Application of GIS and Remote Sensing Technologies in Solid Waste Management: A Case Study of Nyahururu Municipality

Solid waste management forms an integral part of most town management. Urban waste management has been a major problem in most Kenyan cities therefore a need for a comprehensive study utilizing remote sensing and GIS technology. An increase in commercial, residential and infrastructure development due to the population growth in Nyahururu town had been the major contributor for the solid waste menace in the town. The sprawling of unplanned informal settlements around the town had also contributed to this problem where ineffective mechanisms to deal with the resulting solid waste generated had not solved the problem conclusively. Landfill siting is a critical and rather overlooked method of sustainable management of solid waste in most Kenyan towns. Currently, Nyahururu uses an open dumping site system that has aggravated the need for proper siting of landfills. The siting process was guided by principle of eco friendliness, socially acceptable and economically feasible. The key factors that had been employed for the ultimate determination of suitable landfill locations include proximity to road networks, proximity to drainage patterns, slope, geology, land use land cover, proximity to settlements and proximity to urban and shopping centers. GIS and Remote sensing techniques were employed to achieve the desired results. GIS-based Multi-Criteria Evaluation methodology was followed to have advantages of both GIS and Multi-Criteria Evaluation method and solved spatial decision making problems. Remotely sensed satellite imagery provided an effective platform for determination of land use land cover characteristics of the study area. Analytical Hierarchy Processes (AHP) pair-wise comparison module derived weights for all driving factors. The weighted driving factors were integrated in a GIS platform and produced landfill suitability map of Nyahururu municipality. The finding of this study showed that 9.862 % of the study area was most suitable, 56.52 % was moderately suitable, 6.11 % was poorly suitable and 27.49 % was Unsuitable/restricted.