Prediction of Conifer Pigment Content Based on Color Parameters and Hyperspectral Characteristics
WANG Yi-heng1, SUN Kun1, WEN Zhe1, SUO Ying-bo2, ZHANG Qu1, WANG Ge-rong1, WEI Jin-hua1*
1. Forestry College of Beihua University, Jilin 132013, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Abstract：Spectral imaging technology is widely used in the field of non-invasive determination of physical and chemical parameters of plants, and scholars have also explored the correlation between pigments and color parameters. However, it has not been reported that the models fitted using color parameter values and spectral parameter values as independent variables and pigment content, respectively, are compared and optimized. In this experiment, five conifer species were used as research objects, and 11 spectral vegetation parameters, including blue edge amplitude Db, yellow edge amplitude Dy, red edge amplitude Dr, green peak amplitude Rg, red valley amplitude Rr, blue edge area SDb, yellow edge area SDy, red edge area SDr, ratio vegetation index RVI, difference vegetation index DVI, and normalized vegetation index NDVI, were screened as the basis of spectral analysis in this paper. The measured conifer color parameter values and spectral parameter values were used as independent variables, respectively. Stepwise multiple linear regression (SMLR) was used to estimate the pigment content to establish a model, with R2 and RMSE as evaluation criteria, and the parameter combinations with the highest model accuracy were compared and selected for practice. The results of the study indicate that: (1) There are differences in leaf pigment content, color phase parameter values, and reflectance spectral between tree species (p<0.05). (2) The leaf spectral reflectance of Pinus koraiensis Sieb. et Zucc. was significantly lower in Pinus sylvestris var. mongolicaLitv., Pinus banksiana Lamb and Pinus densifloraSieb. et Zucc. (p<0.05). The original spectrum of conifer species shows “blue valley phenomenon” and “red valley phenomenon” near 500 and 680 nm in the visible band, and “green peak phenomenon” and “red edge phenomenon” near 550 and 760 nm bands; the first-order differential spectral reflectance produces dramatic changes near 700 nm. (3) Pigment content was significantly correlated with color parameters, spectral reflectance, and spectral characteristic parameters, and there was a significant linear relationship. (4) When anthocyanins and chlorophyll were combined with L, a* and L, a*, b*, and S color parameters as independent variables, respectively, the fitted model R2 was the highest, 0.588 and 0.638, respectively. In contrast, carotenoids, chlorophyll a, and chlorophyll b were all combined with FD652, FD700, SDb, SDy, RVI, DVI, and NDVI spectral parameters as independent variables. The fitted model R2 was the highest, 0.779, 0.786, and 0.774, respectively. In this study, a hyperspectral camera, color difference instrument and UV-Vis spectrophotometer were used to realize rapid prediction of needle pigment content. Based on a significant correlation between color parameter value and spectral value and pigment content, the parameter combination with the highest accuracy of the established model was successfully selected. Different methods and parameter values could be selected according to the accuracy requirements and research conditions in predicting of needle pigment.
王艺恒，孙 昆，温 喆，锁应博，张 曲，王戈戎，魏进华. 基于色彩参数和高光谱特征的针叶树种色素含量预估[J]. 光谱学与光谱分析, 2022, 42(02): 537-543.
WANG Yi-heng, SUN Kun, WEN Zhe, SUO Ying-bo, ZHANG Qu, WANG Ge-rong, WEI Jin-hua. Prediction of Conifer Pigment Content Based on Color Parameters and Hyperspectral Characteristics. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 537-543.
