Ground Penetrating Radar
Abstract: Microwave radar testing of tree trunks is one of the ways for the trunk interior evaluation. The interpretation of the radar images can be a very complex task - among others due to the roughness of the tree bark. This paper studies the influence of a surface roughness on radar data of observed cylindrical objects, trees in particular. During our study, we did numerical simulations and laboratory measurements to compare radar data obtained by testing a cylinder with a smooth and an irregular surface. Then, several real trees with different surfaces and internal structures were tested to validate our findings. Those experiments indicate that the presence of a rough and irregular bark can significantly inhibit our ability to study the internal structure of the tree with the radar. On the other hand, if the bark is smooth, it is possible to infer the internal composition of the tree even for highly heterogeneous specimens.
Trees are a very important part of humans’ lives. They are crucial for oxygen production, they are a necessary source of construction material, they have an indisputable effect on the climate and they have a significant influence on well-being in urban areas. Then, it is inevitable to pay attention to their condition. As a result of a natural degradation of wood and a human intervention to trees and their habitats, the stability of tree trunks is constantly decreasing which leads to endangering people and infrastructures. In order to prevent collapses of trees, it is highly important to investigate their internal structure . From the macroscopic point of view, tree trunks are composed of bark, sapwood and heartwood with different mechanical properties. Within them, we can observe natural defects (knots, reaction wood, cross grain, etc.) or biological degradation (caused by fungi, insects etc.).
There are several destructive and non-destructive methods for tree trunk evaluation . Core drilling, knife test or penetrometer testing are examples of the destructive ways for tree trunk inspection . Gilbert et al. , Lin et al.  or Brancheriau et al.  used ultrasonic tomography to detect decayed wood in living tree trunks. This method is based on emitting sonic waves into the trunk and evaluating its mechanical state by the sonic waves responses. Guyot et al.  and Elliott et al.  used electrical resistance tomography (ERT) for evaluating the internal structure of tree trunks. ERT measures the subsurface distribution of electrical resistance (which is a function of humidity, density, etc.) with several electrodes following a specific geometric pattern along an investigated object. Van den Bulcke et al.  used X-ray tomography for analysing tree rings to measure the age of trees. Ground-penetrating radar (GPR) is being increasingly used as a non-invasive device for tree trunk interior inspection. It is based on emitting electromagnetic waves into media and capturing them after scattering on the internal structures of the studied object.
Nicolotti et al.  compared three non-destructive methods for the tree trunk investigation, namely, electric, ultrasonic and georadar tomography. Al Hagrey  also tested the same techniques for tree trunks and the root zone in order to evaluate their moisture. Butnor et al.  used the GPR in order to detect decays in tree trunks and provided a comparison between data obtained from gymnosperms and angiosperms. Lorenzo et al.  used the GPR to test a root zone of trees and tree trunks. They observed advantages of using a metal sheet to increase reflections from the other side of the tree. Fu et al.  provided a living tree trunk ray-based tomography using the GPR in a reflection and transmission mode. Mazurek and Łyskowki  tested two shielded antennas (1.6 GHz and 0.8 GHz) for a tree trunk inspection in both reflection and transmission mode in a longitudinal direction to the tree. Li et al.  proposed a ray-based tomography of a living tree trunk and compared a polar and real cross-section shape of a data visualisation. Takahashi and Aoike  combined the reflection and transmission mode of the GPR to distinguish heartwood and decay in logs and living tree trunks.
Tree trunk inspection using GPR is a very complex discipline for several reasons. First, living wood is a humid material with a very variable water content (which can range from about 30% to more than 200% of the weight of wood substance ) which is the cause of the electromagnetic waves attenuation. Second, significant heterogeneity and anisotropy of wood further complicate the interpretation of the radar data as the relative permittivity of wood depends also on a grain direction [19,20]. Third, an irregular shape of a tree trunk does not allow for a good contact or at least a constant distance between a radar antenna and the tree trunk surface. Having various distances between the surface and the antenna leads to irregular surface reflections in radar images which are not straightforward to filter out. And last, but not least, in order to inspect tree trunks, it is essential to keep a good contact of the antenna with bark for better impedance matching. This is not always possible due to the bark roughness.
The influence of the surface roughness on radar data was already very well described by Pinel et al.  who studied wave scattering from multilayered random rough surfaces for road applications. Tosti et al.  dealt with the roughness of a railway ballast during evaluation of its dielectric properties with the GPR. Ardekani et al.  studied scattering and attenuation of radar signal due to a vegetation cover. Lambot et al.  and Jonard et al.  observed the soil roughness influence on the monostatic GPR signal inversion to retrieve surface moisture.
The objective of this paper is to see the influence of the tree trunks rough surface on GPR images. In that respect, we simulated two cylindrical configurations with a smooth and rough surface using a finite difference time domain simulator, namely, gprMax2D [26,27]. To check the validity of the numerical simulations, two laboratory measurements on a corresponding cylindrical model were done. The laboratory model contained also a smooth and a rough surface. Finally, four radar acquisitions on real trees with various surfaces and internal structures were carried out. Two trees with a rough bark and two trees with a smooth bark were chosen. Both pairs of trees consisted of a relatively healthy tree and a tree with a visible cavity. Data were processed using two filtering methods: free-space response subtraction and the average background removal. For more intuitive readability of the GPR images, the cartesian radargrams were projected to the polar coordinates.
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