A Depth-Adaptive Filtering Method for Effective GPR Tree Roots Detection in Tropical Area

This study presents a technique for processing step-frequency continuous-wave (SFCW) ground-penetrating radar (GPR) data to detect tree roots. SFCW GPR is portable and enables precise control of energy levels, balancing depth and resolution tradeoffs. However, the high-frequency components of the transmission band suffer from poor penetrating capability and generate noise that interferes with root detection. The proposed time-frequency filtering technique uses a short-time Fourier transform (STFT) to track changes in frequency spectrum density over time. To obtain the filter window, a weighted linear regression (WLR) method is used. By adopting a conversion method that is a variant of the chirp Z transform (CZT), the time-frequency window filters out frequency samples that are not of interest when doing the frequency-to-time domain data conversion. The proposed depth-adaptive filter window can self-adjust to different scenarios, making it independent of soil information, and effectively determines subsurface tree roots. The technique is successfully validated using SFCW GPR data from actual sites in a tropical area with different soil moisture levels, and the 2-D radar map of subsurface root systems is highly improved compared to existing methods.

Automated summary

This study introduces a technique for using step-frequency continuous-wave (SFCW) ground-penetrating radar (GPR) data to detect tree roots. The proposed time-frequency filtering technique utilizes a short-time Fourier transform (STFT) to track changes in frequency spectrum density over time. A depth-adaptive filter window is used to effectively determine subsurface tree roots, and the technique is successfully validated using SFCW GPR data from actual sites in a tropical area with different soil moisture levels, significantly improving the 2-D radar map of subsurface root systems compared to existing methods.