:: Volume 7, Issue 2 (12-2017) ::
JGST 2017, 7(2): 201-214 Back to browse issues page
A New Model for Forecasting Recovery Period of the Urmia Lake Water Level and Assessment of Spatiotemporal Changes of its Stabilization Using Remote Sensing
A. Zareei , H. Emami
Abstract:   (402 Views)

Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. In this study, the surface area changes of Lake Urmia, Iran were investigated. Lake Urmia, with an area varying from 5200–6000 km2 in the 20th century, is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. The lake is the habitat for a unique bisexual Artemia (a species of brine shrimp), and becomes a host for more than 20,000 pairs of Flamingo and about 200–500 pairs of White Pelican every winter. Lake Urmia forms a rare and important ecologic, economic and geo-tourism zone and was recognized as a Biosphere Reserve by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 1975. In addition, the lake helps moderate the temperature and humidity of the region, providing a suitable place for agricultural activities. Assessment and monitoring of Lake water level changes, in order to protect them in terms of importance, nature, and location are considered at the national, regional and global levels. However, in recent years to improve the water level in the lake, activities have been carried out but unfortunately due to a decrease in the water level of the lake is in a critical state. Therefore, it is necessary the increase or decrease of Urmia Lake water level and its impact on the environment to be monitored on a regular basis and provided correctly scheming and decision-making to effectively improve its situation. In this research, a new model for forecasting recovery period (compared to 14 years ago) of the Urmia Lake water level is conducted and assessment of spatiotemporal changes of its stabilization using  Landsat 5 and 7 and 8 multi-temporal imageries during the period 2002 to 2016. In the proposed model have been considered two main factors, average annual rainfall catchment area and the activities taking place in recent years. To this end, to assess spatial-temporal changes of Urmia Lake water level, four different water body extraction indices, including water ratio index (WRI), automatic index extraction of water (AWEI), normalized difference Water Index (NDWI) and the normalized difference vegetation index (NDVI) were used. Then, the performance of each of the four indices was compared with a base map and it was determined the error of each index that NDWI was the lowest error compared to the other three indices. As a result, the proposed model was presented based on the results of this index in three different state, taking into account the various weights to previously mentioned factors. The results showed a marked reduction (78%) of the Urmia Lake water level occurred in the period 2002 to 2014 compared to 2002. In contrast, from 2014 to 2016, the Urmia Lake water level has increased 57.33 % and it has been reached the relative stability condition. This relative stability is unstable and depends on two main previously mentioned. In addition, the results of the proposed model in three different state are shown that based on the increasing trend in the second period and taking into consideration the different weight of the main factors. It will take up at least 11 years (at best), 18 years (the status quo) and maximum 49 years (reduced recovery activity) the Urmia Lake water level returned to its original level in 2002 and it achieved a stable condition. The proposed model is a suitable method and it can be used for any number of recovery activity factors in the future.

Keywords: Urmia Lake, Remote Sensing, Landsat, Spatio-Temporal Changes, Water Body Extraction Indices
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Type of Study: Applicable | Subject: Photo&RS

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Volume 7, Issue 2 (12-2017) Back to browse issues page