An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar
2. Materials and Methods
2.1. The Test Site
2.2. The GPR Survey Technique
2.3. The GPR Equipment
2.4. The Excavation for Validation Purposes
2.5. The Data Processing Framework
2.5.1. Preliminary Signal Processing Stage
- Zero-offset removal: GPR signal can be distorted by a low-frequency signal trend (known as “wow”) or initial direct current (DC) shifts, which can conceal the actual EM reflections. The result is a GPR trace with an average amplitude different from zero, which could affect the results of further signal processing steps. The application of a dewow filter is used to obtain GPR traces with a mean value equal to zero.
- Time-zero correction: In order to compare the reflection time and consequently the depth of the buried targets, it is necessary to set a unique time-zero point for the GPR data. However, due to factors such as the air gap between the transmitting antenna and the soil surface or the ground-level inhomogeneities, the position of the air-ground surface reflection could vary across the different A-scans. To this extent, the air layer between the signal source point and the ground was eliminated across the whole sequence of A-scans.
- Time-varying gain: The GPR signal rapidly attenuates when it propagates through the investigated media. This is due to the dispersive nature of the EM waves, which relates to the electrical properties of the medium. For this reason, the response from deep targets can be barely detected, especially in case of lossy materials. The application of a time-varying gain to each GPR trace compensates for the rapid fall of the signal, equalising the amplitudes and making the response from deeper targets clearer. In the present study, a spherical and exponential (SEC) function was employed to compensate the energy loss by applying a linearly increasing time gain combined with an exponential increase.
- Singular value decomposition (SVD) : The SVD filter aims to reduce the ringing noise, i.e., a repetitive type of clutter with a high correlation between traces, which can easily lead to data misinterpretation. On the other hand, reflections due to potential targets are more random and scattered, and therefore less correlated. The SVD filter operates by decomposing an image into a set of different sub-images, each of which contains features with a gradually increasing correlation. With this approach, ringing noise can be separated from the real response of the targets.
- Frequency-wavenumber (F-K) migration : In a GPR investigation, the response of a target is associated with a hyperbolic feature. This is caused by the difference in the travel time of the EM waves, while the antenna is moved along the scanning transect. Although this output is acceptable for target identification, the tracking of an object (e.g., tree roots) across several B-scans requires a more focused and accurate localisation. The F-K migration transforms an unfocused space-time GPR image into a focused image showing the object’s true location and size with the corresponding EM reflectivity. The velocity of the host medium in this paper is assumed as constant and it was estimated by means of a trial and error procedure between permittivity values over-migrating and under-migrating the data.
2.5.2. Analysis of Discontinuity Elements
2.5.3. Tree Root-tracking Algorithm
- Preliminary hypotheses: The proposed model is based on two main hypotheses regarding:
- The data acquisition method (longitudinal or circular transects), and
- The dielectric properties of the investigated medium.
- Data input: The algorithm expands upon GPR data from the pre-processing phase, in the form of a three-dimensional matrix of real numbers , composed by the signal amplitude values in a random point of coordinates (). The index indicates the number of GPR scans, limited to , the index corresponds to the scan direction, limited to , and is the vertical coordinate going into the ground, limited to . According to a reference polar coordinate system, the coordinates of a random point can be expressed as follows:
- Iterative procedure: The aforementioned assumptions and input information are essential to develop an iterative procedure for the tracking of a root system. Figure 5 shows a flowchart of the methodology followed in this stage.
- Target identification: The algorithm evaluates the amplitude values in a random position of the 3D domain. In order to filter out the amplitude values that did not likely relate to tree roots, a threshold was set. This threshold value is established a priori based on a preliminary analysis of the data collected, in an effort to isolate as many hyperbolas as possible. Hence, the algorithm is set to analyse the domain until a signal amplitude value greater than the threshold is found. This step is necessary to identify the apices of the reflection hyperbolae (i.e., the apices of the roots) and filter out amplitude values unrelated to candidate root targets.
- Correlation analysis: This step is focused on the investigation of further vertices in the closest vicinity of those identified at the target identification stage. This is performed to pinpoint other potential amplitude values greater than the threshold. This analysis has been improved in the present study compared to the original version presented in , as the area in which the correlation is sought has been extended to four further points within the 3D domain, i.e., a(i + 1, j − 1, k − 1), a(i + 1, j + 1, k − 1), a(i + 1, j − 1, k + 1), a(i + 1, j + 1, k + 1) (see Figure 5). This improvement is used to smooth the correlation analysis process, including all the points of the 3D domain that could ideally belong to the development of a root.
- Tracking of the root: The algorithm isolates correlated points, creating a vector for the mapping of individual roots.
- Reconstruction of the root system architecture in a 3D domain: Vectors identified in the previous step are positioned in a 3D environment in order to represent the geometry of the tree root system.
2.5.4. Root Mass Density Estimation
3.1. Preliminary Signal Processing Stage
3.2. Analysis of Discontinuity Elements: the Detection of a Buried Structure
3.3. Tree Root-Tracking Algorithm
3.4. The Root Mass Density Maps
3.5. Results Validation through Excavation
Conflicts of Interest
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|Root Mass Density Zoning|
|Depth [m]||x||y||Minimum Value [m/m3]||Maximum Value [m/m3]||Average Value [m/m3]||Standard Deviation [m/m3]|
|From [m]||To [m]||From [m]||To [m]|
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Lantini, L.; Tosti, F.; Giannakis, I.; Zou, L.; Benedetto, A.; Alani, A.M. An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar. Remote Sens. 2020, 12, 3417. https://doi.org/10.3390/rs12203417
Lantini L, Tosti F, Giannakis I, Zou L, Benedetto A, Alani AM. An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar. Remote Sensing. 2020; 12(20):3417. https://doi.org/10.3390/rs12203417Chicago/Turabian Style
Lantini, Livia, Fabio Tosti, Iraklis Giannakis, Lilong Zou, Andrea Benedetto, and Amir M. Alani. 2020. "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar" Remote Sensing 12, no. 20: 3417. https://doi.org/10.3390/rs12203417