
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12837-X
10.1016/j.heliyon.2024.e36806
e36806
Review Article
Lithological discrimination based on Landsat-9 OLI sensor and field observation data: The bana an-orogenic volcano-plutonic ring complex, West Cameroon line
Mohamed Rachid a
Tamen Jules a
Ousmanou Safianou safianouousmanou.79@gmail.com
ab⁎
Yangouo Fadimatou Kimoun a
Nkouathio David a
a Department of Earth Science, University of Dschang, Dschang, Cameroon
b Department of Geological Research, CONGEO-Engineering, Yaounde, Cameroon
⁎ Corresponding author. Department of Earth Science, University of Dschang, Dschang, Cameroon. safianouousmanou.79@gmail.com
24 8 2024
15 9 2024
24 8 2024
10 17 e3680613 1 2024
21 8 2024
22 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
The western region, encompassing the an-orogenic Bana volcano-plutonic ring complex in Cameroon, underwent comprehensive exploration involving remote sensing analysis, fieldwork investigations, petrographic, and volcanological studies. The primary objective of this work was to integrate remote sensing analysis, fieldwork, and laboratory studies to achieve accurate lithological mapping for future prospective mineral explorations in the study area. Field relationships among co-occurring rock units in the area were examined, utilizing Landsat-9 OLI data. Petrographic analysis, including the use of a polarizing microscope, was conducted on various rock units (15 samples), along with volcanological processes studies. Operational Land Imager (OLI) images of Landsat 9 were processed using algorithms including False Colour Composite (FCC), Decorrelation Stretch (DS), Band Ratio (BR) composite, Principal Component Analysis (PCA), Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) methods to identify distinct rock units in the Bana ring complex. As a result, the later methods permitted to identify the petrographic units of the ring complex, which primarily comprise a volcano-plutonic sequence, along with metamorphic rocks like gneisses. The volcanic units include variety of basalts, trachytes, rhyolites and volcanic tuffs, while the plutonic units including gabbros, diorites, syenites and fine-grained granites. The findings of this study accurately at 99 % have permitted to newly setup a geologic map of the study area with implications for future mineral explorations.

Keywords

Lithological
Landsat-9
Operational land imager OLI
An-orogenic
Bana
Ring complex
==== Body
pmc1 Introduction

An-orogenic complexes are structures with an annular deposit whose emplacement occurred independently of any orogenic event [1]. They appear in relatively stable sites that have essentially undergone distension or sliding. They are the expression of a magmatic event that is located in zones of crustal extension and rifting, within plates or at plate margins. Moreover, they appear in zones of generalised distension, which may or may not result in fractures and the formation of rifts or ripples [2]. Regions of distension without fracture are marked by a bulging of the lithosphere, probably caused by a local increase in the volume of the asthenosphere. The an-orogenic volcano-plutonic complex at Bana consists of basalts with a transitional tholeiitic character, suggesting lithospheric extension and mantle uplift beneath the Cameroon Volcanic Line [3]. A multidisciplinary approach is required to understand how these structures function. Thus, in this paper, field geology by volcanological approach was coupled with remote sensing approach.

Remote sensing is an important source for mapping past volcanic activity [4]. Due to their remote location and intense climatic circumstances, geological research in volcanic areas face several challenges. Many places remain understudied with regard of geological mapping and mineral prospecting, owing to severe conditions and logistical constraints. Few research using Landsat-9 Operational Land Imager (OLI) and Thermal Infrared (TIR) sensor has recently highlighted the use of remote sensing images for mapping geological features in igneous (volcanic/plutonic) regions [[5], [6], [7], [8], [9]]. Nevertheless, remote sensing images in particular, has the potential to offer solutions to the problems and limits involved with geological field reconnaissance and mineral prospection in inaccessible areas such as volcanic areas [5,6,10,11,12,and13]]. The Landsat-9 data (OLI/TIR) is a multi-spectral remote sensing sensor with excellent spectral, spatial, and radiometric resolutions (Table 1), made up of three distinct sensor subsystems that offer observations in three distinct spectral areas of the electromagnetic spectrum: Visible-Near-Infrared (VNIR), Shortwave Infrared (SWIR), and Thermal Infrared (TIR). The selection of Landsat-9 OLI over other sensors such as Landsat 8 OLI and ASTER can be justified by its enhanced radiometric resolution and continuity of the Landsat program's data record, which ensures compatibility with historical Landsat data while offering improved capabilities for lithological mapping.Table 1 Landsat-9 OLI/TIRS spectral characteristics (https://www.usgs.gov/landsat-mission).

Table 1Satellite	Sensor	Band (Wave length (μm))	Spectral region	Radiometric resolution (m)	
Landsat-9	OLI	1 (0.43–0.45)	Coastal aerosol	30 m	
2 (0.45–0.51)	Blue	
3 (0.53–0.59)	Green	
4 (0.64–0.67)	Red	
5 (0.85–0.88)	Near IR (NIR)	
6 (1.57–1.65)	SWIR	
7 (2.11–2.29)	SWIR	
8 (0.50–0.68)	Panchromatic	15 m	
9 (1.36–1.38)	Cirrus	30 m	
TIRS	10 (10.60–11.19)	Thermal infrared (TIR-1)	100 m	
11 (11.50–12.51)	Thermal infrared (TIR-2)	
Using the Environment for Visualizing Images (Envi3.5) software, radiometric calibrations, dark subtraction and quick atmospheric correction (QUAC) techniques (due to lack of surface reflectance) were processed on Landsat-9 OLI bands (VNIR-SWIR) for conversion of raw radiance to surface reflectance data and atmospheric corrections respectively, followed by layer-stacking and subsetting of the Landsat-9 OLI bands to highlight the visual interpretation of geologic formations.

Furthermore, the innovative aspect and main objectives of this study, relies on a multidisciplinary remote sensing approach integration with petrographical analysis and fieldwork investigations, which are thus to: (i) map and discriminate lithological units using False Colour Composite (FCC), Decorrelation Stretch (DS), Band Ratio (BR) composite, and Principal Component Analysis (PCA); (ii) generate a supervised lithological classification map using the Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) algorithms followed with accuracy assessment; (iii) corroborate the obtained remote sensing results with petrography and fieldwork data and (iv) produce a newly geological map of the study area. The approach given here is a quick, precise, time-saving, and economical tool for identifying rock formations, especially during the initial stage of exploration.

2 Location and geologic setting of the study area

The Bana an-orogenic volcano-plutonic ring complex located in the west region of Cameroon, between latitudes N05°7′0″ to N05°11′0″ and longitudes E10°18′0″ to E10°24′0″, area of 55 km (Fig. 1a), is one of the an-orogenic complexes of the Cameroon Volcanic Line (CVL), which includes more than 60 volcano–plutonic complexes along its continental part in an almost perfect N30° alignment [14].Fig. 1 (a) Study area location. (b) Geological map of Cameroon (modified from Refs. [15,6,and29]]). The black rectangle represent the study area.

Fig. 1

The study area belongs to the central Cameroonian domain (Fig. 1b). Several authors have written extensively on this complex [[3], [16], [17], [18], [19], [20], [21], [22], [23]] describing the Bana an-orogenic volcano-plutonic complex as an ovoid intrusive within basalts plateau (Eocene-Oligocene) resting on a granite-gneiss bedrock. Two magmatic series can be distinguished: an alkaline series and a subalkaline to transitional series [3,22]. The alkaline series, which is less important, essentially comprises volcanic rocks (basanites, olivine basalts and hawaiites), while the subalkaline or transitional series [3,22], which is more important, includes both volcanic rocks (tholeitic transitional basalt, andesitic basalt, quartz trachyte, benmoreite, iron-rich rhyolite) and plutonic rocks (syenodiorite, quartz syenodiorite, hyperalkaline and hyperaluminous granite). Volcanic formations [22,23] are represented by pyroclastic deposits (brecciated tuffs, ignimbrite, basaltic lapilli), flows and flow domes (porphyritic and subaphyric plagioclase basalt, olivine basalt, olivine and clinopyroxene basalt, benmoreite, rhyolite, Fe-rich flows) and finally trachytic domes. Geochronological data on the Bana an-orogenic volcano-plutonic complex indicate a Tertiary age. The Lembo granites were dated at 51 +-1 Ma by the Rb/Sr method [19], while the K/Ar method was used to find ages of 38 +- 1 and 42 +-8 Ma on benmoreite [[16], [18]], 30Ma on evolved lavas [17] and 30.1+-1.2Ma on plagioclase in transitional tholeiitic basalts [3]. As a result, in this research, the subalkaline magmatic series are generally considered to be the oldest because they tend to be more prominently represented in the oldest rock records due to their association with early and ongoing tectonic processes such as subduction and continental crust formation. Moreover, based on mapping and field observation, the produced detail geological map and field contact relationship show sharp (indicating intrusive relationship) and gradational contacts (suggesting transition from plutonic to volcanic environment).

3 Materials and methods

3.1 Materials and pre-processing of remote sensing data

Free-cloud Landsat-9 OLI bands (path/row: 186/056) acquired on the February 15, 2022, downloaded from the official website (https://earthexplorer.usgs.gov) of USGS (United States Geological Survey) and georeferenced using WGS84 datum to UTM 32N was utilized for this investigation. Table 1 display the spatial, spectral, and resolutions of the Landsat-9 OLI data. Nevertheless, only the VNIR and SWIR bands were used in this research because of their good spectral properties in distinguishing the study areas rock units. Moreover, the panchromatic (band 8), cirrus (band 9) and TIR bands were not employed in this study. This is due to the fact that TIRS bands are used to derive land surface temperature and have lower spatial resolution (100m). In addition, band 8 and band 9 are used for sharper image definition and cirrus cloud detection respectively, hence, not adequate and adapted for identifying rock units in the study area.

Additionally, recorded sampling point locations of main rock formations (Table 2) and outcrop photographs were used. A sophisticated field survey data complemented with petrographic observation and interpretations from Refs. [[3], [18], [20], [22]], was used to meet the goals of the current study.Table 2 Sampling recording.

Table 2X	Y	Sample ID	Description	X	Y	Sample ID	Description	
10.32444	5.16667	BR1	Aphyric basalt	10.34222	5.16639	*RB19″	Granite	
10.30972	5.16333	RB1	Tuff	10.34944	5.14444	RB20	Olivine basalt	
10.31083	5.16417	RB2	Tuff	10.35694	5.14639	RB21	Rhyolite	
10.31861	5.15861	RB3	Plagioclase basalt	10.32417	5.16722	RB22	Aphyric basalt	
10.3125	5.16583	RB4	Olivine basalt	10.33118	5.16417	RB23	Porphyric trachyte	
10.31389	5.16555	RB5	Tuff	10.32667	5.16778	RB24	Tuff	
10.31222	5.15639	RB6	Tuff	10.34886	5.12364	RBa	Porphyric plagioclase basalt	
10.31861	5.15167	RB7	Trachyte	10.35028	5.12278	RBc	Olivine basalt	
10.32472	5.14305	RB8	Trachyte	10.34889	5.12444	RBd	Porphyric plagioclase basalt	
10.31917	5.14889	RB9	Trachyte	10.33555	5.13472	RBf	Fe-Al-Ti rich basalt	
10.32611	5.14152	RB10	Trachyte	10.31222	5.16528	RBg	Tuff brechique	
10.32944	5.13944	RB10″	Fe-Al-Ti rich basalt	10.32083	5.16389	RBh	Tuff brechique	
10.32972	5.14333	RB11	Olivine basalt	10.33322	5.15761	RHB1	Rhyolite	
10.32778	5.145	RB12	Porphyric plagioclase basalt	10.32575	5.14614	RHB2	Rhyolite	
10.33028	5.14694	RB13	Subaphyric plagioclase basalt	10.33211	5.16097	RHB3	Aphyric trachyte	
10.33028	5.14917	RB14	Trachyte	10.33558	5.15672	*RG1	Granite	
10.32944	5.15111	RB15	Fe-Al-Ti rich basalt	10.33894	5.15953	*RG2	Granite	
10.32583	5.15528	RB16	Porphyric plagioclase basalt	10.34028	5.16117	*RG3	Granite	
10.33055	5.16833	RB17	Porphyric plagioclase basalt	10.34114	5.14839	*RG4	Granite	
10.33361	5.16222	RB18	Aphyric trachyte	10.33853	5.15267	*RG5	Granite	
10.33667	5.16083	*RB19	Syeno-diorite					

3.2 Digital image processing

Fig. 2 depicts a graphical depiction of the general methodologies used for the Landsat-9 OLI images to build a valid lithological map. Landsat 9 OLI is used because it is a powerful tool for lithological mapping due to its spectral, radiometric, and temporal capabilities. However, its limitations in spatial resolution, spectral range, and susceptibility to atmospheric conditions require careful data processing and, at times, the integration of data from other sensors to achieve comprehensive geological insights. The following is a quick summary of the procedures used.Fig. 2 Remote sensing processing methodological flowchart.

Fig. 2

3.2.1 False Colour Composite (FCC) and decorrelation stretch (DS)

To visualize lithological units, FCC is applied on Landsat-9 OLI data to create an artificially colour image by assigning blue, green, and red hues to wavelength areas where they do not belong in nature. In a conventional FCC, for example, blue is allocated to green radiations (0.5–0.6 μm), green is assigned to red radiations (0.6–0.7 μm), and red is assigned to Near Infrared radiation (0.7–0.8 μm).

DS is processed to reduce the strong correlation that is frequent in multi-spectral data sets, resulting in a more colourful colour composite image [24]. Colour pictures produced by strongly linked data sets are frequently extremely boring. Thus, DS necessitates the use of three bands as input. These bands should represent stretched byte data, but they can also be chosen from an open colour display.

3.2.2 Band Ratio (BR) and Principal Component Analysis (PCA)

BR is used to improve the spectral disparities across bands and to mitigate the topographical impacts. When one spectral band is divided by another, an image with relative band strengths is produced. The graphic draws attention to the spectral variances across bands. In this study, the approximate spectral shape for each pixel's spectrum is estimated thereby combining three BR into a colour-ratio-composite (CRC) or FCC picture.

PCA which is a linear transformation tool is used in this study to reorganize the variance in a multi-band image into a new set of image bands. The PC bands are uncorrelated linear combinations of the input bands. A PC transform creates a new set of orthogonal axes with their origin at the data mean and rotates them to maximize the data variance [25]. Moreover, the statistical relationship between the PCs (dependent variables, or rows) and the input bands (independent variables, or columns) is represented by an eigenvector matrix. The eigenvectors themselves show how much each input band contributes to each PC band. This is known as weighing or component loading. The weighting of each input band is calculated by squaring the eigenvector element of the input band. Thus, the overall contribution of all input bands to any particular PC band is the sum of the squares of the eigenvector elements of the PC band.

3.2.3 Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) classification

SAM is a physically based spectral categorization that matches pixels to reference spectra using an n-D angle. The approach calculates the angle between two spectra and treats them as vectors in a space with dimensions equal to the number of bands to estimate spectral similarity [25]. When applied to calibrated reflectance data, this approach is relatively immune to light and albedo effects. Endmember spectra can be retrieved as area of interest (ROI) mean spectra from a picture. In n-D space, SAM compares the angle between the endmember spectrum vector and each pixel vector. Smaller angles indicate more similarity to the reference spectrum [26]. Pixels that are farther away than the maximum angle criterion in radians are not classified [26].

Furthermore, CEM classification approach necessarily use only target spectra to be identified and employs a finite impulse response (FIR) filter to pass through the desired target while minimizing the output energy caused by a background other than the desired targets. To characterise the composite unknown background, a correlation or covariance matrix is utilized [25]. Thus, a series of grey scale images is generated for each endmember rock selected.

3.2.4 Accuracy assessment of supervised classification algorithms

A confusion matrix approach is suggested to assess the accuracy of the aforementioned supervised classification algorithm. The computation of the matrix is processed using the Envi3.5 software thus, determining the overall accuracy (OA) and Kappa coefficient which reflects the global accuracy and performance of the classification technique [27].

3.2.5 Field investigations and laboratory processing

Using a global positioning system (GPS), several points and rock samples (75) were recorded and gathered from Bana complex zone in January 2023. Furthermore, using a polarizing microscope with a built-in camera, the rock samples were thin sectioned for petrographic studies at the University of Dschang, Cameroon. Likewise, the dynamic emplacement of the rock formations of the Bana volcano-plutonic complex were studied following the standard procedures of Reffay [28].

4 Results and discussion

In this investigation, FCC, DS, BR, PCA, SAM and CEM were utilized to assess distinctions among lithologic units and areas linked to hydrothermal alterations. The subsequent section of the study concentrates on the petrological and volcanological part, involving a meticulous examination of thin sections obtained from diligently sampling.

4.1 False Colour Composite (FCC)

A FCC using bands 7, 4, and 3 in the RGB spectrum was created to facilitate lithological mapping (Fig. 3). The composite image exhibited distinct differentiation among the predominant lithological rock units. Volcanic rocks (Vo) manifested as green hue, contrasting with the purple and light blue colorations. Conversely, plutonic rocks (Plu) were identified by cyan and pink tones, while metamorphic rocks (Me) displayed a dark-brown colour. The 743 in RGB composite proves highly effective for lithological discrimination, and additional enhancements have the potential to optimize the outcome benefits in the results.Fig. 3 False colour composite (743) image discriminating lithological units in Bana ring complex.

Fig. 3

4.2 Decorrelation stretch (DS)

In Fig. 4, the FCC image of bands 743 in RGB is presented following the application of decorrelation stretching. This process distinctly delineates the discrimination boundaries among the variously mentioned rock units, particularly emphasizing the differentiation between the two igneous rock types: volcanic (exhibited as dark greenish and bluish colour) and plutonic (displayed as cyan and light greenish colour). It also vividly highlights metamorphic rocks as pink colours, respectively.Fig. 4 Decorrelation Stretch image of 743 delineating lithologic units in the Bana ring complex.

Fig. 4

4.3 Band Ratio (BR)

Rock unit discrimination was achieved through the utilization of the BR technique on Landsat data, as outlined in studies by Refs. [[5], [6], [7], [29], [30], [31], [32], [33]]. In Fig. 5, the FCC image of the BRs (b5-b3/b5+b3), b5, and b3 presented as an RGB image, serves for lithological mapping and the identification of hydrothermal alteration [30]. This BR composite effectively distinguishes volcanic bodies, represented by light and dark green regions. The BR approach distinctly discriminates the plutonic rocks within the specified area by pink and white pixels. Moreover, metamorphic units is distinguished by blue tones (Fig. 5).Fig. 5 Band ratios ((b5-b3/b5+b3), b5, b3) image illustrating rock units in the Bana ring complex.

Fig. 5

4.4 Principal Component Analysis (PCA)

As evident from the outcomes of the standard PCA presented in Fig. 6, the spatial distribution of lithologic rock units within the Bana ring complex, along with the statistical findings (Table 3), indicates that FCC image was generated using PC2, PC1, and PC3 in RGB. This combination yields satisfactory results in differentiating the volcanic units at the western an-orogenic Bana ring complex marked by pink, red, blue and bright tones. Additionally, plutonic units, reveal distinct discrimination areas in the north and eastern parts of the study area, marked by light green and yellow tones (Fig. 6). Conversely, the RGB combination highlights the metamorphic units, particularly in the central and southern regions of the study area by cyan tones.Fig. 6 PCA (PC213) images depicting lithologic units in the Bana ring complex.

Fig. 6

Table 3 PCA eigenvectors and eigenvalues loadings.

Table 3Eigenvectors	Band 2	Band 3	Band 4	Band 5	Band 6	Band 7	Eigenvalues	Variance %	
PC1	0,180083	0,177532	0,281996	−0,811482	0,317935	0,311359	1305690,81	58,14	
PC2	−0,178253	−0,283797	−0,305351	−0,567787	−0,61079	−0,314642	894482,18	39,83	
PC3	−0,196927	−0,482022	−0,575151	−0,085801	0,623085	0,049786	29150,89	1,30	
PC4	0,345691	−0,039081	−0,409191	0,082545	−0,348352	0,763787	8753,59	0,39	
PC5	−0,672124	−0,367719	0,433527	0,045925	−0,126741	0,454878	5752,59	0,26	
PC6	−0,570756	0,720314	−0,375576	−0,053194	0,014352	0,106265	1850,98	0,08	

4.5 Spectral Angle Mapper (SAM) and Constrained Energy Minimization (CEM) classification

Image classification involves the identification and assignment of pixels with comparable spectral characteristics to the same class, often associated with a unique colour [34]. The prevailing classifiers frequently rely solely on colour, operating on individual pixel values across various wavelengths. Each pixel is allocated to a specific class, feature, or cover type based solely on its spectral properties, with no regard for the contextual information provided by surrounding pixels.

In the current investigation, the application of SAM classification serves the purpose of lithological mapping. Training samples, gathered using the USGS spectral library database, are employed in this process. The SAM parametric spectral angle of 0.1 radians is selected due to its precision in achieving supervised classification of distinct rock types. Spectral signatures for various rock units are identified (Fig. 7a). The SAM classification effectively distinguishes between different rocks: volcanic rocks (basalts, trachyte and rhyolite) manifest as red, pink, and cyan. Plutonic rocks exhibit blue, dark-brown, light green, and yellow, while metamorphic rocks mainly gneisses appears pale-brown (Fig. 7b).Fig. 7 Spectral and classification images. (a) Spectral signatures of rock units. (b) SAM classification image illustrating different rock units constituting the Bana ring complex.

Fig. 7

The CEM method was employed to delineate the distribution of seven (07) rock units within the investigated area, represented using various colours (Fig. 8). These rocks encompass basalts, gabbro, rhyolites, diorites, granites, syenites and gneisses. The colour intensity at a specific site is directly proportional to a rock type. Consequently, the generated image (Fig. 8) highlights specific sites deemed potential for lithological and mineral prospects in the study area. Upon scrutinizing the CEM map, several observations can be drawn: (i) gabbro (Fig. 8a) and gneisses (Fig. 8b) are less widespread; (ii) basalts (Fig. 8c), diorites (Fig. 8d) and granites (Fig. 8e) are extensively demarcated; and (iii) syenites (Fig. 8f) and rhyolites (Fig. 8g) are prevalent rocks.Fig. 8 CEM images illustrating the distribution of different rocks. (a) CEM of gabbro. (b) CEM of gneiss. (c) CEM of basalts. (d) CEM of diorites. (e) CEM of granites. (f) CEM of syenites. (g) CEM of rhyolites.

Fig. 8

4.6 Accuracy assessment

Evaluating the accuracy of the final product derived from remote sensing image classification is crucial. This assessment serves as a guarantee of the quality of classification and instils confidence in the users of the product [35]. Numerous methodologies for analysing the accuracy of remotely sensed data have been discussed by various researchers [[5], [6], [7], [27], [36], [37]].

In this study, accuracy assessment involved the utilization of a confusion matrix and derived measures such as overall accuracy, user's accuracy, producer's accuracy, and kappa coefficient (Table 4). The outcomes of these accuracy assessment techniques revealed an overall accuracy of 99.60 %, with a kappa value of 0.99, signifying a commendable agreement between thematic maps generated from the image and the reference data.Table 4 Confusion matrix of SAM accuracy assessment.

Table 4Class	Basalt	Diorite	Gabbro	Granite	Rhyolite	Syenite	Gneiss	Prod. Acc.	User Acc.	
Basalt	100	0.00	0.00	0.00	0.00	0.00	0.00	100	100	
Diorite	0.00	100	0.00	0.00	0.00	0.02	0.00	100	100	
Gabbro	0.00	0.00	100	0.00	0.00	0.00	0.00	100	100	
Granite	0.00	0.00	0.00	99.92	0.00	0.00	0.00	99.92	100	
Rhyolite	0.00	0.00	0.00	0.08	100	0.00	0.00	99.99	100	
Syenite	0.00	0.00	0.00	0.00	0.00	99.98	0.00	99.97	100	
Gneiss	0.00	0.00	0.00	0.00	0.00	0.00	100	100	100	
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00			
Overall Accuracy	99.60 %									
Kappa Coefficient	0.99									

5 Validation/verification (petrography and volcanological emplacement processes)

To validate and complete the remote sensing work, thorough fieldwork investigations were carried out in the Bana volcano-plutonic complex, the rock formations were identified and described, their dynamic emplacement and the relationship between the different rock units were also established. The dynamic emplacement of the volcanic morphologies were described according to the standards of established by Reffay [[28], [38], [39]], while the dynamic processes were classified according to the classification of Gèse [40].

The Bana volcano-plutonic complex of tertiary age was emplaced following several magmatic events that produced various magmatic products. Volcanic formations include domes, flows, dykes and pyroclastic deposits. The plutonic formations are represented by the intrusive massifs of Lembo in the north, Batcha in the south and Batchingou in the east.

5.1 Volcanic formations

An-orogenic ring complexes typically exhibit subvolcanic characteristics, although the volcanic component may be absent. Within the Bana complex, there is considerable diversity in the emitted products, eruptive styles, and constructed formations. Thus, at the complex's centre, elongated domes, including a bulbous dome with simple polarity open to the east, can be observed (Fig. 9a). This structure is identified by its fragmented shell (i) and the enclosed lava (ii).Fig. 9 Relief shapes (a, c and e) and thin sections of plagioclase basalts (b, d and f). (a, c & e) Geomorphological forms of plagioclase basalt. (b, d & f) Microphotographs of plagioclase basalts analysed under crossed polarized light showing plagioclase minerals.

Fig. 9

The predominant volcanic species in the Bana an-orogenic volcano-plutonic complex are plagioclase basalts, showcasing two primary textures such as porphyritic and subaphyric. These plagioclase basalts are further categorized on a sample scale into three types based on the abundance of plagioclase phenocrysts, as outlined by Ref. [20] including those with low abundance (18.35 % of the modal composition) (Fig. 9f), medium abundance (Fig. 9d), and those with very high abundance (52.65 % of the modal composition, featuring megaphenocrysts) (Fig. 9b). The crystals exhibit a range of shapes, from squat to elongated, with sizes varying from less than 1 mm–12 mm. Additional minerals include clinopyroxene and oxides (titanomagnetite and ilmenite) [3].

These intermediate basalts take shape either as flows or domes through fissural volcanism. The centripetal flows, primarily originating from the flanks of the Batcha and Batchingou intrusions, exhibit a characteristic pattern (Fig. 9e). Regarding domes, there are slender ones characterized by narrow, fragmented tops and smooth truncated cones, known as Crypto-truncated cone domes [28] (Fig. 9c). Located to the north of the complex are two elongated coalescing domes, one oriented NE and the other SE, both displaying fairly regular symmetry.

Very minor flows of olivine basalts and olivine-clinopyroxene basalts are found within granites, rhyolites, tuffs, and plagioclase basalts, indicating their conclusion of volcanic activity within the Bana volcano-plutonic complex. Hawaïte rocks, characterized by a porphyry texture, consist of olivine, clinopyroxene, plagioclase, Fe-Ti oxides, feldspar, and biotite (Fig. 10a and b).Fig. 10 Outcrop (a) and microscopic view of hawaïte rock (b) displaying olivine and clinopyroxene minerals.

Fig. 10

The rhyolites form a significant part of the volcanic formations well-represented in the Bana complex. They are present independently in the northern massif, interweaving to connect the Lembo and Batchingou intrusions. Additionally, they appear as a slab at Balembo. In the southern region, these rhyolites are associated with trachytes, shaping a small, low-lying dome resembling a shield. Rhyolites manifest in various forms, including rhyolitic tuffs and a 4-m-thick dyke trending N 70°, cutting through welded pyroclastic tuffs (Fig. 11a). Their emplacement appears to have occurred in a solid state. Typically, exhibiting an orange-yellow weathering patina (Fig. 11b), microscopic analysis reveals the presence of plagioclase, sanidine, biotite, quartz, opaque minerals, and zircon (Fig. 11c). Based on mineral and normative composition, two groups of rhyolites are identified including peralkaline rhyolites and mildly alkaline rhyolites [20].Fig. 11 Rhyolite rocks within welded tuffs (a–b) followed by its microphotograph showing its main constituent minerals (c).

Fig. 11

Trachytes are scarce within the Bana complex, yet they exhibit two distinct facies: a dense dark grey facies (Fig. 12a) and a light grey facies displaying a vacuolar structure (Fig. 12c). These facies are situated to the northwest of the complex, forming a pelee-shaped crypto-dome with an eroded and rounded pointed top. The porphyritic trachytes that crown the summit postdate the aphyritic trachytes that support them during emplacement. Microscopically, they feature a porphyritic microlitic texture consisting of sanidine, plagioclase, oxides, hornblende, clinopyroxene, and anorthosis (Fig. 12b and d).Fig. 12 Dark porphyritic and light grey aphyritic trachyte samples (a & c). Microphotographs of trachyte section main feldspar minerals constituting the ground mass texture (b & d). Kfs: Alkali feldspar; Cpx: Clinopyroxene; Qtz: Quartz; Pl: Plagioclase; Ox: Oxide.

Fig. 12

The Bana complex also comprises diverse types and facies of tuffaceous rocks, encompassing nearly all stratigraphic elements related to tuff emplacement. Corded lavas, appearing as a slab flow in the Bapouh locality, represent the basal surge. Subsequently, there are tuffs with variable grain sizes leading to brecciated tuff facies. These tuffs exhibit both rhyolitic and ignimbritic characteristics. Occasionally, ignimbritic tuffs form small irregular cones, displaying prismations on cooling surfaces that can be either sharp or arbitrary (Fig. 13a). Some outcrops reveal folding on the vertical plane of a large block, indicating deformation during the flow of fluid lava. At the block's base, a penetrative surface is evident, likely marking magmatic fluidity [41]. Petrographically, these ignimbrites consist of fiâmes, basement fragments, quartz, feldspar, pyrite, olivine, and plagioclase (Fig. 13b).Fig. 13 Outcrop (a) and thin section of ignimbrite tuff (b). (a) Ignimbrite outcrop. (b) Microphotograph under crossed polarized light of ignimbrite exhibiting the fiamme structure. Kfs: Alkali feldspar; Ol: Olivine; Qtz: Quartz; Pl: Plagioclase; Py: Pyrite.

Fig. 13

A distinctive feature of the Bana volcano-plutonic complex is the occurrence of Fe-Ti-Al-rich volcanic rocks exposed at two locations, in the north-west and south-west. These rocks lie beneath basalts, benmoreites, and trachytes. Explored in detail by Ref. [21], they are categorized into three types based on texture including the Al-rich fine-grained solid, globose, and banded type. The minerals constituting these rocks include magnetite, ilmenite, hematite, rutile, corundum, andalusite, sillimanite, cordierite, quartz, plagioclase, alkali feldspar, apatite, Fe-Mn phosphate, Al phosphate, micas, and a fine mixture of sericite and silica [21]. Proposes that these rocks originated through pyro-metamorphism of laterite in contact with basaltic magma.

5.2 Plutonic formations

Located to the north of the Bana volcano-plutonic complex, the Lembo granitic intrusion exhibits steep, imbricated domes with two distinct facies: one granitic and the other syenitic. Covering an area of approximately 2 × 0.8 km2, this sub-circular intrusion reaches an elevation of about 2050 m (Fig. 14a). On a smaller scale, the formation presents a greyish-white hue with black dots composed of ferromagnesian and accessory minerals (Fig. 14b). Microscopic examination reveals a heterogranular texture for the hyperalkaline granitic units, with alkali feldspars (orthoses) constituting 50–60 % of the rock, quartz 20–25 %, amphiboles around 15 %, and the remaining portion comprised of opaque and ferromagnesian minerals (Fig. 14c). The syenitic units (syenodiorite, quartz syenodiorite) partially overlies hyperalkaline granites, displaying a coarse-grained, dark grey appearance. Its composition includes quartz (10–20 %), alkali feldspars, plagioclase, clinopyroxenes, amphibole, biotite, apatite, and opaque minerals.Fig. 14 (a) Overlapping granite domes (b) samples of Lembo granite (c) microphotograph section of Lembo granite.

Fig. 14

The Batcha granitic intrusion, ascending to an elevation of approximately 1860 m (Fig. 15), it takes on an elongated E-W ovoid shape, spanning approximately 3.5 × 0.5 km2 [[18], [19], [20]]. This formation consists of meta-aluminous granites, featuring quartz, orthoclase, albite, biotite, and amphibole, as well as hyper-aluminous granites, characterized by quartz, orthoclase, albite, and biotite.Fig. 15 Geomorphologic units of the Bana volcano-plutonic ring complex resulting from the Digital Elevation Model (DEM) of the study area.

Fig. 15

The Batchingou intrusive massif, taking the form of a half crescent (2.5 × 2 km2), it predominantly comprises granites, forming the bedrock for the anorthositic suite formations [[22], [23]]. The granitic units appears as metric to multi-meter-sized outcrops, reaching an altitude of nearly 2097 m (Fig. 15), marking it as the highest peak in the complex. Comprising alkali feldspars, quartz, plagioclase, biotite, apatite, and opaque minerals, this rock formation of the anorthositic suite was emplaced in the form of dykes within the granitic host rock. The suite includes gabbros, gabbronorites, anorthosites, norites, jotunite, tonalite, mugerites, and monzogranite [[20], [22], [23], [41]].

Research conducted on the Bana volcano-plutonic ring complex reveals that the plutonic formations can be categorized into two distinct suites of A-type granitoids, differing in petrological and geochemical characteristics. These are the syenodiorite-peralkalic suite and the metaluminous-weakly peraluminous suite [20]. The numerous intrusions point to the existence of miarolitic cavities and mesoperthite [20]. The emplacement process initiated with gabbroic plutonism of unspecified age, followed by a syenitic-trending granite at 51+-1 Ma, with its magma source believed to be mantle-fed [19].

Based on the integration of remote sensing analysis (FCC, DS, BR, PCA, SAM and CEM), fieldwork investigations and laboratory results using ArcGIS merge tool, an updated geologic map (Fig. 16) of Bana ring complex is generated illustrating the different rock formations.Fig. 16 Updated geologic map of Bana ring complex after Kuepouo [20].

Fig. 16

6 Conclusion

Various remote sensing techniques, including FCC, DS, BR, PCA, SAM and CEM were employed to process Landsat-9 OLI data for the purpose of delineating geological units within the study area. These methodologies yielded valuable results, and the images generated through these techniques demonstrated strong agreement. Utilizing remote sensing data for the analysis of surface and geological features is particularly significant in geological studies covering extensive regions with limited or no in situ data. The integration of multispectral Landsat-9 OLI data with field and laboratory data emerged as a valuable approach for mapping lithological units in the study area. Notably, the SAM classification method exhibited superior accuracy compared to other techniques. Overall, this work will provide geologist with a powerful and efficient toolset for lithological and mineral exploration, hence allowing for better targeting, reduce the costs, save time and enhanced understanding of the geological environment of the study area. Moreover, the research work particularly showed to be effective in detecting alteration zones, which are often associated with mineral deposits.

The main limitations during this work are: the lack of available and/or reliable data such as sufficient whole-rock geochemical analysis and adequate processing algorithms such as machine learning algorithms which likely limits the scope of the research. The future findings will be focused on processing remote sensing data of the study area with machine learning algorithms integrated with geochemical analysis.

Consent for publication

We, the authors, grant our consent for the publication of identifiable details, which can include figures/photographs and/or details within the text to be published in the above journal and article. We the authors confirm that we have seen and given the opportunity to read the article to be published by Heliyon.

Availability of data and materials

The datasets created and/or analysed in the present research are not publicly available in a repository due to the fact that, it is a content of a PhD research work but would be available from the corresponding author on reasonable request.

Funding

No funding nor grant was received for leading this research or assist with the preparation of this manuscript.

CRediT authorship contribution statement

Rachid Mohamed: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jules Tamen: Validation, Supervision. Safianou Ousmanou: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Fadimatou Kimoun Yangouo: Investigation. David Nkouathio: Validation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study is a constituent of Rachid Mohamed ongoing PhD research work. The authors address their exceptional thankfulness to the population and authorities of Bana village for their hospitality and administrative collaboration. Warm feelings of gratitude to anonymous editors and reviewers for their continuous prompt appraisal.
==== Refs
References

1 Bonin B. Lameyre J. Réflexion sur la position et l’origine des complexes magmatiques anorogéniques Bull. Soc. Géol. France 7 1978 45 49
2 Bonin B. Hypersolvus subvolcanic complexes and the youthful Atlantic basin Geol. Mediterr. 1 1974 139 142
3 Kuepouo G. Tchouankoue J.P. Nagao T. Sato H. Transitional tholeiitic basalt in the tertiary Bana volcano-plutonic complex, Cameroon Line J. Afr. Earth Sci. 45 2006 318 332
4 Corumluoglu O. Vural A. Asri I. Determination of Kula basalts (geosite) in Turkey using remote sensing techniques Arabian J. Geosci. 8 2015 10105 10117 http://doi:10.1007/s12517-015-1914-4
5 Safianou O. Fozing E.M. Kwékam M. Yaya F. Leprince D.A.J. Application of remote sensing techniques in lithological and mineral exploration: discrimination of granitoids bearing iron and corundum deposits in southeastern Banyo, Adamawa region-Cameroon Earth Sci. Info. 16 2023 1 27 10.1007/s12145-023-00937-5
6 Safianou O. Fozing E.M. Tcheumenack K.J. Achu M.L. Kamgang T.A.B. Aman S. Rachid M. Kwékam M. Mapping and discrimination the mineralization potential in the granitoids from Banyo area (Adamawa, Cameroon), using Landsat 9 OLI, ASTER images and field observation GeoGeo 2023 100239 10.1016/j.geogeo.2023.100239
7 Safianou O. Yaya F. Jacques W.W. Amadou D.K. Eric M.F. Maurice K. Miranda I. Fuzzy-logic technique for gold mineralization prospecting using Landsat 9 OLI processing and fieldwork data in the Bibemi goldfield, north Cameroon Heliyon 10 2024 2024 e23334 10.1016/j.heliyon.2023.e23334
8 Gahlan H. Ghrefat H. Detection of gossan zones in arid regions using Landsat 8 OLI data: implication for mineral exploration in the eastern Arabian shield, Saudi Arabia Nat. Resour. Res. 27 2018 109 124 10.1007/s11053-017-9341-8
9 Marzouki A. Dridri A. Lithological discrimination and structural lineaments extraction using Landsat 8 and ASTER data: a case study of Tiwit (Anti-Atlas, Morocco) Environ. Earth Sci. 82 2023 125 10.1007/s12665-023-10831-4
10 Bachri I. Hakdaoui M. Raji M. Teodoro A.C. Benbouziane A. Machine learning algorithms for automatic lithological mapping using remote sensing data: a case study from Souk Arbaa Sahel, Sidi Ifni Inlier, western Anti-Atlas, Morocco ISPRS Int. J. Geo-Inf. 8 2019 248 10.3390/ijgi8060248
11 Santos D. Cardoso-Fernandes J. Lima A. Müller A. Brönner M. Teodoro A.C. Spectral analysis to improve inputs to random forest and other boosted ensemble tree-based algorithms for detecting NYF pegmatites in tysfjord, Norway Remote Sens 14 2022 3532 10.3390/rs14153532
12 Ranjithkumar S. Anbazhagan S. Tamilarasan K. Image processing of landsat-8 OLI satellite data for mapping of alkaline-carbonatite complex, southern India Remote Sens Earth Syst Sci 2024 10.1007/s41976-024-00104-4
13 Kamel M. Abdeen M.M. Youssef M.M. Utilization of landsat-8 (OLI) image data for geological mapping of the neo-proterozoic basement rocks in the central eastern desert of Egypt J Indian Soc Remote Sens 50 2022 469 492 10.1007/s12524-021-01465-9
14 Déruelle B. Ngouonouno I. Demaiffe D. The « Cameroon Hot Line » (CHL): a unique example of active alkaline intraplate structure in both oceanic and continental lithospheres./CR Geoscience 33 2007 589 600
15 Penaye J. Kröner A. Toteu S.F. Van Schmus W.R. Doumnang J.C. Evolution of the Mayo-Kebbi region as revealed by zircon dating: an early (ca. 740 Ma) PanAfrican magmatic arc in southwestern Chad J. Afr. Earth Sci. 44 2006 530 542
16 Cantagrel J.M. Jamond C. Lasserre M. Le magmatisme alcalin de la ligne du Cameroun au tertiaire : données géochronologiques K/Ar. Compte Rendu sommaire Séance Soc Géol. France 6 1978 300 303
17 Lassere M. Mise au point sur les granitoïdes its « ultimes » du Cameroun. Gisement, pétrographie, géogronologie Bull. Bureau Rec. Géol. Minière 2 1978 145 159
18 Nana J.M. Le complexe volcano-plutonique de Bana (Ouest Cameroun) Géologie, Pétrologie 1988 Thèse de Doctorat Université de Paris XI. France
19 Cean-Vachette M. Termpier P. Nana J.P. Le granite de Lembo (partie du complexe volcano-plutonique de Bana), témoin du magmatisme Tertiaire du Cameroun Géochronologie. Bull. Soc. Géol. France 3 1991 497 501
20 Kuepouo G. Geology, Petrology and Geochymisry of Tertiary Bana Volcano-Plutonic Complex, Cameroon Line, Central Africa Ph.D Thesis 2004 Kobe University Japan 300p
21 Kuepouo G. Sato H. Tchouankoue J.P. Murata M. FeO*-AL2O3-TIO2-Rich rocks of the tertiary Bana igneous complex, west Cameroon Resource geo 59 No.1 2008 69 86
22 Ziada Tabengo M. Tassongwa B. Tamen J. Nkoumbou C. Njanko T. Assah A.N.E. Tedonkenfack S.S.T. Wouatong A.S.L. Petrology and geochemistry of the Batchingou anorthositic suite rocks (Bana volcano-plutonic complex, Cameroon Volcanic Line): influence on their origin and relation with host granites Geol. J. 1–23 2022 10.1002/gj.4543
23 Ziada Tabengo M. Pétrologie des anorthosites et roches associées de Batchingou, complexe volcano-plutonique de Bana (Ouest-Cameroun) 2022 Thèse de Doctorat Université de Dschang 226p
24 Saleh G.M. El-Arafy R.A. Kamar M.S. Effectively using multispectral remote sensing and GIS techniques for precision radioactive mineral exploration in um Ara – um shilman area, south eastern desert of Egypt Geoinfor Geostat: An Overview 8 2020 5
25 Research Systems, Inc ENVI Tutorials. Research Systems 2008 Inc. Boulder, CO
26 Frutuoso R. Lima A. Teodoro A.C. Application of remote sensing data in gold exploration: targeting hydrothermal alteration using Landsat 8 imagery in northern Portugal Arabian J. Geosci. 14 2021 459 10.1007/s12517-021-06786-0
27 Congalton R.G. Green K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices 1999 Lewis Publishers Boca Raton
28 Annie Reffay Les dômes volcaniques : essai de typologie géomorphologique Norois, N°105, Janvier-Mars 1980 1931 10.3406/noroi.1980.3868
29 Frei M. Jutz S.L. Use of Thematic Mapper data for the detection of gold bearing formations in the eastern Desert of Egypt Proceedings of the 7th Thematic Conference on Remote Sensing for Ore Exploration Geology II 1989 1157 1172
30 Sabins F.F. Remote sensing for mineral exploration Ore Geol. Rev. 14 3–4 1999 157 183
31 Abdelsalam M.G. Stern R.J. Mineral exploration with satellite remote sensing imagery: examples from Neoproterozoic Arabian shield J. Afr. Earth Sci. 28 1999 4a
32 Kusky T.M. Ramadan T.M. Structural controls in the Neoproterozoic Allaqi suture: an integrated field, Landsat TM, and radar C/X SIR SAR images J. Afr. Earth Sci. 35 2002 107 121
33 Gahlan H. Ghrefat H. Detection of gossan zones in arid regions using Landsat 8 OLI data: implication for mineral exploration in the eastern arabian shield, Saudi Arabia. natural resources research Nat. Resour. Res. 27 1 2018 109 124
34 Kamel M. Youssef M. Hassan M. Bagash F. Utilization of ETM+ Landsat data in geologic mapping of wadiGhadir-GabalZabara area, Central Eastern Desert, Egypt. Egypt. J Remote Sens. Space Sci. 19 2016 343 360
35 Foody G.M. Status of land cover classification accuracy assessment Remote Sens. Environ. 80 2002 185 201
36 Koukoulas S. Blackburn G.A. Introducing new indices for accuracy evaluation of classified images representing semi-natural woodland environments Photogramm. Eng. Remote Sens. 67 2001 499 510
37 Kamel M. Abu El Ella E.M. Integration of remote sensing & GIS to manage the sustainable development in the nile valley desert fringes of assiut-sohag governorates upper Egypt J. Indian Soc. Remote Sens.Volume 44 2016 759 774 10.1007/s12524-015-0529-2
38 Annie Reffay Les Dômes Volcaniques Complexes 1982 Norois, n°114, Avril-Juin 229 237 https://doi:10.3406/noroi.1982.4035
39 Annie Reffay L'évolution géomorphologique des dômes volcaniques Norois, N°115, Juillet-Septembre 1982 403 412 10.3406/noroi.1982.4052
40 Gèse B. Sur la classification des dynamismes volcaniques Bull. volcanol. 27 1964 237 257 10.1007/BF02597524
41 Tassongwa B. Cartographie, minéralogie et géochimie des indices d’argile de Balengou de Lembo (flanc Est du complexe de Bana, Ouest-Cameroun) Thèse de Doctorat Université de Yaoundé 1 2019 200p
