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dc.contributor.authorMutitu, Ann, N
dc.description.abstractThe first stage in planning and developing cities in underdeveloped countries is to detect informal settlements and their changes. High spatial resolution satellite imageries are popular in the carrying out the changes in spatial extent of these settlements. However, these imageries are expensive to purchase, and as a result, it may not be affordable to all, particularly in countries with a large number of informal settlements. Using Kawangware, Nairobi as a case study, this project aimed to investigate the phenomenon of informal settlements in Kawangware and its development from 2000 to 2020 by means of remote sensing and GIS. Landsat, which is a Medium Resolution satellite imagery, was used to map this phenomenon. Random Forest classification method was the applied in this project after constructing the training set using some approaches. In the first approach, visual interpretation was done using Google Map imagery and composites in order to select training samples. Open Street Map (OSM) building blocks and street layers were used as the method for training during the second round of classification. The results showed a high-speed growth of the built-up class. Consequently, the informal settlements increased in area, especially on the account of the vegetation and bare ground classes. The results obtained showed that in 2000, the total area representing the informal settlement was 98.46ha and this increased to 99.08ha. This area increased to 143.94ha in 2020, an increase of 4.3% from 2000. Conclusions drawn were that the increase in informal settlements can be attributed to the proximity of Kawangware to formal settlements such as Lavington, Westlands and also Nairobi CBD where the dwellers work so that commuting easily and cheaply since most of them are casual workers. The classification accuracy was 93.63% for the year 2020,86.64% for the year 2010 and 88.71% for the year 2000.The proposed methodology presents the application of freely available remote sensing data to map change detection in an informal settlement of an extent not larger than a city. It was recommended that further studies on the OSM validation should be conducted to improve the reliability of this data source.en_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectMapping Change Detection of Informal Settlements Using Remote Sensing and Gis: Case Study-kawangware, Nairobi (2000-2020)en_US
dc.titleMapping Change Detection of Informal Settlements Using Remote Sensing and Gis: Case Study-kawangware, Nairobi (2000-2020)en_US

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States