Monitoring and Predicting Urban Growth Trends by Using Geo-Informatics Approaches, A Case Study of Hossana City, Southern Ethiopia
Yigezu Lenda Liyuneh1, M. Kartic Kumar2
1Yigezu Lenda Liyuneh, Lecturer, Arba Minch University, Ethiopia
2Dr. M. Kartic Kumar, Assistant Professor, Geomatics Engineering, Wachemo University, Ethiopia.
Manuscript received on 01 July 2024 | Revised Manuscript received on 13 July 2024 | Manuscript Accepted on 15 July 2024 | Manuscript published on 30 July 2024 | PP: 1-8 | Volume-12 Issue-7, July 2024 | Retrieval Number: 100.1/ijisme.F37720811622 | DOI: 10.35940/ijisme.F3772.12070724
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent decades, urban sprawl has been a prominent element of urban expansion, particularly in developing nations like Ethiopia. To deal with this problem, it’s necessary to forecast auto-spreading orientation toward rural areas through time to avoid haphazard urban growth. Although there were many Models applied to investigate urban growth trends all over the world, just a few studies have used these methods to look at Hossana City’s urban expansion. The study used the Cellular Automata (CA) model in concert with MOLUSCE to monitor and evaluate spatial changes in the city over the last two decades. For this, Landsat data (TM, ETM+, and OLI) from the years 2000, 2010, and 2021 were used. A 30m DEM was used to extract several thematic layers such as distance to stream, topography, slope, and aspect. Distance to build up land and road networks were derived from classified LULC maps and OSM respectively. For comparison and assessment of the city’s urbanization extent, Google Earth images were used. For accuracy testing, topo sheets were employed. ENVI software was used to preprocess satellite data and related auxiliary data. Land use and land cover maps were created using the maximum likelihood algorithm of supervised image classification. ArcGIS 10.8 was used to classify land use and land cover, as well as to evaluate accuracy. Overall accuracy and kappa coefficient results were higher than the minimum acceptable levels. The cumulative rate of urban growth in Hossana city has resulted in significant change during the last two decades (2000 to 2021). This reveals that there have been significant changes in several LULC categories, including bare land, agricultural land, water bodies, and green areas, which have declined by (-6.73 percent), (-18.69 percent), (-0.67), and (2.51) percent, respectively. Built-up areas and vegetation, on the other hand, increased by 22.88 percent and 5.73 percent, respectively. Projection of the future urban growth pattern processed through QGIS by using the CA model. As a result of the findings, significant changes in various LULCs are likely to occur between the present study period (2021) and the prediction year (2031). Thus agricultural land will reduce by 1.55 %, while bare land will shrink by 0.5%, but built-up areas and green areas will grow by 3.09 % and 0.91 %, respectively. Vegetation coverage would be reduced by 3.0%, while water bodies would be reduced by 0.17 %. Thus more change was made towards agricultural land and vegetation. Therefore Hossana city’s urbanization rate is greatly expanding on agricultural land. The project output indicated that the increase in built-up of the town brings about high pressure on agricultural land. In general, Geoinformatics techniques enable us for sustainable management of urban sprawl and monitoring of urban expansion and future development.
Keywords: Urban Growth, Geoinformatics, Cellular Automata, LULC, Hossana City.
Scope of the Article: Remote Sensing