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Remote Sens. 2015, 7(9), 12282-12296; doi:10.3390/rs70912282

Mineral Classification of Makhtesh Ramon in Israel Using Hyperspectral Longwave Infrared (LWIR) Remote-Sensing Data

Remote Sensing Laboratory, Tel Aviv University, Tel Aviv 69978, Israel
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Author to whom correspondence should be addressed.
Academic Editors: James Jin-King Liu, Yu-Chang Chan, Magaly Koch and Prasad S. Thenkabail
Received: 21 June 2015 / Revised: 6 September 2015 / Accepted: 11 September 2015 / Published: 21 September 2015
(This article belongs to the Special Issue Remote Sensing in Geology)
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Abstract

Hyperspectral remote-sensing techniques offer an efficient procedure for mineral mapping, with a unique hyperspectral remote-sensing fingerprint in the longwave infrared spectral region enabling identification of the most abundant minerals in the continental crust—quartz and feldspars. This ability was examined by acquiring airborne data with the AisaOWL sensor over the Makhtesh Ramon area in Israel. The at-sensor radiance measured from each pixel in a longwave infrared image represents the emissivity, expressing chemical and physical properties such as surface mineralogy, and the atmospheric contribution which is expressed differently during the day and at night. Therefore, identifying similar features in day and night radiance enabled identifying the major minerals in the surface—quartz, silicates (feldspars and clay minerals), gypsum and carbonates—and mapping their spatial distribution. Mineral identification was improved by applying the radiance of an in situ surface that is featureless for minerals but distinctive for the atmospheric contribution as a gain spectrum to each pixel in the image, reducing the atmospheric contribution and emphasizing the mineralogical features. The results were in agreement with the mineralogy of selected rock samples collected from the study area as derived from laboratory X-ray diffraction analysis. The resulting mineral map of the major minerals in the surface was in agreement with the geological map of the area. View Full-Text
Keywords: hyperspectral remote-sensing; longwave infrared image; emissivity; Makhtesh Ramon; mineral classification hyperspectral remote-sensing; longwave infrared image; emissivity; Makhtesh Ramon; mineral classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Notesco, G.; Ogen, Y.; Ben-Dor, E. Mineral Classification of Makhtesh Ramon in Israel Using Hyperspectral Longwave Infrared (LWIR) Remote-Sensing Data. Remote Sens. 2015, 7, 12282-12296.

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