{"title":"Mapping of salt-affected soils of irrigated lands in arid regions using remote sensing and GIS","authors":"Y. Aldakheel, A. M. Elprince, A.I. Al-Hosaini","doi":"10.1109/RAST.2005.1512614","DOIUrl":null,"url":null,"abstract":"This research aims to develop a methodology to classify the salt-affected soils using remote sensing and GIS. Taking salt crust as a criterion, Landsat TM with bands other than two and six was found effective, to a greater extent, in classifying the extremely and moderately saline area. Integration of GIS was found effective in classifying low and potential saline area as well as in correcting some of such area misclassified as extremely or moderately. The resulted salinity classes were confirmed with field check, existing map and soil analysis. Moreover, the classification result show more details. This study was tended lead to implement the integration of GIS with digital image processing of TM more effectively for classifying and monitoring saline soils in arid regions.","PeriodicalId":156704,"journal":{"name":"Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAST.2005.1512614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This research aims to develop a methodology to classify the salt-affected soils using remote sensing and GIS. Taking salt crust as a criterion, Landsat TM with bands other than two and six was found effective, to a greater extent, in classifying the extremely and moderately saline area. Integration of GIS was found effective in classifying low and potential saline area as well as in correcting some of such area misclassified as extremely or moderately. The resulted salinity classes were confirmed with field check, existing map and soil analysis. Moreover, the classification result show more details. This study was tended lead to implement the integration of GIS with digital image processing of TM more effectively for classifying and monitoring saline soils in arid regions.