Abstract
The integration of Landsat 8 OLI and ASTER data is an efficient tool for interpreting lead–zinc mineralization in the Huoshaoyun Pb–Zn mining region located in the west Kunlun mountains at high altitude and very rugged terrain, where traditional geological work becomes limited and time-consuming. This task was accomplished by using band ratios (BRs), principal component analysis, and spectral matched filtering methods. It is concluded that some BR color composites and principal components of each imagery contain useful information for lithological mapping. SMF technique is useful for detecting lead–zinc mineralization zones, and the results could be verified by handheld portable X-ray fluorescence analysis. Therefore, the proposed methodology shows strong potential of Landsat 8 OLI and ASTER data in lithological mapping and lead–zinc mineralization zone extraction in carbonate stratum.
Highlights
Remote sensing plays a pivotal role in many areas of the geosciences, geography, and environmental sciences
The mapping accuracies of the mineralized limestone (MI) discriminated by the two kinds of data and the three methods were between 49.72% and 80.15%
The result of ASTER data processed by matched filtering method reaches the best accuracy in mapping MI. 2
Summary
Remote sensing plays a pivotal role in many areas of the geosciences, geography, and environmental sciences. New generation, advanced remote sensing has been used in the past few decades in lithological mapping, mineral exploration, and environmental geology.[1,2,3,4] Due to the technical development of remote sensing,[5] many efficient image processing methods have been designed to map boundaries of intrusive bodies and hydrothermal zones, especially in inaccessible regions.[6] Landsat 8 is an Earth observation satellite, which was launched on February 4, 2013, and provides increased coverage of the Earth’s surface It is a free-flyer astrovehicle equipped with two sensors, the operational land imager (OLI) and the thermal infrared sensor. In the VNIR (380 to 780 nm) and SWIR (900 to 2500 nm) regions, iron oxides, Journal of Applied Remote Sensing
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