Determination of flood extend using OLI data (case study: Dezful 2016 flood)
Among the various natural hazards, floods may be considered as the most devastating factor that inflicts great damage on human societies. Therefore, the importance of estimating flood damage and its scope in planning to reduce damages and determine points with high risk is very important. The aim of...
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| Format: | Article |
| Language: | fas |
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Iranian Rainwater Catchment Systems Association
2019-04-01
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| Series: | محیط زیست و مهندسی آب |
| Subjects: | |
| Online Access: | http://www.jewe.ir/article_83227_ecee8f6445e051893cacd665b8f2babc.pdf |
| _version_ | 1797783517801742336 |
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| author | Ali Asghar Torahi Hasan Hasani Moghaddam |
| author_facet | Ali Asghar Torahi Hasan Hasani Moghaddam |
| author_sort | Ali Asghar Torahi |
| collection | DOAJ |
| description | Among the various natural hazards, floods may be considered as the most devastating factor that inflicts great damage on human societies. Therefore, the importance of estimating flood damage and its scope in planning to reduce damages and determine points with high risk is very important. The aim of this study is to determine the extent of flood hazard using OLI satellite data. For this reason, a window of OLI satellite images of Landsat 8 was acquired before and after the Dezful flood of April 25, 2016. First, preprocessing operations include radiometric and atmospheric corrections of images were done, and the principal component analysis was then used to reduce the correlation of the data. Data processing was performed using a Support Vector Machine algorithm with linear and polynomial kernels. In order to train the Support Vector Machine algorithm, training samples for each class (agricultural land, flood extent, water resources, settlement areas, and recreational areas along the river boundary) were harvested at the user level. In order to evaluate the similarity of the classes and the degree of correlation between the samples, the quantitative assessment method of the Jeffries Matusita was performed. The results showed that the flood area was 11593.26 ha, the highest damage was due to agricultural land with a destruction of 8467.45 ha and recreational and tourist areas along the riverbank with a destruction of 2659.14 ha. |
| first_indexed | 2024-03-13T00:27:05Z |
| format | Article |
| id | doaj.art-6e318b0606274fc5b33113f4ad77c70d |
| institution | Directory Open Access Journal |
| issn | 2476-3683 |
| language | fas |
| last_indexed | 2024-03-13T00:27:05Z |
| publishDate | 2019-04-01 |
| publisher | Iranian Rainwater Catchment Systems Association |
| record_format | Article |
| series | محیط زیست و مهندسی آب |
| spelling | doaj.art-6e318b0606274fc5b33113f4ad77c70d2023-07-11T04:44:22ZfasIranian Rainwater Catchment Systems Associationمحیط زیست و مهندسی آب2476-36832019-04-0151243510.22034/jewe.2019.154927.128983227Determination of flood extend using OLI data (case study: Dezful 2016 flood)Ali Asghar Torahi0Hasan Hasani Moghaddam1Assist. Professor, Department of Remote Sensing and GIS, Faculty of Geographic Science, Kharazmi University, Tehran, IranM.A., Department of Remote Sensing and GIS, Faculty of Geographic Science, Kharazmi University, Tehran, IranAmong the various natural hazards, floods may be considered as the most devastating factor that inflicts great damage on human societies. Therefore, the importance of estimating flood damage and its scope in planning to reduce damages and determine points with high risk is very important. The aim of this study is to determine the extent of flood hazard using OLI satellite data. For this reason, a window of OLI satellite images of Landsat 8 was acquired before and after the Dezful flood of April 25, 2016. First, preprocessing operations include radiometric and atmospheric corrections of images were done, and the principal component analysis was then used to reduce the correlation of the data. Data processing was performed using a Support Vector Machine algorithm with linear and polynomial kernels. In order to train the Support Vector Machine algorithm, training samples for each class (agricultural land, flood extent, water resources, settlement areas, and recreational areas along the river boundary) were harvested at the user level. In order to evaluate the similarity of the classes and the degree of correlation between the samples, the quantitative assessment method of the Jeffries Matusita was performed. The results showed that the flood area was 11593.26 ha, the highest damage was due to agricultural land with a destruction of 8467.45 ha and recreational and tourist areas along the riverbank with a destruction of 2659.14 ha.http://www.jewe.ir/article_83227_ecee8f6445e051893cacd665b8f2babc.pdfagricultural landsjeffries matusita indexpcasupport vector machine algorithm |
| spellingShingle | Ali Asghar Torahi Hasan Hasani Moghaddam Determination of flood extend using OLI data (case study: Dezful 2016 flood) محیط زیست و مهندسی آب agricultural lands jeffries matusita index pca support vector machine algorithm |
| title | Determination of flood extend using OLI data (case study: Dezful 2016 flood) |
| title_full | Determination of flood extend using OLI data (case study: Dezful 2016 flood) |
| title_fullStr | Determination of flood extend using OLI data (case study: Dezful 2016 flood) |
| title_full_unstemmed | Determination of flood extend using OLI data (case study: Dezful 2016 flood) |
| title_short | Determination of flood extend using OLI data (case study: Dezful 2016 flood) |
| title_sort | determination of flood extend using oli data case study dezful 2016 flood |
| topic | agricultural lands jeffries matusita index pca support vector machine algorithm |
| url | http://www.jewe.ir/article_83227_ecee8f6445e051893cacd665b8f2babc.pdf |
| work_keys_str_mv | AT aliasghartorahi determinationoffloodextendusingolidatacasestudydezful2016flood AT hasanhasanimoghaddam determinationoffloodextendusingolidatacasestudydezful2016flood |