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Article

ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China

by
Qi Chen
1,2,3,4,
Zhifang Zhao
1,3,4,*,
Jiaxi Zhou
1,3,4,
Ruifeng Zhu
1,
Jisheng Xia
1,
Tao Sun
1,
Xin Zhao
2,5 and
Jiangqin Chao
2
1
School of Earth Sciences, Yunnan University, Kunming 650500, China
2
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
3
Engineering Research Center of Domestic High-Resolution Satellite Remote Sensing Geology for Universities of Yunnan Province, Kunming 650500, China
4
MNR Key Laboratory of Sanjiang Metallogeny and Resources Exploration & Utilization, Kunming 650051, China
5
National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd., Kunming 650031, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(5), 1253; https://doi.org/10.3390/rs14051253
Submission received: 26 January 2022 / Revised: 2 March 2022 / Accepted: 2 March 2022 / Published: 4 March 2022
Browse Figures
Versions Notes

Abstract

:
Hydrothermal alteration minerals are an effective prospecting indicator. Advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite data are some of the most commonly adopted multispectral data for the mapping of hydrothermal alteration minerals. Compared to multispectral data, hyperspectral data have stronger ground object recognition ability. Chinese Gaofen-5 (GF-5) is the first hyperspectral satellite independently developed by China that has the advantages of both wide-width and high-spectral-resolution technology. However, the mapping ability of GF5 data for hydrothermal alteration minerals requires further study. In this study, ASTER and GF-5 satellite data were implemented to map hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. Selective principal component analysis (SPCA) technology was employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using ASTER data, and the mixture tuned matched filtering (MTMF) method was implemented for the extracted hematite, kaolinite, calcite, and dolomite at the sub-pixel level using GF-5 data. When mapping the hydrothermal alteration minerals, the distribution features of the hydrothermal alteration minerals from the Longtoushan Pb-Zn deposit were systematically revealed. A comprehensive field investigation and petrographic study were conducted to verify the extraction accuracy of the hydrothermal alteration minerals. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9 and that the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map hydrothermal alteration minerals well and that they can be promoted as a hyperspectral data source for mapping systematic hydrothermal alteration minerals in the future.

1. Introduction

Hydrothermal alteration minerals, key indicators of hydrothermal deposit mineralization, are effective prospecting indicators. Since remote sensing technology has the advantages of being speedy and cost-effective, multi-source remote sensing satellite data, including multispectral and hyperspectral data, are applied to various hydrothermal deposits for prospect exploration around the world [1,2,3,4,5,6,7,8,9,10,11,12,13].
The most commonly adopted multispectral satellite data for hydrothermal alteration minerals mapping are advanced spaceborne thermal emission and reflection radiometer (ASTER) data [14,15,16,17,18,19,20]. The visible and near-infrared (VNIR) bands of the ASTER satellite data and their shortwave infrared (SWIR) bands are utilized to map iron oxides/hydroxides and argillic, phyllic, and carbonate minerals in a number of published papers [21,22,23,24]. Moreover, thermal infrared (TIR) bands are applied for silicified alteration identification [25,26,27,28,29]. With the development of hyperspectral remote sensing technology, Earth observations and the ability to distinguish ground objects were greatly enhanced, and there was an increase in research in the field of geological prospecting. The HyMap [30], AVIRIS [31], CASI/SASI [32] airborne hyperspectral data, and the Hyperion TG-1 [13] aerospace hyperspectral data are widely used. However, due to the high cost and long data acquisition cycle for HyMap, AVIRIS, and CASI/SASI, as well as the narrow data width and limited data acquisition ability of Hyperion and TG-1, the popularization and application of hyperspectral data are severely restricted [33]. The introduction of China’s Gaofen-5 (GF-5) satellite data has solved the problem of hyperspectral data acquisition.
GF-5 is the first hyperspectral satellite developed independently by China that has the advantages of both wide-width and high-spectral-resolution technology. It can achieve comprehensive atmospheric and terrestrial observations. The visible shortwave infrared hyperspectral camera carried by the GF-5 is a hyperspectral camera and has both wide coverage and can cover a wide spectrum. It can obtain 330 spectral color channels from visible light to shortwave infrared light (400–2500 nm) at a width of 60 km and at a spatial resolution of 30 m, meaning that it has an extremely strong Earth observation capability [34,35]. At present, GF-5 data are used for lithological mapping and silica content estimation in the geological field [36,37]. However, there are few studies on the application of GF-5 data to hydrothermal alteration mineral mapping, and it is urgent to popularize and demonstrate the use of these data for such applications.
The Longtoushan Pb-Zn deposit is one of the main Pb-Zn production bases and is a typical representative of the Pb-Zn deposit in Yunnan, SW China. Pb-Zn mineralization is characteristically associated with hydrothermal alteration zones as well as geological and structural zones. Carbonate alteration, iron-related alteration, silicic alteration, and argillic alteration are the main types of alternations observed in this area. Generally, the range of alteration is limited and mainly occurs around the ore body. The closer to the ore body, the stronger the alteration is, while the farther away from the ore body, the weaker the alteration is. However, no comprehensive remote sensing alteration survey was conducted in the Longtoushan Pb-Zn deposit until now.
In view of this, ASTER and GF-5 satellite data were adopted for hydrothermal alteration mineral mapping in the Longtoushan Pb-Zn deposit, SW China. The main objectives of this study are as follows: (i) to map iron oxide/hydroxides, argillic, quartz, and carbonate hydrothermal alterations using selective principal component analysis (SPCA) [38,39] techniques with the ASTER data; (ii) to identify hematite, kaolinite, calcite, and dolomite minerals by implementing mixture tuned matched filtering (MTMF) [40,41,42] algorithms with the GF-5 data; and (iii) to verify the results via a comprehensive field investigation and a petrographic study.

2. Geological Setting of the Study Area

The study area is located in the Northeast Yunnan, SW China, part of the northern part of the Pb-Zn mineralization area in the Yangtze plate (Figure 1). Influenced by the long-term activities of the Xiaojiang fault and Kangding–Yiliang–Shuicheng fault, the whole area is in a depressed state. Faults and folds, which constitute a NE and NW trending tectonic pattern, are well developed in this region. The faults are closely related to the Pb-Zn mineralization that takes place in this area.
Most of the outcrops observed in the area belong to the following groups: the Jurassic Shaximiao group (J2sx), the Jurassic Suining group (J2s), the Jurassic Ziliujing group (J1z), Triassic Xujiahe group (T2x), Triassic Guanling group (T2gl), Triassic Jialingjiang group (T1j), Permian Xuanwei group (P2x), Permian Emeishan basalt (Pe), Permian Yangxin group (P1y), Carboniferous longevity Wanshoushan group (C1w), Devonian Zaijieshan group (D3zj), Devonian Qujing group (D2q), Devonian Suotoushan group (D2st), Devonian Posongchong group (D1ps), Silurian Caidiwan group (S3c), Silurian Sifengya and Daluzhai group (S2s-d), and Ordovician Daqing group (O2d). The lithology mainly contains limestone, dolomite, basalt, sandstone, siltstone, mudstone, and siliceous rock (Figure 2).
Dolomitization, calcilization, iron oxides/hydroxides, and silicification are the main types of alterations observed in the surrounding rock. The development of carbonation is prevalent in the mining area, which is mainly controlled by a fracture zone, joint fissures, and the surrounding rock. Dolomite and calcite recrystallization is obvious in the mineralized surrounding rock, forming coarse dolomite and calcite crystals, which are produced along the fissure in the form of veinlets and clusters. Iron oxides mainly occur in dolomite as disseminated, veined, and spotted dolomites are associated with galena and sphalerite. Silicification is mainly characterized by the development of quartz clusters in the dissolution cavity accompanied by Pb-Zn mineralization. Generally, the closer to the ore body, the larger the carbonate mineral crystallization, the more developed its aggregates or veinlets are; iron oxides/hydroxides tend to develop in the near-ore range and are obviously enhanced in the medium-coarse crystal dolomite with strong near-ore alteration. Typically, stronger alterations are seen in rocks the closer they are to the ore body.

3. Materials and Methods

3.1. Remote Sensing Data

In this study, ASTER and GF-5 satellite data were utilized to map the hydrothermal alteration minerals associated with Pb-Zn mineralization in the Longtoushan deposit.
The ASTER multispectral satellite was launched on December 18 1999. It includes three VNIR bands (spatial resolution: 15 m, wavelength range: 0.52–0.86 μm), six SWIR bands (spatial resolution: 30 m, wavelength range: 1.6–2.43 μm), and five TIR bands (spatial resolution: 90 m, wavelength range: 8.0–14.0 μm). Each ASTER scene covers 60 × 60 km2 [43,44].
The GF-5 hyperspectral satellite was launched on 9 May 2018. The visible and shortwave infrared multispectral sensor contains 150 VNIR bands (spectral resolution: 5 nm, wavelength range: 0.39–1.03 μm) and 180 SWIR bands (spectral resolution: 10 nm, wavelength range: 1.0–2.5 μm). Each GF-5 scene covers 60 × 60 km2 at a spatial resolution of 30 m [45].
In this study, an ASTER scene that was acquired on 1 February 2005 was downloaded from the United States Geological Survey (USGS) Global Visualization Viewer (GloVis) (https://glovis.usgs.gov/, accessed on 10 January 2022). A GF-5 scene that was acquired on 15 January 2020, was collected from the China Centre for Resources Satellite Data and Application (http://www.cresda.com/CN/, accessed on 10 January 2022). The ASTER and GF-5 satellite imagery are cloud-free and are suitable for mapping hydrothermal alteration mineral information.

3.2. Pre-Processing of Remote Sensing Data

The ASTER and GF-5 images were pre-georeferenced to UTM zone 18 North projections. The SWIR bands of the ASTER data were resampled to 15 m using the nearest neighbor resampling method, which is consistent with the spatial resolution of the VNIR band of the ASTER data. Additionally, the fast line-of-sight atmospheric analysis of hypercubes (FLAASH) method was adopted to convert the radiance-calibrated data to the apparent reflectance, where mid-latitude winter was selected as the atmospheric model and rural was selected as the aerosol model [46,47].
The quality of GF-5 data is affected by sensors and atmospheric factors, so it is necessary to conduct band screening to retain the bands with better quality and to ensure the accuracy of the subsequent processing. The pixel values of bands 193–200 and bands 246–262 were zero and needed to be removed. Bands 151–154 needed to be deleted due to high information overlap with bands 145–150. Then, layer stacking was carried out for the remaining 301 bands. Moreover, the radiometric calibration and the FLAASH atmospheric correction were applied to the GF-5 image to obtain the surface reflectance.

3.3. Hydrothermal Alteration Mineral Mapping Methods

The SPCA approach was utilized to map the hydrothermal alteration minerals, including iron oxide/hydroxides, argillic, quartz, and carbonate for the VNIR, SWIR, and TIR bands of the ASTER satellite data. The mixture-tuned matched filtering (MTMF) algorithm was adopted to extract hydrothermal alteration minerals such as hematite, kaolinite, calcite, and dolomite for the GF-5 image.

3.3.1. Selective Principal Component Analysis (SPCA)

In general, there is a lot of data redundancy among the bands of remote sensing images due to their high correlation. The principal component analysis (PCA) method can reduce the dimensions of remote sensing images (i) and construct (j) unrelated new principal components (PCs) from the original image [38,48,49]. The transformation formula of PCA is as follows:
Y = A X = ∑ m = 1 j A m X m + ∑ m = j + 1 i A m X m
In Formula (1), X represents the original remote sensing image before PCA transformation, Y represents the remote sensing data after PCA transformation, and A represents the PCA transformation matrix.
The PCA method is widely applied on different remote sensing data to map hydrothermal alteration minerals [39,50,51,52,53,54,55,56]. Through principal component analysis, the original band is transformed into several unrelated bands, with one of the principal component bands containing the required hydrothermal alteration mineral information. The eigenvector loadings contain the spectral characteristic information related to the hydrothermal alteration minerals of interest. Negative loading depicts the hydrothermal alteration minerals as bright pixels, while positive loading expresses the hydrothermal alteration minerals as dark pixels.
In this study, SPCA [57] was utilized on the ASTER satellite data to map the specific hydrothermal alteration minerals (Table 1). The essential difference between SPCA and PCA is that the SPCA only selects a subgroup of bands through which different operations can be performed based on the specific need. ASTER bands 1, 2, and 4 and three bands in the VNIR and SWIR were designated to map the iron oxides/hydroxides [58]. ASTER bands 4, 5, and 6 and three bands in the SWIR were selected to identify argillic alterations [59] (Table 1). ASTER bands 10, 12, and 14 and three bands in the TIR were used to detect quartz, and bands 10, 13, and 14 were allocated for carbonate extraction [28,29,60].

3.3.2. Mixture Tuned Matched Filtering (MTMF)

The MTMF method is the synthesis of hybrid modulation and matched filtering and is the combination of signal processing and linear mixing theory [40,41,42]. One of its advantages is that the matched filter does not need other background endmember spectra and also takes into account the constraint condition that the content of each endmember is positive, and the sum is 1 in the mixed modulation technology (Table 1). Hybrid modulation technology uses linear spectrum mixing theory to reduce the probability of spurious signals. This method flow consists of the following steps: (1) the minimum noise fraction (MNF) [61,62] is implemented to remove noise and to reduce dimension of the hyperspectral data; (2) the pixel purity index (PPI) [63] is applied to extract the pure endmembers; (3) the n-Dimensional Visualizer tool [64,65] is applied to draw the characteristic spectrum curve of each endmember; and (4) the MTMF method is utilized to map hydrothermal alteration minerals.

3.4. Field Survey and Laboratory Analysis

In order to verify the efficiency of hydrothermal alteration mineral mapping, we carried out outdoor and indoor work for field investigation and laboratory analysis, respectively. Examples of outdoor work include GPS positioning, photo-taking, and sample collection, and examples of indoor work include thin section preparation and petrographic studies. The GPS was used to determine the precise spatial position of the field survey sites. Photos were taken to record the characteristics of the field survey sites. Samples were collected for later laboratory analysis. Thin sections were prepared, and petrographic studies were carried out under a microscope to analyze the specific hydrothermal alteration minerals contained in the rocks of the study area. In addition, the Kappa coefficient [66] was calculated to assess the accuracy of the hydrothermal alteration minerals mapped by the ASTER and GF-5 data.

4. Results

4.1. Hydrothermal Alteration Minerals Mapping Using SPCA Method with ASTER Data

Iron oxide/hydroxide minerals have high absorption characteristics at 0.40–1.10 μm and high reflection characteristics at 1.60 μm [21]. Considering the spectral ranges of ASTER band 1 (0.52–0.60 μm), band 2 (0.63–0.69 μm), and band 4 (1.60–1.70 μm), the SPCA method was applied to ASTER bands 1, 2, and 4 to map the iron oxide/hydroxide alterations. The principal component (PC) of the PCA transformation contained iron oxide/hydroxide mineral information, showing negative loadings at band 1 and band2 and positive loadings at band 4. Table 2 show the eigenvector matrix of the iron oxide/hydroxide minerals calculated by the SPCA method for ASTER bands 1, 2, and 4. Through analyzing the eigenvector loadings of PC1, PC2, and PC3, it was determined that PC2 contains iron oxide/hydroxide mineral information. PC2 had strong negative loadings at band 1 (−0.447280) and band 2 (−0.796866) and strong positive loadings at band 4 (0.406136). Thus, the bright pixels in the PC2 image were able to be identified as iron oxide/hydroxide mineral information. The iron oxide/hydroxide minerals were broadly identified in the Pe, P1y, and O2d lithological units (Figure 3).
Argillic minerals have high absorption characteristics at ASTER bands 5 (2.145–2.185 μm), 6 (2.185–2.225 μm) [67], and maximum reflection characteristics at band 4 (1.60–1.70 μm) [61]. Therefore, the SPCA method was applied to ASTER bands 4, 5, and 6 to map the argillic minerals. The PC of PCA transformation contained argillic mineral information and showed negative loadings at bands 5 and 6 and positive loadings at band 4. Table 3 show the eigenvector matrix of the argillic minerals calculated by the SPCA method for ASTER bands 1, 2, and 4. By analyzing the eigenvector loadings of PC1, PC2, and PC3, it can be observed that PC2 contains argillic mineral information. PC2 had strong negative loadings at band 5 (−0.503428) and band 6 (−0.605855) and strong positive loadings at band 4 (0.616036). Thus, the bright pixels that can be seen in PC2 could be identified as argillic mineral information. The argillic minerals were broadly mapped in the T3x, J1z, Pe, and P1y lithological units, and some parts of the J2s, J2sx, P2x, D2st, D1ps and S2s-d lithological units are also mapped (Figure 4).
Quartz has minima emissivity characteristics at ASTER band 10 (8.125–8.475 μm) and band 12 (8.925–9.275 μm) and maximum emissivity characteristics at band 14 (10.95–11.65 μm) [28,29,60,65]. Therefore, the SPCA method was utilized on ASTER bands 10, 12, and 14 for quartz mapping. The PC of the PCA transformation contained quartz information and showed negative loadings at band 10 and band 12 and positive loadings at band 14. Table 4 show the eigenvector matrix of the quartz calculated using the SPCA for ASTER bands 10, 12, and 14. The analysis of the eigenvector loadings in PC1, PC2, and PC3 determined that PC2 contains quartz information. PC2 had strong negative loadings at band 10 (−0.488973) and band 12 (−0.526405) and strong positive loadings at band 14 (0.695559). Thus, the bright pixels that could be observed in PC2 were able to be extracted as quartz information. Quartz was mainly extracted in the T3x, T1j, P2x, P1y, and S3c lithological units, and some parts of the J2sx, J2s, J1z, P2x, Pe, C1w, D1ps, and O2d lithological units were also extracted (Figure 5).
Carbonates have maximum emissivity characteristics at ASTER band 13 (10.25–10.95 μm) and minima emissivity characteristics at band 14 (10.95–11.65 μm) [28,29,60,65]. Therefore, the SPCA method was applied to ASTER bands 10, 13, and 14 for carbonate mineral mapping. The PC of the PCA transformation contained carbonate mineral information and showed negative loadings at band 13 and positive loadings at band 14. Table 5 show the eigenvector matrix of the carbonate minerals calculated using the SPCA method for ASTER bands 10, 13, and 14. Through analyzing the eigenvector loadings of PC1, PC2, and PC3, it was determined that PC3 contains carbonate mineral information. PC3 had strong negative loadings at band 13 (−0.781483) and strong positive loadings at band 14 (0.496035). Thus, the bright pixels seen in PC3 were identified as carbonate mineral information. Carbonate minerals were widely identified in the J1z, T2gl, T1j, P2x, P1y, C1w, D2q, D1ps, and O2d lithological units (Figure 6).

4.2. Hydrothermal AlterationMinerals Mapping Using MTMF Method with GF-5 Data

Hematite, kaolinite, calcite, and dolomite were extracted using the MTMF method along with GF-5 data, which are closely associated with the iron oxide/hydroxide, argillic, and carbonate zones. Before using GF-5 data to map the hydrothermal alteration minerals, it is necessary to extract the endmember spectra, and the main steps include: (1) MNF; (2) PPI; and (3) the n-Dimensional Visualizer.
MNF is actually two continuous PCA transformations that are used for image reduction and data separation. After the transformation, the MNF bands are sorted according to the amount of information. In this study, after the MNF transformation of 301 bands of GF-5 hyperspectral data, the information content of the hyperspectral data was concentrated in the first 15 bands, accounting for more than 85%. As such, the first 15 bands were selected for PPI extraction.
In the multi-dimensional space of remote sensing data, each mixed pixel can be regarded as a point inside or on a convex polyhedron composed of vertices (endmembers). The inner product between the unit vector of points in each direction and the spectral vector of each pixel are calculated, and the number of extreme values (including maximum and minimum values) is the PPI. In this study, the first results of the first 15 MNF transformations were selected for PPI calculation, and a reasonable number of iterations and threshold values were set. After repeated operations, the PPI value tended to even out after approaching 9000; that is, the result was close to a pure single-object pixel.
After extracting pure pixel (PPI), the n-Dimensional Visualizer tool was used to draw the characteristic spectrum curves of each of the endmembers, and these were then compared to the pure mineral spectrum in the USGS standard spectral library to realize mineral identification. The results showed that the endmember spectra of hematite, kaolinite, calcite, and dolomite have absorption characteristics at around 400 nm, 2206 nm, 2336 nm, and 2319 nm, respectively (Figure 7).
On the basis of the endmember spectrum extraction and mineral identification, the MTMF method was utilized to map the hydrothermal alteration minerals of hematite, kaolinite, calcite, and dolomite from the GF-5 data derived from the Longtoushan Pb-Zn deposit. After assigning different colors to the different hydrothermal alteration minerals that were detected, an information distribution graphic of the hydrothermal alteration minerals determined in this study area was finally obtained (Figure 8).
The results indicate that hematite, kaolinite, calcite, and dolomite show high surface abundance and spatial distribution. The association between calcite, dolomite, and hematite can be observed in the O2d lithological unit. Calcite and dolomite are associated with the T2gl, T1j, P2x, and D2q lithological units. Hematite and kaolinite are associated with the T3x, Pe, and P1y lithological units. Small amounts of hematite were identified in the T1j, P2x, S3c, and S2s-d lithological units. Kaolinite was widely identified in the J2s, J2sx, T3x, D1ps, and D1st lithological units. A calcite portion was identified in the J2z, P1y, C1w, D3zj, and D1ps lithological units. A portion of dolomite was identified in the S2c and S2s-d lithological units.

4.3. Results of Field Survey and Laboratory Analysis

A special comprehensive field survey was conducted in the Longtoushan Pb-Zn deposit, specifically in the mapped hydrothermal alteration zones. GPS survey, lithology identification, sample collection, and field photographing were implemented to record the precise locations of the hydrothermal minerals and to analyze the lithologic characteristics. Field investigations determined that iron oxide, argillic, calcite, and dolomite are prevalent in this study area (Figure 9). Moreover, the collected samples were split to obtain thin sections, and then the petrographic study was performed on the thin sections. The surface expression of iron oxides and argillic, calcite, dolomite, pyrite, galena, and sphalerite minerals were identified. The coexistence of iron oxide with argillic, dolomite, calcite, pyrite, galena, and sphalerite is shown in Figure 10a. Figure 10b also show that dolomite contains iron oxides that are concentrated in the micro-fractures. Figure 10c show how Fe-hydroxides replaced the original primary pyrite. Figure 10d show iron oxide’s association with argillic minerals and dolomite. Figure 10e show the coexistence of dolomite with pyrite and sphalerite. Figure 10f show the iron oxide’s association with dolomite and galena.

5. Discussion

5.1. Extraction Efficiency Analysis of Hydrothermal Alteration Minerals Based on ASTER and GF5 Data

ASTER satellite data were employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using the SPCA method, and GF-5 satellite data were utilized to detect hematite, kaolinite, calcite, and dolomite at the sub-pixel level using the MTMF method. From the visual qualitative judgments, it was determined that the distribution trend and range of the hydrothermal alteration minerals extracted by the ASTER and GF-5 data are mostly consistent (Figure 3, Figure 4, Figure 5, Figure 6 and Figure 8). In order to qualitatively verify the efficiency of hydrothermal alteration mineral mapping, an accuracy assessment was carried out via a field investigation. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9, and the kappa coefficients were 0.78 and 0.90, respectively (Table 6 and Table 7). This accuracy assessment showed that both the ASTER and GF-5 data could obtain accurate mapping results in the Longtoushan Pb-Zn deposit. With this in mind, this study strongly suggests that the GF-5 data are able to map hydrothermal alteration minerals well and that it can be promoted as a hyperspectral data source for future systematic hydrothermal alteration mineral mapping.

5.2. Analysis of Corresponding Relationship between Extracted Hydrothermal Alteration Minerals and Lithology

Remote sensing data derived from the ASTER and GF-5 satellite were utilized for the systematic mapping of a variety of hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. It was determined that there was a large spatial distribution of iron oxide/hydroxide minerals (hematite) in the T3x, Pe, P1y, and O2d lithological units. The argillic minerals were broadly mapped in the J2s, J2sx, T3x, J1z, Pe, P1y D1ps, D2st, and S2s-d lithological units. It was also determined that there was a large distribution of carbonate minerals (calcite, dolomite) in the J1z, T2gl, T1j, P2x, P1y, C1w, D3zj, D2q, D1ps, S2c, S2s-d, and O2d lithological units. The relationship between the hydrothermal alteration minerals extracted via remote sensing data and lithology is consistent with the geological map (Figure 2). To a certain extent, this also proved the accuracy of the remote sensing hydrothermal alteration mineral extraction implemented in this paper. Moreover, iron oxide/hydroxide and carbonate mineral concentrations showed a close spatial relationship with Pb-Zn mineralization. Therefore, the northeastern sectors, the areas in which the iron oxide/hydroxide and carbonate minerals were concentrated mapped, can be considered as favorable areas for potential prospecting opportunities.

5.3. Analysis of the Influence of Vegetation on Alteration Mineral Extraction from GF-5 Satellite Data

In order to further investigate the specific effects of vegetation on alteration mineral extraction derived from GF-5 satellite data, the fractional vegetation cover (FVC) map was overlaid with the mapped results of the hydrothermal alteration minerals.
The formula to calculate the FVC is shown below [68]:
f = ( N D V I − N D V I s o i l ) / ( N D V I v e g − N D V I s o i l )
where f is the FVC; NDVI is the normalized difference vegetation index; NDVIsoil is the normalized difference vegetation index of pure soil; NDVIveg is the normalized difference vegetation index of pure vegetation. The value of f ranges from 0 to 1, and 1 represents that vegetation coverage is 100 vol%.
The NDVI is calculated by the red and near-infrared reflectances recorded by remote sensing data [69]. The formula is shown below:
N D V I = ρ N I R − ρ R E D ρ N I R + ρ R E D
where ρNIR and ρRED are the reflectances of the near-infrared and red bands, respectively.
Figure 11 show the FVC and the results of the hydrothermal alteration minerals that were mapped based on the GF-5 data. The statistical analysis indicates that the hydrothermal alteration minerals can be efficiently mapped in areas where the FVC is less than 0.82. In most regions with FVC values greater than 0.82, the hydrothermal alteration minerals are unable to be detected from the GF-5 data efficiently. Therefore, in those areas where the FVC is greater than 0.82, the method to map hydrothermal alteration from GF-5 data requires further study.

6. Conclusions

This study has systematically revealed the distribution characteristics of hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit retrieved from ASTER and GF-5 data. The ASTER data were used to map iron oxide/hydroxides and argillic, quartz and carbonate minerals at the pixel level using the SPCA method, and the GF-5 data were utilized to detect hematite, kaolinite, calcite, and dolomite at the sub-pixel level using the MTMF method. The hydrothermal alteration minerals that were mapped based on the ASTER and GF-5 data demonstrated good correspondence with the results of the field investigation. Through the field investigation and a petrographic study, the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9, and the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map the hydrothermal alteration minerals well and can be promoted as a source of hyperspectral data for systematic hydrothermal alteration mineral mapping in the future.

Author Contributions

Conceptualization, Z.Z. and Q.C.; methodology, Q.C. and R.Z.; software, Q.C. and X.Z.; formal analysis, Q.C., resources, J.Z. and Z.Z.; data curation, Q.C. and R.Z.; writing—original draft preparation, Q.C.; writing review and editing, J.Z., T.S. and J.C.; funding acquisition, Z.Z. and J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported by “National Natural Science Foundation of China” (Grant No. 41872251, 41462015, and 42061038), “the Joint Fund of Science Technology Department of Yunnan Province and Yunnan University” (Grant No.2018FY001(-019)), and “the 12th Postgraduate Scientific Research Innovation Project of Yunnan University” (Grant No. 2020192).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Regional geological map of the study area.
Figure 1. Regional geological map of the study area.
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Figure 2. Geological map of the Longtoushan Pb-Zn deposit.
Figure 2. Geological map of the Longtoushan Pb-Zn deposit.
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Figure 3. Iron oxide/hydroxide minerals mapped with ASTER data using the SPCA method.
Figure 3. Iron oxide/hydroxide minerals mapped with ASTER data using the SPCA method.
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Figure 4. Argillic minerals mapped with ASTER data using SPCA method.
Figure 4. Argillic minerals mapped with ASTER data using SPCA method.
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Figure 5. Quartz mapped with ASTER data using the SPCA method.
Figure 5. Quartz mapped with ASTER data using the SPCA method.
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Figure 6. Carbonate minerals mapped with ASTER data using the SPCA method.
Figure 6. Carbonate minerals mapped with ASTER data using the SPCA method.
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Figure 7. The reflectance spectral curves of the endmembers extracted from GF-5 hyperspectral data and of the endmembers derived from USGS spectral library version 7. (a) Hematite; (b) kaolinite; (c) calcite; (d) dolomite.
Figure 7. The reflectance spectral curves of the endmembers extracted from GF-5 hyperspectral data and of the endmembers derived from USGS spectral library version 7. (a) Hematite; (b) kaolinite; (c) calcite; (d) dolomite.
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Figure 8. Mapped hydrothermal alteration minerals with GF-5 data using the MTMF method.
Figure 8. Mapped hydrothermal alteration minerals with GF-5 data using the MTMF method.
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Figure 9. Typical field photographs of the hydrothermal alteration zones. (a) Dolomite and argillic alterations; (b) a close up of iron oxide alterations; (c) a view of the dolomite alterations; (d) a regional view of the argillic alterations; (e) iron oxide and dolomite alterations.
Figure 9. Typical field photographs of the hydrothermal alteration zones. (a) Dolomite and argillic alterations; (b) a close up of iron oxide alterations; (c) a view of the dolomite alterations; (d) a regional view of the argillic alterations; (e) iron oxide and dolomite alterations.
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Figure 10. Petrographic study of hydrothermally altered minerals under a microscope. (a) The association of iron oxide with argillic, dolomite, calcite, pyrite, galena, and sphalerite; (b) dolomite contains iron oxides concentrated in micro-fractures; (c) Fe-hydroxides replaced primary pyrite; (d) association of iron oxide with argillic and dolomite; (e) the association of dolomite with pyrite and sphalerite; (f) the association of iron oxide with dolomite and galena.
Figure 10. Petrographic study of hydrothermally altered minerals under a microscope. (a) The association of iron oxide with argillic, dolomite, calcite, pyrite, galena, and sphalerite; (b) dolomite contains iron oxides concentrated in micro-fractures; (c) Fe-hydroxides replaced primary pyrite; (d) association of iron oxide with argillic and dolomite; (e) the association of dolomite with pyrite and sphalerite; (f) the association of iron oxide with dolomite and galena.
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Figure 11. Overlay analysis of vegetation cover and hydrothermal alteration minerals.
Figure 11. Overlay analysis of vegetation cover and hydrothermal alteration minerals.
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Table
Table 1. Comparison of the advantages and disadvantages between SPCA and MTMF methods.
Table 1. Comparison of the advantages and disadvantages between SPCA and MTMF methods.
Mapping Method of Hydrothermal Alteration MineralsAdvantagesDisadvantages
SPCASimple operation, small calculation quantityThe mapping objects are mainly hydrothermal alteration groups, and the refined mapping ability of hydrothermal alteration minerals needs to be improved
MTMFHigh mapping accuracy of hydrothermal alteration mineralsRelatively complex operation procedure, high remote sensing data quality requirements
Table
Table 2. Eigenvector matrix of the iron oxides/hydroxides calculated using SPCA method for ASTER bands 1, 2, and 4.
Table 2. Eigenvector matrix of the iron oxides/hydroxides calculated using SPCA method for ASTER bands 1, 2, and 4.
Iron oxides/hydroxidesEigenvectorBand 1Band 2Band 4
PC1−0.261966−0.317454−0.911371
PC2−0.447280−0.7968660.406136
PC3−0.8551690.5140310.066761
Table
Table 3. Eigenvector matrix of the argillic minerals calculated using the SPCA method for ASTER bands 4, 5, and 6.
Table 3. Eigenvector matrix of the argillic minerals calculated using the SPCA method for ASTER bands 4, 5, and 6.
ArgillicEigenvectorBand 4Band 5Band 6
PC10.7868800.4287600.443830
PC20.616036−0.503428−0.605855
PC30.036330−0.7501500.660269
Table
Table 4. Eigenvector matrix of the quartz calculated using the SPCA method for ASTER bands 10, 12, and 14.
Table 4. Eigenvector matrix of the quartz calculated using the SPCA method for ASTER bands 10, 12, and 14.
QuartzEigenvectorBand 10Band 12Band 14
PC1−0.444519−0.535711−0.717925
PC2−0.488973−0.5264050.695559
PC3−0.7505380.660236−0.027952
Table
Table 5. Eigenvector matrix of the carbonates calculated using SPCA method for ASTER bands 10, 13, and 14 o.
Table 5. Eigenvector matrix of the carbonates calculated using SPCA method for ASTER bands 10, 13, and 14 o.
CarbonatesEigenvectorBand 10Band 13Band 14
PC1−0.412100−0.622114−0.665693
PC2−0.828818−0.0475240.557496
PC30.378462−0.7814830.496035
Table
Table 6. The accuracy assessment of the SPCA mapping method for hydrothermal alteration minerals based on ASTER data.
Table 6. The accuracy assessment of the SPCA mapping method for hydrothermal alteration minerals based on ASTER data.
Ground Truth Sample
ClassIron Oxide/HydroxideArgillicQuartzCarbonate
Unclassified2422
Iron oxide/hydroxide30035
Argillic32610
Quartz01323
Carbonate00245
Overall accuracy (Percent)kappa coefficient (Percent)
82.60.78
Table
Table 7. The accuracy assessment of the MTMF mapping method for hydrothermal alteration minerals based on GF-5 data.
Table 7. The accuracy assessment of the MTMF mapping method for hydrothermal alteration minerals based on GF-5 data.
Ground Truth Sample
ClassHematiteKaoliniteCalciteDolomite
Unclassified4215
hematite38000
kaolinite03400
calcite00200
dolomite00065
Overall accuracy (Percent)kappa coefficient (Percent)
92.90.90
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Chen, Q.; Zhao, Z.; Zhou, J.; Zhu, R.; Xia, J.; Sun, T.; Zhao, X.; Chao, J. ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China. Remote Sens. 2022, 14, 1253. https://doi.org/10.3390/rs14051253

AMA Style

Chen Q, Zhao Z, Zhou J, Zhu R, Xia J, Sun T, Zhao X, Chao J. ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China. Remote Sensing. 2022; 14(5):1253. https://doi.org/10.3390/rs14051253

Chicago/Turabian Style

Chen, Qi, Zhifang Zhao, Jiaxi Zhou, Ruifeng Zhu, Jisheng Xia, Tao Sun, Xin Zhao, and Jiangqin Chao. 2022. "ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China" Remote Sensing 14, no. 5: 1253. https://doi.org/10.3390/rs14051253

APA Style

Chen, Q., Zhao, Z., Zhou, J., Zhu, R., Xia, J., Sun, T., Zhao, X., & Chao, J. (2022). ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China. Remote Sensing, 14(5), 1253. https://doi.org/10.3390/rs14051253

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