<p justify;\\"="" style="text-align: justify;"> Abstract: The studies monitor the change in urban growth in Kano, northern Nigeria between 1999 and 2003. The Landsat imageries with 30m resolution were used as for detecting changes using both supervised and unsupervised classification. The images for the two periods were downloaded, layer stacked and corrected before the main processing work. Envi 4.8, ArcGIS 10.0 Idrisi for Taiga 17.0 and excel application of Microsoft office 2007 were used for data processing and analysis. The results were presented in maps, tables and charts. For the supervised classification four land cover classes (ie vegetation, bare lands, built up and water body) were used for the classification. The results have shown that the only bare lands had increases within period of study. Vegetation, water body and built up area had all decreases. However in some area especially in GRA vegetation has increase and this has been attributed to urban greening. To distinguish between farm land and tree canopy the study further used five classes by breaking the vegetation classes into two and the results shows that while farmland had decreases, the dense tree canopy has increase in the area for the period of study. The accuracy assessment has shown that the result is between 86 and 99 percent. The study recommended among other things the need for policy makers to look critically into the issues of urban planning and development in the area.
Keywords: Remote, sensing, urban, change, detection, information, system, Kano.
Title: Remote Sensing Studies on Urban Change Detections
Author: Tanimu Isah
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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