Yousif, I., Ahmed, A. (2019). Integration of Land Cover Changes and Land Capability of Wadi El-Natrun Depression Using Vegetation Indices. Egyptian Journal of Soil Science, 59(4), 385-402. doi: 10.21608/ejss.2019.16322.1299
Ibraheem Atya Hussien Yousif; Abd Al Rahman sayed Ahmed. "Integration of Land Cover Changes and Land Capability of Wadi El-Natrun Depression Using Vegetation Indices". Egyptian Journal of Soil Science, 59, 4, 2019, 385-402. doi: 10.21608/ejss.2019.16322.1299
Yousif, I., Ahmed, A. (2019). 'Integration of Land Cover Changes and Land Capability of Wadi El-Natrun Depression Using Vegetation Indices', Egyptian Journal of Soil Science, 59(4), pp. 385-402. doi: 10.21608/ejss.2019.16322.1299
Yousif, I., Ahmed, A. Integration of Land Cover Changes and Land Capability of Wadi El-Natrun Depression Using Vegetation Indices. Egyptian Journal of Soil Science, 2019; 59(4): 385-402. doi: 10.21608/ejss.2019.16322.1299
Integration of Land Cover Changes and Land Capability of Wadi El-Natrun Depression Using Vegetation Indices
1Soil Science Department, Faculty of Agriculture, Cairo University
2Natural Resources Department, Faculty of African Postgraduate studies, Cairo University
Receive Date: 27 August 2019,
Revise Date: 19 October 2019,
Accept Date: 24 October 2019
Abstract
The study objectives were to use vegetation indices techniques for detecting the change of vegetation cover during three periods: from 1984 to 2001, 2001 to 2018, and 1984 to 2018 and to identify the impact of land capability on land cover changes at Wadi El-Natrun Depression, Egypt. Landsat-8 satellite images dated to 1984, 2001 and 2018 were used to detect the changes in land cover in the investigated area (573.53 km2). Four vegetation indices, including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), green normalized difference vegetation index (GNDVI) and optimized soil adjusted vegetation index (OSAVI) were used to identify and detect vegetation changes during the last 34 years. Ground truth points were collected in 2018 to test the accuracy of classified images. Forty five soil profiles were examined to represent different geomorphic units created using a digital elevation model (DEM) and Landsat-8 image. Modified Storie index was used to carry out land capability evaluation of the investigated soils. Results showed that the vegetation indices determined land cover classes with high over all accuracy (< 92 %) and a kappa coefficient greater than 0.9. The soils belonged to grades 2, 3, 4 and 5, representing 0.7, 17.88, 75.15 and 2.09 % of the total area respectively. The low land capability is due to excess of soil salinity, coarse soil texture, shallow soil depth, and poor drainage conditions in addition to the results illustrated that there was a positive correlation between the land capability and extension in vegetation land cover.
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