Fast estimation of stomatal density and stomatal area of plant leaves based on eCognition
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摘要:目的叶片气孔是植物与外界进行物质交换的重要窗口，对环境变化十分敏感。如何快速、精确地获得气孔密度和开放程度数据仍缺乏成熟的方法与技术，本研究旨在探索植物叶片气孔密度及气孔面积的快速测算方法，为今后植物气孔研究工作提供参考。方法以北京市常见绿化树种白蜡、臭椿和国槐叶片为研究对象，采用面向对象分类的eCognition图像处理软件，对叶片气孔显微图像进行多尺度分割和分类识别，根据对象的光谱特征、亮度特征和几何特征构建规则并进行气孔分类和提取。结果气孔分割的最佳参数及自动提取规则组合为：尺度参数120~125、形状参数0.7、紧凑度参数0.9、亮度值160~220、红光波段>95、形状-密度指数1.5~2.2。结论该方法提取气孔密度和气孔面积的精度分别达到99.2%、94.5%，结果较理想，适用于植物叶片气孔信息的快速提取。Abstract:ObjectiveLeaf stomatal is a main channel used as exchange matter between plants and environment, which is very sensitive to environmental changes. How to calculate stomatal area and openness data quickly and accurately still lacks mature methods and techniques. This paper aims to explore the quantitative calculation of leaf stomatal density and stomatal area, and provide reference for future research on plant stomatal by this way.MethodThis study chose the leaf of Fraxinus pennsylvanica, Ailanthus altissima and Sophora japonica as objects, analyzing stomatal information by multi-scale segmentation and classification recognition and classifying the leaf stomatal microscopic images via eCognition image processing software. The stomatal imagines were classified and identified based on the spectral characteristics, bcenterness characteristics and geometric features of the objects.ResultThe results showed that the best parameters of the stomatal division and the combination of automatic extraction rules were: scale parameters 120-125, shape parameter 0.7, compactness parameter 0.9, bcenterness value 160-220, red light band> 95, shape-density index 1.5-2.2.ConclusionThe precision of stomatal density and stomatal area extracted by this method was 99.2% and 94.5%, respectively and the results were satisfactory. So the method is suitable for rapid extraction of stomatal information in plant leaves.
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图 2 多尺度分割与实测密度差值散点图
Figure 2. Scatter plot of d-value between multi-scale segmentation and measured density
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图 3 气孔分割精度
B.亮度值Bcenterness value，L1.蓝光波段Layer 1 blue band，L2.绿光波段Layer 2 green band，L3.红光波段Layer 3 red band，S.形状特征Shape feature
Figure 3. Stomatal segmentation accuracy
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图 4 气孔图像分割及提取过程
a、b、c.尺度参数分别为30、180和120；d.形状参数为0.1，紧致度参数为0.9；e.形状参数为0.9，紧致度参数为0.1；f.形状参数为0.7，紧致度参数为0.9；g、h、i、j、k、l分别为亮度特征、蓝光波段、绿光波段、红光波段、亮度值+红光波段、亮度值+红光波段+形状特征(图示为臭椿)。
Figure 4. Segmentation and extraction process of stomatal image
a, b and c, partitioning scales of 30, 180 and 120; d, shape parameter is 0.1, compactness parameter is 0.9; e, shape parameter is 0.9, compactness parameter is 0.1; f, shape parameter is 0.7 and compactness parameter is 0.9; g, h, i, j, k and l, bcenterness characteristics, blue band, green band, red band, bcenterness value + red band, bcenterness value + red band + shape feature (Ailanthus altissima used as an example).
表 1 3个树种叶片性状特征
Table 1 Leaf traits of three tree species
树种Tree species 叶面积Leaf area/cm2 质地Texture 被毛情况
Leaf hair condition公园Park 街道Street 白蜡Fraxinus pennsylvanica 24.637±1.432 21.866±2.209 革质Coriaceous 光滑Smooth 臭椿Ailanthus altissima 45.017±5.230 36.940±4.043 薄革质Thin coriaceous 粗糙Rough 国槐Sophora japonica 11.134±2.005 8.903±1.002 纸质Papery 被毛Hair cover 
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表 2 分割参数及提取规则对气孔影像解译结果显著性差异分析
Table 2 Significance difference analysis in interpretation results of stomatal images based on segmentation parameters and extraction rules
变异来源
Source of variation参数间Inter-parameter 环境间Inter-environment 树种间Inter-tree F P F P F P 分割参数Segmentation parameter 28.675 0.000 1 14.507 0.982 1 18.092 0.341 0 亮度特征Bcenterness characteristic 63.564 0.004 5 25.991 0.453 2 25.667 0.543 1 光谱特征Spectrum characteristic 45.321 0.003 3 34.987 0.067 5 32.131 0.204 1 形状特征Shape characteristic 7.834 0.002 1 2.012 0.078 5 18.098 0.982 1
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表 3 分类对象特征阈值范围
Table 3 Threshold range of classification object feature
对象Object 亮度特征
Bcenterness characteristics光谱特征Spectrum characteristics 形状特征
Shape feature蓝光波段Layer 1 blue band 绿光波段Layer 2 green band 红光波段Layer 3 red band 气孔Stoma 160~220 >175 165~250 >95 1.5~2.2 非气孔Non-stoma >200 < 185 < 185 < 95 < 1.5
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表 4 最佳分割参数及提取规则组合阈值范围
Table 4 Threshold range of the best segmentation parameters and extraction rules
分割参数Segmentation parameter 提取规则Extraction rule 参数Parameter 阈值范围Threshold range 规则Rule 阈值范围Threshold range 尺度参数Scale parameter 120~125 亮度特征值Bcenterness characteristic value 160~220 形状参数Shape parameter 0.7 红光波段值Layer 3 red band >95 紧致度参数Compactness parameter 0.9 形状特征值Shape characteristic value 1.5~2.2
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表 5 气孔提取精度
Table 5 Accuracy of stomatal extraction
环境
Environment树种
Tree species气孔密度/(个·mm-2)
Stomatal density/(number·mm-2)气孔面积
Stomatal area/μm2提取值
Extraction value镜检值
Measured value差值
Deference value精度
Accuracy提取值
Extraction value镜检值
Measured value差值
Deference value精度
Accuracy街道Street 臭椿Ailanthus altissima 179±9 176±7 2±2 100.0 341±15.7 331±22.4 10.5±1.3 97.1 白蜡Fraxinus pennsylvanica 284±6 285±7 1±1 99.6 185±16.4 196±19.6 11.1±2.6 94.4 国槐Sophora japonica 247±10 249±9 2±1 99.1 310±21.5 318±21.2 8.4±3.2 97.5 公园Park 臭椿Ailanthus altissima 229±7 244±18 4±1 98.3 233±8.4 255±25.3 11.2±1.5 95.7 白蜡Fraxinus pennsylvanica 399±8 167±15 2±2 98.9 396±12.5 158±22.1 13.1±1.8 94.6 国槐Sophora japonica 295±9 198±18 3±1 98.9 298±13.2 184±17.5 17.8±5.4 93.0
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表 6 提取结果与实测值差值显著性分析
Table 6 Significance analysis in the difference between extraction result and measured value
变异来源
Variance source气孔密度Stomatal density 气孔面积Stomatal area 离差平方和
Sum of squares of deviationdf 均方
Mean squareF Sig. 离差平方和
Sum of squares of deviationdf 均方
Mean squareF Sig. 环境间Inter-environment 0.003 1 0.003 0.004 0.948 0.225 1 0.225 1.639 0.201 树种间Inter-tree 2.867 2 1.433 2.195 0.113 0.506 2 0.253 1.842 0.160 误差Error 232.506 357 0.653 48.867 357 0.137 总计Total 235.376 360 49.598 360
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