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

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 values and measured values that were all above r = 0.89]. The multivariate linear models can be used to predicte chlorophyll content in the leaf of Phyllostachys violascens. Considering the calculate method, strategy B is more fit for the dynamic change of chlorophyll content for Phyllostachys violascens at leaf scale.