13 October 2016 Paleochannel delineation using Landsat 8 OLI and Envisat ASAR image fusion techniques in Cholistan desert, Pakistan
Zaheer Ul Islam, Javed Iqbal, Junaid Aziz Khan, Waqas A. Qazi
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Abstract
Sustainability of desert ecosystem is highly dependent upon water availability from different sources. Paleochannels are important sources of groundwater, and exploiting such resources involves their identification/mapping and subsequent investigation for fresh groundwater. A study in which multisensor (optical/infrared Landsat 8 OLI and active microwave Envisat ASAR) images of the Cholistan desert of Pakistan were processed and analyzed to identify and map Hakra River paleochannels is presented. Radiometrically corrected optical and synthetic aperture radar datasets were fused using principal components image fusion method. The paleochannels were extracted from the analysis of this fused output, and normalized difference vegetative index analysis of Landsat 8 OLI atmospheric corrected images was used as supporting information. Identification and alignment of an identified paleochannel was validated with geophysical ground measurements (electrical resistivity and conductivity surveys) and historical records. The presence of high apparent electrical resistivity with corresponding low soil water conductivity values intersects well with the paleochannels identified from the remote sensing data. The results were also confirmed with historical evidence such as old wells beside forts and proposed ground water harvesting sites. The proposed methodology in this study could be adopted in other parts of the world for mapping of paleochannels.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2016/$25.00 © 2016 SPIE
Zaheer Ul Islam, Javed Iqbal, Junaid Aziz Khan, and Waqas A. Qazi "Paleochannel delineation using Landsat 8 OLI and Envisat ASAR image fusion techniques in Cholistan desert, Pakistan," Journal of Applied Remote Sensing 10(4), 046001 (13 October 2016). https://doi.org/10.1117/1.JRS.10.046001
Published: 13 October 2016
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Cited by 11 scholarly publications.
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KEYWORDS
Image fusion

Earth observing sensors

Landsat

Vegetation

Synthetic aperture radar

Soil science

Image segmentation

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