An assessment of land cover change patterns using remote sensing : a case study of Dube and Esikhawini, KwaZulu-Natal, South Africa.

Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.During the past two centuries, land cover has been changing at an alarming rate in space and time and it is humans who have emerged as the dominant driver of change in the environment, resulting in changes of extraordinary magnitudes. Most of these changes occur due to demands placed on the land by the ever-increasing human population and their need for more land for both settlement and food production. Many researchers underscore the importance of recognizing and studying past land-use and land cover changes as the legacies of these changes continue to play a major role in ecosystem structure and function. The objectives of this study were to determine the extent of land cover changes between 1992 and 2008 in the study areas, Esikhawini and Dube located in the uMhlathuze municipality, KwaZulu-Natal, and to both predict and address the implications of the extent of future changes likely to occur in the area by 2016. Three Landsat satellite images of the study area were acquired for the years, 1992, 2000 and 2008. These images were classified into nine classes representing the dominant land covers in the area. An image differencing change detection method was used to determine the extent of the changes which took place during the specified period. Thereafter, a Markov chain model was used to determine the likely distribution of the land cover classes by 2016. The results revealed that aside from Waterbodies and Settlements, the rest of the classes exhibited a great degree of change between 1992 and 2008, having class change values greater than 50%. With regards to the predicted change in the land cover classes, the future land cover change pattern appears to be similar to that observed between 1992 and 2008. The Settlements class will most likely emerge as the dominant land cover in the study area as many of the other classes are increasingly being replaced by this particular class. The overall accuracy of the classification method employed for this study was 79.58% and the results have provided a good overview of the location and extent of land cover changes in the area. It is therefore plausible to conclude that these techniques could be used at both local and regional scales to better inform land management practices and policies