Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 235-239, 2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
24 Sep 2013
E. Khodabandehloo1, A. Alimohamdadi1, A. Sadeghi-Niaraki1, A. Darvishi Boloorani2, and A. A. Alesheikh1 1GIS Dept. Faculty of Geodesy and Geomatics Eng, K. N. Toosi Univ. Of Tech., Tehran, Iran
2University of Tehran Faculty of Geography, Dep. Of Remote Sensing & GIS, Geoinformatics Research Institute (UT-RGI) and University of Tehran, Iran
Keywords: Dust, spatiotemporal modeling, NDVI, regional model, GIS Abstract. Dust aerosol is the largest contributor to aerosol mass concentrations in the troposphere and has considerable effects on the air quality of spatial and temporal scales. Arid and semi-arid areas of the West Asia are one of the most important regional dust sources in the world. These phenomena directly or indirectly affecting almost all aspects life in almost 15 countries in the region. So an accurate estimate of dust emissions is very crucial for making a common understanding and knowledge of the problem. Because of the spatial and temporal limits of the ground-based observations, remote sensing methods have been found to be more efficient and useful for studying the West Asia dust source. The vegetation cover limits dust emission by decelerating the surface wind velocities and therefore reducing the momentum transport. While all models explicitly take into account the change of wind speed and soil moisture in calculating dust emissions, they commonly employ a "climatological" land cover data for identifying dust source locations and neglect the time variation of surface bareness. In order to compile the aforementioned model, land surface features such as soil moisture, texture, type, and vegetation and also wind speed as atmospheric parameter are used. Having used NDVI data show significant change in dust emission, The modeled dust emission with static source function in June 2008 is 17.02 % higher than static source function and similar result for Mach 2007 show the static source function is 8.91 % higher than static source function. we witness a significant improvement in accuracy of dust forecasts during the months of most soil vegetation changes (spring and winter) compared to outputs resulted from static model, in which NDVI data are neglected.
Conference paper (PDF, 369 KB)

Citation: Khodabandehloo, E., Alimohamdadi, A., Sadeghi-Niaraki, A., Darvishi Boloorani, A., and Alesheikh, A. A.: SPATIOTEMPORAL MODELLING OF DUST STORM SOURCES EMISSION IN WEST ASIA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 235-239, doi:10.5194/isprsarchives-XL-1-W3-235-2013, 2013.

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