Title: Environmental monitoring of flood vulnerability in Ogun State for flood risk management.

Background:

Flooding is a natural disaster and also a global crisis. This occurs when there is an overflow of water source due to rise in sea level, accumulation of saturated rainwater on the ground, and commonly from heavy rainfall when natural watercourses do not the capacity to convey excess water. Flooding causes vast devastation including; displacement of people from their means of livelihood resulting in an economic damage, outbreak of waterborne diseases, loss of human lives, and properties.

Aim: Flood mapping and risk assessment in Ogun State using geospatial analysis.

Objectives:

1.      Assess flood vulnerability in Obafemi Awode L.G.A in Ogun State byintegrating spatial data to identify high-risk areas

2.      Develop and share user-friendly, operational tools and guidance to better prepare for and adapt to floods.

Study Area: The study will focus on Obafemi Awode Local Government Area (L.G.A.) in Ogun State, Nigeria. Ogun State is located in the South West Nigeria with an estimated population of about 6.4 million (2022). It is bordered to the South by Lagos State, to the East by Ondo State, to the North by Oyo and Osun States. Its Western border forms part of the national border with the Republic of Benin. The state is divided into 20 local government areas with Abeokuta as the capital city. Ogun has a tropical wet and dry or savanna climate. The major rivers in Ogun State are Ogun, Yewa, and Osun Rivers.

Methodology: The environmental monitoring of flood vulnerability in Obafemi Awode L.G.A., Ogun State would follow a systematic approach as follows:

1.      Land use land cover (LULC) change - Collect satellite imagery on land use maps to understand changes in land development.

2.      Flood Hazard Mapping – Use Digital Elevation Models (DEM) to analyse terrain and drainage patterns, highlighting areas susceptible to flooding.

3.      Normalized Difference Vegetation Index, NDVI to calculate and understand vegetation density using sensor data.

4.      Multi-Criteria Analysis (MCA) – Combine factors like LULC, NDVI, slope, and flow accumulation to assess vulnerability.

5.      Risk Zonation – Classify areas into high, moderate, and low-risk zones, helping

policymakers prioritize flood mitigation efforts.

Limitations:

1.      Difficult to extract Ogun State data from USGS EarthExplorer. Imported shapefile gave an error of having more than 500 points. Finally imported Ogun State KML file created from Google Earth Pro.

2.      Converting the raster data of 2014 classified signatures to polygon using field “Value” resulted in showing only “Gridcode 5 – waterbodies” for all the selected features in the attribute table of output feature class. Corrected by using field “Name”.

3.      Downloaded 2 footprints for SRTM as one could not cover the study area. I had to convert mosaic dataset into a single raster dataset.

4.      Rainfall data could not be downloaded. During analysis, the values of the distance to stream and line density could not be calculated.

5.      Analytical Hierarchical Process (AHP) could not be deduced.

Data Acquisition: Relevant data such as land use/land cover (LULC) change, digital elevation model, flow direction, flow accumulation, and NDVI; for January, 2014 and January, 2024 covering 10-year period were gathered from satellite imagery. The collected data were processed and analysed using Geographic Information System (GIS) and Remote Sensing (RS) techniques to understand and identify flood-prone zones.

Data Analysis:

1.      Land use/land cover (LULC) change refers to the classification of human activities and natural elements on the landscape. Features including: Built up-1, Dense Vegetation-2, Sparse Vegetation-3, Bareland-4, and Waterbodies-5 were extracted and classified using supervised classification. The patterns and trends in land development for 2014 and 2024 were studied.

Diagram 1: Comparison between 2014 and 2024 features

1. Digital Elevation Model (DEM) explores the attributes of terrain such as elevation at any point, slope and aspect. The Shuttle Radar Topography Mission (SRTM) which contains global elevation data was downloaded from USGS EarthExplorer, projected and analysed in Arc Map.

Diagram 2: The attributes of Obafemi Awode LGA terrain

3.      Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared that vegetation strongly reflects and red light that vegetation absorbs. It is used to quantify vegetation greenness and understand vegetation density. The Landsat 8-9 bands; 5 and 4 were downloaded from USGS EarthExplorer and imported into Arc Map. NDVI was calculated using the following formula: (Band NIR - Band R)/ (Band NIR + Band R) delivered as a single band. The range of values is from -1 to +1. NDVI values interpretation are as follows:

·         Below 0 indicates water

·         Between 0 and 0.3 – Barren areas with little or no vegetation cover

·         Between 0.3 and 0.6 – Sparse vegetation cover

·         Between 0.6 and 0.9 – Dense and healthy vegetation cover

·         Above 0.9 – Very dense vegetation cover

The NDVI 2014 ranges from 0.029 to 0.47 while the NDVI 2024 ranges from 0.006 to 0.43 showing a slight reduction in vegetation cover. The diagram 3 depicts the change in vegetation cover from 2014 to 2024.

Diagram 3: Comparison between NDVI 2014 and NDVI 2024

4.      Multi-Criteria Analysis (MCA) and Risk Zonation – Factors such as LULC, slope, NDVI, and flow accumulation were reclassified and used to assess vulnerability. Overlay of the multi-layer factors was applied for risk analysisclassifying areas into high, moderate, and low-risk zones.

Diagram 4: Flood Risk Analysis

Results:

The findings from the study suggest that the incidence of flooding is mostly as a result of various environmental factors such as: urbanization and climate change. From the analysis, it seems that lots of buildings are springing up while waterbodies are reducing. The most land change happened from the conversion of waterbodies to built-up areas covering approximately 476 square kilometres.

Dense vegetation was also converted to built-up areas covering approximately 186 square kilometres. Close to 150 square kilometres of waterbodies were lost to barren land between January, 2014 to January, 2024. Overlay of multi-layer factors indicates that water bodies and wetlands are prone to flooding.

Recommendations:

1.      Government should sensitize the masses of the effect of climate change and how it can be mitigated as regards felling of trees.

2.      Adaptation measures such as flood barriers and improved drainage systems should be implemented to prevent flooding.

3.      Early warning systems should be established so that people can avoid the high-risk flooding areas.

4.      Nature-based solutions such as tree planting should be encouraged.

5.      More studies should be conducted to understand why the waterbodies are drying up.