مقالات رزومه پیام عالمی صف اول

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.

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, digital processing of satellite images was used to investigate the trend of water level changes in this wetland Therefore, for this purpose, ETM + and OLI sensor images related to 2017 and 2000 were obtained from USGS database and processed by image classification (ML) method. Image processing accuracy of both periods based on kappa coefficient was more than 70%. The results of comparing the water body of the wetland in the two periods show a significant increase in water level in the cold season in the wetland and has a significant relationship with rainfall in the region. Due to the lack of ground information related to the condition of the wetland in the first period of the field study, the method used in the present study was able to monitor the changes in the wetland with relatively appropriate accuracy.

In the present study, land use changes in Babavali Basin watershed have been evaluated over a 13- years period. For this research, 2000 Landsat 7 satellite ETM + images and Landsat 8 satellite imagery have been used. After performing the required actions in the preprocessing stage, the maximum probability of the images is classified. The land use map of the region was obtained in 2000 and 2013 the validation results with ground realities and field visits indicate the total accuracy is equal to 0.89 and 0.88. The Kappa coefficient is equal 0.88 and 0.88 in the ETM + images of 2000. Therefore the Landsat 8 of 2013 was acceptable.