Journal of Environment and Earth Science www.iiste.org ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol.4, No.22, 2014 90 Analysis of Sensor Imaging and Field-Validation for Monitoring, Evaluation and Control Future Flood Prone Areas along River Niger and Benue Confluence Ecology, Lokoja, Nigeria Yahaya Usman Badaru 1* Onuh Spencer 2 Musa Yakubu 3 Ibrahim Ishiaku 4 Yakubu Mohammed Nassir 5 1.Applied Remote Sensing Laboratory, Department of Geography, School of Natural and Applied Science Federal University of Technology, Minna, Nigeria 2.Director, Centre for Satellite Development Technology, National Space Research Development Agency (NASRDA), Abuja, Nigeria 3.Project Consultant, Yamiza Nig. Limited, Abuja investment Neighbourhood centre, Garki-2, Nigeria 4.Department of Geography, School of Natural and Applied Science, Federal University of Technology, Minna 5.Director, Shambilat Consults and Partners, Abuja-Nigeria * Emails of the corresponding author: badaruyahayausman@yahoo.com; remotesensing_medicalgeography@yahoo.com; musayaks@yahoo.com Abstract The study area often suffered from flood for the last two year resulting to ecological damages including farmlands, infrastructures, property damage, loss of life and degradation of land-cover. Flood prone areas assessment is conducted using sensor data from space-borne optical sensors with cross-validation by ground- truthing the study area along the two major rivers that converge at Lokoja, otherwise called river-confluence. Maximum likelihood classification (MLC) and ISO-clustering unsupervised classification method of Arcmap- 10.1 using NigeriaSat-1 data is applied to the regimes of up-stream and down-stream of River Niger and River Benue respectively. Based on ground truthing of the study areas, classification of inundated areas closely connected with actual flood prone area was developed. The results of the classifications of flood prone areas were displayed on satellite imagery, of which the percentage differences of change detected from variations of 16 class of land-use (LU) and land-cover (LC) using optical sensor shows that wetland flood plain comprising of runoffs-routes and lowland areas recorded the highest of 14.42% using MLC and 16.02% using ISO-DATA. In the final analysis, the classification accuracy conducted shows that the ecology of flood prone areas can be adequately classified using MLC (54.89%) and ISO-clustering unsupervised classification (45.11%). In the same vein, the result of regression function shows high correlation coefficient of 0.6242 (62%) and high strength in their relationship of which the potential flood runoff-route did correlate with the state of the location of the study area. It is anticipated that remote-sensing data integrated from optical sensors could be used to supplement up- stream, down-stream and runoffs-route to monitor, evaluate and detect floods prone areas. It is therefore significant that government and relevant agencies adopts these findings to help in the monitoring, evaluating and control of future ecological disasters. Keywords:Analysis, lokoja,river niger, river benue, confluence, monitor, evaluate, control, ecology, flood, spatial, temporal 1.0 Introduction Floods are usually caused by excessive runoff from rainfall amount, particularly in the sub-Sahara of West Africa. In other words floods are sometimes described according to their structural distribution and occurrence. According to Brown et al. (2011), flooding may occur as an overflow of water from river, in which the water overflow its usual bank or boundaries, or it may occur due to an accumulation of rainwater (Adler et al. 2003). Floods can also occur in rivers when the flow rate exceeds the capacity of the river channel, particularly at the bends, often cause damage to homes and the environment (Kuenzer et al 2013). According to Huang et al. (2014), in the medieval period people have lived and worked by rivers because the land adjoining water bank is usually flat, fertile and provide easy travel, and access to commerce and industry. Some floods develop slowly, while others such as flash floods (Huang et al.2014), can develop in just a few minutes and without visible signs of rain. Additionally, floods can be local, impacting a neighborhood or community, or very large, affecting entire river basins. Asante et al. (2007) states that remotely sensed data provides potential for flood monitoring, of recent, remote sensing applications (Hong et al. 2004) have been shown to characterize the hydrologic processes (Schumann et al. 2009) at varying spatial and temporal footprints over sparsely gauged river basins (Brakenridge et al. 2006). Remote sensing data integration from multiple sensors (e.g., optical and microwave) have been used within a hydrologic framework for flood monitoring and prediction in river basins (Hirpa et al. 2013) in southern Africa (Zhang et al. 2013). Multispectral remotely sensed estimates provide timely and cost-effective hydrologic (Khan et al. 2012) monitoring in the flood prone basins, irrespective of the geophysical barriers. Floods can be said to be one of the most hydro-meteorological events that have devastating impacts on human and the