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 (RIGGS: Journal of Artificial Intelligence and Digital Business, 2026)
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.