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Open AccessArticle

Mapping Surface Quartz Content in Sand Dunes Covered by Biological Soil Crusts Using Airborne Hyperspectral Images in the Longwave Infrared Region

1
The Remote Sensing Laboratory, Department of Geography, Tel Aviv University, 10 Zelig St., Tel Aviv 69978, Israel
2
Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion 7528809, Israel
*
Author to whom correspondence should be addressed.
Minerals 2018, 8(8), 318; https://doi.org/10.3390/min8080318
Received: 31 May 2018 / Revised: 6 July 2018 / Accepted: 22 July 2018 / Published: 26 July 2018
(This article belongs to the Special Issue Hyperspectral Imaging for Mineral Mapping)
Biological soil crusts (BSCs), composed of cyanobacteria, algae, mosses, lichens, and fungi, are important ecosystem engineers that stabilize the quartz-rich dunes in the Nitzana study area near the Israel–Egypt border. The longwave infrared (LWIR) region of the electromagnetic spectrum is very useful for quartz identification since quartz reflectance in the visible, near infrared, and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral regions lacks identifying features, whereas in the LWIR region, the quartz emissivity spectrum presents a strong doublet feature. This emissivity feature can be used as a diagnostic tool for BSCs development in desert environments, because BSCs attenuate the quartz feature as a function of their successional development. A pair of day and night airborne hyperspectral images were acquired using the Specim AisaOWL LWIR sensor (7.7–12 µm) and processed using an innovative algorithm to reduce the atmospheric interference in this spectral domain. The resulting day and night apparent emissivity products were used to produce a surface quartz content map of the study area. The significant reduction in atmospheric interference resulted in a high correlation (R2 = 0.88) between quartz content in field samples determined by X-ray powder diffraction analysis and emissivity estimations from the airborne images. This, in turn, served as the ground truth to our quartz content map of the surface, and by proxy to the BSC. View Full-Text
Keywords: longwave infrared; Nitzana; quartz; biological soil crust; hyperspectral remote sensing longwave infrared; Nitzana; quartz; biological soil crust; hyperspectral remote sensing
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Weksler, S.; Rozenstein, O.; Ben-Dor, E. Mapping Surface Quartz Content in Sand Dunes Covered by Biological Soil Crusts Using Airborne Hyperspectral Images in the Longwave Infrared Region. Minerals 2018, 8, 318.

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