Integration of Remote Sensing, GIS, and Geochemical Data for Delineating Prospective Zones of Lateritic Nickel Deposits in a Mining Area

Zulfahmi Zulfahmi; Dwi Yolanda Sumbung

doi:10.31004/riggs.v5i2.8398

Authors

Zulfahmi Zulfahmi Universitas Pejuang Republik Indonesia
Dwi Yolanda Sumbung Universitas Pejuang Republik Indonesia

DOI:

https://doi.org/10.31004/riggs.v5i2.8398

Keywords:

Lateritic Nickel, Remote Sensing, GIS, Geochemistry, Prospectivity

Abstract

This study develops an integrated remote sensing, geographic information system (GIS), and geochemical framework for delineating prospective zones of lateritic nickel deposits during early-stage exploration. The research responds to the need for a rapid and spatially consistent method to prioritize drilling targets in tropical ultramafic terrains where subsurface data are commonly limited. Sentinel-2 imagery was processed to derive vegetation, iron oxide, clay mineral, and moisture indicators, while DEM/SRTM and geological data were used to evaluate slope, elevation, lithology, and structural lineaments. A simulated geochemical dataset consisting of Ni, Fe, MgO, SiO2, and Co values from 30 sampling points was integrated with the spatial layers through normalization and weighted overlay analysis. The resulting prospectivity index classified the 2,000 ha study area into low, moderate, high, and very high potential classes. The model identified four prospect zones, with Zone A showing the strongest response, indicated by an average Ni grade of 1.79% and a prospectivity index of 0.78. Zone B was interpreted as a secondary target, whereas Zones C and D require limited or low-priority follow-up. These findings indicate that multisource geospatial and geochemical integration can improve exploration efficiency, reduce interpretation uncertainty, and support systematic target ranking before detailed drilling. The approach remains conceptual and should be validated using actual field measurements, drilling logs, laboratory assays, and objective weighting methods in future applications.

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References

I. E. Agency, Global Critical Minerals Outlook 2025. Paris: IEA, 2025.

N. W. Brand, C. R. M. Butt, and M. Elias, "Nickel laterites: Classification and features," AGSO Journal of Australian Geology & Geophysics, vol. 17, no. 4, pp. 81-88, 1998.

M. Elias, "Nickel laterite deposits: Geological overview, resources and exploitation," in Giant Ore Deposits: Characteristics, Genesis and Exploration, D. R. Cooke and J. Pongratz, Eds. Hobart: University of Tasmania, 2002, pp. 205-220.

E. E. Marsh and E. D. Anderson, Ni-Co Laterite Deposits: U.S. Geological Survey Open-File Report 2011-1259. Reston, VA: U.S. Geological Survey, 2011.

C. R. M. Butt and D. Cluzel, "Nickel laterite ore deposits: Weathered serpentinites," Elements, vol. 9, no. 2, pp. 123-128, 2013, doi: 10.2113/gselements.9.2.123.

P. Freyssinet, C. R. M. Butt, R. C. Morris, and P. Piantone, "Ore-forming processes related to lateritic weathering," in Economic Geology 100th Anniversary Volume, J. W. Hedenquist, J. F. H. Thompson, R. J. Goldfarb, and J. P. Richards, Eds. Littleton, CO: Society of Economic Geologists, 2005, pp. 681-722.

E. J. M. Carranza, Geochemical Anomaly and Mineral Prospectivity Mapping in GIS. Amsterdam: Elsevier, 2008.

G. F. Bonham-Carter, Geographic Information Systems for Geoscientists: Modelling with GIS. Oxford: Pergamon Press, 1994.

Copernicus Data Space Ecosystem, "Sentinel-2 mission and data products," European Space Agency/Copernicus, accessed 2026.

F. F. Sabins, Remote Sensing: Principles and Interpretation, 3rd ed. New York: W. H. Freeman and Company, 1997.

N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote Sensing of Environment, vol. 202, pp. 18-27, 2017, doi: 10.1016/j.rse.2017.06.031.

T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill, 1980.

P. Goovaerts, Geostatistics for Natural Resources Evaluation. New York: Oxford University Press, 1997.

E. H. Isaaks and R. M. Srivastava, An Introduction to Applied Geostatistics. New York: Oxford University Press, 1989.

A. P. Crosta and J. M. Moore, "Enhancement of Landsat Thematic Mapper imagery for residual soil mapping in SW Minas Gerais State, Brazil," in Proc. 7th Thematic Conf. Remote Sensing for Exploration Geology, 1989, pp. 1173-1187.

T. G. Farr et al., "The Shuttle Radar Topography Mission," Reviews of Geophysics, vol. 45, RG2004, 2007, doi: 10.1029/2005RG000183.

G. Matheron, "Principles of geostatistics," Economic Geology, vol. 58, no. 8, pp. 1246-1266, 1963, doi: 10.2113/gsecongeo.58.8.1246.

NASA Earthdata, "Sentinel-2 Multispectral Instrument," National Aeronautics and Space Administration, accessed 2026.

U.S. Geological Survey, "Shuttle Radar Topography Mission/SRTM digital elevation data," USGS, 2018.

N. Zhang, K. Zhou, and X. Du, "Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China," Journal of African Earth Sciences, vol. 128, pp. 84-96, 2017, doi: 10.1016/j.jafrearsci.2016.12.011.