Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 53-59, 2016
https://doi.org/10.5194/isprs-archives-XLII-2-W1-53-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
26 Oct 2016
AUTOMATIC EXTRACTION OF DUNES FROM GOOGLE EARTH IMAGES NEW APPROACH TO STUDY THE DUNES MIGRATION IN THE LAÂYOUNE CITY OF MOROCCO
D. Dakir, H. Rhinane, O. Saddiqi, E. El Arabi, and L. Baidder Geosciences Laboratory, Faculty of Sciences Ain Chock, University Hassan II of Casablanca, Morocco
Keywords: Google Earth, Dune movements, Spatial analysis, Laâyoune, Morocco Abstract. In desert environments, the sandy dune movements are considered as the main threat to the livelihoods and productivity of people living in Laâyoune city, South of Morocco. Studying dunes at a large scale requires intensive fieldwork and a huge dataset. But this task is not always easy because of the high cost and the limited availability of data. In this paper we present a new approach to study and evaluate the dune dynamic using Google earth archive image acquired from 2005, 2011, 2012, and 2013. To achieve this goal, an area with 3577 meter square including 40 barchan dunes, were selected, and the barchan dunes were automatically extracted from these images using Geographic Information System (GIS) and spatial analysis techniques provided by image processing software. The result shows not only the dune shape but also direction of dunes movements, and distance separating dunes. The obtained direction is ranging from North east to South west. The average rate of movement of the extracted dunes between 2005 and 2013 was calculated and is estimated at 32m per year. Validation of these dunes movement of the Laâyoune city was confirmed by the field data and previous studies.
Conference paper (PDF, 1001 KB)


Citation: Dakir, D., Rhinane, H., Saddiqi, O., El Arabi, E., and Baidder, L.: AUTOMATIC EXTRACTION OF DUNES FROM GOOGLE EARTH IMAGES NEW APPROACH TO STUDY THE DUNES MIGRATION IN THE LAÂYOUNE CITY OF MOROCCO, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 53-59, https://doi.org/10.5194/isprs-archives-XLII-2-W1-53-2016, 2016.

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