Spectral Signature Characterization and Remote Mapping of Oman Exotic Limestones for Industrial Rock Resource Assessment

• Published in: Geosciences (Basel) Vol. 8; no. 4; p. 145
• Main Authors: Rajendran, Sankaran, Nasir, Sobhi, El-Ghali, Mohammed, Alzebdah, Khaled, Salim Al-Rajhi, Ali, Al-Battashi, Mohammed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.04.2018
• Subjects: Algorithms; ASTER; ASTER (radiometer); Calcite; Carbonate rocks; Carbonates; Classification; Comparative analysis; Comparative studies; Data processing; Divergence; Dolomite; Dolostone; Earth science; Emission analysis; exotic limestone; Geology; Identification methods; Image classification; Image processing; industrial rock; Landsat; Landsat (ETM+); Landsat satellites; Limestone; mapping; Marble; Oman; Radiation; Radiometers; Reflectance; Remote sensing; Remote sensors; Rock; Rocks; Sensors; Spectra; Spectral bands; spectral signatures; Stone; Thermal emission; Oman
• ISSN: 2076-3263; 2076-3263
• DOI: 10.3390/geosciences8040145

This study demonstrates the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor data to remotely map industrial carbonate rocks known as the ‘Oman exotics’ of the Sultanate of Oman. We measured reflectance spectra of marble using a PIMA™ spectrometer and studied their spectral absorptions distinguishing calcite from spectral absorptions of dolomite of the same region. The spectral band 8 of ASTER is processed by simple decorrelation stretch image processing method to map the exotic limestone rock of the Nakhl region, Oman. Results showed that carbonate rocks displayed distinctive tonal variation on the image. A comparative study with the spectral band 7 of Landsat 7 (ETM+) does not discriminate the calcite-bearing marbles and associated carbonate formations in the studied area. ASTER data were also processed by the application of the Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) image classification algorithms. The results were assessed by the production of a confusion matrix. The study shows the capability of visible near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of the ASTER sensor and potential of the image processing methods to remotely identify industrial carbonate rocks and we recommend this technique to similar regions of the world.