Integration of remote sensing and GIS for mineral exploration and resource estimation in complex geological terrains

 Corresponding author: Indira Gregorio Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Integration of remote sensing and GIS for mineral exploration and resource estimation in complex geological terrains Indira Gregorio * Mines’ Metallurgical and Materials Engineering Department, Colorado School of Mines, USA. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 Publication history: Received 08 October 2025; revised on 15 November 2025; accepted on 18 November 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.25.2.0334 Abstract The integration of Remote Sensing and Geographic Information Systems (GIS) has transformed mineral exploration by enabling rapid, synoptic, and cost-effective assessment of complex geological terrains. Modern exploration increasingly relies on multispectral, hyperspectral, and radar-based remote sensing techniques to detect surface mineralogical signatures, structural discontinuities, alteration zones, and geomorphological features that traditionally required extensive ground surveys. These technologies provide continuous spatial coverage and the ability to monitor inaccessible or rugged regions, offering a broader perspective on regional geology and mineralization patterns before narrowing down to high-potential targets. Within this broader framework, GIS serves as the analytical backbone for organizing, integrating, and interpreting heterogeneous geoscientific datasets. By combining satellite imagery, geophysical surveys, geochemical samples, digital elevation models, and structural information, GIS enables multicriteria decision analysis and spatial modeling to prioritize exploration zones. Advanced techniques such as spectral unmixing, lineament extraction, mineral mapping algorithms, and spatial probability modeling further refine the identification of ore-bearing lithologies and hydrothermal alteration systems. These integrations enhance predictive accuracy, reduce exploration risk, and optimize resource allocation. Narrowing the focus to resource estimation, remote sensing-derived datasets increasingly support quantitative assessments by delineating ore boundaries, estimating volumetric extents, and improving the resolution of 3D geological models. Machine learning and geostatistical techniques embedded within GIS platforms allow extrapolation of subsurface characteristics from surface indicators, improving preliminary reserve classification. Furthermore, these integrated workflows facilitate sustainable exploration by reducing environmental footprints and guiding targeted field verification campaigns. Overall, the combined use of remote sensing and GIS presents a powerful, data-driven approach to mineral exploration and resource estimation, providing both a comprehensive regional overview and precise, high-resolution insights necessary for modern mining decision-making. Keywords: Remote Sensing; GIS; Mineral Exploration; Resource Estimation; Hyperspectral Imaging; Geological Mapping 1. Introduction 1.1. Background: Global Demand for Minerals and Technological Advancements Global demand for minerals has increased significantly as industrialization, infrastructure expansion, and the transition toward renewable energy accelerate worldwide. Critical minerals such as lithium, cobalt, copper, and rare earth elements have become central to modern technological systems, including electric vehicles, battery storage, telecommunications, and advanced manufacturing [1]. This heightened demand places pressure on governments and mining companies to identify new deposits efficiently while minimizing exploration risks and environmental impact. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 122 Traditional geological field surveys, though essential, are resource-intensive and limited in their ability to cover vast or inaccessible terrains [2]. Advancements in remote sensing technologies have transformed mineral exploration by enabling rapid, synoptic assessment of large landscapes. Multispectral, hyperspectral, thermal, and radar sensors allow researchers to detect mineralogical signatures, alteration halos, structural disruptions, and geomorphological indicators relevant to ore formation [3]. The increasing spatial and spectral resolution of modern satellite missions such as Sentinel-2, Landsat 8, and commercial hyperspectral systems has improved the precision of lithological mapping and enhanced discrimination of mineral assemblages [4]. These innovations enable exploration teams to identify potential targets before deploying costly field campaigns. Complementing remote sensing, Geographic Information Systems (GIS) provide robust tools for integrating diverse geoscientific datasets, performing spatial modeling, and supporting multi-criteria analysis [5]. Together, remote sensing and GIS create a comprehensive, scalable workflow for prioritizing exploration zones and supporting informed decisionmaking. The combined approach is particularly valuable in complex terrains where structural, metamorphic, and erosional processes can obscure mineralization patterns detectable only through advanced spectral and spatial analysis [6]. As demand intensifies, integrated geospatial technologies continue to play an increasingly vital role in supporting efficient, data-driven mineral discovery strategies [7]. 1.2. Challenges of Exploration in Complex Geological Terrains Exploration in complex geological terrains poses substantial scientific and operational difficulties. These regions often include highly deformed metamorphic belts, volcanic arcs, faulted sedimentary basins, or deeply weathered shields where traditional indicators of mineralization may be obscured or altered by geological processes [8]. Structural overprinting, metamorphic re-equilibration, and surface cover such as vegetation or regolith can mask lithological boundaries and hydrothermal signatures, complicating field-based mapping efforts. Additionally, rugged terrain and limited accessibility hinder systematic surveying, making it challenging to implement ground sampling, geophysical measurements, or trenching programs. In many regions, logistical barriers drive exploration costs upward and restrict the density of field observations. Remote sensing technologies help overcome these constraints but require sophisticated interpretation to distinguish true mineralogical anomalies from environmental or atmospheric noise [3]. Complex terrains also introduce uncertainty into resource estimation. Variations in rock fabric, alteration intensity, and mineral distribution introduce spatial heterogeneity that complicates subsurface modeling [6]. These challenges underscore the need for integrated approaches that merge remotely sensed datasets with spatial analytics to refine geological understanding. Ultimately, addressing these complexities requires methods capable of capturing regional context while supporting precise, localized assessment of mineral potential [9]. Research Aim, Need for Integrated Remote Sensing–GIS Framework, and Contribution This study aims to evaluate how the integration of remote sensing and GIS can enhance mineral exploration and resource estimation in geologically complex terrains where traditional methods alone may be insufficient [4]. The research addresses the need for a unified framework capable of synthesizing spectral, spatial, structural, and geochemical datasets to improve target identification and minimize uncertainty during early exploration phases [2]. The study contributes by outlining a comprehensive geospatial workflow that leverages multispectral and hyperspectral imagery, digital elevation models, and GIS-based spatial modeling to support efficient mineral prospecting [5]. It demonstrates how integrated datasets can reveal hidden structural controls, detect hydrothermal alteration zones, and refine volumetric estimations necessary for preliminary resource classification [7]. Through a systematic evaluation of tools and processes, the paper offers a structured approach for improving exploration accuracy and reducing operational risk in terrains where geological complexity often obscures mineralization indicators [1]. 2. Remote sensing technologies for mineral exploration 2.1. Multispectral and Hyperspectral Imaging: Principles and Mineralogical Signatures Multispectral and hyperspectral imaging have become indispensable tools for mineral exploration due to their ability to capture diagnostic spectral responses associated with different mineral groups. Multispectral sensors measure Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 123 reflected sunlight across a limited number of broad spectral bands, enabling the identification of major lithological units and broad alteration features [6]. In contrast, hyperspectral sensors acquire data across hundreds of narrow, contiguous spectral bands, allowing for the detection of subtle absorption features linked to specific minerals such as clays, carbonates, iron oxides, and sulfates [7]. This higher spectral resolution supports precise mineral mapping, particularly in terrains where alteration halos or mineralogical variations are difficult to observe through field-based surveys alone. The underlying principle of hyperspectral analysis relies on the fact that each mineral exhibits characteristic absorption features associated with electronic transitions or vibrational processes occurring at the molecular level [8]. By analyzing reflectance curves and matching them to known spectral libraries, geoscientists can detect the presence and abundance of minerals that commonly signal hydrothermal systems, such as kaolinite, sericite, alunite, and hematite [9]. These minerals serve as proxies for identifying potential ore-forming environments even when underlying mineralization is not directly visible at the surface. Advancements in imaging spectroscopy have expanded the utility of remote sensing to geologically complex regions where structural deformation and metamorphism complicate traditional mapping approaches. Hyperspectral imagery enables differentiation between mineral assemblages formed during different geological events, supporting detailed reconstruction of alteration pathways [10]. Additionally, the integration of multispectral and hyperspectral data improves the robustness of classification models by combining broad spatial coverage with detailed spectral fidelity [11]. This dual approach enhances exploratory efficiency, reduces field-mapping uncertainty, and provides a scalable platform for both regional reconnaissance and localized mineral prospecting in diverse geological settings. 2.2. Optical, Thermal, and Radar Sensors in Geological Mapping Optical remote sensing systems remain essential for geological mapping because they offer high spatial resolution across visible and near-infrared wavelengths, enabling the extraction of lithological boundaries and structural features such as fractures and lineaments [12]. Optical sensors are particularly valuable for mapping surface mineralogy in semiarid or exposed regions where vegetation cover is minimal, allowing geological units to be distinguished based on reflectivity and colorimetric variations [6]. However, optical data are limited by atmospheric interference and inability to penetrate vegetation or surface cover, motivating the use of complementary sensor types. Figure 1 Classification of Remote Sensing Sensors Used in Mineral Exploration Thermal infrared (TIR) sensors extend the observational capability of remote sensing by detecting emitted radiation associated with surface temperature. Minerals exhibit distinct thermal emissivity characteristics, enabling the differentiation of silicates, carbonates, and sulfates through emissivity spectra [13]. TIR imaging is particularly effective in identifying quartz-rich lithologies and igneous compositions that may not exhibit strong absorption features in shorter wavelengths [10]. Radar systems, including synthetic aperture radar (SAR), provide structural and textural information independent of lighting conditions and cloud cover, making them highly useful in tropical or persistently cloudy regions [14]. Radar backscatter is sensitive to surface roughness, slope, moisture, and dielectric properties, enabling the detection of Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 124 geological structures such as faults, shear zones, and lithological contacts [15]. These structural insights are crucial for identifying pathways of hydrothermal fluid migration and locating potential mineralized corridors. Together, optical, thermal, and radar sensors offer a complementary suite of geological mapping tools that overcome individual sensor limitations. Their integration provides a more comprehensive representation of surface conditions and subsurface indicators. This combined perspective is structured more clearly when visualized through Figure 1, placed after this subsection, which illustrates the classification of sensors commonly used in mineral exploration [16]. 2.3. Spectral Indices, Alteration Zone Detection, and Lithological Discrimination Spectral indices derived from multispectral and hyperspectral data are essential for identifying mineralogical anomalies associated with hydrothermal alteration and weathering processes. Indices such as the Normalized Difference Vegetation Index (NDVI), Clay Mineral Ratio, and Ferric Iron Index enhance subtle spectral differences that may not be directly observable in raw imagery [17]. These enhancements facilitate rapid detection of alteration zones that commonly accompany ore-forming systems, including argillic, phyllic, and propylitic assemblages [12]. Hydrothermal alteration mapping is especially critical in terrains where mineralization signatures are obscured by complex geological overprinting. By analyzing absorption depths and spectral curvature at key wavelengths, geoscientists can differentiate between mineral groups such as kaolinite–illite series or hematite–goethite pairs, supporting more accurate reconstruction of hydrothermal pathways [9]. This spectral-based approach allows for early targeting of potential mineralized zones before conducting field verification. Lithological discrimination benefits greatly from spectral transformation techniques, including principal component analysis, minimum noise fraction, and matched filtering algorithms. These tools isolate significant spectral information while reducing noise, enabling more reliable classification of rock types across heterogeneous landscapes [11]. Integration with digital elevation models enhances discriminatory power by correlating lithological boundaries with geomorphological parameters such as slope, curvature, and drainage patterns [14]. By combining spectral indices, alteration mapping, and lithological classification, remote sensing provides a multifaceted analytical framework for identifying mineral-rich environments. This integrated approach is particularly valuable in structurally complex terrains where geological units are spatially fragmented or deeply weathered. Ultimately, the synergy of spectral analysis and spatial modeling improves exploration accuracy while reducing reliance on expensive and logistically challenging ground surveys [6]. 3. GIS-based spatial analysis and geological data integration 3.1. GIS as a Decision-Support Framework for Geoscience Geographic Information Systems (GIS) function as an essential decision-support framework in modern geoscience because they enable the integration, management, and visualization of spatially referenced datasets critical for mineral exploration. Traditional geological assessments often rely on fragmented information from field surveys, geophysical campaigns, and satellite observations, but GIS provides a unified environment where these datasets can be harmonized and analyzed coherently [15]. Through layered mapping, GIS allows geoscientists to overlay diverse datasets such as lithology, structural maps, alteration indices, and geophysical anomalies to identify spatial correlations that may indicate subsurface mineralization [16]. Spatial modeling techniques enhance this capability by incorporating mathematical and statistical tools into exploratory workflows. Functions such as buffer analysis, density mapping, and spatial interpolation help quantify geological relationships and reveal patterns not readily visible in raw datasets [17]. Data harmonization is also central to GIS, ensuring consistency between datasets acquired from different sensors, scales, or coordinate systems. This is particularly important when combining remote sensing imagery with point-based geochemical measurements or linebased structural datasets [18]. GIS also supports temporal tracking of geological processes by managing time-series datasets derived from remote sensing missions. This allows users to detect environmental changes, monitor mining disturbances, and evaluate exploration impacts. The ability to generate 2D and 3D visualizations further strengthens interpretive accuracy by enabling geologists to explore complex terrains in a virtual setting [19]. Ultimately, GIS provides the analytical backbone for mineral exploration, enabling efficient data integration, enhanced interpretability, and robust decision-making in regions characterized by geological complexity [20]. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 125 3.2. Integration of Remote Sensing, Geophysical, and Geochemical Datasets The integration of remote sensing, geophysical, and geochemical datasets within a GIS environment significantly improves mineral exploration outcomes by leveraging the strengths of each discipline. Remote sensing provides regional-scale surface information that identifies lithological units, structural fabrics, and hydrothermal alteration zones detectable through spectral signatures [21]. When merged with geophysical datasets such as magnetic, gravity, and electromagnetic surveys explorers gain insight into subsurface conditions that cannot be inferred directly from surface data alone [17]. Geophysical methods help delineate buried faults, intrusive bodies, and density contrasts associated with mineralized systems. Their integration with remote sensing-derived structural interpretations enhances the reliability of mapping fluid pathways and mineral deposition zones [18]. Digital Elevation Models (DEMs) further contribute by revealing geomorphological patterns, drainage anomalies, and terrain breaks that may align with subsurface structures [22]. DEM-derived slope, curvature, and watershed metrics often correlate with lithological boundaries and fault systems, assisting in lineament extraction and structural interpretation. Geochemical datasets including soil, stream-sediment, and rock assays provide ground-truth confirmation of anomalies detected through geophysical and remote sensing techniques. By incorporating these data into GIS, analysts can link surface spectral anomalies with elemental concentrations, increasing confidence in exploration targets [19]. Spatial correlation tools, such as cluster analysis and hotspot mapping, help identify geochemical halos or indicator minerals that support exploration models. Overlay analysis forms the basis of integrating these heterogeneous datasets by enabling weighted combinations of different evidence layers. This generates exploration potential maps highlighting areas where multiple indicators converge. The placement of Table 1 after this subsection supports clarity by summarizing dataset roles in mineral exploration workflows [23]. The combined use of remote sensing, geophysics, and geochemistry within GIS thus augments interpretive depth, reduces uncertainty, and enhances strategic exploration planning in complex geological environments [24]. 3.3. Spatial Multi-Criteria Decision Analysis (SMCDA) and Weighted Overlay Methods Spatial Multi-Criteria Decision Analysis (SMCDA) provides a structured and quantitative approach for ranking exploration targets by integrating diverse geoscientific indicators within GIS. Unlike traditional qualitative interpretation, SMCDA introduces a systematic method for assigning weights to different evidence layers based on their relevance to mineralization processes [18]. Weighted overlay methods, frequently used in mineral prospectivity mapping, allow multiple datasets such as alteration indices, geophysical anomalies, structural density, and geochemical concentrations to be combined into a composite exploration potential model [16]. The process begins with standardizing input layers to a common scale, ensuring compatibility across variables that may differ in units or measurement techniques. Expert judgment, analytic hierarchy processes, or machine learning–based weighting schemes may be used to assign significance scores to each dataset [20]. The weighted layers are then aggregated to produce a final prospectivity map highlighting zones with the highest exploration likelihood. SMCDA is particularly advantageous in complex terrains where geological signals are fragmented or ambiguous. By quantifying relative contributions of each factor, the method reduces subjective interpretation and increases transparency in target prioritization [22]. Additionally, sensitivity analysis helps evaluate the influence of weight adjustments, improving model robustness. The integration of remote sensing, geophysical, and geochemical datasets into SMCDA facilitates holistic evaluation of mineral systems by capturing both surface and subsurface indicators [21]. Overall, SMCDA strengthens decision-making by combining scientific rigor with spatial analytics, enabling exploration teams to allocate resources more effectively and identify high-potential targets under uncertainty [19]. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 126 Table 1 Primary Geoscientific Datasets and Their Role in Mineral Exploration Models Dataset Type Description Typical Parameters / Data Products Role in Mineral Exploration Models Remote Sensing Data (Multispectral and Hyperspectral) Satellite or airborne spectral imaging used to detect mineralogical signatures and surface alteration patterns. Spectral reflectance curves, band ratios, vegetation indices, alteration indices (e.g., FDI, LSI). Identifies alteration zones, maps lithologies, supports early reconnaissance, and provides regional-scale mineral prospectivity signals. Geophysical Data (Magnetic, Gravity, Radiometric, EM) Subsurface physical property datasets that reveal structural and lithological variations. Total magnetic intensity, Bouguer anomaly, K/Th/U radiometrics, resistivity models. Maps deep structures, identifies faults and intrusions, assists in locating ore-hosting lithologies or conductive/ magnetic anomalies associated with mineralization. Geochemical Data (Soil, Stream Sediment, Rock Sample Assays) Surface sampling datasets capturing elemental concentrations and geochemical anomalies. Major and trace element concentrations, pathfinder elements, anomaly threshold maps. Reveals geochemical halos, detects mineralizing trends, improves target prioritization when combined with geological layers. Digital Elevation Models (DEMs) Terrain-height data derived from LiDAR, photogrammetry, or radar. Slope, aspect, curvature, topographic roughness metrics. Assists structural/lineament extraction, drainage analysis, geomorphological interpretation, and supports terrain-correction of other datasets. Geological Maps (1:50,000 – 1:250,000) Digitized or scanned national/regional geological mapping datasets. Lithological boundaries, fault traces, fold axes, stratigraphy, age domains. Provides foundational geospatial framework, constraints for validation, and training layers for mineral prospectivity modeling. Drillhole and Borehole Data Direct subsurface data from drilling campaigns. Lithology logs, assay values, geotechnical logs, downhole geophysics. Validates exploration models, calibrates geophysical inversion, supports 3D geological modeling, and provides ore grade continuity data. Structural and Lineament Data Derived from RS, DEMs, or field mapping. Fault orientations, fracture density, lineament networks. Crucial for understanding mineralizing fluid pathways, targeting structurally controlled mineralization, and evaluating tectonic controls. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 127 3.4. 3D GIS and Subsurface Modeling Enhancements Advancements in 3D GIS have further expanded the analytical capability of mineral exploration workflows. Threedimensional geological models integrate drillhole data, geophysical inversions, and structural interpretations to create volumetric representations of ore bodies and host lithologies [24]. These models allow geoscientists to visualize the geometry, depth, and continuity of subsurface units, improving resource estimation accuracy and reducing drilling risk [17]. Subsurface modeling also enhances structural interpretation by enabling the reconstruction of fault networks, intrusive geometries, and stratigraphic variations in areas where surface evidence alone is insufficient. Integration with remote sensing-derived surface features ensures that 3D models remain spatially consistent with observable terrain characteristics [20]. With the GIS foundations established, the next section explores how these integrated datasets are operationalized into mineral exploration workflows, emphasizing predictive modeling, target generation, and uncertainty reduction. 4. Integrated remote sensing–GIS workflow for mineral exploration 4.1. Regional Reconnaissance: Broad-Scale Mapping and Terrain Screening Regional reconnaissance forms the initial phase of mineral exploration, providing a broad-scale understanding of geological, geomorphological, and structural patterns that may indicate mineralizing systems. Remote sensing is particularly valuable at this stage because it enables synoptic coverage of large and often inaccessible regions, offering an efficient means of identifying lithological domains, major structural trends, and surface alteration signatures [22]. Multispectral and hyperspectral datasets allow explorers to differentiate rock units, classify landforms, and detect spectral anomalies that may reflect hydrothermal activity or weathering processes associated with ore deposits [23]. The use of Digital Elevation Models (DEMs) further enhances terrain screening by revealing slope variations, drainage patterns, and terrain roughness, attributes often correlated with subsurface geological structures [24]. DEM derivatives such as hillshade, aspect, and curvature highlight subtle geomorphic features that may align with fault networks or lithological contacts. When integrated into GIS, these remote sensing layers form a foundational geological framework for regional analysis. Terrain screening also benefits from machine learning methods capable of analyzing multi-dimensional geoscientific datasets to identify emergent patterns indicative of mineral potential [25]. By combining spatial modeling and spectral analysis, regional reconnaissance narrows broad landscapes into prioritized domains suitable for more detailed structural or alteration mapping. This stage significantly reduces operational uncertainty by ensuring that subsequent exploration efforts focus on areas with the highest geological promise [26]. 4.2. Lineament Analysis, Structural Mapping, and Tectonic Framework Interpretation Lineament analysis is a critical component of structural geology because it enables the detection of fractures, faults, shear zones, and lithological boundaries that often control mineralizing fluid pathways. Remote sensing techniques particularly those using optical and radar datasets allow geoscientists to extract linear and curvilinear features associated with tectonic deformation [27]. Radar imagery is highly effective for structural mapping because it is sensitive to surface roughness and dielectric contrasts, supporting lineament identification even in vegetated or cloudcovered regions [28]. Optical datasets complement radar observations by providing high-resolution imagery that captures tonal differences and shadow textures useful for detecting brittle deformation zones. DEM-based derivatives such as shaded relief and slope maps further refine structural interpretations by emphasizing geomorphic discontinuities and ridge–valley alignments [29]. Together, these layers form a structural mosaic that reveals tectonic domains, fault networks, and regional stress orientations. GIS-based lineament density mapping enhances tectonic framework interpretation by quantifying fracture intensity and identifying structural corridors that may serve as conduits for hydrothermal fluids [22]. These areas often correlate with metallic and non-metallic mineralization, particularly in orogenic, volcanic, or basin-hosted systems. Crossvalidation of remote sensing–derived structures with magnetic or gravity lineaments increases interpretive reliability and helps resolve ambiguities arising from surface cover or erosional modification [30]. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 128 The importance of structural analysis is emphasized by its placement in the exploration workflow, bridging broad regional reconnaissance and alteration mapping. To illustrate this integration visually, Figure 2, inserted after this subsection, presents a schematic of structural lineament mapping derived from combined remote sensing and GIS analysis [24]. This figure reinforces how structural data guide subsequent exploration stages by highlighting deformation zones with high mineralization potential. Figure 2 Structural Lineament Mapping Derived from Remote Sensing and GIS Integration 4.3. Hydrothermal Alteration Mapping and Mineral Prospectivity Zonation Hydrothermal alteration mapping represents a pivotal step in mineral exploration because alteration minerals provide direct or indirect evidence of ore-forming processes. Remote sensing plays a central role in capturing diagnostic spectral signatures associated with clay, sulfate, iron oxide, and carbonate minerals that often mark hydrothermal zones [23]. Hyperspectral analysis enables high-fidelity differentiation of alteration assemblages such as argillic, phyllic, and propylitic zones, allowing geoscientists to reconstruct fluid pathways and locate potential ore centers [26]. GIS-based integration enhances alteration analysis by combining spectral indices with structural layers, geophysical anomalies, and geochemical variables. This synthesis supports the development of mineral prospectivity zonation maps that highlight areas where multiple indicators converge [27]. Weighted overlay and fuzzy logic classifiers are commonly applied to combine alteration intensity with tectonic and lithological evidence, improving the predictive accuracy of prospectivity models. Multi-temporal analysis further strengthens exploration outcomes by revealing dynamic changes in spectral signatures associated with weathering, vegetation shifts, or anthropogenic disturbance. DEM-derived terrain parameters, when integrated with alteration data, help determine whether spectral anomalies align with topographic highs, depressions, or structural intersections features commonly associated with hydrothermal deposition [29]. By identifying spatial relationships between alteration halos and structural corridors, hydrothermal mapping reduces uncertainty and guides the selection of areas for detailed predictive modeling and drilling. This harmonized approach advances mineral exploration workflows by providing a scientifically grounded basis for prioritizing prospective zones within complex geological terrains [25]. 4.4. Deriving Targeted Exploration Zones through Predictive Modeling Predictive modeling operationalizes the integrated datasets developed through regional reconnaissance, structural mapping, and alteration analysis, enabling the delineation of targeted exploration zones. GIS-based predictive models incorporate spatial statistics, machine learning algorithms, and geological expert knowledge to generate prospectivity surfaces that reflect the relative likelihood of mineral occurrence across a study area [30]. Inputs typically include fault density, alteration intensity, lithological contacts, geophysical gradients, and geochemical anomalies, each weighted according to its relevance to mineralization processes [27]. Global Journal of Engineering and Technology Advances, 2025, 25(02), 121–137 129 Machine learning methods such as random forests, support vector machines, and neural networks are increasingly utilized to capture nonlinear relationships between geological variables and known mineral occurrences [22]. These models excel in heterogeneous terrains where traditional linear approaches may fail to resolve complex geological interactions. Validation against known deposits and field evidence ensures predictive robustness. Composite prospectivity maps generated through predictive modeling highlight exploration targets that satisfy multiple mineralization criteria. These outputs guide drilling campaigns, resource estimation efforts, and field verification programs by reducing spatial uncertainty and focusing on geologically favorable zones [28]. Predictive modeling thus serves as the culmination of integrated geospatial workflows, transforming multi-source datasets into actionable exploration intelligence. By synthesizing structural, spectral, geomorphic, and geochemical indicators, it strengthens the scientific basis for high-confidence targeting while reducing financial and operational risk in mineral exploration [24]. 5. Resource estimation using remote sensing–GIS outputs 5.1. Remote Sensing Indicators for Volumetric and Ore Boundary Estimation Remote sensing provides a powerful foundation for preliminary volumetric assessment and ore boundary delineation by capturing surface mineralogical, geomorphological, and structural signatures that often correlate with subsurface resource distribution [22]. Spectral absorption features detected through multispectral and hyperspectral imaging offer indirect indications of mineral abundance and compositional variation across an ore system [28]. These mineralogical gradients can be spatially analyzed to estimate lateral continuity of alteration halos, enabling preliminary delineation of ore boundaries before drilling begins [29]. Digital Elevation Models (DEMs) further support volumetric estimation by defining morphological patterns associated with lithological contacts, vein systems, or intrusive centers. Slope and curvature derivatives often correlate with lithologic transitions, enabling remote identification of geological boundaries that influence ore geometry [30]. Radar datasets add value by highlighting structural fabrics and penetrative features that may control ore continuity. Remote sensing also contributes to depth inference by integrating spectral proxies with known geological models. For example, the spatial distribution of iron oxides, clay minerals, or carbonates may serve as surface indicators of hydrothermal processes that extend into the subsurface, forming the basis for extrapolating ore-system geometry [31]. When fused with geophysical or geochemical datasets, these remotely sensed indicators strengthen the preliminary estimation of ore volume and shape. Although remote sensing alone cannot determine exact subsurface dimensions, its utility lies in guiding drilling campaigns and constraining initial models [18]. By narrowing the spatial search window and identifying geologically coherent boundaries, remote sensing significantly improves the efficiency of volumetric assessment and accelerates the transition to more advanced resource estimation workflows grounded in subsurface data [32]. 5.2. GIS-Based Geostatistical Modeling and Reserve Classification GIS-based geostatistical modeling enhances mineral resource estimation by integrating spatial analysis techniques with predictive statistical frameworks that quantify ore distribution and grade variability. Ordinary kriging, indicator kriging, inverse distance weighting, and co-kriging remain widely applied approaches for interpolating grade values across an ore body using drillhole or sampling data [33]. These geostatistical methods rely on spatial autocorrelation principles captured through variogram modeling, enabling analysts to assess continuity, anisotropy, and spatial heterogeneity within mineral deposits. GIS provides the computational environment necessary to manage and visualize the input datasets such as drillhole coordinates, assay data, geological boundaries, and structural features allowing seamless incorporation into geostatistical estimators [28]. 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