Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

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.