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Journal of Arid Land  2019, Vol. 11 Issue (3): 319-331    DOI: 10.1007/s40333-019-0096-4
Assessment of desertification in Eritrea: land degradation based on Landsat images
G GHEBREZGABHER Mihretab1,2, Taibao YANG1,*(), Xuemei YANG3, Congqiang WANG1
1 Institute of Glaciology and Eco-Geography, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2 College of Business and Social Sciences, Adi Keihi 59, Eritrea
3 Gansu Desert Control Research Institute, Lanzhou 730070, China
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Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-arid regions. In this study, we used a decision tree method based on NDVI (normalized difference vegetation index), SAVI (soil adjusted vegetation index), and vegetation cover proportion to quantify and analyze the desertification in Eritrea using Landsat data of the 1970s, 1980s and 2014. The results demonstrate that the NDVI value and the annual mean precipitation declined while the temperature increased over the past 40 a. Strongly desertified land increased from 4.82×104 km2 (38.5%) in the 1970s to 8.38×104 km2(66.9%) in 2014: approximately 85% of the land of the countrywas under serious desertification, which significantly occurred in arid and semi-arid lowlands of the country (eastern, northern, and western lowlands)withrelatively scarce precipitation and high temperature. The non-desertified area, mostly located in the sub-humid eastern escarpment, also declined from approximately 2.1% to 0.5%. The study concludes that the desertification is a cause of serious land degradation in Eritrea and may link to climate changes, such as low and unpredictable precipitation, and prolonged drought.

Key wordsdesertification      Landsat images      NDVI index      SPI analysis      Eritrea     
Received: 10 April 2018      Published: 10 June 2019
Corresponding Authors: Taibao YANG     E-mail:
Cite this article:

G GHEBREZGABHER Mihretab, Taibao YANG, Xuemei YANG, Congqiang WANG. Assessment of desertification in Eritrea: land degradation based on Landsat images. Journal of Arid Land, 2019, 11(3): 319-331.

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