Abstract
The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing city areas. Landsat satellite imageries of three different time periods, i.e., Landsat Thematic Mapper (TM) of 1982, 2000 and 2018 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site, quantify the changes in the Osogbo and its peripheral areas from 1982 to 2018 over a period of 36 years. These data sets were imported in ArcGIS 10.3, ERDAS Imagine and IDRIS Selva, satellite image processing softwares to create a false colour composite (FCC), supervised classification methodology was employed using maximum likelihood technique. The images of the study area were categorized into four different classes namely Core-urban, Peri-urban, Vegetation, water body. The results indicate that during the last thirty-six (36) years, Core-Urban land and water body have been increased by 2.74% (38.20 km2) and 0.98% (13.69 km2) while Peri-Urban land, and vegetation cover have decreased by 0.35% (5.00 km2), and 3.36 % (46.87 km2), respectively. The results quantify the land cover change patterns in the city and its peripheral area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyse changes in land cover over time that can be used as inputs to land management and policy decisions.
Highlights
Remotely sensed data made possible to study the changes in land cover in less time, at low cost and with better accuracy (Kachhwala, 1985) in association with Geographical Information System (GIS) that provides suitable platform for data analysis, update and retrieval (Chilar, 2000)
El Gammal et al (2010) have used several Landsat images of different time periods (1972, 1982, 1987, 2000, 2003 and 2008) and processed these images in ERDAS and ARC-GIS software to analyse the changes in the shores of the lake and in its water volume
Bhagawat (2011) presented the change analysis based on the statistics extracted from the four land use/land cover maps of the Kathmandu Metropolitan by using GIS
Summary
Remotely sensed data made possible to study the changes in land cover in less time, at low cost and with better accuracy (Kachhwala, 1985) in association with GIS that provides suitable platform for data analysis, update and retrieval (Chilar, 2000). This information assists in monitoring the dynamics of land use resulting out of changing demands of increasing population.
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