塔柏刺形叶特征与叶面积估算模型

doi:10.11707/j.1001-7488.LYKX20220379

摘要/Abstract

摘要：

目的: 刺形叶小且密生，传统的叶面积测定带来诸多不便，本研究旨在构建快速、准确测量塔柏刺形叶叶面积的模型。方法: 以塔柏刺形叶为材料，采用游标卡尺测量1 270片塔柏刺形叶的叶长（LL）、叶基宽（LBW）、最大叶宽（LW_max）以及叶厚（LT），使用Photoshop CS5图像处理软件对叶片的图像进行处理并计算叶面积（LA）。应用SPSS统计软件对叶形态学指标和叶面积进行统计分析，探究刺形叶形态学指标与叶面积的关系，并构建塔柏刺形叶叶面积估算模型。结果: 在塔柏刺形叶5个形态学指标中, LA的变异系数最大（CV=0.301），数值分布在7.307~7.556 mm²（95%CI），且LA与LL、LW_max呈显著正相关（r=0.858，0.794）。塔柏LA的最优多变量线性回归模型为：Y=?3.879+0.718 X₁+5.679 X₂?1.177 X₃（R²=0.914，RMSE=0.667，AIC=2 060.969），其中X₁、X₂、X₃分别为LL、LW_max、LBW，预测精度为96.21%。LA的最优单变量模型为基于LL的模型：LA=?1.686+1.003 X（R²=0.725，RMSE=1.191，AIC=3 238.133），预测精度为91.16%。结论: 本研究为准确估算塔柏刺形叶叶面积提供了简洁的方法，也为研究刺形叶性状指标之间的关系提供了数据基础。

关键词: 塔柏, 刺形叶, 叶面积, 估算模型, 图像处理

Abstract:

Objective: Leaf area is an important parameter in evaluating a plant's ability to photosynthesize and how well it can adapt to its surroundings. The tiny and dense growth of spiny leaves causes numerous difficulties in the traditional measurement of leaf area. Therefore, the aim of this study was to construct a rapid and accurate model for measuring the leaf area of the spiny leaves in Juniperus chinensis cv. Pyramidalis. Method: The leaf length (LL), base width (LBW), maximum leaf width (LW_max) and leaf thickness (LT) of 1 270 leaves from J. chinensis were measured using a vernier caliper, and the leaf area (LA) was determined through leaf image using Photoshop CS5 software. Then the relationships between the morphological indexes and leaf area were analyzed to further construct an estimation model of leaf area for the spiny leaves of J. chinensis using SPSS statistical software. Result: LA had the greatest coefficient of variation (CV=0.301) among the five morphological indexes and ranged from 7.307–7.556 mm² (95% CI). LA was significantly and positively correlated with LL and LW_max (r=0.858, 0.794). The optimal multivariate linear regression model for LA was Y=?3.879+0.718 X₁+5.679 X₂?1.177 X₃ (R²=0.914, RMSE=0.667, AIC=2060.969), where X₁, X₂ and X₃ were LL, LW_max and LBW respectively, with a prediction accuracy of 96.21%. The optimal univariate model for LA was based on LL, Y=?1.686+1.003 X (R²=0.725, RMSE=1.191, AIC=3238.133), with a prediction accuracy of 91.16%. Conclusion: This study provides a concise method for accurately estimating the area of spiny leaf of J. chinensis and a basic data for studying the relationship between spiny leaf traits.

Key words: Juniperus chinensis cv. Pyramidalis, spiny leaf, leaf area, estimation model, image processing

中图分类号:

S791.45

王露露,张有福,陈一博,陈春艳,宋晨慧. 塔柏刺形叶特征与叶面积估算模型[J]. 林业科学, 2023, 59(5): 81-87.

Lulu Wang,Youfu Zhang,Yibo Chen,Chunyan Chen,Chenhui Song. Leaf Characteristics and Leaf Area Estimation Model of the Spiny Leaf in Juniperus chinensis cv. Pyramidalis[J]. Scientia Silvae Sinicae, 2023, 59(5): 81-87.

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统计量Statistics	指标Measurements
统计量Statistics	LA/mm²	LL/mm	LW_max/mm	LBW/mm	LT/mm
样本量Sample size	1 270	1 270	1 270	1 270	1 270
平均数Mean	7.433	9.004	1.014	0.780	0.385
平均数的标准误差 SE of mean	0.063	0.052	0.006	0.004	0.002
标准偏差 Standard deviation	2.235	1.907	0.209	0.126	0.061
均值的95％置信区间上限 Upper 95％ CI of mean	7.556	9.110	1.026	0.788	0.388
均值的95％置信区间下限 Lower 95％ CI of mean	7.307	8.898	1.003	0.774	0.382
最小值Minimum	1.192	2.550	0.500	0.400	0.200
最大值Maximum	14.124	12.700	1.600	1.200	0.600
变异系数 Coefficient of variation	0.301	0.212	0.206	0.162	0.158
偏度 Skewness	?0.629	?1.027	0.041	0.078	0.096
峰度Kurtosis	?0.090	0.431	?0.160	0.092	?0.207
平均绝对误差 Mean absolute deviation	1.787	1.510	0.164	0.098	0.050

指标 Index	LL/mm	LW_max/mm	LBW/mm	LT/mm	LA/mm²
LL/mm
LW_max/mm	0.486^**
LBW/mm	0.219^**	0.499^**
LT/mm	0.375^**	0.589^**	0.441^**
LA/mm²	0.858^**	0.794^**	0.341^**	0.503^**

方法 Methods	模型 Model $Y=a+\displaystyle\sum {b}_{i}{X}_{i}$ $ i=1—4 $	自变量X_i Independent Variable X_i	系数 Coefficient	标准误差 Standard Error	显著性 Significance	共线性诊断 VIF	95%置信区间 95% Confidence Interval
方法 Methods	模型 Model $Y=a+\displaystyle\sum {b}_{i}{X}_{i}$ $ i=1—4 $	自变量X_i Independent Variable X_i	系数 Coefficient	标准误差 Standard Error	显著性 Significance	共线性诊断 VIF	下限 Lower	上限 Upper
全入 Complete substitution method	i=1	（常量Constant）	?3.755	0.170	<0.001		?4.088	?3.423
	R²=0.914	LL	0.720	0.012	<0.001	1.330	0.695	0.745
	RMSE =0.666	LW_max	5.780	0.140	<0.001	1.964	5.506	6.055
	AIC=2 059.602	LBW	?1.104	0.204	<0.001	1.353	?1.540	?0.704
		LT	?0.800	0.439	0.069	1.576	?1.662	0.062
逐步 Stepwise method	i=2	（常量Constant）	?1.686	0.175	<0.001		?2.030	?1.342
	R²=0.725	LL	1.003	0.019	<0.001	1.000	0.965	1.041
	RMSE =1.191
	AIC=3 238.133
	i=3	（常量Constant）	?4.497	0.117	<0.001		?4.726	?4.267
	R²=0.911	LL	0.725	0.013	<0.001	1.302	0.700	0.749
	RMSE =0.678	LW_max	5.331	0.116	<0.001	1.302	5.104	5.558
	AIC =2 492.517
	i=4	（常量Constant）	?3.879	0.156	<0.001		?4.184	?3.573
	R²=0.914	LL	0.718	0.012	<0.001	1.314	0.693	0.742
	RMSE =0.667	LW_max	5.679	0.128	<0.001	1.653	5.427	5.931
	AIC=2 060.969	LBW	?1.177	0.200	<0.001	1.300	?1.569	?0.785

模型 Model	自变量X_i Independent Variable X_i	参数Parameters			统计量Statistics
模型 Model	自变量X_i Independent Variable X_i	a	b	N	R²	RMSE	AIC	P
线性函数 Linear function ${\rm{LA}}=ax+b$	LL	1.003	?1.686	1 016	0.725	1.191	3 238.133	<0.001
	LW_max	8.544	?1.270	1 016	0.620	1.400	3 567.673	<0.001
	LBW	5.323	3.115	1 016	0.077	2.183	4 469.881	<0.001
	LT	17.974	0.402	1 016	0.222	2.004	4 295.968	<0.001
指数函数 Exponential function ${\rm{LA}}=a{{\rm{e}}}^{bx}$	LL	1.366	0.180	1 016	0.776	0.187	3 401.459	<0.001
	LW_max	1.653	1.419	1 016	0.566	0.260	3 899.519	<0.001
	LBW	3.198	0.973	1 016	0.086	0.378	4 519.311	<0.001
	LT	2.109	3.081	1 016	0.216	0.350	4 379.768	<0.001
幂函数 Power function ${\rm{LA}}=a{x}^{b}$	LL	0.356	1.367	1 016	0.805	0.174	3 261.810	<0.001
	LW_max	7.060	1.427	1 016	0.630	0.242	3 691.125	<0.001
	LBW	8.320	0.756	1 016	0.093	0.376	4 508.263	<0.001
	LT	22.468	1.520	1 016	0.242	0.344	5 033.099	<0.001

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