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Remote Sens. 2014, 6(8), 7005-7025; doi:10.3390/rs6087005

Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic

1
Remote Sensing Laboratory, Tel Aviv University, Tel Aviv 69978, Israel
2
Department of Remote sensing, Czech Geological Survey, Prague 1 11821, Czech Republic
3
Sokolovska uhelna a.s., Sokolov CZ-35645, Czech Republic
*
Author to whom correspondence should be addressed.
Received: 20 May 2014 / Revised: 17 July 2014 / Accepted: 22 July 2014 / Published: 29 July 2014
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Abstract

Remote-sensing techniques offer an efficient alternative for mapping mining environments and assessing the impacts of mining activities. Airborne multispectral data in the thermal region and hyperspectral data in the optical region, acquired with the Airborne Hyperspectral Scanner (AHS) sensor over the Sokolov lignite open-pit mines in the Czech Republic, were analyzed. The emissivity spectrum was calculated for each vegetation-free land pixel in the longwave infrared (LWIR)-region image using the surface-emitted radiation, and the reflectance spectrum was derived from the visible, near-infrared and shortwave-infrared (VNIR–SWIR)-region image using the solar radiation reflected from the surface, after applying atmospheric correction. The combination of calculated emissivity, with the ability to detect quartz, and SWIR reflectance spectra, detecting phyllosilicates and kaolinite in particular, enabled estimating the content of the dominant minerals in the exposed surface. The difference between the emissivity values at λ = 9.68 µm and 8.77 µm was found to be a useful index for estimating the relative amount of quartz in each land pixel in the LWIR image. The absorption depth at around 2.2 µm in the reflectance spectra was used to estimate the relative amount of kaolinite in each land pixel in the SWIR image. The resulting maps of the spatial distribution of quartz and kaolinite were found to be in accordance with the geological nature and origin of the exposed surfaces and demonstrated the benefit of using data from both thermal and optical spectral regions to map the abundance of the major minerals around the mines. View Full-Text
Keywords: AHS; airborne remote-sensing; LWIR multispectral remote-sensing; SWIR hyperspectral remote-sensing; Sokolov open-pit mine; land emissivity; mineral mapping AHS; airborne remote-sensing; LWIR multispectral remote-sensing; SWIR hyperspectral remote-sensing; Sokolov open-pit mine; land emissivity; mineral mapping
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Notesco, G.; Kopačková, V.; Rojík, P.; Schwartz, G.; Livne, I.; Dor, E.B. Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic. Remote Sens. 2014, 6, 7005-7025.

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