Fadoul Nuri, Atiyat Abdalla and Ahmed Hamid, Amna and Doka M. Ali, El Abbas and Mohamed Salih, Eltegani (2016) Assessment of Vegetation Cover Degradation Using Remote Sensing and GIS Techniques along Sudanese Red Sea Coast (Suakin to Ashad). Journal of Geography and Geology, 8 (1). pp. 55-64. ISSN 1916-9779
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Abstract
This study aimed to assess the vegetation cover degradation in the Sudanese Red Sea coast (from Suakin to Ashad) after the drought during the period from 2000 - 2011. Remote Sensing and GIS techniques were used beside field survey to conduct the study. Moderate Resolution Imaging Spectrometer (MODIS) terra 2000 -2001, 2005-2006 and 2010-2011 time-Series images mainly the 16 days Normalized Difference Vegetation Index (NDVI) product and Enhanced Thematic Mapper plus (ETM+) images dated 2005 and 2010 were used. Unsupervised classification methods were used to detect vegetation cover of the study area. Based on field survey investigations, beside the data collected on the study area and image interpretation, it was evident that season 2005-2006 and season 2006-2010 are good seasons in the vegetation cover compared to season 2000-2001. Five land cover classes were detected; wet land, bare land and three classes of vegetation cover (dense vegetation, moderately dense vegetation and sparse vegetation cover). Spectral signatures of the three dominant land cover vegetation species were detected. Areas of the three classes of vegetation cover area (dense vegetation, moderately dense vegetation and sparse vegetation cover) were calculated per km2. The study concluded that MODIS could be used as a cost effective tool in assessing land cover changes and monitoring vegetation cover degradation.As well, it could also be used to detect fairly the different vegetation species in arid and semiarid regions.
Item Type: | Article |
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Subjects: | Academic Digital Library > Geological Science |
Depositing User: | Unnamed user with email info@academicdigitallibrary.org |
Date Deposited: | 08 Jun 2023 07:05 |
Last Modified: | 18 Jan 2024 11:43 |
URI: | http://publications.article4sub.com/id/eprint/1716 |