{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T08:32:16Z","timestamp":1722069136477},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab163","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T11:08:52Z","timestamp":1635419332000},"page":"333-341","source":"Crossref","is-referenced-by-count":1,"title":["Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification"],"prefix":"10.1093","volume":"66","author":[{"given":"W","family":"Jiji","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , Dr. Sivanthi Aditanar College of Engineering, Tiruchendur - 628215, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G","family":"Merlin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , Dr. Sivanthi Aditanar College of Engineering, Tiruchendur - 628215, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A","family":"Rajesh","sequence":"additional","affiliation":[{"name":"Vikram Sarabhai Space Centre , Thiruvananthapuram - 695022, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"2023022013493077900_","doi-asserted-by":"crossref","DOI":"10.1029\/2007JC004177","article-title":"Extracting the 2004 Indian Ocean tsunami signals from sea surface height data observed by satellite altimetry","volume":"113","author":"Hayashi","year":"2008","journal-title":"J. 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