Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Chen, Yansi; Wang, Yunchen; Zhang, Feng; Dong, Yulong; Song, Zhihong; Liu, Genyuan

doi:10.3390/min13091153

Cited by 5 publications

(4 citation statements)

References 131 publications

(311 reference statements)

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“…The application of remote sensing technology for lithological mapping has gained widespread application due to its ability to detect and differentiate surface characteristics that are not visible to the naked eye. Various remote sensing techniques are employed for lithological mapping, including hyperspectral imaging [1] and multispectral imaging [10,23]. Multispectral imaging involves collecting data in a few broad spectral bands with moderate spectral resolution and rapid coverage of large areas [24].…”

Section: Data Used and Methodsmentioning

confidence: 99%

“…Advanced remote sensing enhancing processes can be used to recognize patterns and features in data and classify them into different lithological units. Integrating remote sensing and field investigation processes is faster, more efficient, and more accurate than the traditional "field survey" [10]. On the other hand, remote sensing techniques can process large amounts of data from various sources, such as multispectral and hyperspectral satellite imagery, field data, and geological and topographical maps [11].…”

Section: Introductionmentioning

confidence: 99%

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Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

El-Haddad,

Youssef,

Mahran

et al. 2024

J. Geogr. Cartogr.

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In the domains of geological study, natural resource exploitation, geological hazards, sustainable development, and environmental management, lithological mapping holds significant importance. Conventional approaches to lithological mapping sometimes entail considerable effort and difficulties, especially in geographically isolated or inaccessible regions. Incorporating geological surveys and satellite data is a powerful approach that can be effectively employed for lithological mapping. During this process, contemporary RS-enhancing methodologies demonstrate a remarkable proficiency in identifying complex patterns and attributes within the data, hence facilitating the classification of diverse lithological entities. The primary objective of this study is to ascertain the lithological units present in the western section of the Sohag region. This objective will be achieved by integrated Landsat ETM+ satellite imagery and field observations. To achieve our objectives, we employed many methodologies, including the true and false color composition (FCC&TCC), the minimal noise fraction (MNF), principal component analysis (PCA), decoration stretch (DS), and independent component analysis (ICA). Our findings from the field investigation and the data presented offer compelling evidence that the distinct lithological units can be effectively distinguished. A recently introduced geology map has been incorporated within the research area. The sequence of formations depicted in this map is as follows: Thebes, Drunka, Katkut, Abu Retag, Issawia, Armant, Qena, Abbassia, and Dandara. Implementing this integrated technique enhances our comprehension of geological units and their impacts on urban development in the area. Based on the new geologic map of the study area, geologists can improve urban development in the regions by detecting building materials “aggregates”. This underscores the significance and potential of our research in the context of urban development.

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Section: Data Used and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

El-Haddad,

Youssef,

Mahran

et al. 2024

J. Geogr. Cartogr.

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“…A substantial amount of spectral information can be derived from satellite-based hyperspectral data, allowing mineral compositions to be determined from the spectra [9]. Yet, in addition to spectral confusion, difficulties in data processing, relatively narrow swath widths, and atmospheric interference, high-resolution hyperspectral images are prone to spectral interference [10]. Thus, a single pixel in the image provides coverage for a large ground surface area (e.g., 1000 m 2 ), making selections of pure pixel spectra for training samples in the supervised classifier difficult and challenging; furthermore, the lithological classification accuracy is potentially low as a result [11,12].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Bahrami,

Esmaeili,

Homayouni

et al. 2024

Minerals

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Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.

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“…Researchers used integrated remote sensing data and spatial analysis techniques to map lithological units. 21,22 Chen et al 23 believed that using a strategy of combining multi-source remote sensing technologies or integrating remote sensing with auxiliary data can be effective for indirect lithological identification in vegetated areas.…”

Section: Introductionmentioning

confidence: 99%

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Chen,

2023

J. Appl. Rem. Sens.

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.Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.

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Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Cited by 5 publications

References 131 publications

Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

Integrating remote sensing and field investigation for lithological mapping of Per-Eonile to Neonile sequences west of Sohag city, Egypt: Impact on urban development

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

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