Remote Sensing and Spectral Characteristics of Desert Sand from Qatar Peninsula, Arabian/Persian Gulf

Remote sensing data can provide valuable information about the surface expression of regional geomorphologic and geological features of arid regions. In the present study, several processing techniques were applied to reveal such in the Qatar Peninsula. Those included preprocessing for radiometric and geometric correction, various enhancement methods, classification, accuracy assessment, contrast stretching, color composition, and principal component analyses. Those were coupled with field groundtruthing and lab analyses. Field groundtruthing included one hundred and forty measurements of spectral reflectance for various sediment exposures representing main sand types in the four studied parts in Qatar. Lab investigations included grain size analysis, X-ray diffraction and laboratory measurements of spectral reflectance. During the course of this study three sand types have been identified: (i) sabkha-derived salt-rich, quartz sand, and (ii) beach-derived calcareous sand and (iii) aeolian dune quartz. Those areas are spectrally distinct in the VNIR, suggesting that VNIR spectral data can be used to discriminate them. The study found that the main limitation of the ground spectral reflectance study is the difficulty of covering large areas. The study also found that ground and laboratory spectral radiance are generally higher in reflectance than those of Landsat TM. This is due to several factors such as atmospheric conditions, the low altitude or different scales. Whereas for areas with huge size of dune sand, the Landsat TM spectral has higher reflectance than those from field and laboratory. The study observed that there is a good correspondence or correlation of the wavelengths maximum sensitivity between the three spectral measurements i.e lab, field and space-borne measurements.

[1]  Christian Walter,et al.  Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data , 2006 .

[2]  Gregory D. Bierly,et al.  Mineralogical characterization and transport pathways of dune sand using Landsat TM data, Wahiba Sand Sea, Sultanate of Oman , 1999 .

[3]  S. Z. Friedman Mapping urbanized area expansion through digital image processing of LANDSAT and conventional data. [Orlando, Florida; Seattle, Washington; and Boston, Massachusetts] , 1980 .

[4]  E. J. Milton,et al.  Processing of High Spectral Resolution Reflectance Data for the Retrieval of Canopy Water Content Information , 1998 .

[5]  Ronald E. McRoberts,et al.  Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery , 2009 .

[6]  Nicholas C. Coops,et al.  Estimating stand structural details using nearest neighbor analyses to link ground data, forest cover maps, and Landsat imagery , 2008 .

[7]  F. Howari,et al.  Mineralogical and gemorphological characterization of sand dunes in the eastern part of United Arab Emirates using orbital remote sensing integrated with field investigations , 2007 .

[8]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[9]  P. S. Jr. Chavez Image Processing techniques for Thermatic Mapper data , 1984 .

[10]  J. Shipman,et al.  Using landform and vegetative factors to improve the interpretation of Landsat imagery: land-cover units associated with major landform conditions were readily classified with reasonable accuracy to level 3 and at times to level 4 , 1984 .

[11]  P. Chavez,et al.  STATISTICAL METHOD FOR SELECTING LANDSAT MSS RATIOS , 1982 .

[12]  A. R. Newton,et al.  Discriminating rock and surface types with multispectral satellite data in the Richtersveld, NW Cape Province, South Africa , 1993 .

[13]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[14]  G. Hunt Visible and near-infrared spectra of minerals and rocks : I silicate minerals , 1970 .