Tree Roots Characterization Through Spectral Analysis and Machine Learning of Ground Penetrating Radar Data

Spectral analysis of data acquired using ground penetrating radar (GPR) allows for the evaluation of the amplitudes and frequencies associated with reflections generated by subsurface materials and their interaction with electromagnetic waves. This interaction produces a unique response for each material type. In this study, we tested two widely used time-frequency tools (the short-time Fourier transform [STFT] and power spectral density [PSD]) to characterize the subsurface roots of three distinct tree species: Jacaranda mimosifolia , Libidibia ferrea , and Handroanthus impetiginosus . Furthermore, we developed an artificial neural network (ANN) to distinguish the evaluated species, complementing the spectral analysis. GPR data were collected using a 900 MHz antenna within an area containing all 3 species. Through spectral and ANN analysis of 200 A-scans (single radar traces) per species, we were able to differentiate them in the frequency domain, demonstrating the potential of signal processing techniques for mapping tree roots. Validation was achieved through excavation of the site around L. ferrea (which was suppressed), enabling the accurate identification of each root encountered. Using spectral analysis and ANN, it was possible to differentiate the root system of the 3 species evaluated using GPR data.