Combination of Landsat-8 and Sentinel-2 images in order to detect alterations of porphyry deposits (Masjed Daghi), northwest Iran

Combination of Landsat-8 and Sentinel-2 images in order to detect alterations of porphyry deposits (Masjed Daghi), northwest Iran

Introduction

Remote sensing is one of the widely used methods in geology and exploration of mineral deposits and plays an important role I identifying changes. Different methods of remote sensing have made it possible to investigate and study a wide range with accuracy, speed and less cost. Since the change related to porphyry mineralization have a suitable expansion; therefore, this type of deposits can be suitable index in the methods and discoveries of porphyry deposits. Masjed Daghi copper porphyry area is located 35 km east of Julfa in the East Azerbaijan province. The formation of epithermal gold veins on the porphyry deposit and the spatial and temporal relationship of two deposits have been investigated.

The simultaneous use of Sentinel-2 sensor and Landsat-8 satellite and supervised classification methods based on machine learning was done for the first time in this research on Masjed Daghi porphyry deposit in northwest Iran.

Material and methods

In this research, Landsat-8 and Sentinel-2 satellite images have been used to highlight and sportively the changes in the region. Also the methods of band composition, band ratio (BR), and regression least square method (Ls-Fit) have been used to determine the position of alteration zones. Also, supervised classification methods based on machine learning such as: maximum similarity (ML), neural network methods (NN), and support vector machine (SVM) and combining these three classifications using the maximum voting method (MV) for the correctness and accuracy of the use of these images have been used.

Results and discussion

The oldest rock unit of the region includes flysch sediment of Eocene age associated limestone, shale and conglomerate. The lithological volcanic composition are Eocene trachyandesite associated Oligocene monzodiorite. These host rock suffering potassic, phyllic, argillic, propylitic and silicic alteration. Mineralization system consist of two types of porphyry and epithermal systems. The most important minerals are molybdenite, magnetite, pyrite, bornite, chalcopyrite, and sphalerite in the porphyry system and pyrite, chalcopyrite, sphalerite, gold associated quartz, barite, and anhydrite in the epithermal system.

Landsat-8 satellite images of the three supervised classifications of ANN, ML and SVM have the accuracy of 77.71%, 70.48% and 79.23% respectively. In the Sentinel-2 sensor, the tree supervised classifications of ANN, Ml and SVM in Masjed Daghi region have the accuracy of 78.69%, 59.16% and 7.75%, respectively. The obtained results show the superiority of Sentinel-2 sensor over Landsat-8 in highlighting the variations in the study area of Masjed Daghi. Also, by comparing to kappa coefficients obtained from Landsat-8 and Sentinel-2, it emphasizes the superiority of Sentinel-2. Among the classifications applied on the images, SVM classification is more accurate in both satellites and sensors; this point indicates the better performance of Support Vector Machine (SVM) algorithm. But ML classification in Landsat-8 has a better performance that sentinel-2, which the kappa coefficient results will also confirm this issue. The output results of overall accuracy (OA) for the maximum voting (MV) method compared to support vector machine algorithm method have increased by about 3.75% in the Landsat-8 satellite. Maximum voting with 82.38% overall accuracy and 0.6868 kappa coefficient for Landsat-8 satellite indicate; the combining the output data of the classification with the maximum voting method improve the identification of changes. Also, for the Sentinel-2 coefficient f 0.7070 has increased by about 4.5% compared to the support vector machine method.

Conclusion

1. Landsat-8 and Sentinel-2 data and its compliance with the geological range of Masjed Daghi region show that the Sentinel-2 sensor has better and higher accuracy than the Landsat-8 satellite.

2. The classification of the support vector in the Landsat-8 satellite and Sentinel-2 sensor in the study area of Masjed Daghi has a higher accuracy and kappa coefficient, and the Sentinel-2 sensor has a higher accuracy and precision then the Landsat-8 satellite.

3. Sentinel-2 and Landsat-8 data show the overall accuracy output for the maximum voting method compared to the support vector machine algorithm method in satellite Landsat-8 has increased by about 75.3%, which shows that combining the output data of the classifications using the maximum voting approach has improved the identification of changes.

4. Sentinel-2 detector has a very high accuracy in the presented classifications and in the maximum ratio voting method due to its better spectral and spatial power to the Landsat-8 satellite.

Keywords: Arasbaran, Landsat-8, Masjed Daghi, Porphyry copper, Sentinel-2