scholarly journals Application of Remote Sensing in Earth Sciences – A Review

2021 ◽  
Vol 10 (05) ◽  
pp. 045-051
Author(s):  
Adel Shirazy Shirazy ◽  
Aref Shirazy ◽  
Hamed Nazerian

The application of remote sensing sciences in the field of geology is very diverse and wide. One of its most important applications in earth sciences is geological mapping. Mineral exploration using remote sensing techniques is done in different ways, one of them is the mapping alteration zones related to mineral resources. Given the importance of remote sensing and geosciences in today's industry and given that deposit-related alteration areas are one of the most important exploratory keys. in this review study the mapping methods and alteration zones detection using remote sensing techniques and other applications of remote sensing in earth sciences and its generalities are explained.

2021 ◽  
Vol 11 (3) ◽  
pp. 1123-1138
Author(s):  
Mohamed Taha AlMakki Mohamed ◽  
Latifa Shaheen Al-Naimi ◽  
Tochukwu Innocent Mgbeojedo ◽  
Chidiebere Charles Agoha

AbstractIn recent years, various geological activities and different mineral prospecting and exploration programs have been intensified along the Red Sea hills in order to elucidate the geological maps and to evaluate the mineral potentials. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (GIS) techniques for geological mapping and prospecting for gold mineralization in the area. The study area is located in northeast Sudan and covers an area of about 1379 km2. Different digital image processing techniques were applied to Landsat 8 Operational Land Imager image in order to increase the discrimination between various lithological units and to delineate wall rock alteration which represents target zones for gold mineralization. Image sharpening was performed to enhance the spatial resolution of the images for more detailed information. Contrast stretching was applied after the various digital processing procedures to produce more interpretable images. The principal component analysis transformations yielded saturated images and resulted in more interpretable images than the original data. Several ratio images were prepared, combined together and displayed in RGB color composite ratio images. This process revealed the existence of alteration zones in the study area. These zones extend from the northeast to the southwest in the acid meta-volcanic and silica barite rocks. The enhanced satellite images were implemented in the GIS environment to facilitate the final production of the geological map at scale 1:400,000. X-ray fluorescence analyses prove that selected samples taken from the wall rock alteration zones are gold-bearing.


Author(s):  
Sara Salehi

Lithological mapping using remote sensing depends, in part, on the identification of rock types by their spectral characteristics. Chemical and physical properties of minerals and rocks determine their diagnostic spectral features throughout the electromagnetic spectrum. Shifts in the position and changes in the shape and depth of these features can be explained by variations in chemical composition of minerals. Detection of such variations is vital for discriminating minerals with similar chemical composition. Compared with multispectral image data, airborne or spaceborne hyperspectral imagery offers higher spectral resolution, which makes it possible to estimate the mineral composition of the rocks under study without direct contact. Arctic environments provide challenging ground for geological mapping and mineral exploration. Inaccessibility commonly complicates ground surveys, and the presence of ice, vegetation and rock-encrusting lichens hinders remote sensing surveys. This study addresses the following objectives: 1. Modelling the impact of lichen on the spectra of the rock substrate; 2. Identification of a robust lichen index for the deconvolution of lichen and rock mixtures and 3. Multiscale hyperspectral analysis of lithologies in areas with abundant lichens.


1987 ◽  
Vol 22 (S2) ◽  
pp. 225-249 ◽  
Author(s):  
P. S. Griffiths ◽  
P. A. S. Curtis ◽  
S. E. A. Fadul ◽  
P. D. Scholes

2020 ◽  
Vol 12 (8) ◽  
pp. 1261 ◽  
Author(s):  
Hodjat Shirmard ◽  
Ehsan Farahbakhsh ◽  
Amin Beiranvand Pour ◽  
Aidy M Muslim ◽  
R. Dietmar Müller ◽  
...  

There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.


2007 ◽  
Vol 40 (4) ◽  
pp. 1998
Author(s):  
G. K. Nikolakopoulos ◽  
D. A. Vaiopoulos ◽  
G. A. Skianis

During the last decades remote sensing imagery has contributed significantly to mineral exploration. Motivated by the increasing importance of hyperspectral remote sensing, this study investigates the potential of the current-generation satellite hyperspectral data for geological mapping. A narrow-band Hyperion image, acquired in summer 2001, was used. The study area is situated at Milos island. Two different approaches were used for the reduction of the Hyperion bands. First, on the basis of histogram statistics the uncalibrated bands were selected and removed. Then the Minimum Noise Fraction was used to classify the bands according to their signal to noise ratio. The noisiest bands were removed and sixty bands were selected for further processing. In order to make meaningful comparisons between image spectra and laboratory reflectance spetra, the image radiance values must be corrected (calibrated) to reflectance by removing the atmospheric effects. Atmospheric corrections techniques were applied to the selected Hyperion bands. The comparison of the Hyperion hyperspectral data with the JPL spectral library gave quite encouraging results. Further processing of the data has to be done using the image analysis algorithms that have been developed specifically to exploit the extensive information contained in hyperspectral imagery.


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