Wetland change detection in Nile swamps of southern Sudan using multitemporal satellite imagery

Abstract

In this study, the maximum likelihood supervised classification and the post-classification comparison change detection are applied in order to monitor the wetlands by assessing and quantifying the wetland cover changes in the Nile swamps of southern Sudan, called the Sudd, by using the ERDAS IMAGINE software. Three multispectral satellite imageries, acquired in the wet season from 1986 to 2006 by Landsat TM and Landsat ETM+ images, are classified into five main land cover classes namely water, vegetation, urban, wetland-vegetation, and wetland-no vegetation, by using the maximum likelihood supervised classification. A pixel-by-pixel comparison was then performed over the classified thematic map images. The post-classification change detection results show a 3.69% decrease in the wetland-vegetation areas and a 2.68% decrease in the wetland-no vegetation areas within the period 1986 to 1999. In addition, a noticeable increase is observed in the wetland-vegetation areas within the period 1999 to 2006 in the Sudd area as 14.95% of the land cover classes' areas, excluding the wetland-vegetation areas are changed into wetland-vegetation areas while there was a decrease of 5.18% in the wetland-no vegetation areas within the period 1999 to 2006. The objective of this paper is to introduce precedence in studying the wetland cover changes over the Sudd area which can help the output planners develop water resources management projects leading to enhance the life conditions in the Sudd region.