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Hyperspectral Differences Between New and Old Leaves of Dominant Tree Species in Changbai Mountain |
CHEN Jun-jie1, YU Quan-zhou1, 2*, TANG Qing-xin1, LIANG Tian-quan1, JIANG Jie1, ZHANG Hong-li1 |
1. Geographic Information Science, School of Geography and Environment, Liaocheng University, Liaocheng 252059, China
2. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Abstract Revealing the differences in foliar traits and leaf spectral characteristics between new and old leaves is important for the non-destructive monitoring of vegetation physiological and ecological parameters and can provide more theoretical support for quantitative remote sensing in forestry. This study sampled new and old leaves of 8 tree species in Changbai Mountain, and their reflectance spectra were measured. Then, the first-order derivative transformations and spectral indices were calculated. Foliar traits such as specific leaf area (SLA), leaf water content, leaf nitrogen content, and leaf carbon content were measured in our laboratory. Using variance analysis and correlation analysis, the differences in physicochemical properties and spectral characteristics between the old and new leaves of different tree species were investigated, and the differences in related coefficients were also analyzed between the old and new leaves. The results show that: (1) Multiple foliar traitsof thetree species showed significant differences between old and new leaves. Except for leaf carbon content, which did not differ significantly between old and new leaves, the other three traits showed significant variability between old and new leaves. (2) The differences in the spectral characteristics of different tree species were inconsistent between the old and new leaves. Only the old and new leaves of Betula costata, Ulmus laciniata, Acer buergerianum, and Pinus koraiensis showed more obvious differences in spectral curve characteristics. Betula costata showed significant differences in the spectral trilateral characteristics. (3) The correlation between leaf traits and spectra showed significant differences between old and new leaves, and the spectra have different abilities to indicate leaf traits. Near-infrared spectrum spectral reflectance is a better indicator of leaf nitrogen content for old leaves than for new leaves. In contrast, many spectral indices indicate better water and leaf carbon content in newer and older leaves. This study shows that there are not only differences in leaf properties and spectral characteristics but also differences in their correlations between old and new leaves. This study has a guiding significance for selecting representative leaves in the non-destructive observation of forest leaf properties.
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Received: 2023-04-25
Accepted: 2023-11-03
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Corresponding Authors:
YU Quan-zhou
E-mail: yuquanzhou2008@126.com
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[1] Pan Yude, Richard A Birdsey, Fang Jingyun, et al. Science, 2011, 333(6045): 988.
[2] LIU Shen-tao, GAO Peng, LIU Pan-wei, et al(刘胜涛, 高 鹏, 刘潘伟, 等). Acta Ecologica Sinica(生态学报), 2017, 37(10): 3302.
[3] XIE Ya-lin, LEI Xiang-dong, WANG Hai-yan, et al(解雅麟, 雷相东, 王海燕, 等). Forest Research(林业科学研究), 2019, 32(4): 57.
[4] Ian J Wright, Peter B Reich, Mark Westoby, et al. Nature: International Weekly Journal of Science, 2004, 428(6985): 821.
[5] XIAO Hua-shun, ZHANG Gui, FU Zhao-xi, et al(肖化顺, 张 贵, 符朝曦, 等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2018, 38(7): 8.
[6] LI Ya-juan, CAO Guang-min, LONG Rui-jun(李亚娟, 曹广民, 龙瑞军). Grassland and Turf(草原与草坪), 2015, 35(4): 32.
[7] MIAO Yan-ming, LÜ Jin-zhi, BI Run-cheng(苗艳明, 吕金枝, 毕润成). Bulletin of Botanical Research(植物研究), 2012, 32(4): 425.
[8] Pérez Harguindeguy N, D-az S, Garnier E, et al. Australian Journal of Botany, 2016, 64(8): 715.
[9] MENG Ting-ting, NI Jian, WANG Guo-hong(孟婷婷, 倪 健, 王国宏). Chinese Journal of Plant Ecology(植物生态学报), 2007,(1): 150.
[10] YU Quan-zhou, LIU Yu-jie, ZHOU Lei, et al(于泉洲, 刘煜杰, 周 蕾, 等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报), 2019, 39(12): 93.
[11] NIE Lei-chao, CUI Li-juan, LIU Zhi-jun, et al(聂磊超, 崔丽娟, 刘志君, 等). Acta Ecologica Sinica(生态学报), 2023,(12): 1.
[12] FU Zuo-qin, LÜ Mao-kui, LI Xiao-jie, et al(付作琴, 吕茂奎, 李晓杰, 等). Chinese Journal of Ecology(生态学杂志), 2019, 38(3): 648.
[13] Kikuzawa K, Lechowicz M J. Ecological Research Monographs, 2011: 57.
[14] GE Lu-lu, MENG Qing-quan, LIN Yu, et al(葛露露, 孟庆权, 林 宇, 等). Acta Botanica Boreali-Occidentalia Sinica(西北植物学报), 2018, 38(3): 544.
[15] Hallik L, Niinemets U, Kull O. Plant Biology (Stuttgart, Germany), 2012, 14(1): 88.
[16] Tucker Compton J. Remote Sensing of Environment, 1979, (2): 127.
[17] Carl F Jordan. Ecology, 1969, 50(4): 663.
[18] Huete A, Didan K, Miura T, et al. Remote Sensing of Environment, 2002, 83(1): 195.
[19] Gamon J A, Peñuelas J, Field C B. Remote Sensing of Environment, 1992, 41(1): 35.
[20] Penuelas J, Filella I, Biel C, et al. International Journal of Remote Sensing, 1993, 14(10): 1887.
[21] le Maire G, Francois C, Dufrene E. Remote Sensing of Environment, 2004, 89(1): 1.
[22] Geneviève Rondeaux, Michael Steven, Frédéric Baret. Remote Sensing of Environment, 1996, 55(2): 95.
[23] Broge N H, Leblanc E. Remote Sensing of Environment, 2001, 76(2): 156.
[24] Daniel A Sims, John A Gamon. Remote Sensing of Environment, 2002, 81(2): 337.
[25] Cho Moses A, Sobhan Istiak M, Skidmore Andrew K. Remote Sensing and Modeling of Ecosystems for Sustainability Ⅲ, 2006, 6298: 629805.
[26] LI Yong-mei, WANG Hao, ZHAO Yong, et al(李永梅, 王 浩, 赵 勇, 等). Acta Agriculturae Zhejiangensis(浙江农业学报), 2022, 34(4): 781.
[27] PAN Qing-mei, ZHANG Jin-song, ZHANG Jun-pei, et al(潘庆梅, 张劲松, 张俊佩, 等). Forest Research(林业科学研究), 2019, 32(6): 1.
[28] YAO Xia, ZHU Yan, TIAN Yong-chao, et al(姚 霞, 朱 艳, 田永超, 等). Scientia Agricultura Sinica(中国农业科学), 2009, 42(8): 2716.
[29] SONG Xiao, XU Duan-yang, HUANG Shao-min, et al(宋 晓, 许端阳, 黄绍敏, 等). Chinese Journal of Applied Ecology(应用生态学报), 2020, 31(5): 1636.
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