Danboyi Joseph Amusuk
Geoscience and Digital Earth Centre (INSTEG), Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia;
Waziri Umaru Federal Polytechnic PMB 1034, Birnin Kebbi, Kebbi state, Nigeria
Mazlan Hashim
Geoscience and Digital Earth Centre (INSTEG), Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Amin Beiranvand Pour
Institute of Oceanography and Environment (INOS), University Malaysia Terengganu (UMT) 21030 Kuala Nerus, Terengganu, Malaysia
Jabar Habashi
Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Lithological mapping in semi-arid regions has witnessed a phase of transformation due to advancement in remote sensing technology. This has permitted a more comprehensive understanding of surface lithological units. This review explores the evolution of remote sensing mapping techniques and their diverse uses at semi-arid regions, underscoring the significance of the mapping procedure and the prospects. Remote sensing technology has been advancing with moderate to high resolution spaceborne and airborne sensors, unmanned aerial vehicle (UAV) technology and LiDAR (light detection and ranging). These have significantly enhanced capacity, accuracy and the scope of lithological mapping procedures. Especially, the advancement of machine learning and Artificial Intelligent (AI) in automated remote sensing data analysis has ignited more precise ways of identifying and classification of lithological units. Using hybrid remote sensing/machine learning mapping techniques has extended the horizon of geological studies where mineral exploration, water resource management, land use planning, environmental assessments, and risk mitigation are particularly considered. The maps derived provide deeper insights into accurate delineation of mineral deposits, identification of potential sources of water, and aiding those making informed decision making for land development and resource management. The importance of hybrid remote sensing/ machine learning techniques lies with the profound contributions made through geological history, resource exploration, environmental preservation, and risk management directed to fragile ecosystems such as semi-arid environments. The future of the hybrid methodologies holds promise for further advancements in integrating various data sources, exploitation of their contextual properties, refining AI algorithms for faster and more accurate analysis, and methodologies that are specific to environments. These evolving technologies and diverse applications present a trajectory targeted at more comprehensive utilization of geological resources and improvement of environmental stewardship even to fragile regions.
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Copyright © 2024 Danboyi Joseph Amusuk, Mazlan Hashim, Amin Beiranvand Pour, Jabar Habashi
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