Using GIS and remote sensing to map suitable sites for wind farms: a case study of Nkonkobe Municipality, Eastern Cape.

Issues relating to energy use, renewable energy introduction and climate change have received a lot of attention from governments throughout the world in the past two decades. Many developing nations like South Africa heavily rely on coal and other non-renewable resources for their production of energy. The processing of the non-renewable resources into secondary energy forms is according to many energy experts a chief contributor to climate change. Climate change is affecting agricultural production in many developing countries that entirely rely on rainfall for their production. The loss in production in South Africa is increasing the level of poverty in low-income earners and unemployed people. Also a lot of households found in rural areas of South Africa have no access to electricity. Electricity in South Africa is generated and distributed by Eskom a state owned company. In April 2008 Eskom failed to meet power demand and started introducing load shedding to maintain and upgrade their power plants. The load shedding stopped for while and was introduced again in 2014 when the power utility faced the challenge of failing to produce power to meet demand again due to problems with their power stations. The ongoing energy provision problems in South Africa can be alleviated by introducing alternative sources like renewable energy. Introduction of renewable energy in poorly developed areas like Nkonkobe Municipality can help households‘ access electricity and reduce load on the national grid. The aim of this study was to locate suitable sites for setting up wind farms in Nkonkobe municipality in South Africa. Wind energy as a resource is abundant in many areas of South Africa and areas to set wind farms for optimum harness of wind power were identified using GIS, remote sensing and multi-criteria decision making techniques. Wind speed, distance from settlements, distance from main roads, distance from national, slope and land use/land cover were chosen as the factors to consider in selecting a suitable site. Data was collected from South Africa Weather Services, University of Fort Hare and online free data sources. Thematic maps for all the factors were developed in ArcGIS and fed into Nkonkobe Municipality database developed for the study. Thematic maps were assigned weights before being overlaid using weighted overlay tool. Weights for the factors were determined using Analytical hierarchy Process‘ pairwise comparison approach. Weighted overlay of the thematic maps produced a map showing suitable areas based on the weight influence of each factor. The output map was compared to results obtained from using Boolean approach and weighted sum method as a way of checking the accuracy of obtained suitable sites. Results showed there are three sites that can be used for setting up wind farms in the study area.Thesis (MSc) (Applied Remote Sensing and GIS) -- University of Fort Hare, 201