Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions. The article begins by introducing the sources and processing methods of RS data, which serve as the foundation for subsequent analysis. Moreover, it highlights the techniques and methodologies employed for lithological classification in vegetated areas. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact accurate lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to enhance classification accuracy and the exploration of novel RS technologies and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

This study addresses engineering challenges associated with sandy residual deposits in the coastal zone of Quanzhou, China, characterized by high void ratios (e > 0.8), low cohesion (c < 10 kPa), and strong liquefaction tendencies induced by marine dynamic forces. Focusing on the beach sands of Shenhu Bay and Qingshan Bay, 123 in situ dynamic penetration tests and 12 laboratory physical–mechanical tests (including water content, particle gradation, relative density, and triaxial shear strength) were conducted. The correlations between the physical and mechanical properties of these coastal sandy soils and their foundation bearing capacity were systematically analyzed. Results reveal that the sands, predominantly medium-to-fine grains with 8–15% biogenic debris, are generally in a loose-to-medium dense state (relative density ~34%), with negligible cohesion. Shear strength depends primarily on the internal friction angle (28.89–37.43°). Correlation analyses show that water content (17.8–31.92%) and particle gradation parameters (uniformity coefficient C_u and curvature coefficient C_c) significantly influence bearing capacity, with bearing capacity increasing by 12.15% per 14.12% rise in water content and 35% per 0.518 increase in C_c. An improved foundation bearing capacity model based on the Prandtl–Reissner theory is proposed by integrating particle gradation and water content, tailored for beach foundations in Quanzhou. Model validation demonstrates an average error of approximately 15%, outperforming traditional models. These findings provide valuable theoretical support for assessing foundation stability in building construction projects in Quanzhou and similar coastal regions.

In this study, we investigate the bearing capacity characteristics and controlling mechanisms of coastal beach sand in Quanzhou Bay, Fujian Province, China. The results provide support for coastal engineering construction and vehicle trafficability assessment in beach areas, while field sampling, laboratory static plate load tests, and data-based modeling were conducted to examine the effects of moisture content, particle size distribution, and relative density on the bearing behavior of beach sand. In total, 52 groups of static load tests were performed, with the results showing that relative density is the dominant controllable factor affecting the bearing capacity of coastal beach sand. When the relative density increased from 40% to 65%, the ultimate load increased by 80%, and the deformation modulus increased by 139.9%. The optimal relative density range was approximately 52–65%, and the improved particle size distribution enhanced bearing performance. The ultimate load of well-graded sand was 60% higher than that of poorly graded sand, and moisture content showed a threshold effect, with the best mechanical performance occurring at a moisture content of about 7%, whereas excessive moisture content significantly reduced the bearing capacity. Under natural conditions, the proportional limit load of medium-dense coastal beach sand in Quanzhou Bay was approximately 200 kPa, the ultimate load was 250 kPa, and the characteristic value of bearing capacity was 125 kPa, while the dominant failure mode was general shear failure. A semi-empirical bearing capacity model was also developed; its average relative error was 10.35%, indicating that it has both physical meaning and engineering applicability. The findings provide a reference for foundation design evaluation in Quanzhou Bay and similar coastal beach areas.