A Multi-Data Approach Using Remote Sensing, GIS, and Geochemistry for Lateritic Nickel Exploration

Ian Suryani J; Gina Audina P. Alhabsyi; Andi Nurul Isma Yogie W. A

doi:10.31004/riggs.v5i2.8545

Authors

Ian Suryani J Universitas Pejuang Republik Indonesia
Gina Audina P. Alhabsyi Universitas Pejuang Republik Indonesia
Andi Nurul Isma Yogie W. A Universitas Pejuang Republik Indonesia

DOI:

https://doi.org/10.31004/riggs.v5i2.8545

Keywords:

Lateritic Nickel, Remote Sensing, GIS, Geochemistry, Prospectivity Mapping

Abstract

This study proposes a multi-data integration approach combining remote sensing, Geographic Information Systems (GIS), and geochemical data to enhance the accuracy of lateritic nickel prospectivity mapping. As the global demand for nickel, particularly for use in stainless steel production and electric vehicle batteries, increases, more efficient and accurate exploration methods are needed. This research evaluates the extent to which integrated datasets improve exploration outcomes compared to single-method approaches. The methodology integrates Sentinel-2 image processing, spectral index analysis (iron oxide, clay minerals, NDVI), GIS-based spatial modeling (lithology, slope, elevation, lineament density), and geochemical analysis (Ni, Fe, MgO, SiO2, Co). Using a simulated 2,000 ha study area, four prospect zones (A-D) were identified. Zone A exhibited the highest potential with an average Ni grade of 1.79% and a Prospectivity Index (PI) of 0.78, indicating a priority for exploration drilling. The results show that multi-data integration significantly improves mapping accuracy, with an improvement of 10-20% compared to single-method approaches. This study demonstrates the potential of integrating remote sensing, GIS, and geochemical data for early-stage lateritic nickel exploration, offering a systematic and cost-effective framework. The integration enhances exploration decision-making, reduces uncertainty, and allows for targeted exploration, ensuring more efficient use of resources. Future research should focus on enhancing the model through machine learning and geostatistical approaches to improve the accuracy and applicability of the model in real-world scenarios.

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