Nonlinear model prediction of needle chlorophyll content of Picea koraiensis Nakai at different needle ages based on hyperspectral features - Wang, Yiheng; Zhang, Jinsong; Diao, Shu; Xue, Liang (Frontiers in Forests and Global Change, 2023)
Pigment content is a critical assessment indicator in the study of plant physiological metabolism, stress resistance, ornamental characteristics and forest health. Spectral imaging technology is widely used for rapid and non-destructive determination of plant physicochemical parameters. To address the shortcomings of previous models of spectral reflectance prediction of chlorophyll content of needles only from the perspective of traditional algorithms and ignoring physical models, this research integrates variable complexity and refined classification of physical models to validate the increased accuracy of both the conventional partial least squares method and the traditional neural network algorithm. The results of the conifer chlorophyll models of Picea koraiensis Nakai with different needle ages based on spectral reflectance and vegetation index parameters showed that the improved nonlinear STA-BP neural network model approach (STA-BP) (R2 of 0.73-0.89) and the nonlinear Stacking-PLS partial least squares model approach (Stacking-PLS) (R2 of 0. 67- 0.85) is slightly more robust than the traditional algorithms nonlinear BP model (R2 of 0.63-0.82) and linear PLS model (R2 of 0.60-0.76), This finding suggests that the nonlinear fitting of chlorophyll content in needles of different needle ages in P. koraiensis Nakai surpasses the traditional linear model fitting methodology. Furthermore, the model fitting of chlorophyll content in conifers of different needle ages outperforms the mixed P. koraiensis Nakai model, suggesting that chlorophyll models using needle refinement classification help to improve model robustness. This study provides data and theoretical support for rapid and non-invasive characterization of physiological and biochemical properties of needles of different needle ages using spectral imaging techniques to predict growth and community structure productivity of forest trees in the coming years.