Monitoring of Urban Growth Patterns in Rapidly Growing Bahir Dar City of Northwest Ethiopia with 30 year Landsat Imagery Record - Mengistie Kindu; Daniela Angelova; Thomas Schneider; Martin Döllerer; Demel Teketay; Thomas Knoke (ISPRS International Journal of Geo-Information, 2020)
Monitoring urban growth patterns is an important measure to improve our understanding of land use/land cover (LULC) changes and a central part in the proper development of any city. In this study, we analyzed the changes over a period of 30 years (1985–2015) in Bahir Dar, one of the rapidly growing cities of northwest Ethiopia. Satellite images of Landsat TM (1985, 1995, and 2008), and OLI (2015) were used. The classification was carried out using the object-based image analysis technique and a change analysis was undertaken using post-classification comparison in GIS as a novel framework. An accuracy assessment was conducted for each reference year. Eight LULC types were successfully captured with overall accuracies ranging from 88.3% to 92.9% and a Kappa statistic of 0.85 to 0.92. The classification result revealed that cropland (66%), water (12.5%), and grassland (6%) were the dominant LULC types with a small share of areas covered by built-up areas (2.4%) in 1985. In 2015, cropland and water continued to be dominant followed by built-up areas. The change result shows that a rapid reduction in natural forest cover followed by grassland and wetland occurred between the first (1985–1995), second (1995–2008), and third (2008–2015) study periods. On the contrary, build-ups increased in all three periods by 9.3%, 121.3%, and 44.8%, respectively. Although the conversion between the LULC classes varied substantially, analysis of the 30-year change matrix revealed that about 31% was subject to intensive change between the classes. Specifically, the built-up area has increased by 250.5% during the study years. The framed approach used in this research is a good repeatable example of how to assess and monitor urban growth at the local level, by combining remote sensing and GIS technologies. Further study is suggested to investigate detailed drivers, consequences of changes, and future options.