基于叶形分类的木本植物单叶片面积预测模型

doi:10.17521/cjpe.2024.0044

植物生态学报 ›› 2024, Vol. 48 ›› Issue (12): 1683-1691.DOI: 10.17521/cjpe.2024.0044 cstr: 32100.14.cjpe.2024.0044

基于叶形分类的木本植物单叶片面积预测模型

陈香蕾¹(), 崔树娟¹, 赵晨军¹, 顾洪亮¹, 陈晓萍²^,³, 李锦隆³, 孙俊¹^,³^,^*()

¹安庆师范大学资源环境学院, 安徽安庆 246052
²枣庄学院旅游与资源环境学院, 山东枣庄 277160
³福建师范大学地理科学学院, 福州 350007

收稿日期:2024-02-08 接受日期:2024-08-23 出版日期:2024-12-20 发布日期:2024-12-20
通讯作者: *孙俊(sunjunfjnu@aliyun.com)
基金资助:
国家自然科学基金(32071555)

Predictive model of single leaf area for woody plants based on leaf morphology classification

CHEN Xiang-Lei¹(), CUI Shu-Juan¹, ZHAO Chen-Jun¹, GU Hong-Liang¹, CHEN Xiao-Ping²^,³, LI Jin-Long³, SUN Jun¹^,³^,^*()

¹School of Resources and Environment, Anqing Normal University, Anqing, Anhui 246052, China
²College of Tourism and Resources Environment, Zaozhuang University, Zaozhuang, Shandong 277160, China
³College of Geographical Science, Fujian Normal University, Fuzhou 350007, China

Received:2024-02-08 Accepted:2024-08-23 Online:2024-12-20 Published:2024-12-20
Contact: *SUN Jun(sunjunfjnu@aliyun.com)
Supported by:
National Natural Science Foundation of China(32071555)

摘要/Abstract

摘要： 植物叶片形态特征和叶面积反映了植物重要的生理生态功能。该研究在不同物种中建立椭圆形和披针形2种叶片形态的叶面积预测模型, 以便无损、准确地预测叶面积的变化。采集了亚热带3个研究区59个物种, 共计4 061片叶片, 利用叶面积与叶片长度、叶片宽度之间的关系进行建模, 同时与叶片实际面积进行比较, 采用均方根误差、决定系数和预测精度来检验模型的适用性。结果表明: 1)不同物种叶面积的大小与叶片长度、叶片宽度间均存在显著的线性相关关系, 受长宽积的影响最大, 其相关系数为0.997; 2)以长宽积为自变量建立椭圆形和披针形叶面积分类预测模型, 其均方根误差为0.996和1.017, 决定系数分别为0.998和0.990, 分类预测精度为95.32%和94.89%, 且优于整体拟合精度92.76%; 3)进一步研究发现, 基于物种叶片长宽比均值分类的预测模型精度分别为95.53%和94.86%, 且预测模型精度随着物种间叶片长宽比变异系数增大而显著降低。该研究基于叶片形态分类建立的以长宽积为自变量的叶面积预测模型, 可以为椭圆形和披针形叶面积的快速测定提供便捷的方法。同时, 细分叶片形态能够增加模型的准确性, 减少叶片长宽比变异对叶面积预测模型的影响。

关键词: 分类拟合, 椭圆形叶片, 叶片长宽积, 叶形, 披针形叶片

Abstract:

Aims Leaf morphology and area variations are essential for understanding plant physiological and ecological functions. This study aims to develop non-destructive and accurate prediction models for leaf area with elliptical and lanceolate shapes across different species.

Methods A total of 4 061 leaf samples from 59 species across three subtropical natural reserves were collected. The relationships between leaf area and leaf length, width were modeled, and modeled leaf areas were compared with actual leaf areas. Model suitability was evaluated through root mean square error, coefficient of determination, and prediction accuracy.

Important findings The results indicate: 1) Significant linear correlations exist between leaf area and leaf length, width across different species, with the product of length and width having the most significant impact, achieving a correlation coefficient of 0.997. 2) Models based on length-width products for elliptic and lanceolate leaves showed root mean square errors of 0.996 and 1.017, determination coefficients of 0.998 and 0.990, and prediction accuracies of 95.32% and 94.89%, surpassing the overall accuracy of 92.76%. 3) Further analysis showed predictive model accuracy of 95.53% and 94.86%, based on mean length-to-width ratios of 59 species, decreasing as leaf length-to-width ratio increased. Therefore, these shape-classified models, using length-width products, offer a quick, accurate method for estimating leaf area in elliptic and lanceolate leaves. Additionally, refining leaf morphology classification improves model accuracy and minimizes the effect of length-to-width ratio variation on predictions.

Key words: classified fitting, elliptical leaf, leaf length-times-width, leaf shape, lanceolate leaf

陈香蕾, 崔树娟, 赵晨军, 顾洪亮, 陈晓萍, 李锦隆, 孙俊. 基于叶形分类的木本植物单叶片面积预测模型. 植物生态学报, 2024, 48(12): 1683-1691. DOI: 10.17521/cjpe.2024.0044

CHEN Xiang-Lei, CUI Shu-Juan, ZHAO Chen-Jun, GU Hong-Liang, CHEN Xiao-Ping, LI Jin-Long, SUN Jun. Predictive model of single leaf area for woody plants based on leaf morphology classification. Chinese Journal of Plant Ecology, 2024, 48(12): 1683-1691. DOI: 10.17521/cjpe.2024.0044

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2024.0044

https://www.plant-ecology.com/CN/Y2024/V48/I12/1683

图/表 8

表1 叶面积模型检验误差及预测精度统计量

Table 1 Statistical test error and prediction accuracy for the leaf area model

统计量 Statistic	符号 Symbol	公式 Formula	理想值 Ideal value
均方根误差 Root mean square error	E1	$E1=\sqrt{\frac{\mathop{\sum }^{}{{({{y}_{i}}-{{{\hat{y}}}_{i}})}^{2}}}{n}}$	0
预测精度 Predicted precision	E2	$E2=\frac{1}{n}\sum{\left [ 1-\left ( \frac{\left \| y_{i}-\hat{y}_{i} \right \| }{y_{i}} \right )\times 100\% \right ] }$	1
决定系数 Coefficient of determination (R²)	E3	$E3=1-\frac{\mathop{\sum }^{}{{({{y}_{i}}-{{{\hat{y}}}_{i}})}^{2}}}{\mathop{\sum }^{}{{({{y}_{i}}-{{{\bar{y}}}_{i}})}^{2}}}$	1

表2 两种不同叶片形态的长宽比特征

Table 2 Length-to-width ratio related characteristics for two different types of leaves

类型 Type	平均值 Mean value	标准差 Standard deviation	样本数 Sample size	变异系数 Coefficient of variation
椭圆形 Elliptic shape	2.21	0.450	2 936	20.36
披针形 Lanceolate shape	3.55	0.473	1 125	13.23

表3 叶片形态学特征间的Pearson相关性分析

Table 3 Pearson correlation analysis of leaf morphological characteristics

指标 Measurement	LL	LW	LL × LW	LL - LW	LL + LW	LL²	LW²	LL/LW
LW	0.680^**
LL × LW	0.870^**	0.904^**
LL - LW	0.935^**	0.376^**	0.662^**
LL + LW	0.976^**	0.823^**	0.942^**	0.836^**
LL²	0.969^**	0.667^**	0.897^**	0.902^**	0.948^**
LW²	0.621^**	0.966^**	0.909^**	0.319^**	0.767^**	0.643^**
LL/LW	0.417^**	-0.290^**	-0.110^**	0.711^**	0.220^**	0.346^**	-0.278^**
LA	0.857^**	0.907^**	0.997^**	0.645^**	0.933^**	0.886^**	0.914^**	-0.260^**

图1 三种叶面积预测模型的拟合效果对比。A, 整体拟合。B, 椭圆形叶片的拟合。C, 披针形叶片的拟合。

Fig. 1 Comparison of fitting results between three leaf area prediction models. A, Fitting for whole leaves. B, Fitting for elliptic leaves. C, Fitting for lanceolate leaves.

表4 叶面积预测模型的预测精度及误差统计量

Table 4 Statistical analysis of prediction accuracy and error for leaf area prediction models

方法 Method	均方根误差 Root mean square error	R²	预测精度 Predicted precision (%)	样本量 Sample number
椭圆形 Elliptic shape	0.897 6	0.997 5	94.90	1 706
披针形 Lanceolate shape	0.902 2	0.996 3	96.18	652
整体拟合 Overall fitting	1.580 7	0.991 3	91.34	3 050

图2 基于检验样本数据的叶面积预测模型拟合效果对比。A, 整体拟合。B, 椭圆形叶片的拟合。C, 披针形叶片的拟合。

Fig. 2 Comparison of fitting results of leaf area prediction models based on sample data. A, Fitting for whole leaves. B, Fitting for elliptic leaves. C, Fitting for lanceolate leaves.

图3 两种预测模型下检验样本数据预测叶面积与实测叶面积的残差分布。A, 整体拟合的残差分布。B, 分类拟合的残差分布。

Fig. 3 Residual distribution between the predicted leaf area and the observed leaf area fitted using the sample data analyzed with two prediction models. A, Residual distribution fitted for all leaves. B, Residual distribution fitted for classified leaves.

表5 检验样本数据在叶面积预测模型下验证的预测精度及误差统计量

Table 5 Statistical prediction accuracy and error of the leaf area prediction models for test sample data

方法 Method	均方根误差 Root mean square error	R²	预测精度 Predicted precision (%)	样本量 Sample number
椭圆形 Elliptic shape	0.996 0	0.998 9	95.32	504
披针形 Lanceolate shape	1.017 5	0.990 9	94.89	207
整体拟合 Overall fitting	1.666 7	0.997 3	92.76	1 011

参考文献 41

[1]	Aydinsakir K, Büyüktaş D (2009). Non-destructive leaf area estimation in carnation plants. Akdeniz Üniversitesi Ziraat Fakültesi Dergisi, 22, 83-89.
[2]	Chen XP, Lyu M, Wang MT, Hu DD, Sun J, Zhong QL, Cheng DL (2021). Trade-off relationship between light interception and leaf water shedding at different canopy positions of 73 broad-leaved trees of Yangji Mountain in Jiangxi Province, China. Scientia Sinica (Vitae), 51, 91-101.
	[ 陈晓萍, 吕敏, 王满堂, 胡丹丹, 孙俊, 钟全林, 程栋梁 (2021). 江西阳际峰73种阔叶植物叶片光截获和水分排除策略对树冠位置的响应研究. 中国科学: 生命科学, 51, 91-101.]
[3]	Cheng JJ, Mi XC, Ma KP, Zhang JT (2011). Responses of species-abundance distribution to varying sampling scales in a subtropical broad-leaved forest. Biodiversity Science, 19, 168-177.
	[ 程佳佳, 米湘成, 马克平, 张金屯 (2011). 亚热带常绿阔叶林群落物种多度分布格局对取样尺度的响应. 生物多样性, 19, 168-177.] DOI
[4]	Diao J, Lei XD, Hong LX, Rong JT, Shi Q (2010). Single leaf area estimation models based on leaf weight of Eucalyptus in Southern China. Journal of Forestry Research, 21, 73-76.
[5]	Feng DX, Shi SJ (2005). Research on night measurement methods of leaf area. Chinese Agricultural Science Bulletin, 21(6), 150-152. DOI
	[ 冯冬霞, 施生锦 (2005). 叶面积测定方法的研究效果初报. 中国农学通报, 21(6), 150-152.]
[6]	Gamiely S, Randle WM, Mills HA, Smittle DA (1991). A rapid and nondestructive method for estimating leaf area of onions. HortScience, 26, 206. DOI: 10.21273/HORTSCI.26.2.206.
[7]	Gielis J (2003). A generic geometric transformation that unifies a wide range of natural and abstract shapes. American Journal of Botany, 90, 333-338. DOI PMID
[8]	Guo YH, Zhao CB, Li SQ, Xu HX, Huang TQ, Lin SQ, Chen JW, Yang XH (2023). Study on inheritance patterns of leaf traits and fruit weight in loquat. Journal of Tropical and Subtropical Botany, 31, 62-68.
	[ 郭乙含, 赵崇斌, 李舒庆, 徐红霞, 黄天启, 林顺权, 陈俊伟, 杨向晖 (2023). 枇杷叶片性状与单果质量的遗传规律研究. 热带亚热带植物学报, 31, 62-68.]
[9]	Hu QP (2014). The Study of Plant Identification Technology Based on Leaves’ Shape Features. Master degree dissertation, Xidian University, Xi’an.
	[ 胡秋萍 (2014). 基于叶片形状特征的植物识别技术研究. 硕士学位论文, 西安电子科技大学, 西安.
[10]	Keramatlou I, Sharifani M, Sabouri H, Alizadeh M, Kamkar B (2015). A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Scientia Horticulturae, 184, 36-39.
[11]	Li JH, Peng GQ, Yang DM (2017). Effect of stem length to stem slender ratio of current-year twigs on the leaf display efficiency in evergreen and deciduous broadleaved trees. Chinese Journal of Plant Ecology, 41, 650-660.
	[ 李俊慧, 彭国全, 杨冬梅 (2017). 常绿和落叶阔叶物种当年生小枝茎长度和茎纤细率对展叶效率的影响. 植物生态学报, 41, 650-660.] DOI
[12]	Li XY, Yang PL, Ren SM, Zhang SY (2009). The model of prediction of transpiration for fruit tree based on leaf area and canopy radiation. Acta Ecologica Sinica, 29, 2312-2319.
	[ 李仙岳, 杨培岭, 任树梅, 张少炎 (2009). 基于叶面积与冠层辐射的果树蒸腾预测模型. 生态学报, 29, 2312-2319.]
[13]	Li YQ, Wang ZH (2021). Leaf morphological traits: ecological function, geographic distribution and drivers. Chinese Journal of Plant Ecology, 45, 1154-1172.
	[ 李耀琪, 王志恒 (2021). 植物叶片形态的生态功能、地理分布与成因. 植物生态学报, 45, 1154-1172.] DOI
[14]	Li ZZ, Xie F, Liu XM, Liu YY, Shao JW (2013). A comparative study on the accuracy of two commonly used methods of leaf area measurement. Chinese Agricultural Science Bulletin, 29(19), 193-197. DOI
	[ 李治中, 谢菲, 刘小梅, 刘云云, 邵剑文 (2013). 2种常用叶面积测量方法准确性的比较研究. 中国农学通报, 29(19), 193-197.]
[15]	Liu YJ, Liu YL, Wang CK, Wang XC (2023). Comparison of leaf cost-benefit relationship for five pinnate compound-leaf tree species in temperate forests of Northeast China. Chinese Journal of Plant Ecology, 47, 1540-1550.
	[ 刘艳杰, 刘玉龙, 王传宽, 王兴昌 (2023). 东北温带森林5个羽状复叶树种叶成本-效益关系比较. 植物生态学报, 47, 1540-1550.] DOI
[16]	Mazzini RB, Ribeiro RV, Pio RM (2010). A simple and non- destructive model for individual leaf area estimation in citrus. Fruits, 65, 269-275.
[17]	Nahas S, Arce OE, Ricci M, Romero ER (2019). Leaf area estimation of individual leaf and whole plant of chickpea (Cicer arietinum L.) by means of regression methods. Revista Agronómica del Noroeste Argentino, 39, 99-106.
[18]	Peppe DJ, Royer DL, Cariglino B, Oliver SY, Newman S, Leight E, Enikolopov G, Fernandez-Burgos M, Herrera F, Adams JM, Correa E, Currano ED, Erickson JM, Hinojosa LF, Hoganson JW, et al. (2011). Sensitivity of leaf size and shape to climate: global patterns and paleoclimatic applications. New Phytologist, 190, 724-739. DOI PMID
[19]	Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, Bret-Harte MS, Cornwell WK, Craine JM, Gurvich DE, Urcelay C, Veneklaas EJ, Reich PB, Poorter L, Wright IJ, et al. (2013). New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 61, 167-234.
[20]	Qu YC, Jiang YH, Chen HY, Zhang JG, Zhang XQ (2024). Dynamic change of stand leaf area for Chinese fir plantation. Acta Ecologica Sinica, 44, 5609-5620.
	[ 屈彦成, 江怡航, 陈涵玥, 张建国, 张雄清 (2024). 杉木人工林林分叶面积动态变化规律研究. 生态学报, 44, 5609-5620.]
[21]	Ren SM, Zhang WQ, Ou JJ, Luo YL, Chen YF, Chen M (2022). Leaf morphology and leaf area estimation model of two species of Lagerstroemia. Northern Horticulture, (21), 74-81.
	[ 任四妹, 张文琪, 欧鉴基, 罗银玲, 陈云飞, 陈梅 (2022). 两种紫薇属植物叶片形态及面积估算模型. 北方园艺, (21), 74-81.]
[22]	Schrader J, Pillar G, Kreft H (2017). Leaf-IT: an Android application for measuring leaf area. Ecology and Evolution, 7, 9731-9738. DOI PMID
[23]	Schrader J, Shi P, Royer DL, Peppe DJ, Gallagher RV, Li Y, Wang R, Wright IJ (2021). Leaf size estimation based on leaf length, width and shape. Annals of Botany, 128, 395-406. DOI PMID
[24]	Serdar Ü, Demirsoy H (2006). Non-destructive leaf area estimation in chestnut. Scientia Horticulturae, 108, 227-230.
[25]	Shao J, Chen XP, Li JL, Hu DD, Wang MT, Zhong QL, Cheng DL (2021). Nitrogen and phosphorus contents and resorption efficiency of thirty broadleaved woody plants in Yangjifeng, Jiangxi, China. Chinese Journal of Applied Ecology, 32, 1193-1200. DOI
	[ 邵静, 陈晓萍, 李锦隆, 胡丹丹, 王满堂, 钟全林, 程栋梁 (2021). 江西阳际峰30种阔叶树叶片氮磷含量及再吸收效率. 应用生态学报, 32, 1193-1200.] DOI
[26]	Shi P, Li Y, Niinemets Ü, Olson E, Schrader J (2021a). Influence of leaf shape on the scaling of leaf surface area and length in bamboo plants. Trees, 35, 709-715.
[27]	Shi P, Liu M, Yu X, Gielis J, Ratkowsky DA (2019). Proportional relationship between leaf area and the product of leaf length and width of four types of special leaf shapes. Forests, 10, 178. DOI: 10.3390/f10020178.
[28]	Shi P, Ratkowsky DA, Li Y, Zhang L, Lin S, Gielis J (2018). A general leaf area geometric formula exists for plants—Evidence from the simplified Gielis Equation. Forests, 9, 714. DOI: 10.3390/f9110714.
[29]	Shi P, Yu K, Niklas KJ, Schrader J, Song Y, Zhu R, Li Y, Wei H, Ratkowsky DA (2021b). A general model for describing the ovate leaf shape. Symmetry, 13, 1524. DOI: 10.3390/sym13081524.
[30]	Song LY, Yang T, Xia SG, Yin Z, Liu X, Li SP, Sun RB, Gao HJ, Chu HY, Ma C (2022). Soil depth exerts stronger impact on bacterial community than elevation in subtropical forests of Huangshan Mountain. Science of the Total Environment, 852, 158438. DOI: 10.1016/j.scitotenv.2022. 158438.
[31]	Suárez Salazar JC, Melgarejo LM, Durán Bautista EH, Di Rienzo JA, Casanoves F (2018). Non-destructive estimation of the leaf weight and leaf area in cacao (Theobroma cacao L.). Scientia Horticulturae, 229, 19-24.
[32]	Toumoulin A, Kunzmann L, Moraweck K, Sack L (2020). Reconstructing leaf area from fragments: testing three methods using a fossil Paleogene species. American Journal of Botany, 107, 1786-1797. DOI PMID
[33]	Tsukaya H (2005). Leaf shape: genetic controls and environmental factors. The International Journal of Developmental Biology, 49, 547-555.
[34]	Wang XL, Lu CY (2022). Morphological parameters, total organic carbon and total nitrogen contents of leaves of Laguncularia racemosa. Wetland Science, 20, 747-753.
	[ 王秀丽, 卢昌义 (2022). 拉关木叶片的形态参数、总有机碳和全氮含量. 湿地科学, 20, 747-753.]
[35]	Wu J, Hu SZ, Mao SY, Zou K, Zheng QY, Qiu QH, Shi JM (2020). Single leaf area model of Phyllostachys edulis based on leaf morphology. Scientia Silvae Sinicae, 56(8), 47-54.
	[ 巫娟, 胡姝珍, 茅思雨, 邹凯, 郑淇元, 邱啟璜, 施建敏 (2020). 基于叶片形态的毛竹单叶叶面积模型. 林业科学, 56(8), 47-54.]
[36]	Yan XL, Ji MC, Wu LL (2010). Study on bryophyte flora from Yangjifeng Nature Reserve, Jiangxi Province, China. Journal of Zhejiang University (Agriculture and Life Sciences), 36, 348-354.
	[ 严雄梁, 季梦成, 吴璐璐 (2010). 江西省阳际峰自然保护区苔藓植物区系研究. 浙江大学学报(农业与生命科学版), 36, 348-354.]
[37]	Yao W, Niinemets Ü, Yao W, Gielis J, Schrader J, Yu K, Shi P (2022). Comparison of two simplified versions of the gielis equation for describing the shape of bamboo leaves. Plants, 11, 3058. DOI: 10.3390/plants11223058.
[38]	You WB, Li Y, Zhou Y, He DJ (2023). Edge effect of Pinus massoniana forest converted into tea plantation on topsoil carbon content in Wuyishan National Park. Scientia Silvae Sinicae, 59(10), 41-49.
	[ 游巍斌, 李颖, 周艳, 何东进 (2023). 武夷山国家公园马尾松林改为茶园后影响表层土壤碳含量的林缘效应. 林业科学, 59(10), 41-49.]
[39]	Yu X, Shi P, Schrader J, Niklas KJ (2020). Nondestructive estimation of leaf area for 15 species of vines with different leaf shapes. American Journal of Botany, 107, 1481-1490. DOI PMID
[40]	Zhao NQ, Chen XN, Xu GF, Qiao JR, Yu M, Xu YX, Shi SY, Li BZ, Guo Y (2024). Leaf area measurement and empirical model construction of 14 garden greening plants. Journal of Temperate Forestry Research, 7(1), 9-14.
	[ 赵纳祺, 陈晓娜, 徐光甫, 乔靖然, 于猛, 许亚欣, 石善宇, 李彬州, 郭跃 (2024). 14种园林绿化植物的叶面积测量及经验模型构建. 温带林业研究, 7(1), 9-14.]
[41]	Zhao Q, Liu S, Chen K, Wang SJ, Wu CZ, Li J, Lin YM (2021). Change characteristics and influencing factors of soil organic carbon in Castanopsis eyrei natural forests at different altitudes in Wuyishan Nature Reserve. Acta Ecologica Sinica, 41, 5328-5339.
	[ 赵青, 刘爽, 陈凯, 王世君, 吴承祯, 李键, 林勇明 (2021). 武夷山自然保护区不同海拔甜槠天然林土壤有机碳变化特征及影响因素. 生态学报, 41, 5328-5339.]

基于叶形分类的木本植物单叶片面积预测模型

Predictive model of single leaf area for woody plants based on leaf morphology classification

全文

PDF (PC)

附录附件

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 41

相关文章 4

编辑推荐

Metrics

本文评价

[1]	余继梅, 吴福忠, 袁吉, 金遐, 魏舒沅, 袁朝祥, 彭艳, 倪祥银, 岳楷. 全球尺度上凋落物初始酚类含量特征及影响因素[J]. 植物生态学报, 2023, 47(5): 608-617.
[2]	李耀琪, 王志恒. 植物叶片形态的生态功能、地理分布与成因[J]. 植物生态学报, 2021, 45(10): 1154-1172.
[3]	薛伟, 李向义, 朱军涛, 林丽莎, 王迎菊. 遮阴对疏叶骆驼刺叶形态和光合参数的影响[J]. 植物生态学报, 2011, 35(1): 82-90.
[4]	赵聪蛟, 邓自发, 周长芳, 关保华, 安树青, 陈琳, 陆霞梅. 氮水平和竞争对互花米草与芦苇叶特征的影响[J]. 植物生态学报, 2008, 32(2): 392-401.