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Assessing Potential Flood Risk Zones Around AGULU Lake Using High Resolution Satellite Image and Digital Terrain Model (DTM) Ekweonu, F.K1, Igbokwe, J

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Assessing Potential Flood Risk Zones Around AGULU Lake Using High Resolution Satellite Image and Digital Terrain Model (DTM) Ekweonu, F.K1, Igbokwe, J ISSN 2321 3361 © 2019 IJESC Research Article Volume 9 Issue No.8 Assessing Potential Flood Risk Zones around AGULU Lake using High Resolution Satellite Image and Digital Terrain Model (DTM) Ekweonu, F.K1, Igbokwe, J. I2, Baywood, C. N3 Department of Surveying & Geoinformatics Nnamdi Azikiwe University Awka, Nigeria Abstract: The aim of this thesis is to assess potential flood risk zones around AGULU Lake using High Resolution Satellite image and Digital Terrain Model DTM. The study area is AGULU Lake, in Agulu town under Anaocha Local Government Area of Anambra state, in south east Nigeria. AGULU Lake lies within the Anambra Basin, where monstrous effects of flood, soil and gully erosion and landslides destroy lives and property. The objectives of the research are to map the landcover/landuse pattern around Agulu. Access the accuracy of the resultant landcover/landuse map using kappa statistics. To model the risk zones around Agulu lake using elevation, slope, flow accumulation and distance to drainage channels. To delineate the areas affected by the various risk zones around Agulu lake. The methodology flow chart shows different stages and analysis involved in delineating flood zone area within the study area, data requirements includes QuickBird 2017 imagery covering the study area, 2meter Digital Terrain model covering the study area and shape file of 2km by 2km land area covering Agulu Lake and Environ. Software and hardware requirements that were used in carrying out such study, the software requirement includes ArcGIS 10.2, ENVI 5.2, Microsoft excel. The methodology adopted image processing and classification using object-based classifier into 5 Class categories, Agulu Lake, Residential Area, Farm Land, Low Forest and Road. Ground truthing picking of sample points for accuracy assessment and to identify the features on ground to determine the landcover/ landuse of the study area. The hydrological elements were created from the DTM after filling the void. These elements include flow accumulation, Slope, Elevation and Drainage Network. The different data sets were reclassified for information generation such as DTM and slope creation, all data was integrated in a GIS environment using multi-criterion decision tools (WLC) Weight Linear Combination for preparation of flood risk maps into very high risk, high risk, moderate risk and low risk zones using equal interval of separation based on elevation. The results indicated that very high-risk zone occupied 22.53% of the entire study area, covering an area of 96.88 hectares, while high risk zone occupied 23.80%, covered an area of 102.34 hectares. Moderate risk zone occupied 25.61%, covering 110.13 hectares while low risk zone occupied 28.04% covering an area of 120.55 hectares. The very high-risk zone also covers 6 buildings and 8 plots of farmland, these features are particularly at very high risk of potential flooding in the study area. However, it is recommended that adequate measures be taken by the state government and local government to inform and relocate buildings in area of very high-risk zones. The study clearly shows that satellite remote sensing data have emerged as a viable alternative for assessing flood risk. Different techniques exist that manage and analyze the impact of flood some of these techniques have not been effective in management of flood disaster. Remote sensing technique presents itself as an effective and efficient means of managing flood disaster. Keywords: Flooding, Flood Risk, Risk Zones, DTM. I. INTRODUCTION Ayhan 2006). Floods are among the most recurring and devastating natural hazards, impacting human lives and Flood is considered to be one the most devastating and causing severe economic damage throughout the world. It is frequently occurring natural hazards in the world (Komolafe et understood that flood risks will not subside in the future, and al 2015). The devastating effects of flood are recorded in terms with the onset of climate change, flood intensity and frequency of mortality and economic risk by both national and will threaten many regions of the world. In Nigeria, flood international agencies (Akanni and Bilesanmi, 2011). Although accounts for the highest occurring natural hazards, with great research claims that the mortality rate is reducing globally due consequences on the life and property (Aderogba, 2012). Due to the established early warning systems in some countries to the torrential over the Niger River and its tributaries located (mostly the developed), but in some localities, especially in the in Benin and Nigeria, led to the opening of flood gates of the developing and under developed countries, those living in the Kainji, Jebba and Shiriro Dams, resulting to a dearth and coastal areas, increasing deaths are witnessed because of their considerable material loss in 1999. Shortly after that level of exposures and vulnerability(Komolafe et al 2015). Kumadugu Yobe valley (Northern Nigeria) experienced a Floods can be generally considered in two categories: flash devastating flood again in 2001 (Amaize, 2011). The 2012 floods, the product of heavy localized precipitation in a short flood in Nigeria was believed to have resulted from the time period over a given location; and general floods, caused combination of intense rainfall and Cameron Lagdo Dam by precipitation over a longer time period and over a given effect, these devastating floods affected about 14 states that river basin (Atay and Ayhan 2006). Flooding is the most border the Niger-Benue River. The worst affected state common environmental hazard, due to the widespread includes Kogi, Edo, Anambra and Delta States. This flood geographical distribution of river valleys and coastal areas, and incident has been characterized as the most devastating since the attraction of human settlements to these areas (Atay and the last 40 years (Felix Ndidi 2013). The current trend and IJESC, August 2019 23481 http://ijesc.org/ future scenarios of flood risks demand accurate spatial The methodology adopted image processing and classification information on the potential hazards and risks of floods. using object-based classifier into 5 Class categories, Agulu Techniques utilizing satellite remote sensing data can provide Lake, Residential Area, Farm Land, Low Forest and Road. objective information that help to detect floods and to monitor Ground truthing picking of sample points for accuracy their spatiotemporal evolution. This research emphasizes on assessment and to identify the features on ground to determine the use of remote sensing techniques in analyzing potential the landcover/ landuse of the study area. The hydrological flood risk in Agulu Lake. Satellite images have shown the elements were created from the DTM after filling the void. capabilities to extract relevant information needed to model These elements include flow accumulation, Slope, Elevation and manage the impact of flood. Currently there has not been and Drainage Network. The different data sets were any flood assessment carried out in Agulu Lake using remote reclassified for information generation such as DTM and slope sensing tools. So, the need to carry out this research is if not creation, all data was integrated in a GIS environment mandatory for future prediction of flood risk around Agulu and using multi-criterion decision tools (WLC) Weight Linear also produce a map showing possible flood zone areas to create Combination for preparation of flood risk maps into very high awareness for the individuals, cooperate organizations, risk, high risk, moderate risk and low risk zones using equal government and nongovernmental organization, industries etc. interval of separation based on elevation. flood prone areas within the community. IV. RESULTS II. STUDY AREA In this section, results of image analysis as obtained from the The study area is Agulu lake in Agulu Town under Anaocha image segmentation and flood potential risk mapping are Local Government Area of Anambra State in south east presented. Most of the discussions are supported by maps, Nigeria. Agulu Lake lies within the Anambra Basin, where tables and illustrative graphs. monstrous effects of flood, soil and gully erosion and landslides destroy lives and property. The study area lies A. Land Cover/Land Use Mapping within latitudes 60 06’-60 08’ N and longitudes 70 00’- 70 In mapping the landcover/landuse 2km by 2km around Agulu 02’E, in the Anambra Basin. Existence of Anambra Basin is as Lake, four different classes were identified to include a result of folding and uplift of Abakaliki-Benue fold belt in residential, low forest, farmland, lake and roads. the santonian stage (Egboka 2006). Early rainfall occurs usually in January/February with full commencement of rainy season in March and stopping in November of each year. The dry season commences from November and ends in February. The mean highest annual rainfall usually recorded around July to October is about 1952mm (Igbokwe, 2010). Fig2d shows the map of Agulu lake and its environs at a scale of 1:12500. Figure.4.1 Landcover / Landuse map of 2km by 2km area covering Agulu Lake and Environ Fig 4.1 shows the results of the landcover/landuse classification of 2km by 2km area around Agulu lake, the results indicate that residential accounted for the 36.50% of the land cover/use with an area of 156.95 hectares while low forest had the 48.48 % with an area of 208.44 hectares, farmland had 6.53% with an area of 28.09 hectares and Agulu lake had 8.47% with an area of 36.42 hectares. The landcover / landuse distribution is shown in table 4.1. Figure.1. (a) Map of Nigeria showing Anambra State, (b) Anambra Table.4.1. Landcover /Landuse distribution of 2km by 2km Showing Anaocha LGA, (c) Anaocha LGA showing Agulu Lake; (d) land area covering Agulu lake and environ Satellite image of Agulu Lake. Class Name Area (Ha) Percentage (%) III. METHODOLOGY Residential 156.95 36.50 3.1 Data Requirements Low Forest 208.44 48.48 Data used in this research include: Farmland 28.09 6.53 I.
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