采用染色示踪技术的土壤优先流自动分割与量化系统

针对土壤染色图像色度不一致、染色/非染色区域对比度低的特点，以及现有土壤染色图像分割方法自动化程度弱的问题，该研究提出一种土壤优先流自动分割与量化系统。该系统采用基于H分量改进的模糊C均值方法（Fuzzy C-Means Based on H Component and Morphology，HM-FCM）实现染色区域的自动分割，运用数学统计法提取总染色面积比、基质流深度、优先流比等特征参数，实现对土壤染色区域的量化分析，以揭示优先流的发育程度。并基于2种林地染色图像验证了系统性能。试验结果表明：1）HM-FCM法对于天然次生林和榛子林图像均具有最佳分割效果，其分割准确率为87.9%和83.3%，调和平均值为90.5%和80.3%；2）2种林地土壤染色区域总体集中于0～50 cm土层，优先流具有不同发育程度（P<0.05）。该系统可为优先流路径的空间演变提供技术支持和理论依据。

Automatic segmentation and quantitative analysis of soil preferential flow using dye tracer technology

Abstract: Preferential flow is widely considered to be a common phenomenon of water movement in soil. Currently, dye tracer can be one of the most efficient ways to characterize the preferential flow using soil-stained images. However, the general image processing software, such as Photoshop, Image Pro Plus, and Image J, cannot specifically extract the soil-stained images with inconsistent chromaticity and low contrast between dyed and non-dyed areas. A larger error occurs normally in the subsequent quantitative analysis for the preferential flow pathways. This study aimed to propose an automatic segmentation for preferential flow pathways using dyed tracer images and to further improve the accuracy and efficiency of quantification. An image processing was performed on the dyeing images of preferential flow, thereby quantitatively analyzing specific parameters. Firstly, brilliant blue dye was used to stain subsurface flow pathways in soil plots from natural secondary forest and hazelnut shrub forest during simulated rainfall events under dry conditions. The dyed tracer images were converted into the hue-saturation-value (HSV) space for the extraction of hue (H) component, in order to improve the contrast of dyed images and highlight the preferential flow path. Fuzzy C-means based on H component and morphology (HM-FCM) was selected to automatically segment the dyeing area. Morphological opening and closing arithmetics were used to fix under- and over-segmentation in the images. Secondly, mathematical statistics were selected to quantificationally analyze multiple indicators of soil preferential flow in the high-precision graphs of natural secondary forest and hazelnut shrub forest. The specific parameters included total dyeing area ratio, matrix flow depth, preferential flow ratio, and fractal dimension. The proposed segmentation well accurately identified the distribution of preferential flow pathways in forest soil and automatically segmented the dyeing area. Furthermore, multiple indicators were achieved for the subsequent evaluation of preferential flow and topological structure. Specifically, the preferential flow in the natural secondary forest occurred earlier than that in the hazelnut forest, whereas, the development degree of preferential flow in the natural secondary forest soil was higher than that in hazelnut forest soil. The dyeing areas of the two forests were generally concentrated in the soil layer of 0-50 cm, where the dyeing area ratio of hazelnut forest was higher than that of natural secondary forest. The water infiltration behaved mostly the uniform flow with less preferential flow. It was found that HM-FCM effectively segmented the soil dyeing areas of two forests. The segmentation accuracy was 87.9% for the images of natural secondary forest, and the harmonic mean was 90.5%, whereas, the segmentation accuracy was 83.3% for the images of hazelnut shrub forest, and the harmonic mean was 80.3%. There were different development degrees in the priority flow (P<0.05). The proposed automatic segmentation can be widely expected to identify the preferential flow and migration in the underground soil of various woodlands for sustainable forestry.