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Article

Application of Multi-Source Remote Sensing and Topographic Factor Integration in the Exploration of Ion-Adsorption Type Rare Earth Deposits: A Case Study from Houaphanh Province, Laos

by
Yakang Ye
1,2,
Chenwei Li
1,*,
Ozias Rachid Vladmir Zerbo
1,
Xinyu Yang
1,
Wenbo Sun
1,
Yifan Xing
1,
Yujie Qian
1 and
Cheng Yu
1
1
College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China
2
Institute of Multipurpose Utilization of Mineral Resources, Chinese Academy of Geological Sciences, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
Minerals 2025, 15(11), 1160; https://doi.org/10.3390/min15111160
Submission received: 14 September 2025 / Revised: 21 October 2025 / Accepted: 29 October 2025 / Published: 3 November 2025
(This article belongs to the Special Issue Ion-Adsorption-Type REE Deposits)
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Abstract

Ion-adsorption type rare earth element (IREE) deposits are critical strategic resources formed under strong lithological, geomorphological, and weathering controls. In Houaphanh Province, Laos, widespread granitic intrusions and tropical monsoon weathering provide favorable conditions for IREE mineralization; however, exploration is limited by rugged terrain, dense vegetation cover, and sparse geological data. This study integrates Landsat 9, ASTER multispectral, and digital elevation data to enhance IREE exploration. Band ratio and principal component analysis (PCA) were applied to extract lithological and alteration features, while six topographic parameters describing elevation, slope, relief amplitude, incision depth, surface roughness, and elevation variability were derived from ASTER GDEM data. These datasets were combined using a weighted overlay to delineate favorable geomorphic zones. Six prospectives zones were identified, and field verification at Nongkhang confirmed 19 IREE ore bodies. The results demonstrate that integrating spectral and topographic indicators significantly improves the accuracy for IREE prediction in tropical, densely vegetated regions, offering a transferable framework for similar geological settings worldwide.

1. Introduction

Rare earth elements (REEs) are critical strategic materials for modern technologies and the development of green energy [1,2]. Among global REE resources, ion-adsorption type deposits (IREE) formed through the prolonged weathering of rocks are particularly important because of their low environmental impact and high economic value. These deposits typically form through prolonged tropical weathering, which concentrates REEs in clay-rich regolith [3,4]. Laos hosts widespread Indosinian-age (255–200 Ma) granitic intrusions that have been extensively weathered under tropical monsoon conditions, producing the clay-rich regolith favorable for IREE development [5].
Despite these favorable conditions, systematic IREE exploration in Laos remains limited due to rugged terrain, and sparse geological data. The region is predominantly forested, with tropical evergreen and deciduous forests interspersed with bamboo and secondary regrowth, which further complicates traditional field-based surveys. Remote sensing offers an effective means to overcome these challenges by enabling large-scale, lithological, and alteration mapping [6].
To address these limitations, this study integrates multispectral remote sensing with digital elevation model (DEM)-derived topographic analysis to delineate IREE prospectivity zones. ASTER and Landsat 9 imagery were used to extract lithological and alteration features through band ratio and principal component analysis (PCA), while six topographic factors were derived from ASTER GDEM data, quantifying geomorphic influences on mineralization. A weighted overlay analysis was applied to integrate these datasets, and field validation in the Nongkhang area [7] confirmed 19 IREE ore bodies within a 3.547 km2 mineralized zone, demonstrating the reliability of this integrated approach for IREE exploration in tropical terrains.

2. Regional Geological Setting

2.1. Geological Background

Houaphanh in northern Laos lies within the southern segment of the Ma Jiang suture zone, occupying a transitional position between the eastern Tethyan and western Pacific domains, and records a complex Paleotethyan to Mesozoic evolution involving subduction-related magmatism and Indosinian–Mesozoic intraplate reorganization (Figure 1a) [8].
During the Tethyan stage, repeated subduction and crustal melting generated widespread I-type and S-type granitic intrusions. Subsequent Indosinian compressional tectonism in the Mesozoic triggered extensive magmatism, producing large intermediate- to acidic-composition plutons that now dominate the bedrock of Houaphanh Province and serve as parent rocks for IREE mineralization [8,9].
Since the Cenozoic, the region has remained tectonically stable under a humid tropical monsoon climate influenced by the Indian Ocean. High rainfall (112.81 mm annually) and temperature promote intense chemical weathering, creating favorable conditions for the formation of ion-adsorption (lateritic) type REE deposits [3,8].

2.2. Genesis and Occurrence Conditions of Ion-Adsorption Type REE Deposits

REE deposits are broadly classified as endogenic or exogenic according to their formation processes. Endogenic types, including carbonatite-related, alkaline igneous, and hydrothermal-vein deposits, originate from magmatic or hydrothermal activity and host REEs in discrete minerals such as monazite, xenotime, and bastnäsite. Exogenic types form through surface weathering, sedimentation, or secondary enrichment and include placer, weathering-crust, and ion-adsorption deposits [9,10].
Intense weathering releases REEs from primary minerals, after which they become weakly bound to clay minerals such as kaolinite and illite, forming regolith-hosted deposits where REEs occur in adsorbed form rather than as discrete minerals [3,11]. IREE deposits represent a distinctive exogenic subtype formed by the prolonged chemical weathering of REE-enriched granitic rocks [12]. Residual primary REE phases (e.g., monazite, xenotime) may persist locally, but they generally constitute a minor component.
The development and preservation of IREEs are strongly influenced by climatic and geomorphic conditions. Warm, humid tropical to subtropical climates promote deep weathering, while moderate slopes and gentle relief favor regolith stability. Excessive erosion removes weathered material and limits enrichment, whereas stable terrain allows the long-term accumulation of REE-bearing clays [3,11].

2.3. Metallogenic-Potential of Ion-Adsorption Type REEs in Houaphanh, Laos

Houaphanh hosts extensive Permian–Triassic biotite monzogranites enriched in rare earth elements (Figure 1b) [4]. These granitic rocks are composed mainly of feldspar which readily weathers to form clay minerals, and quartz, which remains largely as a residual phase. Their high silica and alumina contents provide favorable parent material for REE adsorption onto clay minerals [9,11].
Topography exerts a major control on the development and preservation of REE-bearing regoliths. Situated along the western margin of the Annamite Range, Houaphanh is characterized by rugged mountains trending NW-SE, with elevations ranging from below 300 m in valleys to over 2000 m at peaks. Much of the terrain lies above 1000 m, where dense forest covers and intense weathering favor regolith development and retention. The Ma River system, which drains the region, shapes erosion patterns and regolith stability. Areas with moderate slopes, shallow incision, and gentle relief provide optimal conditions for the preservation of REE-enriched weathering crusts [13].
Taken together, the presence of REE-enriched granitic parent rocks, a humid tropical climate, and favorable geomorphic conditions provide an excellent geological–geochemical setting for IREE formation, making Houaphanh Province one of the most promising target areas for REE exploration in Laos.

3. Methods

This study integrates multispectral remote sensing data with digital elevation model (DEM)-derived topographic factors to identify favorable zones for IREE mineralization in Houaphanh Province, Laos. The methodological workflow involves three main components: (1) remote sensing-based lithological and alteration extraction, (2) topographic factor derivation, and (3) multi-source data integration for metallogenic prediction.
All parameters were standardized and reclassified in ArcGIS 10.8 to a uniform scale to facilitate subsequent spatial integration and analysis. Lithological, alteration, and topographic datasets were integrated using a weighted overlay approach within the ArcGIS platform to generate a metallogenic favorability map. Since no well-documented IREE occurrences exist within the study area to serve as calibration references for terrain-based weighting, this study applied equal weighting coefficients to the topographic parameters within their respective favorable ranges. This approach ensures a balanced representation of geomorphic influences on regolith preservation, although it does not account for variations in factor significance. The resulting map highlights zones where favorable lithology, strong alteration anomalies, and stable geomorphic conditions coincide, indicating high potential for IREE mineralization.
Field validation involved systematic geological field surveys, core drilling, and sampling (Figure 2). The surveys focused on mapping lithology, structures, and weathering profiles to identify favorable zones for ion-absorbed rare earth mineralization. Representative samples were collected from drill for chemical analysis (Figure 2c) [7].
The overall methodological workflow is illustrated in Figure 3.

3.1. Remote Sensing Data and Preprocessing

The primary datasets include ASTER multispectral imagery and ASTER GDEM elevation data which were obtained from NASA’s Earth data platform (https://search.earth.nasa.gov). ASTER provides 14 spectral bands covering the visible–near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) ranges 15, 30, and 90 m, respectively (Table 1) [14,15]. For analysis, all imagery was resampled to a uniform spatial resolution of 30 m to ensure consistency with the ASTER GDEM data. This resolution was sufficient to meet the mineral prospectivity prediction objectives of this study. Landsat 9 imagery was obtained from USGS Earth Explorer to complement ASTER data for regional lithological mapping. Following the approach of [16], diagnostic spectral band ratios were selected to enhance mineral-specific absorption and reflection features.
All satellite imagery was preprocessed to ensure optimal data quality. Geometric correction, radiometric calibration, and atmospheric correction were performed using ENVI 5.6. To minimize interference from vegetation, water bodies, and clouds, masking was applied using spectral indices: the normalized difference vegetation index (NDVI = (NIR − R)/(NIR + R)) for vegetation removal and the normalized difference water index (NDWI = (B2 − B6)/(B2 + B6)) (Figure 4) for identifying water and cloud-covered areas. These preprocessing steps ensured that only bare land and weathering crusts were retained for subsequent analysis. The ASTER GDEM dataset, with a spatial resolution of 30 m, was then employed to derive geomorphological parameters, selected for its proven reliability in rugged tropical terrain [17].

3.2. Data Analysis Methods

3.2.1. Band Ratio Method

Rock-forming minerals display characteristic absorption and reflection features across specific wavelength ranges, allowing remote sensing to differentiate lithological units [15]. To enhance spectral contrast and emphasize diagnostic mineral features, several band ratios were calculated based on the absorption and reflection properties of specific minerals within the ASTER spectral range.
Within the visible to near-infrared (VNIR) range, the Fe3+ index (b2/b1) (Figure 5a) was calculated to enhance ferric iron absorption between 0.55 and 0.65 µm, and the Fe2+ index (b1/b2 + b5/b3) was applied to detect ferrous iron-bearing silicates that exhibit absorption features between 0.6 and 1.2 µm [18,19].
In the shortwave infrared (SWIR) range, the clay mineral index ((b5 + b7)/b6) was computed to capture Al–OH absorption near 2.2 µm associated with clay minerals such as kaolinite and muscovite, while the dark mineral index ((b6 + b9)/b8) was designed to detect Mg–OH absorption around 2.34 µm, typical of biotite-, amphibole-, and chlorite-bearing rocks [18,19].
In the thermal infrared (TIR) range, spanning 8–14 µm, four indices were applied to identify major rock-forming minerals. The silicate index (b11/b14) was used to detect Si–O stretching vibrations between 8.3 and 11.3 µm, the mafic index (b12/b13) to estimate mafic mineral abundance inversely correlated with SiO2 content. The quartz index (b11/(b10 + b12) × (b13/b12)) was calculated to map quartz-bearing lithologies such as granites and quartz sandstones [20], and the calcite index (b14/b13) to identify carbonate rocks with absorption near 11.2 µm [13,18].
Six of the seven calculated indices were used to construct band index maps (Figure 6), which were then merged using a false-color composite (FCC) technique [13,18] to improve lithological discrimination and highlight alteration zones associated with weathering crust development and potential REE enrichment.
The Landsat 9 band ratio b4/b2 was used as an iron oxide index, enhancing ferric-iron absorption in the visible range, where Band 2 (blue) records absorption and Band 4 (red) (Table 2) [21] highlights strong iron-related reflectance (Figure 7b). The b6/b7 ratio served as a clay and hydroxyl index, emphasizing Al–OH absorption features near 2.2 µm, typical of kaolinite and muscovite (Figure 7c). In contrast, the b6/b5 ratio identified ferrous and dark-mineral-bearing lithologies, such as biotite- and amphibole-rich rocks.
A false-color composite (RGB = b4/b2, b6/b7, b6/b5) (Figure 7d) was then generated to integrate these indices, effectively enhancing the spectral contrast of oxidation, hydroxyl, and ferrous alteration zones associated with weathering and potential REE enrichment [16].

3.2.2. Principal Component Analysis (PCA)

Principal component analysis (PCA) was applied to selected ASTER bands to isolate spectral signatures of alteration minerals associated with weathering and potential REE enrichment. Diagnostic spectral bands corresponding to key alteration minerals were selected based on their characteristic absorption and reflection features, and multispectral data were subjected to orthogonal transformation to separate correlated spectral information [22,23].
For iron-oxide alteration, Bands 1–4 were analyzed (Figure 5), and the second principal component (PC2) best represented ferric iron (Fe3+) anomalies due to the contrasting eigenvector between Bands 2 and 3 (Table 3).
For hydroxyl-bearing clay alteration, Bands 1, 4, 6, and 7 were selected (Table 4), with PC2 effectively enhancing Al-OH absorption while suppressing vegetation interference. The resulting principal component images were combined into RGB composites to visualize the spatial distribution of Fe3+ and Al-OH anomalies.
These enhanced images provided key indicators of surface weathering intensity and potential REE enrichment zones (Figure 8).

3.2.3. Topographic Factor Analysis

The digital elevation model (DEM) provides a quantitative representation of surface relief and offers a comprehensive basis for analyzing geomorphological features [24,25]. Topographic parameters can be classified into macroscopic factors (e.g., relief amplitude, incision depth, surface roughness, and coefficient of elevation variation), which describe regional geomorphological characteristics, and microscopic factors (e.g., elevation, slope, and aspect), which depict local microtopographic variations [26,27,28].
Based on ASTER GDEM data, six (06) topographic factors known to influence regolith formation and preservation of weathering crust were extracted: elevation, slope, relief amplitude, incision depth, surface roughness, and the coefficient of elevation variation [29]. Elevation plays a key role in determining the intensity of weathering, with optimal development of IREE deposits typically occurring between 1000 and 1300 m (Figure 9a). Slope and relief amplitude are key indicators of erosional dynamics, with moderate values conducive to the preservation of regolith (Figure 9b,c) [26,30].
Incision depth (<50 m) and surface roughness (<1.6) (Figure 9d) indicate low erosional intensity and relatively stable terrain (Figure 9f), while the coefficient of elevation variation describes local surface heterogeneity (Figure 9d,e) [20].

4. Remote Sensing Interpretation Results

4.1. Lithological and Alteration Interpretation

Remote sensing interpretation shows that the Houaphanh region is dominated by Mesozoic argillaceous clastic rocks and widely distributed granitic intrusions. The Mz2 unit (red mudstone) is most extensive, occurring mainly in the central and northeastern parts of the study area, whereas Mz1 (argillaceous clastic rocks) and Pz1–Pz3 (Paleozoic sedimentary rocks) appear as smaller patches in the southwest and northeast (Figure 10a).
Large granitic bodies are primarily exposed in the Nongkhang and Mengban areas, where they intrude the surrounding Mesozoic-Paleozoic strata (Figure 10a). These intrusions exhibit crosscutting relationships with surrounding Mz2, Mz1, and some Pz units. In addition, mica schist is mainly concentrated in the northeastern corner of the study area, forming continuous banded outcrops together with other Paleozoic strata.
Band ratio and PCA analyses highlight distinct alteration anomalies associated with these granitic exposures (Figure 10b). Iron-Oxide (Fe3+) and hydroxyl-bearing (Al-OH) anomalies exhibit strong spatial overlap, indicating zones of intense weathering and potential REE enrichment. Using the mean (μ) ± N × standard deviation (σ) classification, high anomaly intensities were concentrated around Nongkhang, Mengbang, and Xam Neua areas, consistent with the distribution of granitic parent rocks and favorable geomorphic conditions (Figure 8). These results suggest that lithological boundaries between granitic intrusions and surrounding clastic units represent key zones for the development of weathering crusts and secondary REE enrichment.

4.2. Results of Topographic Factor Analysis

Topographic analysis based on ASTER GDEM data reveals strong terrain variability across the study area. The landscape generally slopes from the southwest highlands to the northeast lowlands. (Figure 9a). The northwestern region exhibits high relief, deep incision, and pronounced roughness, typical of tectonic-erosional terrain (Figure 9b–f).
In contrast, the eastern and central regions, particularly the Nongkhang and Mengban areas, display moderate elevation, slope, and incision depth, forming gently undulating landforms favorable for regolith preservation.
Among the six analyzed parameters, elevation, slope, incision depth, relief amplitude, and surface roughness showed the strongest influence on regolith development, while the coefficient of elevation variation exhibited minimal variation and is therefore excluded from the final favorability model. The selected five factors were standardized and weighted equally to generate a topography-based metallogenic favorability map (Figure 11a). Areas satisfying all five favorable thresholds were extracted as geomorphically optimal zones for IREE mineralization (Figure 11b).

4.3. Integrated Interpretation Results

By integrating lithological, alteration, and topographic datasets, six prospective zones (b1–b6) were delineated across the study area (Figure 12). These targets represent regions where Fe3+ and hydroxyl anomalies coincide spatially with favorable geomorphic conditions.
Among them, Target b1, located in the Nongkhang area, represents the most prominent and spatially extensive anomaly. It occurs near granitic intrusions and red mudstone units, where strong alteration signatures and favorable topographic conditions indicate high exploration potential. Targets b2 and b3 share similar lithological and geomorphic characteristics with b1, displaying concentrated anomalies and moderate slopes that favor REE accumulation. In contrast, Target b4, situated at the southern margin of the study area, shows no clear surface exposure of granitic rocks; however, the presence of strong spectral anomalies suggests possible buried sources. Target b5, located south of Xam Neua, exhibits favorable topography but is affected by significant human disturbance and displays weak alteration anomalies, making it a lower-priority exploration target.
Finally, Target b6 lies in the easternmost part of the study area, where anomalies are limited and do not overlap with favorable geomorphic factors, indicating low metallogenic potential.

5. Prediction of Ion-Adsorption Type Rare Earth Deposits

5.1. Remote Sensing–Based Prospecting Model for Ion-Adsorption Type REEs

Integration of spectral indices and topographic factors revealed a clear spatial relationship between geomorphology, lithology, and alteration anomalies associated with IREE mineralization. High values of the Fe3+, clay mineral, and hydroxyl indices were consistently observed in areas where weathering crusts are well developed (Figure 10b). These anomalies commonly coincide with red mudstone and granitic units, confirming that such lithologies play a key role in REE enrichment (Figure 10a).
Topographic metrics related to elevation, gradient, and relief complexity exhibited a strong correlation with regolith development. Areas with moderate slopes, shallow incision depths, and gentle relief closely align with the most pronounced alteration anomalies, emphasizing the strong link between geomorphology and REE enrichment (Figure 9).
Overlay analysis combining these spectral and geomorphic datasets showed that Fe3+ and hydroxyl anomalies are found around granitic intrusions and sandstone zones, especially in favorable geomorphic areas (Figure 11). This relationship indicates a consistent spatial connection among four key elements: parent rock, weathering crust, alteration signal, and geomorphic background. The integrated interpretation offers a reliable remote sensing-based model for predicting IREE deposits in tropical regions.

5.2. Integrated Overlay Analysis and Validation of Prediction Results

Field verification was carried out in the Nongkhang area (target b1 in Figure 12), corresponding to the high-priority b1 target zone identified in the integrated analysis. Drilling data from 36 boreholes revealed 19 IREE ore bodies distributed across an area of approximately 3.547 km2 [7]. The medium- to high-grade mineralization zones closely align with areas showing high values in the clay mineral, Fe3+, silicate, and mafic indices (Figure 13c).
Comparison between false color composites images (Figure 13a,b) and spatial distribution of medium- to high-grade mineralization (Figure 13c) indicates that most mineralized bodies occur within regions of strong alteration response and favorable topography. Of the 19 confirmed ore bodies, 16 are located within topographically suitable zones characterized by moderate slopes and low depths, further validating the reliability of the integrated model.
High values of the clay mineral, silicate, and dark mineral indices are observed in the zones represented by Figure 13a-1 and Figure 13b-6. Although the lithology is dominated by granite, these areas lie outside the favorable topographic range, and only four medium- to high-grade mineralized points were identified, suggesting that geomorphic conditions limited the development of weathering crusts. In contrast, zones corresponding to Figure 13a-2 and Figure 13b-5 display high Fe3+, clay mineral, and mafic index values, appearing as red-to-orange anomalies in the FCC imagery. Nine (09) REE ore bodies were delineated in these zones, indicating strong mineralization potential. The spatial coincidence of Fe3+ and mafic index highs reflects intense weathering, while their overlap with favorable topographic factors supports the formation of weathering-crust-type–type REE deposits.
At the southernmost part of the Nongkhang region (area 3), the silicate index shows high values, but Fe3+ responses and iron-staining anomalies are weak, and only four mineralized points were found. This suggests that, although the area is underlain by intermediate-acidic intrusive rocks, the degree of weathering is insufficient to produce a thick weathering crust capable of REE enrichment.
Overall, the strong correspondence between predicted high-favorability zones and field validation results confirms the accuracy of the integrated remote sensing and topographic approach. The findings demonstrate that both alteration intensity and geomorphic stability are critical for the formation of ion-adsorption-type REE deposits, and the applied method provides a rapid, cost-effective tool for identifying IREE targets in tropical, densely vegetated terrains.

6. Conclusions

  • Lithological and alteration information derived from ASTER and Landsat 9 imagery using band ratio and principal component analysis (PCA) effectively identified granitic and weathered crustal zones favorable for REE enrichment. When combined with key geomorphological parameters extracted from DEM data, these datasets provided a comprehensive framework for delineating potential mineralization zones
  • The integrated analysis revealed strong consistency among lithological, alteration, and topographic indicators, defining six prospective areas. Field validation in the Nongkhang zone confirmed 19 IREE ore bodies within the predicted high-favorability zone, demonstrating the accuracy and practical applicability of this method. Integrating spectral and geomorphological information enhances the prediction of regolith-hosted REE deposits, particularly in tropical, densely vegetated regions where traditional surveys are constrained. Future work should incorporate higher-resolution hyperspectral data and advanced machine learning algorithms to refine the model further and improve exploration efficiency.
  • Integrating spectral and geomorphological information enhances the prediction of regolith-hosted REE deposits, particularly in tropical, densely vegetated regions where traditional surveys are constrained. Future work should incorporate higher-resolution hyperspectral data and advanced machine learning algorithms to refine the model further and enhance prediction accuracy.

Author Contributions

Conceptualization, Y.Y.; Methodology, C.L., X.Y. and W.S.; Formal analysis, Y.X.; Investigation, X.Y., W.S., Y.X., Y.Q. and C.Y.; Data curation, Y.Y., X.Y., O.R.V.Z., W.S., Y.Q. and C.Y.; Writing—original draft, Y.Y., C.L., X.Y. and O.R.V.Z.; Supervision, Y.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by Deep Earth Probe and Mineral Resources Exploration—National Science and Technology Major Project, No. 2025ZD1006600 (2025ZD1006602), the China Geological Survey Project (Grant No. DD20230900109), the National Natural Science Foundation of China (Grant No. 42302068), and the Sichuan Provincial Natural Science Foundation for Young Scholars (Grant No. 24NSFSC0820).

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Geological setting of the study area. (a) Simplified tectonic map of Laos and adjacent regions; the blue polygon delineates the study area. (b) Lithological distribution of the Houaphanh region. PR, Pz, and Mz denote the Precambrian, Paleozoic, and Mesozoic eras, respectively (modified after [8]).
Figure 1. Geological setting of the study area. (a) Simplified tectonic map of Laos and adjacent regions; the blue polygon delineates the study area. (b) Lithological distribution of the Houaphanh region. PR, Pz, and Mz denote the Precambrian, Paleozoic, and Mesozoic eras, respectively (modified after [8]).
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Figure 2. Field validation and schematic representation of the weathering-crust profile in the Bannanao deposit area, Nongkhang District, Xam Neua City, Houaphanh Province. (a) Outcrop profile at the drilling site; (b) surface soil horizon at the same site; (c) drill-core samples from the completely weathered layer. Samples were collected from borehole BSNZK003, where preliminary results indicate a moderate degree of mineralization. No bedrock exposure was observed at the base of the profile. The diagram on the right presents a schematic illustration of an ion-adsorption-type rare-earth weathering crust (modified after [7]).
Figure 2. Field validation and schematic representation of the weathering-crust profile in the Bannanao deposit area, Nongkhang District, Xam Neua City, Houaphanh Province. (a) Outcrop profile at the drilling site; (b) surface soil horizon at the same site; (c) drill-core samples from the completely weathered layer. Samples were collected from borehole BSNZK003, where preliminary results indicate a moderate degree of mineralization. No bedrock exposure was observed at the base of the profile. The diagram on the right presents a schematic illustration of an ion-adsorption-type rare-earth weathering crust (modified after [7]).
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Figure 3. Workflow of the integrated analysis combining multispectral imagery with DEM-derived topographic parameters.
Figure 3. Workflow of the integrated analysis combining multispectral imagery with DEM-derived topographic parameters.
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Figure 4. (a) ASTER RGB 3-2-1 false-color composite image (Red or dark red indicates vegetation, while white to light blue primarily represents exposed surfaces. White or light gray indicates bare rocks or sediments, and deep blue or black represents water bodies or shaded areas.). (b) Vegetation, water, and cloud masking results based on spectral indices (black areas represent masked vegetation, clouds, and water bodies).
Figure 4. (a) ASTER RGB 3-2-1 false-color composite image (Red or dark red indicates vegetation, while white to light blue primarily represents exposed surfaces. White or light gray indicates bare rocks or sediments, and deep blue or black represents water bodies or shaded areas.). (b) Vegetation, water, and cloud masking results based on spectral indices (black areas represent masked vegetation, clouds, and water bodies).
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Figure 5. (a,b) Typical spectral reflectance curves of rock-forming minerals in the visible–near-infrared (VNIR)(B1–B3), shortwave infrared (SWIR) (B4–B9), and thermal infrared (TIR) (B10–B14) ranges. Labels B1–B14 correspond to ASTER bands 1–14.
Figure 5. (a,b) Typical spectral reflectance curves of rock-forming minerals in the visible–near-infrared (VNIR)(B1–B3), shortwave infrared (SWIR) (B4–B9), and thermal infrared (TIR) (B10–B14) ranges. Labels B1–B14 correspond to ASTER bands 1–14.
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Figure 6. Band ratio index maps of the Houaphanh region. (a) Fe3+ Index, (b) Dark Mineral Index, (c) Silicate Mineral Index, (d) Clay Mineral Index, (e) Quartz Index, (f) Calcite Index.
Figure 6. Band ratio index maps of the Houaphanh region. (a) Fe3+ Index, (b) Dark Mineral Index, (c) Silicate Mineral Index, (d) Clay Mineral Index, (e) Quartz Index, (f) Calcite Index.
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Figure 7. Landsat 9 images (a) True-color composite image of the Huaphan region in Laos, RGB: 4-3-2; (b) Single-band image of the iron oxide index (b4/b2); (c) Hydroxyl alteration anomaly (b6/b7); (d) RGB color composite with where red represents a band ratio of 4/2, green represents a band ratio of 6/7, and blue represents a band ratio of 6/5. The more vibrant a color appears in a particular region, the higher the corresponding index or the concentration of a specific ratio within that region is likely to be.
Figure 7. Landsat 9 images (a) True-color composite image of the Huaphan region in Laos, RGB: 4-3-2; (b) Single-band image of the iron oxide index (b4/b2); (c) Hydroxyl alteration anomaly (b6/b7); (d) RGB color composite with where red represents a band ratio of 4/2, green represents a band ratio of 6/7, and blue represents a band ratio of 6/5. The more vibrant a color appears in a particular region, the higher the corresponding index or the concentration of a specific ratio within that region is likely to be.
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Figure 8. False-color composite images of alteration anomalies. (a) Iron oxidation alteration. (b) Hydroxyl alteration. For enhanced visualization, red, green, and blue channels were assigned to principal components associated with respective alteration anomalies. In this composite, the second principal component (PC2) highlights enrichment zones for both types of alteration anomalies.
Figure 8. False-color composite images of alteration anomalies. (a) Iron oxidation alteration. (b) Hydroxyl alteration. For enhanced visualization, red, green, and blue channels were assigned to principal components associated with respective alteration anomalies. In this composite, the second principal component (PC2) highlights enrichment zones for both types of alteration anomalies.
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Figure 9. Topographic factor analysis maps of the Houaphanh region: (a) elevation (m); (b) slope (°); (c) relief amplitude (m); (d) incision depth (m); (e) coefficient of elevation variation (dimensionless); (f) surface roughness (dimensionless).
Figure 9. Topographic factor analysis maps of the Houaphanh region: (a) elevation (m); (b) slope (°); (c) relief amplitude (m); (d) incision depth (m); (e) coefficient of elevation variation (dimensionless); (f) surface roughness (dimensionless).
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Figure 10. Lithological and alteration interpretation results. (a) Integration of multi-source remote sensing band ratios with the geological and mineral map of Laos. (b) Distribution of alteration anomalies derived from principal component analysis.
Figure 10. Lithological and alteration interpretation results. (a) Integration of multi-source remote sensing band ratios with the geological and mineral map of Laos. (b) Distribution of alteration anomalies derived from principal component analysis.
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Figure 11. Results of topographic factor analysis. (a) Mineral prospectivity index (red = low, blue = high). (b) Favorable metallogenic terrains identified in this study.
Figure 11. Results of topographic factor analysis. (a) Mineral prospectivity index (red = low, blue = high). (b) Favorable metallogenic terrains identified in this study.
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Figure 12. Comprehensive overlay interpretation map integrating lithology, alteration anomalies, and topographic factors in the Houaphanh region.
Figure 12. Comprehensive overlay interpretation map integrating lithology, alteration anomalies, and topographic factors in the Houaphanh region.
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Figure 13. Composite images and field validation of mineral targets. (a) False-color composite image (R: Fe3+ Index, G: Clay Mineral Index, B: Silicate Index). (b) False-color composite image (R: Mafic Index, G: Clay Mineral Index, B: Dark Mineral Index). (c) Landsat 9 image showing alteration anomaly zones, favorable metallogenic terrains, and confirmed medium- to high-grade mineralization sites from field validation. The numbers in Figure 13a,b represent: 1: Section of weathered basaltic sandstone; 2: Areas with high Fe3+ index and high clay mineral index; 3: Areas with medium-acidic intrusive rocks; 4: Areas with high clay mineral index; 5: Areas with high magnesium-iron index; 6: Areas with high dark mineral index; The white lines represent the boundaries between different regions.
Figure 13. Composite images and field validation of mineral targets. (a) False-color composite image (R: Fe3+ Index, G: Clay Mineral Index, B: Silicate Index). (b) False-color composite image (R: Mafic Index, G: Clay Mineral Index, B: Dark Mineral Index). (c) Landsat 9 image showing alteration anomaly zones, favorable metallogenic terrains, and confirmed medium- to high-grade mineralization sites from field validation. The numbers in Figure 13a,b represent: 1: Section of weathered basaltic sandstone; 2: Areas with high Fe3+ index and high clay mineral index; 3: Areas with medium-acidic intrusive rocks; 4: Areas with high clay mineral index; 5: Areas with high magnesium-iron index; 6: Areas with high dark mineral index; The white lines represent the boundaries between different regions.
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Table 1. Spectral band parameters and wavelength ranges of the ASTER sensor.
Table 1. Spectral band parameters and wavelength ranges of the ASTER sensor.
Band NumberBand TypeWavelength Range (µm)Off-Nadir Angle (°)Spatial Resolution (m)
Band 1 (B1)Visible and Near Infrared0.52–0.60+/−2415
Band 2 (B2)Visible and Near Infrared0.63–0.69+/−2415
Band 3 (B3)Visible and Near Infrared0.76–0.86+/−2415
Stereo BackwardVisible and Near Infrared0.76–0.86+/−2415
Band 4 (B4)Shortwave Infrared1.60–1.70+/−8.5530
Band 5 (B5)Shortwave Infrared2.145–2.185+/−8.5530
Band 6 (B6)Shortwave Infrared2.185–2.225+/−8.5530
Band 7 (B7)Shortwave Infrared2.235–2.285+/−8.5530
Band 8 (B8)Shortwave Infrared2.295–2.365+/−8.5530
Band 9 (B9)Shortwave Infrared2.360–2.430+/−8.5530
Band 10 (B10)Thermal Infrared8.125–8.475+/−8.5590
Band 11 (B11)Thermal Infrared8.475–8.825+/−8.5590
Band 12 (B12)Thermal Infrared8.925–9.275+/−8.5590
Band 13 (B13)Thermal Infrared10.25–10.95+/−8.5590
Band 14 (B14)Thermal Infrared10.95–11.65+/−8.5590
Table 2. Key OLI-2 spectral requirements and band average performance. Both instruments met requirements and their spectral similarity enhances continuity between the two instruments.
Table 2. Key OLI-2 spectral requirements and band average performance. Both instruments met requirements and their spectral similarity enhances continuity between the two instruments.

#Spectral Band		Req Min Lower Band Endge (nm)Req Max Upper Band Edge (nm)OLI
Lower (nm)		OLI
Upper (nm)		OLI-2 Lower (nm)OLI-2
Upper (nm)
1.
Coastal Aerosol
433453435451435450
2.
Blue
450515452512452512
3.
Green
525600533590532589
4.
Red
630680636673636672
5.
NIR
845885851879850879
6.
SWIR 1
156016601566165115651651
7.
SWIR 2
210023002107229421052294
8.
Panchromatic
500680504676503675
9.
Cirrus
136013901363138413631384
Table 3. Eigenvector loadings of principal components derived from ASTER Bands 1–4.
Table 3. Eigenvector loadings of principal components derived from ASTER Bands 1–4.
EigenvectorsBand 1Band 2Band 3Band 4
PC10.1196010.0944310.8671180.474221
PC20.2259330.352579−0.4882870.765649
PC3−0.55244−0.706362−0.0844640.434429
PC40.793385−0.606484−0.0504790.012974
Table 4. Eigenvector loadings of principal components derived from ASTER Bands 1, 4, 6, and 7.
Table 4. Eigenvector loadings of principal components derived from ASTER Bands 1, 4, 6, and 7.
EigenvectorsBand 1Band 4Band 6Band 7
PC10.2076020.815090.4003950.363613
PC20.125594−0.568020.5341790.613377
PC30.969494−0.09893−0.18084−0.13263
PC4−0.034770.056523−0.722240.68845
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Ye, Y.; Li, C.; Vladmir Zerbo, O.R.; Yang, X.; Sun, W.; Xing, Y.; Qian, Y.; Yu, C. Application of Multi-Source Remote Sensing and Topographic Factor Integration in the Exploration of Ion-Adsorption Type Rare Earth Deposits: A Case Study from Houaphanh Province, Laos. Minerals 2025, 15, 1160. https://doi.org/10.3390/min15111160

AMA Style

Ye Y, Li C, Vladmir Zerbo OR, Yang X, Sun W, Xing Y, Qian Y, Yu C. Application of Multi-Source Remote Sensing and Topographic Factor Integration in the Exploration of Ion-Adsorption Type Rare Earth Deposits: A Case Study from Houaphanh Province, Laos. Minerals. 2025; 15(11):1160. https://doi.org/10.3390/min15111160

Chicago/Turabian Style

Ye, Yakang, Chenwei Li, Ozias Rachid Vladmir Zerbo, Xinyu Yang, Wenbo Sun, Yifan Xing, Yujie Qian, and Cheng Yu. 2025. "Application of Multi-Source Remote Sensing and Topographic Factor Integration in the Exploration of Ion-Adsorption Type Rare Earth Deposits: A Case Study from Houaphanh Province, Laos" Minerals 15, no. 11: 1160. https://doi.org/10.3390/min15111160

APA Style

Ye, Y., Li, C., Vladmir Zerbo, O. R., Yang, X., Sun, W., Xing, Y., Qian, Y., & Yu, C. (2025). Application of Multi-Source Remote Sensing and Topographic Factor Integration in the Exploration of Ion-Adsorption Type Rare Earth Deposits: A Case Study from Houaphanh Province, Laos. Minerals, 15(11), 1160. https://doi.org/10.3390/min15111160

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