ASTER VNIR‐SWIR Based Lithological Mapping of Granitoids in the Western Junggar Orogen (NW Xinjiang): Improved Inputs to Random Forest Method

Abstract

Although advanced spaceborne thermal emission and reflection radiometer multispectral analysis for lithological mapping has been widely applied, traditional methods such as band ratios (BR) and principal component analysis (PCA) are still hampered by cumbersome data processing and poor classification performance. In this study, we utilize improved data inputs for random forest (RF) to extract lithological information of granitoids, which are the predominant rock type for intrusion‐related polymetallic ore deposits in the western Junggar Orogen (NW Xinjiang). Based on spectral absorption features of minerals (e.g., orthoclase, K‐feldspar, hornblende, biotite, plagioclase, and oligoclase), image statistical information and textural features, we tested different combinations of bands, BR, PCA, and texture using RF method, and found that the combination of B13678 + T1 (Mean texture) achieved the highest weighted‐F1 score for granitoids, with an accuracy of 87.32%. Compared to the support vector machine, RF effectively distinguishes lithological differences between different types of granitoid and wallrocks, especially the granite, granodiorite, and alkali granite in the Akebasito intrusion, as well as the alkali granite, plagiogranite and biotite granite in the Karamay intrusions. Moreover, the large number of rare metal deposits (including Cu, Au, Mo, etc.) distributed near the granitoid intrusions in the western Junggar, our result facilitates the analysis of regional tectonic evolution and mineralization controlling.

Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery

“…The Western Junggar region is located in a crucial position in the southwestern part of the Central Asian orogenic belt, with highly developed Late Paleozoic magmatic rocks. Common rock-forming minerals such as silicate and carbonate minerals exhibit obvious emission spectra in the thermal infrared bands, making lithology extraction through remote sensing relatively straightforward [ 17 , 18 ].…”

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

“…Furthermore, remote sensing lithological classification can be employed in environmental monitoring, such as detecting rock dissolution pollution in water bodies and mapping wetland vegetation distribution, among others. In conclusion, the use of remote sensing technology for lithological classification holds significant practical importance in geological research, resource exploration, disaster prediction and prevention, and environmental monitoring [15][16][17][18][19][20][21].…”