Lithological discrimination and mineralogical mapping using Landsat-8 OLI and ASTER remote sensing data: Igoudrane region, jbel saghro, Anti Atlas, Morocco

Baid, Soukaina; Tabit, Abdelhalim; Algouti, Ahmed; Algouti, Abdellah; Nafouri, Imane; Souddi, Sabir; Aboulfaraj, Abdelfattah; Ezzahzi, Salma; Elghouat, Akram

doi:10.1016/j.heliyon.2023.e17363

Cited by 4 publications

(3 citation statements)

References 73 publications

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“…Multispectral sensors are cost-effective and have higher spatial resolution, making data acquisition and processing relatively straightforward. As a result, multispectral data finds wide applications in remote sensing-based lithological classification and has reached a relatively mature stage of development [57][58][59][60][61][62][63][64][65][66]. However, there is still untapped potential for further advancements in the use of multispectral data in lithological classification.…”

Section: Discussionmentioning

confidence: 99%

GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China

Chen,

Yang,

Han

et al. 2023

Remote Sensing

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Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field.

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Section: Discussionmentioning

confidence: 99%

GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China

Chen,

Yang,

Han

et al. 2023

Remote Sensing

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“…The second rotation uses PCs derived from the original image data after they have been whitened by the first rotation and rescaled by noise standard deviation. Similar to the PCA output bands, the first MNF band contains the highest variance, and the second MNF band contains the second-highest variance [45,46]. As the last three components that were included are strongly affected by noise, only the first seven MNF components were selected for lithological mapping (Table 3).…”

Section: Feature Extractionmentioning

confidence: 99%

Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF

Xi,

Jiang,

Liu

et al. 2023

Applied Sciences

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Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA) and minimum noise fraction (MNF), were used with grey-level cooccurrence matrices (GLCMs) to increase the quality of the input datasets. The ICA could discriminate between rock unit datasets better than the other approaches. In contrast, GLCM performed poorly when used independently. The overall classification accuracies were 60.71%, 62.63%, 64.34%, 65.21% and 58.87% for the 10 bands of Sentinel-2, PCA, MNF, ICA and GLCM, respectively. Then, five datasets were combined as a single group and applied in RF classification. Sentinel-2 obtained an overall accuracy of 73.96% and performed better than the other single-dataset approaches used in this study. Furthermore, the classification result of RF was achieved better performance than that of the support vector machine algorithm (SVM). During feature selection processing, ReliefF, the most successful pre-processing algorithm, was employed to preliminarily perform feature screening. Then, the optimal dataset was selected on the basis of the importance ranking of RF. A total of 20 more important predictors were selected from 114 original features using the ReliefF-RF model. These predictors were used in the lithological mapping, and an overall accuracy of 77.63% was reached.

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“…The most recent remote sensing works in this study area, notably Anaba Fotze et al ( 2019) and Ketchaya et al (2021) focused on mapping regional hydrothermal and structural alteration using Landsat OLI 8 images. However, to provide more accurate and localized information on hydrothermal alteration and structural mapping, ASTER data, which offers better precision and efficiency compared to Landsat OLI 8 (Baid et al, 2023), was used. This approach is innovative in this context as it has never been used before in this study area.…”

Section: Introductionmentioning

confidence: 99%

ASTER Data Processing by Discrete Wavelets Transform and Band Ratio Techniques for the Identification of Lineaments and Hydrothermal Alteration Zones in Poli, North Cameroon

Ahamadou,

Stella,

Arsène

2023

GEP

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The aim of this study is to carry out hydrothermal alteration mapping and structural mapping using ASTER images in order to identify indices that could guide mining exploration work in the Poli area and its surroundings. To achieve this, the ASTER images were first preprocessed to correct atmospheric effects and remove vegetation influence. Secondly, a lineament mapping was conducted by applying Discrete Wavelet Transform (DWT) algorithms to the First Principal Component Analysis (PCA1) of Visible Near-Infrared (VNIR) and Shortwave Infrared (SWIR) bands. Lastly, band ratio methods were applied to the VNIR, SWIR, and Thermal Infrared (TIR) bands to determine indices of iron oxides/hydroxides (hematite and limonite), hydroxyl-bearing minerals (chlorite, epidote, and muscovite), and the quartz index. The results obtained showed that the lineaments were mainly oriented NE-SW, ENE-WSW, and E-W, with NE-SW being the most predominant direction. Concerning hydrothermal alteration, the identified indices covered almost the entire study area and showed a strong correlation with lithological data. Overlaying the obtained lineaments with the hydrothermal alteration indices revealed a significant correlation between existing mining indices and those observed in the field. Mineralized zones generally coincided with areas of high lineament density exhibiting significant hydrothermal alteration. Based on the correlation between existing mining indices and the results of hydrothermal and structural mapping, the results obtained can then be used as a reference document for any mining exploration in the study area.

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Lithological discrimination and mineralogical mapping using Landsat-8 OLI and ASTER remote sensing data: Igoudrane region, jbel saghro, Anti Atlas, Morocco

Cited by 4 publications

References 73 publications

GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China

GF-2 Data for Lithological Classification Using Texture Features and PCA/ICA Methods in Jixi, Heilongjiang, China

Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF

ASTER Data Processing by Discrete Wavelets Transform and Band Ratio Techniques for the Identification of Lineaments and Hydrothermal Alteration Zones in Poli, North Cameroon

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