Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.

Active layer thickness (ALT) is a sensitive indicator of response to climate change. ALT has important influence on various aspects of the regional environment such as hydrological processes and vegetation. In this study, 57 ground-penetrating radar (GPR) sections were surveyed along the Qinghai-Tibet Engineering Corridor (QTEC) during 2018-2021, covering a total length of 58.5 km. The suitability of GPR-derived ALT was evaluated using in situ measurements and reference datasets, for which the bias and root mean square error were approximately -0.16 and 0.43 m, respectively. The GPR results show that the QTEC ALT was in the range of 1.25-6.70 m (mean: 2.49 +/- 0.57 m). Observed ALT demonstrated pronounced spatial variability at both regional and fine scales. We developed a statistical estimation model that explicitly considers the soil thermal regime (i.e., ground thawing index, TIg), soil properties, and vegetation. This model was found suitable for simulating ALT over the QTEC, and it could explain 52% (R-2 = 0.52) of ALT variability. The statistical model shows that a difference of 10 degrees C.d in root TIg is equivalent to a change of 0.67 m in ALT, and an increase of 0.1 in the normalized difference vegetation index (NDVI) is equivalent to a decrease of 0.23 m in ALT. The fine-scale (<1 km) variation in ALT could account for 77.6% of the regional-scale (approximately 550 km) variation. These results provide a timely ALT benchmark along the QTEC, which can inform the construction and maintenance of engineering facilities along the QTEC.

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (theta) and the soil dielectric constant (epsilon). The theta-epsilon relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai-Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai-Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as upsilon-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within +/- 0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m(3)/m(3). While when the GPR velocity interpretation error is larger than +/- 0.004 m/ns, a piecewise formula calculation method, combined with the upsilon-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai-Tibet plateau permafrost regions.

Using ground-penetrating radar (GPR), we measured and estimated the ice thickness of the Baishui River Glacier No. 1 of Yulong Snow Mountain. According to the position of the reflected media from the GPR image, combined with the radar waveform amplitude and polarity change information, the ice thickness and the changing medium position at the bottom of this temperate glacier were identified. Water paths were found in the measured ice, including ice caves and crevasses. A debris-rich ice layer was found at the bottom of the glacier, which produces strong abrasion and ploughing action at the bedrock surface. This results in the formation of different detrital layers stagnated at the ice-bedrock interface and numerous crevasses on the bedrock surface. Based on the obtained ice thickness and differential GPS data, combined with Landsat images, the kriging interpolation method was used to obtain grid data. The average ice thickness was 52.48 m and between 4740 and 4890 m above sea level, with a maximum depth of 92.83 m. The bedrock topography map of this area was drawn using digital elevation model from the Shuttle Radar Topography Mission. The central part of the glacier was characterized by small ice basins with distributed ice steps and ice ridges at the upper and lower parts.