Extraction of Water Surface Changes of Miankale International Wetland using Landsat-8 satellite Images and Fusion of Supervised Classifiers

Document Type : Original Article


1 Master of Science in Civil Engineering, Expert of Ab Varesh Pars Company, Yosefabad, Tehran, Iran

2 Ph.D. of the Water Resource Engineering, Khuzestan Water and Power Authority (KWPA), Ahwaz, Iran.


Among environmental changes, water plays a very vital role in the political, social and economic issues of countries, which can be used as one of the most practical sources of water supply available to humans and animals. Investigating surface water fluctuations in terms of importance, location and nature has gained special importance in recent years. Miankale International Wetland with an area of 68,000 hectares in the north of Iran is of special importance. First, by performing radiometric and atmospheric corrections on Landsat-8 satellite images for the years 2013 to 2023 and using the Gram Schmidt integrator to increase spatial resolution, to extract the NDWI, MNDWI, AWEI and WI2015 indices in order to differentiate the water level. Wetland was treated from non-water. In order to classify images, supervised classifiers such as maximum likelihood, support vector machine (SVM) and artificial neural network (ANN) were used. Also, in order to improve the results, the output of the classifiers was merged using the majority voting method. The results of the research showed that the majority voting method was chosen as the most suitable classification method with the highest level of accuracy. In 2023, compared to 2013, water level of the wetland has decreased from 452.351 km2 to 298.059 km2 (34.11%), and the wetland drought has increased from 6.209 km2 to 160.19 km2 (more than 2000%). The obtained results can be useful for management decisions to preserve more natural resources of our country.


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