Table of content
1.1 Introduction
1.2 Justification of Study
1.3 Statement of Research Problem
1.4 Research Objectives
1.5 Scope of Study and Anticipated Outcomes
2.1 Study Area
2.1.1 Drainage
2.1.2 Climate and Vegetation
2.1.3 Geology and Soil
2.2 Materials and Methods
2.2.1 UAV Data acquisition and mission planning
2.2.2 Identification of flood vulnerable zones
3.1 Results and Discussion
3.1.1 Digital Elevation Model
3.1.2 Flood Vulnerable Zones
4. Discussion
5. Conclusion and Recommendation
EXECUTIVE SUMMARY
Flooding is the most recurring, widespread, disastrous and frequent natural hazard of the world. Global climate change and poor governance has increase flooding in many regions of the world with severe and devastating effect on the urban poor, who are vulnerable and find it difficult to recover without external aid (Blakhie 1998; Zwenzner and Voigt, 2009; Agbonkhese et al 2014).
In Nigeria, flooding is largely caused by indiscriminate dumping of refuse on drainage channels to channel adjustment and poor drainage conditions, as observed by (Agbonkhese et al 2013). Flooding in Nigeria occurs in three main forms which are: river flooding, urban flooding and coastal flooding (Gwari 2008; Adeoti 2010). According to ActionAid (2006), urban flooding can be recognized when; major rivers are flowing through urban areas, small streams in urban areas are rising quickly after heavy rain and wet season flooding in lowland and coastal cities and localized flooding which occurs many times in a year due to few and blocked drains (ActionAid 2006).
This study used unmanned aerial vehicles (UAV) and a hydraulic model HEC-RAS in a GIS and Remote Sensing environment for the study area, generates the inundation and flooded area. It provides a starting point on a direction of strategy making to reduce the damage. The results showed that 25m from the river are very vulnerable to flood in the study area, while 50 and 100m are moderate and less vulnerable to the flood. In Galadimawa, 20.4 ha (68.5%) of the land area is vulnerable. Also in Trade more estate, about (354) buildings are within the flood vulnerable zones, of which (156) buildings are within the 25metres vulnerable zones covering 17.6 ha (32.5%) of land area (208) buildings 36.5 ha (67.5%) of land area are within the 50metres vulnerable zones. Building a structure along the river is also one of the factors contributing to flood and elevation of the study area. This project can serve as a tool for applying remote sensing and GIS in disaster management. In the age of all embracing flood plain management, these sophisticated technologies can be very useful for the planners to formulate effective strategy for combating the perpetual natural disaster of river flooding.
It can be concluded that remote sensing, GIS, and GPS together with flood modeling technique have successfully been applied to prepare the Flood Risk Maps for the Galadimawa roundabout and Trade more Estate in support of disaster preparedness and mitigation activities. For the first time in Abuja Municipal Area Council, UAV derived remote sensing data was utilized successfully for extracting flood extent and thereby to calibrate/validate HEC-RAS model output. The study had produced a series of (25meter, 50meter and 100meter buffer) Hazard maps followed by Vulnerability and Risk Maps corresponding Statistics for the moderate and low risks were found to be as follows; 50m and 100 respectively. A household survey further revealed that approximately 19 building is highly vulnerable for flood event whilst the remaining 74% and 7% of them fall into moderate and low vulnerability classes, respectively.
Most of the studies area showed a very high-resolution DEM, therefore, UAV can be our dependence on high resolution terrain data. A detailed mapping depict the trace of past floods event that help us in this direction. Clearly, identifying pattern of the flood will help to prevent it and reduce more damage by taking necessary steps in time. This technology can be very useful for a planner to generate an effective strategy to prevent this disaster. Hence this tool can be used by a policymaker, which will help them to make a decision before the flood to prevent more economic and social loss.
In recommendation, further improvement of this research could be to validate the flood risk maps, for a more accurate and reliable flood warning system to be developed. The obtained results supported that the UAV-derived DEMs could be an appropriate alternative to the LiDAR DEM and other satellite data for flood modelling on a small to large scale.
INTRODUCTION
1.1 Background of Study
Flooding is the most recurring, widespread, disastrous and frequent natural hazard of the world. Global climate change and poor governance has increased flooding in many regions of the world, with severe and devastating effect on the urban poor, who are vulnerable and find it difficult to recover without external aid (Blakhie 1998; Zwenzner and Voigt, 2009; Agbonkhese et al 2014).
Sada (1998), defined flooding as unusual high rates of discharge often leading to inundation of lands adjacent to streams, and it is usually caused by intense or prolonged rainfall. Flunk (2006), asserts that flooding arises from heavy rainfall, structural failures and a host of human-induced factors. Also, flood also depends on rainfall amount and rate, past moisture conditions, topography, soil type and land use (Flunk 2006). According to Agbola et al. (2012), UN-Water (2011) and Agbonkhese et al. (2014), rapid population growth, urbanization, exploitation of natural resources, industrialization and location of infrastructure exacerbate flooding.
In Nigeria, flooding is largely caused by indiscriminate dumping of refuse on drainage channels to channel adjustment and poor drainage conditions (Agbonkhese et al 2013). Flooding in Nigeria occurs in three main forms which are: river flooding, urban flooding and coastal flooding (Gwari 2008; Adeoti 2010). According to ActionAid (2006), urban flooding can be recognized when; major rivers are flowing through urban areas, small streams in urban areas are rising quickly after heavy rain and wet season flooding in lowland and coastal cities and localized flooding which occurs many times in a year due to few and blocked drains (ActionAid 2006).
Urban flooding has resulted in major loss of lives and livelihoods, and destruction of economic and social infrastructures such as; electricity, roads, water supply and railway lines. According to (UN-Water 2011), flood disasters has severely killed many people with devastating economic damage on people living in low-lying coastal zones and other areas at risk of flooding or extreme weather conditions. Also, flooding in cities can contaminate water supplies and increase the spread of epidemic diseases, cholera, typhoid, malaria, diarrhea and other water-borne diseases. A need to develop a standardized methodology to monitor and map out flood risk and vulnerability is also due to the necessity to support the sustainable development goal 13.1. The SDG goal 13.1 aims to strengthen resilience and adaptive capacity to climate related hazards and natural disasters in all countries. (UN, 2015).
Flood hazard and its impacts can be mitigated by providing reliable information to the public and relevant stakeholders about the flood risk through flood inundation maps. Also accurate flood hazard maps are very essential for planning and emergency action plans (El Morjani et al., 2017).
Remote Sensing and Geographic Information Systems (GIS) have become important tools for flood hazard and risk management. The application geospatial technology for flood hazard assessment have been documented by literatures (Spachinger, Dorner, Metzka, Serrhini, & Fuchs, 2008; (Demir & Kisi, 2016). GIS can be used to analyze flooded areas, visualize the extent of flooding, damage assessment and flood risk maps (Elkhrachy, 2015).
1.2 Research Objectives
i. Identify and map the flood prone zones in the Galadimawa and Trade More Estate areas
ii. Assess the vulnerability of buildings and infrastructure to flood in the areas.
1.3 Statement of Research Problem
Floods have constituted a major problem in Abuja Municipal Area Council. Floods have been identified as one of the key issues preventing Africa’s expanding population of city dwellers from escaping poverty and obstructing the United Nations’ 2020 aim of achieving significant improvement in the lives of the people living in Africa’s regions (Douglas et al, 2008). This is due to the fact that the majority of African towns lack the infrastructure to withstand extreme meteorological conditions, as well as the means and equipment to monitor them. Flood mitigation design in African towns, development along floodplains, and dam collapses, as well as other environmentally unsound activities, all contribute to placing the African population at danger.
In Nigeria, nearly 90 percent of natural hazard causing damages are because of flood events (Ayanlade et al., 2013). Floods have been a common and recurring occurrence in the country, wreaking havoc on human livelihoods and infrastructure development. A review of the flood incidents in Galadimawa and Trade more areas in Abuja Municipal Area Council (AMAC), Federal Capital Territory (FCT), like other flood-prone locations in Nigeria, indicates that flood has become one of the key concerns of citizens and government decision makers. The area is usually ravaged by flood during rainy seasons each year. The most significant was the flood of 12th, September 2021, which claimed lives and destroyed property worth billions of naira(source)
With the likelihood of floods increasing as a result of environmental changes, poor land use planning and absence of an adequate drainage system, there is an urgent demand for reliable flood maps for effective disaster risk management.
In addition, the ability to determine flood risk for the Souss River basin and its resident populations will strengthen its flood management capacity by providing the information necessary for decision makers to: advocate for resources to improve emergency preparedness; support emergency response; and help to identify, plan, and prioritize areas for mitigation activities to minimize the effects of flood hazard. Moreso, the ability to determine flood risk for the Souss River basin and its resident populations will strengthen its flood management capacity by providing the information necessary to decision makers to: advocate for resources to improve emergency preparedness; support emergency response; and help to identify, plan, and prioritize areas for mitigation activities to minimize the effects of flood hazard. The assessment of flood risk zones in the area will provide information to decision makers to advocate for resources to improve emergency preparedness, support emergency response, identify, plan, and prioritize areas for mitigation activities to minimize the effects of flood hazard. Specifically, this research aim at using UAV derived data to assess the vulnerability of the Galadimawa and Trade More Estate to flood hazard.
1.4 Justification of Study
Remote Sensing (RS) and Geographic Information Systems (GIS) have become essential tools for flood hazard management. Optical and Radio Detection and Ranging (RADAR) based sensors provide access to data for flood hazard mapping (Haq, Akhtar, Muhammad, Paras, & Rahmatullah, 2012; Memon, Muhammad, Rahman, & Haq, 2015; Anusha & Bharathi, 2020) However, the acquisition of satellite images is expensive and the data is perturbed by weather conditions. In addition, their low temporal resolution and coarse spatial resolution are impediment to accurate flood models (Iizuka et al., 2018; Karamuz, Romanowicz, & Doroszkiewicz, 2020).
On the Unmanned Aerial Vehicle (UAV) is a cost-effective technology, provides a new dimension in aerial photography, ecosystem mapping and monitoring. In recent times, UAV-based data are filling the gap between land-based measurements and traditional, air-borne or satellite photogrammetry. It captures imagery of high spatial and temporal resolutions that are free of occlusions, clouds and surpasses the limitations of other satellite borne data (Tomaštík, Mokroš, Saloš, Chudỳ, & Tunák, 2017). UAV generated data provides accurate, geo-referenced data that is more up-to-date and higher resolutions than most publicly available data. In addition, UAV enables near real time application and low-cost alternatives to the traditional manned aerial photogrammetry (Eisenbeiss, 2009).
Digital elevation model (DEM) is one of most important input data in hydrological modelling. Structure from Motion (SFM) is the method of generating three-dimensional structure from two-dimensional images. This is achieved by stereo vision; whereby images are captured from different position with an overlap. Afterwards, depth information is calculated by positional difference between images taken from different position (Lim, Ye Seul La, Phu Hien Park, Jong Soo Lee & Pyeon, Mu Wook Kim, 2015). Therefore, applying the Structure From Motion photogrammetric technique on images captured by UAV, point cloud can be generated (Puliti, Olerka, Gobakken, & Næsset, 2015; Tomaštík et al., 2017; Wallace, Lucieer, Malenovsk, Turner, & Petr, 2016). Subsequently, DEM and orthophotos can be generated from the point clouds through photogrammetric processing (Bendig et al., 2015; Zarco-Tejada et al., 2014). The DEM serve as input for flood inundation modelling. This data can be used in conjunction with other variables to determine appropriate and cost-effective flood control measures needed to protect a community.
1.5 Scope of Study and Anticipated Outcomes
This study will examine the use of UAV-based data for flood mapping and estimate the number of building and facilities at risk within the Galadimawa and Trade more areas. Some of the terms and concepts associated with flood mapping and disaster risk management will be discussed along with the applications of Remote Sensing (RS) and Geographical Information System (GIS) in managing flood events. The focus of this study is to map the flood risk areas, identify causative factors based on Land Use and estimate its impact. The flood maps can further be utilized for easy and quick identification of areas of potential flood hazard to minimize losses. In addition, this methodology can be used as a tool by the authorities concerned to control and apply management and land zoning, reduce flood hazards, protect the environment, provide insurance and development planning.
STUDY AREA
2.1 Study Location
The study was conducted at Galadimawa and Trade More Estate (Figure 1 and Figure 2), all within the Abuja Municipal Area Council (AMAC), Federal Capital Territory (FCT) Abuja.
Figure 1: Study area showing the Abuja Municipal Area Council
2.1.1 Drainage
The drainage pattern generally varies from trellis to dendritic. The area is drained by many rivers in and around Abuja including Rivers Wupa and other smaller seasonal southerly-flowing streams form the tributaries. The rivers depend on rainfall for their recharge. As such, their stakes are high in rainy season and decrease appreciably in the dry season.
2.1.2 Climate and Vegetation
The climate is tropical continental climate characterized by two distinct seasons; the dry and rainy seasons. The rainy season spans from March/April to September/October, while the dry season extends from October to May. The mean annual rainfall is 1630mm, while temperatures vary from 220C around December/January to 350C in March/April. The weather conditions reflect the rugged nature of the area with the hills and inselbergs occasionally inducing orographic (relief) rainfall in their immediate vicinity (Abam & Ngah, 2013).The area falls within the Guinea Savanna Ecological zone, the vegetation is characterized by thorn bushes and trees, herbs, shrubs, and mango trees. Although there are patches of rainforest in the Gwagwa plains, especially in the gullied train to the south and the rugged south-eastern parts of the area. The dominant vegetation of the area is however classified into three savannah types: The Park or Grassy Savannah, the savannah woodland and the shrub savannah.
2.1.3 Geology and Soil
Landform consists of two major types: the hills and the plains (Ibilewa et al., 2021). The study area is predominantly underlain by the Precambrian basement complex rocks. The basement complex of Nigeria has been classified in several ways. But the most recent and widely accepted is the classification of which is basically classified into three main geological units namely; migmatite-gneiss complex, older granite suite sometimes called the intrusive suite and the schist belt.
The two main types of soils are the sedimentary belt in the southern and south-western extremities of the area and the pre-Cambrian Basement complex rock country which accounts for more than 80 per cent of the area (Oyawoye 1977). The sedimentary formation consists mainly of fine-grained sandstones with inclusions of grits, siltstone and clay. While the Basement complex consists of a wide variety of rock types which is further classified into three broad groups: The igneous rocks made up mostly of biotite grantie, rhyolite, syenite, gabbro diorite. The granites account for most of the rock domes and massive hills in the north-eastern and north-western parts of the area.
MATERIALS AND METHODS
2.1 UAV Data acquisition and mission planning
The UAV data was the primary data used for this study. The images were acquired using Phantom 4 DJI UAV on the 10th and 11th of March 2022. The drone deploy software was used to define the flight plan at the two study sites (Galadimawa and Trade More) Figure 3 and 4. The survey was carried out on a sunny and less winding mid-day time. The UAV utilizes the Global Positioning System (GPS) and Obstacle Sensing System to automatically stabilize itself, navigate between obstacles (Birute Ruzgiene 2004). A total of 11 flight strips for both locations were captured. Galadimawa covered an area of 98 hectares and Trademore has 249 hectares. A total of nine hundred and eighty-seven (987) images were captured at both locations.
The spatial quality of the ortho-mosaicked images depends on flight height and percentage of overlap. Dandois, Olano, & Ellis, (2015), assessed the relationship between optimal flight altitude, overlap, weather conditions for UAV data acquisition and quality of the point cloud. The study clinched that higher point density, proper alignment and image matching in the photogrammetry processing is highly correlated with an increase in forward and side overlap and flight height. Therefore to ensure high quality of the photogrammetry outputs, an overlap of 100m and height of 100m above the ground were used for image acquisition. Also, the speed of the UAV has an effect on the images, to avoid blurred images moderate speed was adopted. Image acquisition parameters of the data are shown in Table 1. The images captured during the flight were saved in the drone repository and later retrieved for processing.
Table 1: UAV data acquisition parameters
| Parameter | Value |
| Altitude | 100m |
| Angle | Nadir (90) |
| Front overlap | 100% |
| Side overlap | 100% |
| Speed | Moderate |
2.2 Data Processing
The acquired data was processed using the sequential workflow defined in the Agisoft Photoscan software to derive the DTM and Orthophotos. The methodological steps are summarized in the Figure 5.
The initial stage was photo aligning and, in this stage, the software finds matching points between overlapping images, estimates camera position for each photo and builds sparse point cloud model. Thereafter, the build dense point cloud was computed. This stage increases the density of the 3D points computed from the former stage. The build dense point cloud calculates depth information for each camera to be combined into a single dense point cloud. The densification of the sparse point cloud increases the accuracy of the Digital Surface Model and orthophotos that will be generated subsequently (Agisoft, 2022). Finally, the build mesh and texture was applied to the images to derive the Digital Elevation Model and orthophotos. The DEM derived was imported into ArcGIS 10.8 for further spatial analysis.
2.3 Identification of flood vulnerable zones
The DEM derived from the photogrammetry processing was used to identify the flood prone areas.
The lowest portion of the elevation range was extracted from the DEM using a raster query in ArcGIS. The data was vectorized and the entire lowland area was computed. Also, the river channel in the study area were digitized as a line feature and buffer zones were created. It has been observed from the previous flood incidents, that flood water usually extends up to 50 metres from the river course (Agency Report, 2022). Therefore, proximity analysis of 25m and 50m was done based on the recommendation from Federal Capital Development Authority (FCDA) around the stream channel. The proximity analysis was done to determine buildings and infrastructure that are vulnerable to flood in the area.
3.1 RESULTS AND DISCUSSION
The orthophotos derived from the photogrammetry processing from the study areas are shown in Figure 5.
3.1.2 Digital Elevation Model
In Galadimawa, the elevation ranges between 405 and 445metres (Figure 6). The area with elevation of 405m is stream channel and depression, hence, they are the most vulnerable areas. Within these areas are farmlands, excavation sites and buildings downstream. This explains why the area is been ravaged by flood annually during rainy season.
However, in Trade more area the elevation ranges between 356 and 403metres (Figure 7). The area with elevation of 356m is a natural water way, within which buildings and infrastructure have taken over, thereby impeding the flow of water when it rains. This explains why the estate is highly vulnerable to flood disaster.
3.1.3 Flood Vulnerable Zones
Results from the proximity analysis conducted in Galadimawa based on the guideline provided by the FCDA, stimulates that the distance of buildings from the river course should be 25metes. However, it is been observed from the previous flood incidents, that flood water usually extends up to 50 metres from the river course. Based on this, a proximity analysis of 25 and 50 metres were carried out. Figure 7 show the results of the proximity analysis. Within the 25metres, no building is vulnerable, but five structures within the 50metres are vulnerable and this covers 20.4 ha (68.5%) of the land area. Also, two buildings and a part of the African University of Science and Technology (AUST) intersected between the 25 and 50metres flood susceptible zones. A total of eight structures are within the flood vulnerable zones.
Whereas in trade more estate, three hundred and four (354) buildings are within the flood vulnerable zones, of which one hundred and fifty-six (156) buildings are within the 25metres buffer zones covering 17.6 ha (32.5%) of the total land area. While two hundred and eight (208) buildings are within the 50metres buffer zone, and this covers 36.5 ha (67.5%) of the land area (Table 1 and Figure 7).
Table 1: Flood vulnerable zones of buildings affected
| Buildings affected | Buffer Zones | Land area |
| 208 | 50m | 36.5 ha (7.5%) |
| 156 | 25m | 17.6 ha (32.5%) |
| 354 buildings are within the flood vulnerable zones | ||
4. Discussion
In projects of a small to medium scale, unmanned aerial vehicles (UAVs) are becoming more widely acknowledged as a useful instrument for collecting data that is critical for flood risk analysis (Karamuz, Romanowicz, & Doroszkiewicz, 2020). UAVs have been proven as a significant alternative to traditional data capture, particularly when it comes to mapping applications with high spatial and temporal resolution, and they provide a low-cost alternative to traditional manned aerial photogrammetry (Uysal, Toprak, & Polat, 2015). In flood risk modelling, the DEM is widely used in a variety of applications, including simulation, surface analysis and damage assessment (Sulaiman, et al., 2012). This study corroborates with that of Karamuz et al.(2020), that utilized UAV derived DEM data to assess flood prone areas in an urban environment.
Rapid UAV mapping can also provide updated topography information, whose changes due to human activities can strongly influence flood dynamics. Also, the low flight height and high accuracy of UAVs-derived DEMs can be effective in urban environmental surveys, where features which cannot be properly detected by satellite or airborne imaging survey technologies (Annis et al., 2020).
During the field survey in Galadimawa area, factors may have contributed to flooding and loss of lives in recent time includes: blockage of drainage, narrow drainages and culverts that are inadequate to evacuate the flooded water timely due to high volume of water in the area during high intensity rains and construction on water ways. Other anthropogenic activities were sand scooping and top soil erosion.
In Trademore more estate the findings were not different, as the field study revealed several factors attributed to flooding in the area. The attributes include building on flood plains, blockage of constructed drainages, inadequate and size of drainage diameter to timely evacuate flood water.
Land use and development changes soil and water movement, thus influencing flooding. Construction on flood zone increase the risk of floods (Park, Choi, & Yu, 2021). The soil is replaced with impervious surfaces during construction. As a result of the interference, more water accumulates in surrounding streams and rivers, essentially raising the average flow rate year-round If the water cannot seep into the soil as it used to, it can be problematic during flood seasons. It can also obstruct floodwaters’ normal flow, potentially diverting them to the structures (Gholami, Saravi, & Ahmadi, 2010). In addition, this hinders natural retention of water and alters subsurface or groundwater flow, leading to increased formation of flood waves and volume or size of flood wave discharge.
Conclusion
It was concluded that this work investigated the potential of UAV derived DEMs to assess flood vulnerable areas in an urban environment. Finding reveals that the area is highly prone to flood disaster. In Galadimawa, 20.4 ha (68.5%) of the land area is vulnerable and a total of eight structures are within the flood vulnerable zones. In trade more estate, about three hundred and four (354) buildings are within the flood vulnerable zones, of which one hundred and fifty-six (156) buildings are within the 25metres vulnerable zones covering 17.6 ha (32.5%) of land area and two hundred and eight (208) buildings 36.5 ha (67.5%) of land area are within the 50metres vulnerable zones. The finding also reveals obstruction of the natural water ways due to construction of buildings, infrastructure and situation of the area on low elevations might be responsible for the flood.
The approach adopted in this work provides a quick and accurate support to decision-makers for the identification of hazard areas, in order to define measures intended at mitigating the flood risk and for the implementation of flooding protection policies.
In recommendation, further improvement of this research could be to validate the flood risk maps, for a more accurate and reliable flood warning system to be developed. The obtained results supported that the UAV-derived DEMs could be an appropriate alternative to the LiDAR DEM and other satellite data for flood modelling on a small to large scale.
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List of MDAs that will implement the recommendation
- National Emergency Management Agency (NEMA)
- Integrated Flood Management (IFM)
- Federal Ministry of Environment (FMEnv)
- Community Based Citizen Flood Management Committee (CFMC)
- Federal Emergency Management Agency (FEMA)
- Flood Hazard Management Program (FHMP)
- National Flood Insurance Program (NFIP)
- Associated Programme on Flood Management (APFM)