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

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 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 DOI: 10.5846/stxb201707211305 作者: 作者单位: 中国科学院地理科学与资源研究所,中国科学院地理科学与资源研究所,中国科学院地理科学与资源研究所,中国科学院华南植物园,中国科学院地理科学与资源研究所,中国科学院地理科学与资源研究所,中国科学院华南植物园,广东省生态气象中心 作者简介: 通讯作者: 中图分类号: 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） Study of multiple vegetation indices reveals photosynthetic phenology in a subtropical evergreen forest Author: Affiliation: Institute of Geographic Sciences and Natural Resources Research, CAS,Institute of Geographic Sciences and Natural Resources Research, CAS,,,,,, Fund Project: National Key R&D Plan，Key Program of the Chinese Academy of Sciences，The National Natural Science Foundation of China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 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（NDFI685和NDFI760），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（R2=0.60，P < 0.01），其次为CCI（R2=0.55，P < 0.01），而NDFI能够作为辅助指数，有效提高NDVI（R2=0.68，P < 0.001）和CCI（R2=0.67，P < 0.001）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。 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). 参考文献 相似文献 引证文献