Analysing the road reserve encroachment in Maseru Lesotho using remote sensing and image analysis

• Main Author: Ralitsoele, Teboho
• Other Authors: Sithole, George
• Format: Dissertation
• Language: English
• Published: Faculty of Engineering and the Built Environment 2021
• Subjects: Road reserve; Encroachment; Qoaling; Maqalika; Maseru; Lesotho; Remote Sensing; GIS; Image analysis
• Online Access: http://hdl.handle.net/11427/33912

The increasing rate of urbanization and the problem of road reserve encroachment mean that there is no space for road expansion and sometimes for maintenance and road furniture, these and other problems have exposed the problem of road reserve encroachment. The main aim of this study was to investigate methods of finding the road reserve encroachment in Maseru Lesotho using aerial photos. The study used single image analysis and multiple image analysis methods. In single image analysis, the study used three methods of image classifications to find objects that are in the road reserve. Under classification, the study used both supervised and unsupervised image classifications. For supervised classification, the study used the direct image classification method where the aim was to look for every object found in the road reserve. For the indirect approach, the study looked for the ground to find objects in the road reserve. For unsupervised image classification, the study assumed that small clusters are encroachment. In multiple images analysis, the study used the 2015 and 2017 images to determine permanent objects found to have encroached road reserves. Here the assumption was that encroachment does not change over time, which means that unchanged objects during the change detection have encroached on the road reserve. The confusion matrix was used to tell the best performing method and the results show that the indirect method, both in Qoaling and Maqalika performed best. All the methods showed that there was an encroachment on a road reserve, and found that permanent objects were; houses, shops, and shopping centers. The study recommended the use of images with higher resolution and more bands, also that images be taken frequently.