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פותח על ידי קלירמאש פתרונות בע"מ -
Mapping surface quartz content in sand dunes covered by biological soil crusts using airborne hyperspectral images in the longwave infrared region
Year:
2018
Source of publication :
Minerals
Authors :
רוזנשטיין, עופר
;
.
Volume :
Co-Authors:

Weksler, S., The Remote Sensing Laboratory, Department of Geography, Tel Aviv University, 10 Zelig St, Tel Aviv, Israel, Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion, Israel;Ben-Dor, E., The Remote Sensing Laboratory, Department of Geography, Tel Aviv University, 10 Zelig St, Tel Aviv, Israel

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Total pages:
1
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Abstract:

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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Note:
Related Files :
Biological soil crust
Hyperspectral remote sensing
Longwave infrared
Quartz
עוד תגיות
תוכן קשור
More details
DOI :
10.3390/min8080318
Article number:
318
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
36639
Last updated date:
02/03/2022 17:27
Creation date:
15/08/2018 08:31
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Scientific Publication
Mapping surface quartz content in sand dunes covered by biological soil crusts using airborne hyperspectral images in the longwave infrared region

Weksler, S., The Remote Sensing Laboratory, Department of Geography, Tel Aviv University, 10 Zelig St, Tel Aviv, Israel, Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center, HaMaccabim Road 68, P.O. Box 15159, Rishon LeZion, Israel;Ben-Dor, E., The Remote Sensing Laboratory, Department of Geography, Tel Aviv University, 10 Zelig St, Tel Aviv, Israel

Mapping surface quartz content in sand dunes covered by biological soil crusts using airborne hyperspectral images in the longwave infrared region .

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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Scientific Publication
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