Integrated Sentinel-2 and UAV Remote Sensing for Rare-Metal Pegmatite–Greisen Exploration: Evidence from the Central Kalba–Narym Belt, East Kazakhstan

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Authors: Marzhan Rakhymberdina, Roman Shults, Baitak Apshikur, Yerkebulan Bekishev, Yevgeniy Grokhotov, Azamat Kapasov, Damir Mukyshev
Cited by: 3
References: 54

Abstract

Rare-metal pegmatite–greisen systems are commonly small, structurally controlled, and difficult to delineate using conventional mapping alone. This study proposes a multiscale remote-sensing workflow for prospecting Li–Nb–Ta–Cs mineralisation in the Kalba–Narym rare-metal belt (East Kazakhstan) by integrating Sentinel-2 multispectral imagery, UAV-derived centimeter-scale orthomosaics, structural (lineament) analysis, and field-based mineralogical–geochemical validation. Sentinel-2 responses were first calibrated using known occurrences to derive alteration proxies related to greisenisation, silicification, Na-metasomatism, and oxidation. These proxies were combined into an Integrated Hydrothermal Alteration Index (IHAI) to highlight areas where multiple alteration processes overlap. Lineament mapping from Sentinel-2 and DEM products indicates dominant NW–SE and NE–SW structural trends, zones of elevated lineament density and intersection systematically coincide with high IHAI values. UAV orthomosaics refine satellite-scale anomalies by resolving quartz-vein networks, fracture corridors, and surface-alteration textures that are not detectable at 10–20 m resolution. Mineralogical and geochemical data confirm that high-IHAI targets correspond to albitised pegmatites and greisenised rocks enriched in Li, Nb, Ta, and Cs. The results demonstrate that combining freely available Sentinel-2 data with UAV observations and targeted ground validation provides a cost-effective and transferable framework for reducing false positives and prioritising exploration targets in structurally complex granitoid terranes.

Fields of Study & Subjects

Geochemistry and Geologic Mapping Geological and Geochemical Analysis Remote-Sensing Image Classification

Publication details

Journal / source
Geosciences
Publisher
MDPI AG
Volume
16
Issue
3
Pages
130–130
Publication date
21 March 2026
Language
EN