Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

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.

Automated summary

Remote sensing technology has played a significant role in geological exploration and mineral resource assessment. This paper provides a comprehensive overview of the challenges and opportunities in remote sensing-based lithological identification in vegetated regions. It reviews prior research, remote sensing data sources, and classification methodologies. The paper also addresses limitations and proposes promising avenues for future research, including the integration of multi-source data and exploration of novel remote sensing techniques and algorithms.