANG Shaoqiang,CHEN Jinghu,ZHOU Guoyi,ZHANG Leiming,LI Yanmu,MENG Ze and CHEN Diecong.Study of multiple vegetation indices reveals photosynthetic phenology in a subtropical evergreen forest[J].Acta Ecologica Sinica,2018,38(16):5771-5781. |
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| Authors: | QIAN Zhaohui WANG Shaoqiang CHEN Jinghu ZHOU Guoyi ZHANG Leiming LI Yanmu MENG Ze and CHEN Diecong |
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| Institution: | Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China,South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China and Guangdong Ecological Meteorological Center, Guangzhou 510640, China |
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| Abstract: | Remote sensing is an effective method to assess terrestrial vegetation photosynthetic physiology and productivity dynamics at a regional scale. The conventional spectral vegetation index such as normalized difference vegetation index does not accurately reveal the photosynthetic phenology of subtropical evergreen forests because canopy structure is relatively stable across seasons. This study calculated the conventional canopy structural vegetation index (normalized difference vegetation index, NDVI), photosynthetic physiological and biochemical vegetation index (chlorophyll/carotenoid index, CCI), and chlorophyll fluorescence vegetation index (normalized difference fluorescence indices, NDFI) respectively, using the spectral reflection data from the automated multi-angular spectro-radiometer at the Dinghu Mountain Forest Ecosystem Research Station in Guangdong, China. We compared and analyzed their differences in tracking gross primary productivity (GPP) as measured by eddy covariance at the canopy level. A multivariate linear regression model was built to improve the fitting accuracy of GPP seasonal dynamics in this subtropical evergreen forest. The results show:for this mixed subtropical evergreen forest, 1) GPP was significantly correlated with all three indices, and the correlation with NDVI was the strongest (R2=0.60, P < 0.01); 2) CCI could not replace NDVI as a better vegetation index to reveal GPP seasonal dynamics (R2=0.55, P < 0.01); 3) NDFI could be used as a secondary index to effectively improve assessment of photosynthetic phenology (R2=0.68,P < 0.001). |
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| Keywords: | subtropics evergreen forest photosynthetic phenology vegetation index |
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