基于色彩参数和高光谱特征的针叶树种色素含量预估

doi:10.3964/j.issn.1000-0593(2022)02-0537-07

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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.

Key words：Conifer species; Pigment content; Plant spectrum; Chromatic parameters

Received: 2021-01-21 Accepted: 2021-02-24

ZTFLH:

S791.24

Corresponding Authors: WEI Jin-hua E-mail: weijh99@163.com

Cite this article:

WANG Yi-heng,SUN Kun,WEN Zhe, et al. Prediction of Conifer Pigment Content Based on Color Parameters and Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 537-543.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)02-0537-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I02/537

[1] Skudra I，Ruza A. Rural Sustainability Research，2017，37（332）：29.
[2] LIU Chang，SUN Peng-sen，LIU Shi-rong(刘畅，孙鹏森，刘世荣). Forestry Scientific Research（林业科学研究），2017，30（1）：88.
[3] WANG Gai-ping，ZHANG Lei，YAO Xue-bing，et al（王改萍，张磊，姚雪冰，等）. Journal of Nanjing Forestry University·Natural Science Edition（南京林业大学学报·自然科学版），2020，44（5）：41.
[4] PAN Li-qin，LI Ji-yuan，WEI Hai-zhong，et al（潘丽芹，李纪元，韦海忠，等）. Molecular Plant Breeding（分子植物育种），2020，18（2）：579.
[5] ZHAO Yi-xuan，SUN Gui-fang，YANG Jian-wei，et al（赵艺璇，孙桂芳，杨建伟，等）. Forestry and Ecological Sciences（林业与生态科学），2019，34（1）：93.
[6] Buddenbaum H，Stern O, Paschmionka B, et al. Int. J. Remote Sens.，2015，36（18）：4590.
[7] Li Y，Shao W，Dong R，et al. Journal of Near Infrared Spectroscopy，2018，26（2）：95.
[8] JING Chuan-bao，ZHOU Wei-qi，QIAN Yu-guo（靖传宝，周伟奇，钱雨果）. Journal of Ecology（生态学报），2019，（22）：8383.
[9] WANG Ya-hui，CHEN Er-xue，GUO Ying，et al（王雅慧，陈尔学，郭颖，等）. Forestry Scientific Research（林业科学研究），2020，（1）：11.
[10] PAN Qing-mei，ZHANG Jin-song，ZHANG Jun-pei，et al（潘庆梅，张劲松，张俊佩，等）. Forestry Scientific Research（林业科学研究），2019，（6）：1.
[11] ZHANG Shu-bin，WANG Xiang-ping，WU Peng，et al（张树斌，王襄平，吴鹏，等）. Journal of Ecology（生态学报），2018，38（22）：7990.
[12] LI He-sheng（李合生）. Principles and Techniques of Plant Physiological Biochemical Experiment(植物生理生化实验原理和技术). Beijing: Higher Education Press（北京：高等教育出版社），2006.
[13] Horler D N H，Dockray M，Barber J，et al. Advances in Space Research，1983，3（2）：273.
[14] Blackburn G A. Remote Sensing of Environment，1998，66（3）：273.
[15] ZI Li，XIE Fu-ming，SHU Qing-tai，et al（字李，谢福明，舒清态，等）. Journal of Fujian Agricultural and Forestry University·Natural Science Edition（福建农林大学学报·自然科学版），2019，48（1）：55.
[16] ZANG Zhuo，LIN Hui，YANG Min-hua（臧卓，林辉，杨敏华）. Journal of Central South Forestry University of Science and Technology（中南林业科技大学学报），2013，33（1）：35.
[17] HOU Yan-jun, DONG Lin-lin(侯艳军，董琳琳). Laser & Optoelectronics Progress(激光与光电子学进展)，2021，58(3)：350.
[18] Li Y，Sun Y，Jiang J，et al. Plant Methods，2019，15：73.
[19] Russell Main，Moses Azong Cho，Renaud Mathieu，et al. ISPRS Journal of Photogrammetry and Remote Sensing，2011，66：751.
[20] BAI Xue-qi，ZHANG Xiao-li，ZHANG Ning，et al（白雪琪，张晓丽，张凝，等）. Journal of Beijing Forestry University（北京林业大学学报），2016，38（11）：16.
[21] LIU Xin，YANG Guang，CHEN Hao-yu，et al（刘昕，杨光，陈昊宇，等）. Journal of Northeast Forestry University（东北林业大学学报），2020，48（2）：54.