基于微环境参数集的茎干水分变化规律与估算模型研究

Variation Rule and Estimation Model of Stem Water Content Based on Micro-environment Parameter Set

以杨树为研究对象,通过分析茎干水分与微环境参数集的变化关系,提出了茎干水分的估算模型。采用主成分分析法选取微环境参数集的最大主成分PC1作为特征变量,解决了微环境参数集之间的共线性问题,并在保留98.89%的原始数据信息的同时,达到了数据降维的目的,从而降低了模型复杂度。以PC1作为输入变量,茎干水分作为输出变量,基于两者的周期性变化规律建立了茎干水分与PC1的斜椭圆模型。在微环境状况相近的6个晴天,该模型的平均误差小于0.05%,均方根误差小于0.06%,决定系数大于0.94,能够较好地依据微环境参数集估算出实时的茎干含水率。但是由于植株个体存在形态指标上的差异,不同植株茎干水分与PC1的斜椭圆模型的预估参数也会存在差异,针对不同季节和气象环境需要分别建立茎干水分的估算模型。

Stem water content is an important parameter for evaluating plant physiological water conditions. Poplar trees were selected as research objects. The estimation model of stem water content was proposed by analyzing the variation relationship between stem water content and micro-environment parameter set. Considering the multi-collinearity between micro-environment parameter set,the maximum principal component PC1 of micro-environment parameter set was chosen as a feature variable via principal component analysis. The feature variable retained 98. 89% of the original data information. In addition,the complexity of model was simplified by reducing the data dimension. PC1 as the input variable and stem water content as the output variable,the oblique ellipse model between the two was established. In six sunny days with similar micro-environment,the mean error of the model was less than0. 05%,root mean square error of the model was less than 0. 06%,and decision coefficient of the model was greater than 0. 94. The oblique ellipse model can precisely estimate real-time stem water content,but because of the differences in morphological indexes of poplar trees,the estimated parameters of different oblique ellipse models between stem water content and PC1 were different. Moreover,the estimation model of stem water content should be respectively established according to different seasons and weather environment.