Document Type : Research Paper
Authors
1 MSc Remote Sensing and GIS- Research water and Soil,
2 Associate Professor of Soil Conservation and Watershed Management Research Organization, Education and Agriculture
3 MSc Remote Sensing and GIS,, Geological Survey and Mineral Exploration of Iran
4 Assistant Professor, Department of RS-GIS, science and Research Branch, Islamic Azad university, Tehran, Iran
5 Associate Professor of Renewable Energies and Environment Faculty of New Sciences and Technologies, Tehran University
Abstract
In the last decade, the use of remote sensing has played an important role in identifying and assessing natural disasters, especially floods. Among these techniques, the Support Vector Machine algorithm (SVM) and Change Detection technique can be mentioned. The main objective of this study was to evaluate the capability of these techniques in determining the effects of flood in Gori Belmak Wetland and Poldokhtar triple wetlands in the north of Molab and outlet of Poldokhtar watersheds in Lorestan Province, which was faced with flood in April 2019. The land use maps of the region were prepared by applying supervised classification method and the SVM on the Landsat 8 satellite image in the 2013, 2015, 2017 and 2019. Validation of the maps and techniques using indicators of kappa and overall accuracy, showed the high accuracy of maps prepared. The kappa coefficient was calculated to be 0.87, 0.84, 0.83 and 0.87 for the maps of the studied years and the overall accuracy was 90.02, 89.51, 88.11 and 90.32, respectively. By extracting the water class, the changes that occurred on the water body of the wetlands were detected. The results showed that Gori Belmak Wetland, undergo extensive changes due to reasons such as drought in 2015, increase of 112.08 ha of surrounding arable lands between 2013 and 2019, as well as topographic features, especially lower slope than the three wetlands. In 2019, with the storage of flood, this wetland increased to 47.08 ha compared to 2017 and reached an area of 146.15 ha. The similarity of the results obtained in this study with the results of the research conducted in the study area by the Copernicus Emergency Management Service (EMS) and the Geoinformatics Unit research team on the flood of 2019 indicates the high accuracy of the used techniques and results of the present research.
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