Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification.
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| Title: | Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification. |
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| Authors: | Jiji, W1 (AUTHOR) jijivevin@yahoo.co.in, Merlin, G1 (AUTHOR), Rajesh, A2 (AUTHOR) |
| Source: | Computer Journal. Feb2023, Vol. 66 Issue 2, p333-341. 9p. |
| Subjects: | Soil salinity, Indian Ocean Tsunami, 2004, Tsunamis, Feature extraction, Feature selection, Image processing |
| Abstract: | The research attempts to know the salinity level and how much agricultural land was affected by the 2004 Indian Ocean tsunami in the study area. Nagapattinam province of Tamilnadu, which was strongly hit by the 2004 Indian Ocean tsunami, is selected as the demonstration site. IKONOS and QuickBird image for the periods 2003 (pre-tsunami, IKONOS), 2004 (immediate tsunami, IKONOS) and 2006 (post-tsunami, QuickBird) have been used in this study. The purpose of this research is to detect the effects of the tsunami in the study area. Multispectral images were obtained in order to detect the salt-affected soils and compare the data with images after the tsunami. The near-infrared reflectance spectra contain significant information related to soil components. The development of remote sensing techniques can support the monitoring and efficient mapping of salinity level in soil for environmental assessment. In this research, we have implemented the techniques in four levels: image preprocessing, extracting land region, feature extraction and image classification. Image preprocessing is the first step in the image processing chain and is usually necessary prior to image classification and analysis. In the second level, land regions are extracted from the study area using the Soil-Adjusted Vegetation Indices. In the feature extraction level, we have extracted 15 salinity features from the image data and performed the feature selection method to select the best five features of soil. In the classification stage, the salt-affected land region is classified into three classes (C1, highly saline; C2, saline; and C3, non-saline) using the fuzzy logic method. In the immediate tsunami and after-tsunami data, 95.15- and 121.4-hectare area, respectively, were detected as a salt-affected area. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jiji%2C+W%22">Jiji, W</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jijivevin@yahoo.co.in</i><br /><searchLink fieldCode="AR" term="%22Merlin%2C+G%22">Merlin, G</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rajesh%2C+A%22">Rajesh, A</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Journal%22">Computer Journal</searchLink>. Feb2023, Vol. 66 Issue 2, p333-341. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Soil+salinity%22">Soil salinity</searchLink><br /><searchLink fieldCode="DE" term="%22Indian+Ocean+Tsunami%2C+2004%22">Indian Ocean Tsunami, 2004</searchLink><br /><searchLink fieldCode="DE" term="%22Tsunamis%22">Tsunamis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The research attempts to know the salinity level and how much agricultural land was affected by the 2004 Indian Ocean tsunami in the study area. Nagapattinam province of Tamilnadu, which was strongly hit by the 2004 Indian Ocean tsunami, is selected as the demonstration site. IKONOS and QuickBird image for the periods 2003 (pre-tsunami, IKONOS), 2004 (immediate tsunami, IKONOS) and 2006 (post-tsunami, QuickBird) have been used in this study. The purpose of this research is to detect the effects of the tsunami in the study area. Multispectral images were obtained in order to detect the salt-affected soils and compare the data with images after the tsunami. The near-infrared reflectance spectra contain significant information related to soil components. The development of remote sensing techniques can support the monitoring and efficient mapping of salinity level in soil for environmental assessment. In this research, we have implemented the techniques in four levels: image preprocessing, extracting land region, feature extraction and image classification. Image preprocessing is the first step in the image processing chain and is usually necessary prior to image classification and analysis. In the second level, land regions are extracted from the study area using the Soil-Adjusted Vegetation Indices. In the feature extraction level, we have extracted 15 salinity features from the image data and performed the feature selection method to select the best five features of soil. In the classification stage, the salt-affected land region is classified into three classes (C1, highly saline; C2, saline; and C3, non-saline) using the fuzzy logic method. In the immediate tsunami and after-tsunami data, 95.15- and 121.4-hectare area, respectively, were detected as a salt-affected area. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/comjnl/bxab163 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 333 Subjects: – SubjectFull: Soil salinity Type: general – SubjectFull: Indian Ocean Tsunami, 2004 Type: general – SubjectFull: Tsunamis Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jiji, W – PersonEntity: Name: NameFull: Merlin, G – PersonEntity: Name: NameFull: Rajesh, A IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00104620 Numbering: – Type: volume Value: 66 – Type: issue Value: 2 Titles: – TitleFull: Computer Journal Type: main |
| ResultId | 1 |
