A Review of Morphometric Studies of Drainage Basins in Nigeria (Published)
The review chronicles morphometric studies of drainage basins across the regions of Nigeria. This is done against the backdrop of the importance of the drainage basin in terms of its morphometric characteristics, water resources exploration, planning and development, and its associated environmental problems of flooding, erosion and drought. Both conventional and non-conventional methods are classified into pre- and Remote Sensing (RS) and Geographical Information Systems (GIS) era, where they were used. The results of analysis showed that the linear, areal and relief attributes of the basin varied across the country. The Nigeria drainage basins range: Basin Area(Ab ) 40.75km2 to 9,926km2, Stream Order(Nu) 3rd to 7th, Stream Number(N) 13 to 9205, Stream Length (Lu) 9.6km to 30,232.80km, Bifurcation Ratio (Rb) 0.9 to 6.0, Drainage Density(Dd)0.03 to 1.27, Drainage Frequency(Nf) 0.01 to 1.35km-1/ km-1, Drainage Texture( Dt) 0.005 to 4.99, Form Factor(Ff) 0.02 to 0.85, Circularity ratio (CR) 0.10 to 0.81, Elongation ratio (ER) 0.14 to 1.40, Length of overland flow(Lg) 0.08-1.95, Infiltration number (IF) 0.02-1.71, Ruggedness number(Rn) 3.68 to 112.59, Hypsometric Integral (HI) 0.22 to 0.99. The basins were found to be predominantly a dendritic system pattern, suggesting that there were no structural controls. Basins with bifurcation ratio below the threshold of 3 to 5 were less susceptible to flooding and erosion while those above are more prone with a strong structural control on the drainage pattern. Catchments with low values of infiltration number were noted to experience higher infiltration and lower surface runoff. Where form factor values are small, the basins are elongated and have low peak flow of longer duration. Whereas basins with high values have high flows of shorter duration.
Keywords: : GIS, Morphometric, RS, basins, streams
Effect of Urbanization on Land Use Land Cover in Gombe Metropolis (Published)
This study examined the integration of Remote Sensing and Geographic Information System (RS/GIS) for analyzing land use and land cover dynamics in Gombe Metropolitan, the Gombe State capital for the period 1976 to 2016. Land sat (TM) images of 1976, 1996and 2016 were used. The study employed supervised digital image classification method using Erdas Imagine 9.2 and Arc GIS 10.5 software and classified the land use into undisturbed vegetation, sparse vegetation, Settlements, Farmlands, Rock outcrops, Bare surfaces. The images were analyzed via georeferencing, image enhancement, image resampling and classification. The results obtained showan increasing settlements (from 0.36% – 4.01%) and farmlands (from 24.8% – 51.2%), over a decreasing of other LULC classes (bare surfaces, undisturbed and sparse vegetation, and rocky outcrops) for the time period of 1976 to 2016. These results could help city planners and policy makers to attain and sustain future urban development. It is therefore recommended that encouragement should be given to people to build towards the outskirts, like New mile 3 and Tumfure,etc through the provision of incentives and forces of attraction that is available at the city center in these areas to avoid the problem of overcrowdings.
Keywords: : GIS, Change Detection, Remote Sensing, Urbanization, gombe, land use
Remote Sensing Application in Forest Monitoring: An Object Based Approach (Published)
Object-based methods for image analysis have the advantage of incorporating spatial context and mutual relationships between objects. Few studies have explored the application of object-based approaches to forest classification. This paper introduced an object based method to SPOT5 image to map the land cover in Yen Nhan commune in 2015. This approach applied multi-resolution segmentation algorithm of eCognition Developer and an object based classification framework. In addition, forest maps from 2000 to 2015 were used to analyze the change in forest cover in each 5 years period. The object based method clearly discriminated the different land cover classes in Yen Nhan. The overall kappa value was 0.73 was achieved. The estimation of forest area was 89.05 % of forest cover in 2015. By overlaying achieve forest maps of 2000, 2005, 2010 and the classified map of 2015 shows vegetation changed during 2000-2015 remarkably.
Keywords: : GIS, Change Detection, Forest Classification, Remote Sensing, SPOT 5 Image