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Addis Ababa University (2012)

MAPPING OF SOIL SALINITY IN SEGO IRRIGATION FARM, SOUTHERN ETHIOPIA USING GEOSPATIAL TOOLS

Zewdu, Shegena

Titre : Mapping of Soil Salinity in Sego Irrigation Farm, Southern Ethiopia Using Geospatial Tools

Auteur : Shegena, Zewdu

Université de soutenance : Addis Ababa University

Grade : Master of Science in Geo-information Science 2012

Résumé
Salinization is the major problem of the irrigated agriculture in arid and semi-arid areas, which adversely reduce the productivity of agricultural lands. Managing salinity so as to minimize its environmental impact is a prerequisite for the long-term sustainability of irrigated agriculture. The main objective of this study was to assess the level of salinity in Sego irrigated farm and to map temporal and spatial distribution of salt affected soils to aid further management. The study employed normal image classification and developing models from NDSI versus ECe and from different thematic layers to map soil salinity using geospatial tools. Classification for Landsat image of 1984, 1995 and 2010 showed that land under intensive cultivation has significantly decreased in the 26 years period where 13 ha is put under fallow per year. The analysis of NDSI shows also the strongly saline soils have increased by 6 ha/yr. Empirical model was developed from ECe vs NDSI of 2010 image using regression analysis and it has shown coefficient of relation of 66%. The model was extended for the whole area and it has revealed that 2.0 % of the study area is strongly saline where 54.7 % of the area is non-saline. Overlay model was also developed from water table, landform, land management type, soil texture and land cover and the result showed about 26.6 % of the study area is non-saline where 39.5 % and 2.8 % is moderately and strongly saline respectively. Validation of the models was made to test their predication capability and hence overlay model has revealed better correlation coefficient of 68.0 % to the measured ECe. The map of salt affected soils derived from the overlay salinity model was used to assess the distribution of salt affected soil with respect to water table and soil type. The result showed that most of the salt affected areas are on shallow water table where the strongly saline soil accounting to 2.8 % is on the shallow water table. The distribution of salt affected area with respect to soil type shows that Cambisols and Fluvisols are greatly affected by salinity where Salonchaks and Solonetz are almost found on salt affected areas. Generally the result indicates that geospatial tools are efficient and feasible techniques for detecting salt affected areas from satellite images and different thematic maps.

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