Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery

• Main Authors: Abdelrahim A.M. Salih, El-Tyeb Ganawa, Anwer Alsadat Elmahl
• Format: Article
• Language: English
• Published: Elsevier 2017-04-01
• Series: Egyptian Journal of Remote Sensing and Space Sciences
• Subjects: Land degradation; Spectral mixture analysis; Segmentation; Landsat; Classification and semiarid
• Online Access: http://www.sciencedirect.com/science/article/pii/S1110982316301703

The severe Sahel catastrophe in 1968–1974 as well as repeated famines and food shortage that have hit many African countries during the 1970s have highlighted the need for further research concerning land degradation and environmental monitoring in arid and semi-arid areas. Land degradation, and desertification processes in arid and semi-arid environment were increased in the last four decades, especially in the developing countries like Sudan. To test to what extent remote sensing and geographical information science (GIS) methodologies and techniques could be used for monitoring changes in arid and semi-arid regions and environment, these methodologies have long been suggested as a time and cost-efficient method. In this frame, spectral Mixture Analysis (SMA), Object-based oriented classification (Segmentation), and Change Vector Analysis are recently much recommended as a most suitable method for monitoring and mapping land cover changes in arid and semi-arid environment. Therefor the aim of this study is to use these methods and techniques for environmental monitoring with emphasis on desertification and to find model that can describe and map the status and rate of desertification processes and land cover changes in semi-arid areas in White Nile State (Sudan) by using multi-temporal imagery of the Landsat satellite TM (1987), TM (2000), and ETM+ (2014) respectively. The paper also discusses and evaluates the efficiency of the adapted methodologies in monitoring the land degradation processes and changes in the arid and semi-arid regions.