Accuracy Assessment of Soil Salinity Map in Yazd-Ardakan Plain, Central Iran, Based on Landsat ETM+ Imagery (original) (raw)
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Soil salinity is a major concern in the Uzbekistan. Fergana valleys agricultural lands, it negatively affects plant growth, crop yields, whereas in central part of the valley is semi-desert and desert affects agricultural areas due to subsidence, corrosion and ground water quality, leading to further soil erosion and land degradation. Traditional soil salinity assessments have been doing by collecting of soil samples and laboratory analyzing of collected samples for determining totally dissolved soils (TDS) and electro conductivity, but, Geo-informatic systems (GIS) and Remote Sensing (RS) technologies provides more efficient, economic and rapid tools and techniques for soil salinity assessment and soil salinity mapping. Main goals of this research are to map soil salinity of Fergana valley, to show relation of its result with traditional analysing and analysing withGIS technology As a source of satellite images has been used Landsat-8 OLI. Research areas every arable land validity ...
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ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC...
E3S Web of Conferences
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Application of remote sensing indices for mapping salt- affected areas by using field data methods
International Journal of ADVANCED AND APPLIED SCIENCES, 2017
Salinity is one of the oldest and most important environmental problems. Salinization is defined as presence of excessive salts on the top layer of the soil, resulting in deterioration of its chemical and physical properties. This is a form of land degradation turning into a major cause of low agricultural productivity in the South Khorasan province, Iran. The criteria defining saltaffected areas are based on electrical conductivity (EC) values. Kaji Playa is an endorheic basin that located in a distance of 190 km from the south of Birjand city in the South Khorasan province, Iran. The salt affected soils of Kaji Playa drainage basin cover approximately 39% of the study area and the EC values change from 4.2 dS.m-1 to 245 dS.m-1 (decisiemens per meter). Salinity Mapping is an expensive process, and a multi-scale strategy is essential to achieve a rapid and effective assessment of its extent and severity. Advantages of using remote sensing technology include saving time, wide coverage are faster than ground methods and facilitate long term monitoring. In this paper, we offered a project, which monitors soil salinity conditions in the study area using fieldwork, soil sample analysis, and a multi-temporal analysis of Landsat ETM+ data. The fieldwork was done to measure the soil salinity and gather ground truth for image classification. The alternative methodology of this study is based on the interpretation and calculation of salinity index from satellite data. The paper main aim included mapping and monitoring of salinity conditions for environmental management at basin level. According to the results, the soil salinity map produced by satellite index of NDSI (Normalized Difference Salinity Index) had an overall accuracy of 84% and Kappa index of 67%, indicating an acceptable accuracy for this classification.