基于eCognition植物叶片气孔密度及气孔面积快速测算方法

Fast estimation of stomatal density and stomatal area of plant leaves based on eCognition

【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。

[Objective]Leaf 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. [Method] This 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,brightness characteristics and geometric features of the objects. [Result]The 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,brightness value 160-220,red light band 〉95,shape-density index 1. 5-2. 2. [Conclusion] Theprecision 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.