Author(s)

Ali Liaghat^1*, Nima Tavanpour²

¹Department of Water Engineering Shiraz Branch, Islamic Azad University, Shiraz, Iran.
²Department of Water Engineering, Faculty of Agriculture, Shiraz University, Shiraz, Iran.

About one third of the water needed for agriculture in the world is generated by melting snow. Snow cover, surface and ground water recharge are considered as sustainable and renewable resources. It is therefore necessary to identify and study these criteria. The aim of this study is to determine the spatial and temporal distribution of snow cover in the district of the Sheshpir basin in Fars province (in south of Iran). Ground-based observation of snow covers, especially in mountainous areas, is associated with many problems due to the insufficient accuracy of optical observation, as opposed to digital observation. Therefore, GIS and remote sensing technology can be partially effective in solving this problem. Images of Landsat 5^TM and Landsat 7^TM satellites were used to derive snow cover maps. The images in ENVI 4.8 software were classified by using the maximum likelihood algorithm. Other spatial analyses were performed in ARC-GIS 9.3 software. The maximum likelihood method was accuracy assessed by operation points of testing. The least and the average of overall accuracy of produced maps were found to be 91% and 98%, respectively. This demonstrates that the maximum likelihood method has high performance in the classification of images. Overall snow cover and the review of terrain through the years 2008-2009 and 2009-2010 showed that snow cover begins to accumulate in November and reaches its highest magnitude in February. Finally, no trace of snow can be observed on the surface of the basin in the month of May. By average, 34% of the basin is covered in snow from November through to May.

RS, GIS, Maximum Likelihood Algorithm, Snow Cover, Spatial and Temporal Distribution

Liaghat, A. and Tavanpour, N. (2016) Investigation of Spatial and Temporal Distribution of Snow Cover by Using Satellite Imagery (Case Study: Sheshpirdam Basin). Open Journal of Geology, 6, 330-340. doi: 10.4236/ojg.2016.65028.