雷竹叶绿素与高光谱植被指数关系及其反演模型  

Chlorophyll content in Phyllostachys violascens related to hyper-spectral vegetation indices and development of an inversion model

作　　者：李亚丹[1,2] 杜华强[1,2] 周国模[1,2] 谷成燕[1,2] 徐小军[1,2] 孙少波[1,2] 高国龙[1,2] 

机构地区：[1]浙江农林大学浙江省森林生态系统碳循环与固碳减排重点实验室,浙江临安311300 [2]浙江农林大学环境与资源学院,浙江临安311300

出　　处：《浙江农林大学学报》2015年第3期335-345,共11页Journal of Zhejiang A&F University

基　　金：浙江省杰出青年科学基金资助项目(LR14C160001);国家自然科学基金资助项目(31370637);浙江省林业碳汇与计量创新团队资助项目(2012R10030-01);浙江省本科院校中青年学科带头人学术攀登计划项目(pd2013239);浙江农林大学农林碳汇与生态环境修复研究中心预研基金资助项目

摘　　要：雷竹Phyllostachys violascens快速生长过程中,采用ASD便携式野外光谱测量仪和CCM-200手持式叶绿素仪对研究区样竹反射光谱曲线和相对叶绿素进行连续观测,在此基础上分析了植被指数与雷竹叶绿素在不同观测时间的相关关系,并构建了叶绿素反演模型。研究结果表明:1绿度指数(GM),红边指数(Vog3),双重差值指数(DD),修正型归一化指数(m ND705),修正型比值指数(m SR705)和红边拐点指数(REP)等6个高光谱植被指数在整个生长过程均与雷竹叶绿素有较好的相关关系,而其他植被指数在某些时间或时间段里与雷竹叶绿素具有较好的关系,且在观测末期,几乎所有植被指数与叶绿素均有较好的相关性;2采用以上6个植被指数建立的一元线性模型,在99%置信水平下的相关系数均在0.85以上,且2种方案所建立的多元线性模型能够对雷竹叶绿素进行高精度的预测,预测与实测叶绿素之间的相关系数在0.89以上。Reflectance data and relative chlorophyll content for Phyllostachys violascens at leaf scale were measured during the growth period from April, 5th to June, 18 th using a portable Analytical Spectral Devices（ASD） field spectrometer and a hand-held Chlorophyll Content Meter（CCM）-200. Correlation analyses were conducted between hyper-spectral vegetation indices and chlorophyll content based on the data. Then individual univariate linear inversion models were developed for chlorophyll content and hyper-spectral vegetation indices, such as red edge indexes GM, Vog3, double difference index DD, modified normalized differential vegetation index m ND705, modified simple ratio m SR705, and Red-edge positions（REP）. Also multivariate linear models for selected hyper-spectral vegetation indices and chlorophyll content were tested. Multivariate linear models are designed in two methods, strategy A is based on the 20 Phyllostachys violascens samples, and each data for the sample is the average for all the 14 times. On the contrary, strategy B is based on the data of 14 times, which average the 20 samples for each time. Results over the entire growth period showed（1） significant（P〈0.01） correlations between chlorophyll content and hyper-spectral vegetation indices, GM（r =0.866 3）, Vog3（r = 0.927 4）, DD（r = 0.880 6）, m ND705（r = 0.917 9）, m SR705（r = 0.924 9）, and REP（r =0.895 4）. At the end of the growth period, all vegetation indices had a favorable relationship with chlorophyll content, showing as the high correlation coefficients, although some indexes perform bad in most other time periods;（2） Using the univariate linear model, correlation for hyper-spectral vegetation indices and chlorophyll content showed r 〉0.85. The multivariate linear models of the six hyper-spectral vegetation indices listed above and chlorophyll content using two strategies, both accurately predicted chlorophyll content of Phyllostachys violascens [with correlation coefficients between predicted va

关 键 词：植物学 雷竹 叶绿素 高光谱植被指数 相关性 模型 

分 类 号：S718.4[农业科学—林学]