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基于探地雷达的白蜡树根系形态特征及生物量定量分析

Quantitative analysis of root morphologies and biomass of ash tree using ground penetrating radar

  • 摘要: 为了无损揭示树木地下根系的生长形态和空间分布,该研究提出了一种基于探地雷达(ground penetrating radar, GPR)的根系形态特征及生物量定量分析方法,以中国北方白蜡树(Fraxinus chinensis Roxb)为研究对象,首先在野外使用900 MHz的GPR对其根系进行方形网格法扫描;其次从GPR的扫描结果中进行根系定位、直径估测以及根系构型(root system architecture, RSA)三维重建,在此基础上将根系视为圆台体模型定量分析其长度、表面积、体积以及空间生长角度等形态特征,并建立根体积-生物量的关系模型以实现根系生物量的估计;最后将根系定量分析结果与整根挖掘测量结果进行对比。结果表明:GPR重建9年生白蜡树RSA的总长度、总表面积、总体积、生物量分别为786.5 cm、12588.9 cm219969.9 cm3和0.131 g/cm2,与整根挖掘结果相比,GPR定量分析各特征参数和生物量的准确率分别为66.77%、80.72%、93.84%和76.09%,表明了基于GPR无损定量分析根系形态特征和生物量具有可行性和有效性。该研究为树木根系生长的无损监测以及根系生物量积累的动态评估提供了有效手段。

     

    Abstract: Root morphologies can dominate the plant growth to absorb the water and nutrients. It is also required for the patterns of root morphology in the plant adaptation to ecological environments. However, some challenges are posed from the invisibility of plant root systems buried underground during the quantitative analysis of root morphologies. This study aims to achieve the non-destructive interpretation of root morphologies and root biomass. A quantitative analysis was also proposed on the root morphologies and root biomass using ground penetrating radar (GPR). A simple approach was provided to non-destructively reveal the growth form and spatial distribution of underground tree roots. The nine-year-old Chinese ash tree (Fraxinus chinensis Roxb.) was taken as the research subject in northern China. Initially, a 900 MHz GPR system was employed to conduct a square grid scan of the root system. A GPR C-scan data volume was generated with the root morphologies. Subsequently, the vertices of the root system reflection hyperbola in each B-scan image were extracted to spatially locate the root systems within the C-scan data volume. Additionally, the total propagation time of radar waves through the root systems was derived from the A-scan images intersecting the root system hyperbolic vertices. The root system diameter was then estimated after operation. After that, the localization of root system was facilitated to reconstruct the three-dimensional (3D) spatial distribution of the root system architecture (RSA). Finally, the root systems were modeled as the frustum-of-a-cone, in order to quantitatively analyze morphological characteristics, such as the length, surface area, volume, and spatial growth angles. A relationship model was established between the root volume and biomass. Non-destructive estimation of root biomass was realized using root volume characteristics. The feasibility and accuracy of the quantitative analysis was also validated for the root morphologies and root biomass. The whole root excavation was evaluated after validation. The research results indicated that: (1) The GPR was effectively detected the buried root systems, with a correct identification rate of 64.2%, primarily located at the depths of 0.1 to 0.4 m (76.9%); (2) The accurate locating root system was achieved in the vertices of the reflection hyperbola from the GPR C-scan data volume. There was an average root point localization error of 15.8%; (3) The diameter of root system was effectively estimated, according to the total propagation time of radar waves, indicating an average estimation error of 26.4%; (4) The quantitative analysis was feasible on the root morphologies using the frustum-of-a-cone model. The total length, total surface area, and total volume of the RSA reconstructed by GPR were 786.5 cm, 12588.9 cm², and 19969.9 cm³, respectively, with the estimation accuracies of 66.77%, 80.72%, and 93.84%, respectively; (5) The root volume was effectively represented the root biomass, with an estimated biomass of 0.131 g/cm² and an accuracy rate of 76.09%. The root-soil mechanics can also be expected to non-destructively monitor the root growth. Additionally, the dynamic assessment of root biomass accumulation was enhanced to understand the root growth and survival mechanisms. The great contribution are gained for the ecological adaptation strategies of the roots in complex environments. The finding can also provide the technical support for the forestry production and ecological protection.

     

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