Flood Vulnerability Mapping of River Ngadda Using Geospatial and Remote Sensing Techniques Maiduguri Metropolis, Borno State.

Flood disaster has been threating to animal, plants, land, human and non-human resources over the years. This have increased concerns on the need to investigate various vulnerable areas to ood for decision making. As a mitigative measures to loss of lives, properties, and livelihood which could arise from ood disaster, understanding zones at risk to ooding is vital to support all stages of disaster management including early warning and planning. River Ngadda is a major river within Maiduguri metropolis. Its plains and banks are known to have different activities and resources. When the river overows its shores, neighboring lands could suffer destruction, there could be loss of lives and properties. This project aims to investigate ood vulnerability along River Ngadda identifying the shores, plains, and banks of the river at risk. The research adopts ground truth information, the use of Aster DEM (30m) and Sentinel 2 (10m) Satellite Imagery dataset through Geospatial and Remote Sensing techniques to assess undulating nature of the area under study, identify areas liable to ood and analyzed the ood plain. Further analysis was carried out to obtain the Land use and Landcover (LULC) within the study area using a supervised classication and areas at various ood risk were also identied. This research results presented the river plains, ood vulnerable impact on LULC, it was found that 13.17 hectares of built-up areas fall within the very high ood risk zone, over sixty six percent (66%) of the study area appears fall within the moderate to very high vulnerable ood risk zone and the production of the ood plains, LULC, risk and hazard map of the study area was presented. This result could aid early warning, planning and decision making for River Ngadda and its environs.


Introduction
River and its systems are very important to man. It is a natural resource which human cannot live without (Karr and Chu, 2000). In the historical times, the entire civilization grew around rivers which serve as a continent's circulatory systems (Karr, 1998). River and their adjacent oodplain corridors ful ll a variety of functions both as part of the natural ecosystem and for a variety of human uses. However, the rivers and its systems with its great positives also have negative effects when been altered or distorted, this could arise from nature such as excess rainfall, river over ow or human activities imposed on them thereby resulting to ood (Karr, 1998). Consequences of these changes such as river ow withdrawal; physical disturbance of riverbeds; river ow redistribution in time; river ow redistribution in space; and water clogging causes ood is oftentimes disastrous hence causing great damage (Govorushko, 2007).
Floods are very serious and frequent disaster occurring in many parts of the world. They are one of the most common natural hazards worldwide representing the most common global hazard causing phenomenal losses (Tingsanchali, 2012). Throughout human history, swelling rivers and oods have taken a heavy toll on properties and lives and caused more economic losses than any other hazard. River over ow and excessive rainfall from climate change, cause ood disaster into the river surrounding community hence large destruction of lives and properties.
Gwange community within Maiduguri Municipal Council, has River Ngadda along the side of the community. Destructions from ood along the plains of River Ngadda has been recorded over the years.
The main cause of river ooding is the occurrence of heavy rainfall during the raining season (Mala, Nyanganji and Mukhtar, 2012). Apart from climatic and hydrological phenomenon, the presence of natural or man-made obstructions in the path such as lack of drainage, oating debris, blocked drains, failure of different structures, over own dam, causes ooding. Flooding, discharge of excessive water exceeding the channel capacity, and Flash oods, are the most pervasive and costly natural hazards faced by the habitants of Maiduguri.

Flood hazards
The problem of ood hazard is particularly prominent in the plains where the gradient of the river channel is very low (Mala, et al., 2012). Although, many scholars speci es that some river gradually gets dry during the dry season, but it is not uncommon to have oods in a city caused by excessive rainfall during the raining season (Nzeribe, et al, 2014).
The societies of developing countries are vulnerable to oods because of rst, socioeconomic conditions in terms of poverty and lack of development (Mirza, 2003) second, most of the dams in developing countries are not multipurpose (Oruonye, 2015).; third, during oods, planning, design and implementation of the measures are inadequate and ineffective fourth, rural areas depend heavily on agriculture and are generally more affected than urban areas; fth, lack of education; sixth, lack of nonstructural measures; and lastly, there is a lack of adequate human and material resources to tackle the massive disaster-like oods that occurred in the past (Mirza, 2003;Balica, Dinh, & Popescu, 2015).
Because of their vulnerability often millions of peoples become homeless, and hundreds of thousands are in need for food and medicines (Ahmad, 2003).
In Nigeria, Alau dam constructed on River Ngaddabul (Daura, Gisilanbe and Waziri 1997); caused ooding which emerged from a heavy rainfall of 12th to 13th September 1994. This led to a human displacement in different parts of Northern Nigeria, having Maiduguri town as one of the most affected locations.
According to Nigeria Floods Sep 1994 UN DHA Situation Reports over 400000 displaced and homeless persons of which 180,000 were in immediate need for accommodation. Destructions of properties recorded from the ood include houses, roads, communication routes and shutdown of network providers, contamination of water, increase in health deterioration, damage of livestock and large hectares of farmlands. Unplanned areas of poor habitation systems and facilities within the ood plains of the metropolis were most affected by the ood presenting a rise in economic draw back (Mala, Nyanganji and Mukhtar, 2015).
Floods as identi ed as one of the most common natural disasters that looms the world has been estimated by Dilley et.al 2005in Bhuiyan, (2014 as having more than one-third of the world's land area to be ood pone and affecting over eighty percent of the world's population. Flood hazard mapping is a vital component for appropriate planning in ood prone areas. It creates understandable charts and maps which aids planning and decision-making during identi cation of risk Bapalu, and Sinha, (2005), in addition, it facilitates prioritization of mitigated efforts and response plans (Emmanuel Udo, Ojinnaka, Baywood, and Gift, 2015;and Forkuo, 2011).
Investigating ood vulnerability along the rivers is very crucial in disaster planning and management. In recent years the impacts of oods have gained importance because of the increasing amount of people, economic activities and ecosystems that are impacted by its adverse effects (Jonathan, et al., 2020).

Flood Vulnerability Mapping
Development of ood vulnerability map in arid areas is a signi cant issue that needs to be addressed. Areas surrounded by rivers are more of a concern mostly because that large amount of rain fall, and ow of water could cause ood. In years 1995 and 2015, oods were estimated to represent about 43% of all weather-related catastrophes, reaching more than 3,000 documented events of 157,000 loss of lives and estimated minimum economic losses worth US$ 662 billion (Wahlstrom andGuha-Sapir 2015 in Kabenge, et al, 2017). Their widespread existence and destruction drives scientists to investigate this phenomenon thoroughly.
To foster easy management and planning of ood disaster and monitoring, various approaches have been used in mapping out ood prone areas of which the recent technology has been seen in the use of remote sensing and GIS techniques.
The rst integration of remote sensing with ood monitoring mentioned in literature dates to the 1970s, where data from Landsat 1 helped to analyse the Mississippi ood of 1973 (Deutsch and Ruggles, 1974).
Since then, scientists have continued to use satellite data as auxiliary sources for multiple reasons, e.g., change detection, or observation of ood boundaries (Moore and North, 1974;Jeyaseelan, 2003;Bello and Aina, 2014;and Klemas, 2015) Remote sensing is widely used in all stages of disaster management, from the mapping of ood-prone areas, through ood detection, early warning, evacuation planning, damage assessment and spatial planning (Jeyaseelan, 2003;Bello and Aina, 2014;Klemas, 2015). Various remotely sensed data and approaches have proven effective for disaster planning and responses. The combined passive and active remote sensing systems presented ood damage of urban, rural, and infrastructural categories, in which remotely sensed Landsat data was critical in ood stimulation and loss estimate in Japan (Dutta and Herath, 2001).
According to Wang, and Xie, (2018); and Jeyaseelan, (2003), remote sensing provides critical data for mapping water resources, measuring hydrological uxes, monitoring drought and ooding inundation, while geographic information systems (GIS) provide the best tools for water resources, drought, and ood risk management. It creates understandable charts and maps which aids planning and decision-making during identi cation of risk in addition, it facilitates prioritization of mitigated efforts and response plans (Emmanuel et al.,2015;and Forkuo, 2011).
Bangladesh ood hazard mapping research approach with the use of NOAA AVHRR data and Geographic Information Systems (GIS) resulted in the production of ood hazard maps. The research focused on the use of depth of ooding and duration of ooding for assessment, prediction and analysis obtained from the satellite images. Imrul (2006) in his ndings on Bangladesh ood research, emphasized on the remote sensing and GIS supported for early warning and complete disaster risk management cycle.
Delineating the ood prone areas in the Tapi catchment of India was well achieved with the use of Digital Elevation Model (DEM), GPS data, IRS-1DLISSIV satellite imagery, river hydraulic analysis and geospatial technologies (Singh & Sharma, 2009).
The expected damages and produced risk map of river ood for Dhaka East was conducted with information derived from water level and discharge records, topographic maps, land use map, secondary depth duration-damage function, and the monetary value of land use speci c property (Gain & Hoque, 2013). The vector-based digital terrain model (DTM), Triangulated Irregular Network (TIN) was developed for the study area by digitization of the contour lines and point elevations using ArcGIS 9 for ood inundation mapping with the HEC-RAS simulation.
In a similar way, Chen, Ito, Sawamukai and Tokunaga, (2015) investigation on ood hazard assessment emphasized on ood hazards in Kujukuri Plain of Chiba Prefecture, Japan. In the research, ood management and environmental protection sector gained from leveraging on using a multi-criteria decision analysis and GIS techniques incorporating six factors of river system, elevation, and depression areas in the research.
Flood risk is a combination of threat, consequence and vulnerability identifying functions of green infrastructural landscapes (Carter, Handley, Butlin & Gill, (2018). Its management and adaptation of cities to climate change disaster management and planning within some certain jurisdictions and distant locations some areas liable to ood could be managed through green infrastructural development. Gain and Hoque (2013) argument was supported with the ndings which showed that to achieve ood risk of a location, the success could be attributed to a combination of Geospatial technologies modelling the surface water run-off and the DTM within the boundary area under study (Carter, et. al., 2018).
Just like other parts of the world, research have been carried out in Nigeria on ood and its effect in various parts of the country, unfortunately Maiduguri has had few research studies on risk mapping and ood vulnerability as well as its impact on lives and properties using geo spatial techniques. The research earlier conducted appears to have larger coverage or scale with little or no concentration towards a particular the section of River Ngadabul close environs hence recommending improved and updated investigation using better technologies. Mala, et al., (2012) considered a research on gully development along river Ngaddbul ood plain of Maiduguri, their approach utilised measurements of gully length, width, and depth at four locations. The results showed that Ngaddabul annual ood, rainfall amount, runoff taking advantage of footpaths, wheeled-tracks, soil texture and human factors could cause gully development, erosion and ood without identifying built up areas liable to ood. Some other researchers have considered assessment of ood within Maiduguri and its environs using different methodology. Jimme, Bashir and Adebayo (2016) studied the Spatial Distribution Pattern and Terrain Analysis of Urban Flash Floods and Inundated Areas in Maiduguri Metropolis. His study used data generated from Shuttle Raddar Topography Mission and ndings presented low lying locations within the study area, identifying potential areas liable to ash ood with a conclusion on recommendation on a continued vulnerability mapping improved which this research will provide.
Similarly, Kaka, Mayomi and Daura's research in (2019) found that the Topography plays a great role on Flood Vulnerability in Maiduguri using a 30m Asterdem Data. Recommendations made were on priority consideration given to areas liability to ood during disaster response and further studies on vulnerable areas which this research is aimed at using higher resolution imagery.
In this study, application of Remote Sensing (RS) and Geographic Information System (GIS) techniques will be applied using Sentinel dataset of 10m resolution and a 30m Aster DEM of the study area compared to previous studies and in line with recommendations from earlier researchers to explore ood vulnerability mapping within the study area. Vulnerability assessment for settlement and other land use would be identi ed enhancing the results obtained of different structures and facilitates prone to damages caused by ood generating of a ood risk mask (a map layer showing the extent of ood impact upon occurrence) and ready to be distributed to disaster management authorities.
Hydrologic analysis will be combined with socioeconomic resources and constraints with the help of GIS data processing software ArcGIS 10.4, Sentinel, imagery at a speci c resolution used to detect change in river morphology and to identify ood plains. DEM will be incorporated into the software for ood elevation estimates and risk map zones identi ed.
Primary data on past hazards, socio-economic condition, vulnerability, response capability in the recent past would be collected to develop vulnerability and risk map. This study therefore identi es vulnerable areas, ood plains and risk zones of within the study area.

Study Area
River Ngadabul is located within the Metropolis of Maiduguri Local Government Area of Borno state located at the North eastern part of the country was the study site as shown in Figure 2 With the use of the listed equipment and data, the approach adopted is shown in the Flow Chart of Section 2.2.

Flow chart
The ow chart (see Figure 3.2) presents an overview of the methodology adopted

Data Collection
Mixed method of data collection was done. While primary data was collected on the eld upon site visitation, elevation coordinate of areas where ood had been experienced in the past was obtained and noted, overview of the area was also noted. The complete dataset for the research was obtained from secondary sources.
Sentinel 2 imagery from Copernicus Open Access Hub for the study area was extracted de ning the criteria of 2021 Sentinel-2A satellite imagery with a Multi-spectral instrument. This was further sorted by sensing date, product type, cloud cover of ranges zero through to six (0-6.0). With the criteria set, the imagery of June 10th, 2021 Sentinel 2A Satellite imagery with a cloud cover percentage of 0.001669 and 0.0 cloud shadow percentage was extracted.
Similarly, AsterDEM of 30m resolution with a minimum elevation of the study area was obtained 2.5 Data Processing 2.5.1 Sentinel-2 Upon obtaining the dataset, a supervised processing was carried out using ArcGIS software. Although the imagery had a re ectance of 60 m. The Sentinel-2, 10m resolution dataset of Band 2,3,4 and 8 were the appropriate bands for the research. The obtained imagery was inputted into the map interface presenting its natural color, the study area was clipped using the Geoprocessing tool. Further processing of band combination was carried out to present the false color combination for easy interpretation (See Figure  2.3). This gave rise to the next training samples for land use land cover classi cation.
A schema was created, and the different sample classes were speci ed with the conventional colors. Using the polygon feature type, the samples were further trained for the different classes, merged, collapsed, and reclassi ed. The data was classi ed into four various land uses and land cover types after adopting the supervised training model.

AsterDEM
Furthermore, the AsterDEM raster was of 30m resolution with a minimum elevation of 296 meters and a maximum of 327meters. The study area was rst clipped, further processing and reclassi cation was performed on the data set to obtain actual range values and classi cation for Digital Elevation Model of the study area. This was also guided but the obtained primary eld data of the study areas where ood had been experienced in the past to aid the production of Flood plain Map and Flood prone areas.

Analysis And Results
In line with the objectives of this research, the analysis was carried out using a multi-criteria approach. Data management tool and spatial analytical tool to identify Flood risk areas and Flood plains. This further transcended to identifying ood risk zones within the study area and built-up areas at very high risk were identi ed.

DEM and Flood plain Analysis
Statistical data clustering of the Jenks optimization method was adopted for the analysis. Jenks Natural breaks of the study area was performed in which the sum of squared deviations for array means (SDAM) and sum of squared derivation for class means (SDCM) was derived to obtain its Goodness of variance t (GVF) whose value lies between 0 through to 1.

GVF = (SDAM -SCDM) / SDAM
The above method resulted to a reclassi cation of the DEM of which a customize extension activated and a good color ramp was chosen.
Similar procedure was used for the identi cation of different plains and ood risk areas. This procedure generated a grid codes, counts and values for both themes.

Flood Risk
Again, with the same spatial analytical method, the grid code was generated for the ood risk areas. The obtained grid code was given appropriate symbology to produce a risk map (see Figure 3.3).

Flood Risk Zone
Furthermore, the ood zone was cross tabulated, the map algebra and raster calculator performed different functions during the spatial analysis. The includes the use a multiplier with the results (grid codes/ values) obtained from topographic analysis and ood risk areas to map out vulnerable ood risk zones (see Figure 3.4).
Flood plain / Flood Topo Risk area =Flood Risk Zone (Vulnerability) This analysis presented grid codes shown in Table 3.1 and classi ed into ve (5) Flood Risk Zones using the common vulnerability scoring system and severity level.  Table  3.2).
Count x Pixel Area = Area in Sq.m (m 2 ) Where the pixel area is 30m by 30m This area of each ood vulnerability risk zone was further analyzed and presented in its percentage as shown in Figure 3.5 using the formula below (Flood risk zone / Total ood risk zone ) x 100 From the above, it is apparent that more areas within the study area is liable to ood and over 50% lies between the moderate to very high risk which expresses concerns of which activities or settlement should not be permitted.

Land use Landcover and Flood zone
Having identi ed zones vulnerable to ood and area determined, the Land use Land cover raster (see Figure 3.6) was vectorized. The built-up areas only were geo-processed and its intersection with the Very high ood risk zones upon overlay (see Figure 3.7) was determined and the area was calculated (see Table 4.5).

Discussion
River Ngadda and its environs understudy presented an interesting and concerning output from its analysis. It was apparent that the procedures used for this research as described by and Jeyaseelan, (2003) stood as a good approach to obtaining the aim of the research.
The objective of assessing the undulating nature of the area of interest, was met, and used for further analysis, this supports Forkuo (2011) argument, which in turn was concurred by Emmanuel et al., (2015) in their opinion stating that analytical output from the assessment could support planning and prioritization efforts to mitigation and response.
This research ndings agree with Mala, et al., (2012) argument specifying that areas of low or very low plain are risk to ood especially during the period with high amount of rain fall. About 30.51% within the study area lie on the low and very low plain of high ood risk and very high ood risk where different activities are ongoing, and infrastructure are present while 7.03% lies on the highland of very low ood risk (see Figure 4.5). This implies that the greater percentage of the study area is at risk and requires adequate attention.
Furthermore, the area estimation of each ood zones analyzed for and as shown in Table 4.4 and Table  4.5 respectively shows the extent of possible damage when a ood occurs as well as resources that could be lost using satellite imagery spatial analysis described by Blasco, Bellan, and Chaudhury, (1992) and in same argument by Brouder (1994) and Demir and Kisi, (2016). While Table 4.4 presents area in hectares for each zone, Table 4.5 in similar way identi ed that 13.17 hectares of built-up areas could be affected by ood upon its occurrence.
In addition, analyzing ood risk impact on various land use land cover presented the different classes of built-up, vegetation, water, and bare land at different risk zones. This, in addition to the area extent obtained as described above, fostered understanding of which natural and human resources lie within the study area and to what extend will the built-up areas be affected within the very high ood risk zone (see Identifying resources present within the study area also could aid the understanding of possible economic and social infrastructure which could be lost at the occurrence of ood hazard (see Figure 4.6) this supports Horritt et al., (2003) argument on the possibility of the detection of ooded vegetation or vegetation prone to ooding using remote sensing techniques.
Lastly, the development of the vulnerable risk maps, and ood plain maps from the multicriteria approach and analysis adopted using various distinct color composite as argued by Rasid and Pramanik, (1990) shown in gure 9 and gure 14 presents a simple, explicit, coherent, and logical comprehension on zones of ood risk for early warning and complete disaster risk management cycle.

Summary
The River Ngadda and its surrounding plains from this research has increased body of knowledge to support disaster risk management processes, ranging from early warning planning, response, rescue, rehabilitation, and relocation. The multi-criteria approach of Chen,et al., (2015) proved relevant for ood vulnerability mapping.
This research further presents an in-depth knowledge of the study area which could be referenced by researchers for further study or analysis of the area of interest.
In addition, planners involved in land use and construction works could bene t from this study as it poses to be a document to support required information for development purposes.

Recommendations
Medium and low ood risk zones where drainage system or water ow system is not available, should have individuals discouraged from residing within those locations or implement any livelihood activity ongoing as ood water from high-risk zones could possibly over ow there by constituting negative impact within that zone.
Flood plain regulations should be established, existing ood plain laws should be enforced and adhered to as a measure to reduce adverse river ood impact.
Tree planting and afforestation could be encouraged within river plains as a measure to reduce velocity of river ow in turn support sustainable soil land conservation and management.
Adoption to Land use planning or recharacterization of the vulnerable areas, construction of drainage system, ood control embarkments, and dredging works could be a measure to support reduction of ood impact within the study area.
Lastly, it is recommended that further research could be carried out with a different methodology and additional information analyzed for comparison purposes or extended areas for a wholesome comprehension, in-depth and wider coverage on Ngadda riverbanks, boundary, and plains.

Conclusion
From the research ndings, it is apparent that ood has an impact in the society and understanding the nature of the terrain especially within riverbanks and its environs is paramount to avoid destruction of properties and save lives. Resident including their lives and properties that lies on areas prone to ooding are at great danger regardless of their vulnerability status, this is especially because ooding cannot be completely avoided but its effects reduced.. If appropriate authorities do not give adequate attention and prepare for any future ood occurrence, it will have great impact on human and non-human resources.
Lives and livelihood could be lost, and properties damaged which would present a setback in the socioeconomic status within the environs and state at large.
In conclusion, the relevance and applicability of this research extends to the effectiveness and e ciency of the use of GIS and Remote sensing technologies for disaster risk management as its nding and produced maps could be relevant as a vital tool for early warning, planning, decision-making purposes, researchers, and other appropriate authorities.