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American Journal of  
Geospatial Technology (AJGT)

Geospatial Assessment of  Oil Spill’s Impact in Obio/Akpor Local Government Area
Rivers State, Nigeria

Victor Ayodele Ijaware1*, Kanu Ifeyinwa Florence Mary2

Volume 3 Issue 1, Year 2024
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v3i1.2906
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: May 10, 2024

Accepted: June 07, 2024

Published: June 10, 2024

Environmental degradation resulting from oil spills has emerged as a critical issue requiring 
immediate attention. This study uses geospatial techniques to investigate the impact of  oil 
spills in Obio/Akpor Local Government Area of  Rivers State. Landsat images, adminis-
trative map of  the study area and historical oil spill data were data used in carrying out 
this study. The images were pre-processed through atmospheric and geometric corrections. 
Spatial analysis of  four remote sensing indices-Normalized Difference Vegetation Index 
(NDVI) for assessing vegetation health, Normalized Difference Water Index (NDWI) for 
detecting water bodies, Land Surface Temperature (LST) for measuring surface temperature, 
and Normalized Difference Built-Up Index (NDBI) for identifying built-up areas-was con-
ducted using the raster calculator in ArcGIS.  The study employed the Analytical Hierarchy 
Process (AHP) to assign weights to these indices based their relative significance regarding 
oil spill impact. Subsequently, reclassification and integration of  this information were done 
to generate Oil Spill Index (OSI) maps for 2003, 2013, and 2023. Validation of  the Oil Spill 
Index (OSI) of  various points before and after an oil spill incident was performed, specifi-
cally contrasting 2003 with 2013 and 2023 OSI values. This was to affirm the accuracy and 
predictive capability of  the OSI maps in identifying oil spill-prone areas. Findings revealed 
OSI values for 2003 and 2013, indicating a range of  1.04 to 3.48 for 2003, with a noticeable 
increase observed in 2013, expanding the range to 3.08 to 4.72. Similarly, a comparison of  
OSI values between 2003 and 2023 indicates OSI values, ranging from 1.64 to 3.92 in 2003, 
then in 2023 after the oil spill incidents, there were notable changes in the OSI values, with 
the range shifting to 3.2 to 4.84. This suggests a significant increase in the impact of  oil spill 
on the affected areas over the two-decade. This comprehensive index map serves as a tool 
for monitoring and assessing the effects of  oil spills on the study area, thereby achieving the 
research objectives effectively.

Keywords
Normalized Difference Vegetative 
Index (NDVI), Normalized 
Difference Water Index (NDWI), 
Land Surface Temperature (LST), 
Normalized Difference Built-Up 
Index (NDBI), Oil Spill Index, 
Analytical Hierarchy Process 
(AHP)

1 Department of  Surveying and Geoinformatics, School of  Environmental Technology, Federal University of  Technology, Akure
  Ondo State, Nigeria
* Corresponding author’s e-mail: vaijaware@futa.edu.ng

INTRODUCTION
Crude oil is a naturally occurring liquid found beneath the 
earth’s surface, which can be converted into fuel (Grema 
et al., 2023). It is a mixture of  hydrocarbons (Sephton and 
Hazen, 2013). However, the extraction process due to 
poor management has led to oil spills. An oil spill occurs 
when liquid petroleum is accidentally or purposefully 
released into the environment. According to Ejiba et al., 
(2016), oil spills are caused by deteriorating infrastructure, 
malfunctioning machinery, operational accidents, 
sabotage, and theft. The dangers associated with oil 
spills are numerous and could make life unbearable for 
the inhabitants of  impacted regions. Environmental 
problems brought on by oil spill contamination grew in 
severity throughout the 20th century. 
Among the nine Niger Delta states, Rivers State accounted 
for more than half  of  all oil spill occurrences reported 
by Shell Petroleum Development Company (SPDC) 
(Mohamadi et al., 2016). Obio/Akpor Local Government 
Area (Study Area) has experienced several oil spill 
incidents. For example, on November 6th, 2020, a Punch 
newspaper publication by Dennis Naku, reported the oil 
spill incident that occurred at Umuchem community in 
Obio/Akpor LGA. Concerned that there would be an 

explosion, the residents of  the Umuchem community 
signaled the alarm.  Another incident occurred on 
December 19 when a pipeline operated by the Shell 
Petroleum Development Company, SPDC, burst and 
spilled crude oil in the Eneka community in Obio/Akpor 
Local Government Area (Udoma, 2019). On December 
3rd, 2003, an oil spill occurred from an 8” pipeline 
between Agbada FS and Nkpoku manifold operated by 
SPDC, at Rukpokwu community in Obio/Akpor LGA 
of  Rivers State (Bassy, 2004). It was estimated that 81 
barrels of  crude oil leaked into the environment. 
Oil spills have the potential to destroy the ecosystem 
if  left unchecked or properly managed (Agunobi et al., 
2014). In addition, the residents of  Obio/Akpor Local 
Government Area have cried out for assistance regarding 
the frequent oil spills, to Shell Petroleum Development 
Company (SPDC), humanitarian organizations, and state 
and local governments (Naku, 2020). Therefore, it is 
necessary to properly analyze data and offer information 
on this matter. Hence, this study investigated the extent, 
severity, and impact of  these spills using environmental 
parameters such as vegetation, waterbodies, land surface 
temperature, and built-up areas through geospatial 
analysis. The following research questions enable the 



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research aim to be achieved: 
(i) What are the temporal patterns in NDVI, NDWI, 

LST, and NDBI?
(ii) What is the relative significance of  each criterion in 

accurately expressing the extent and severity of  oil spills 
in the study area?

(iii) What is the oil spill index of  the study area?
(iv) How accurate is the oil spill index?

This study addresses these issues using geospatial 
method, utilizing both remote sensing data and analytical 
framework to provide valuable insight into the temporal 
dynamics of  oil spill contamination in the study area.  

LITERATURE REVIEW 
The compilation of  research examined in this study shows 
considerable advancements in methodological, as well as 
the complexities encountered in the context of  oil spill 
investigation, particularly using geospatial technology. 
Park et al., (2016) examined the oil spill pattern from 
various types of  accidents and contaminants. They 
utilized temporal, geographical, and spatiotemporal 
analysis to examine the environmental incidents 
associated with oil spills that occurred in North Dakota 
between 2000 and 2014 as a result of  the oil boom. 
Ivanov and Zatyagalova, (2014) mapped oil spills in the 
marine environment in the Gulf  of  Thailand, the Caspian 
Sea, the black sea, and the Sea of  Okhotsk using GIS 
and SAR technologies. In addition, Whanda, et al., (2016) 
evaluated the geographic cluster and pattern of  443 oil 
spill incident sites using three geospatial methodologies 
and ground data. Rajendran et al., (2021) developed an oil 
spill index by using the spectral band of  the sentinel-2, 
(OSI = (B3+B4)/B2) to map marine oil spills. In addition, 
they used the drone images from the incident to verify the 
results of  the remote sensing. In Nigeria, relevant studies 
have been carried out on oil spills, to mention a few; 

Mohamadi et al., (2016) investigated oil spills’ influence 
on vegetation in Nigeria and it determinants. They made 
use of  ENVL and GIS to produce a multi-endmember 
spectral mixture analysis (MESMA) model. They were 
interested in the causes and consequences of  vegetation 
loss brought on by oil spills. While, the research carried 
out by Balogun et al., (2020) focused on the effect of  oil 
spills and the recovery pattern of  wetlands and coastal 
vegetation by employing multispectral satellite imagery 
from Landsat and machine learning models. In addition, 
Dutsenwai et al., (2017) study in Ogoni Land, Rivers State, 
observed vegetation changes to the intensity of  oil spills, 
by employing statistical methods and remote sensing 
data (Landsat imageries) for the Normalized Difference 
Vegetation Index (NDVI). 
Synthesizing the collective body of  relevant studies, one 
observes a rich tapestry of  methodologies, findings, 
and scholarly insights. Yet, a focused examination of  
the integration of  remote sensing indices – specifically 
NDVI, NDWI, LST, and, NDBI – in the context of  oil 
spill impact assessment remains conspicuously absent. 
This study endeavours to bridge this gap by employing 
the Analytic Hierarchy Process (AHP) to judiciously 
assign weights to these indices, reflecting the magnitude 
and severity of  oil spills. The culmination of  this 
methodology is the creation of  an innovative oil spill 
index map, a methodological novelty as per the extant 
literature. Furthermore, validation against historical data 
substantiates the accuracy of  the index, underscoring the 
study’s contribution towards a more comprehensive and 
integrated framework for environmental monitoring and 
oil spill management.  

MATERIALS AND METHODS
Study Area 
Obio-Akpor Local Government Area (Figure 1) is 

Figure 1: Study Area



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situated geographically between Latitudes 4°45′N and 
4°55′N and Longitudes 6°55′E and 7°05′E. According 
to Ayo, et al. (2017), Obio/Akpor LGA is bordered to 
the west by Emohua LGA, to the east by Oyigbo LGA, 
to the south by Port Harcourt LGA, and to the north 
by Ikwerre LGA. Obio Akpor LGA is one of  the 23 
local governments in Rivers State, which is located in 
what is known as the south southern part of  Nigeria, 
Niger Delta Region. Rumuodu-Maya is home to its 
administrative center.  The Port Harcourt metropolis 
comprises the LGAs of  Eleme, Port Harcourt, and Obio-
Akpor (Ayo et al., 2017). Obio-Akpor is one of  the four 
local governments dominated by the Ikwerres. Fishing, 
farming, lumbering, and hunting are the region’s ancestral 
occupations. It had 464,789 residents in 2006, 487,751 
in 2015, and 540,308 in 2020, according to estimates. 
Construction, engineering, civil service, administration, 
manufacturing, mining, sand dredging, printing, public 
service, etc. are among the new occupations that have 
emerged in this region as a result of  greater urbanization 
brought on by the growing population (Okwakpam & 
Augustine, 2019). The research area’s equatorial location 
is near the equator, where typical temperatures range 
from 25 to 28 degrees Celsius and yearly precipitation 
ranges from 2000 to 2500 millimeters between April and 
October. Due to its latitudinal location, the region’s high 
temperatures throughout the year generate increased 
humidity (Eludoyin et al., 2011; Menegbo, 2022). The 
local vegetation typical of  the Niger Delta includes 
mangrove forests, raffia palm groves, tropical rain forests, 
and mangrove areas along the shore (Eludoyin et al, 2011; 
Menegbo, 2022).

Data Collection and Processing
The flowchart of  the research methodology is as shown 
in Figure 2. 
The study on oil spills impact assessment employs a 
comprehensive approach integrating geospatial analysis 
and remote sensing data. Table I depicts the data 
acquisition, which involves sourcing information from 
reputable and reliable sources, covering various aspects 

relevant to the study, including remote sensing imagery, 
geographic information, and historical oil spill data. 
The Landsat imagery covering the year 2003, 2013, 
and 2023, with a resolution of  30m, was acquired from 
EarthExplorer Website (http://earthexplorer.usgs.gov/), 
which is operated by the United States Geological Survey 
(USGS). The administrative map of  the study area was 
obtained from Rivers State Ministry of  Lands and Surveys, 
which is a governmental department responsible for land 
management and surveying in the state. The historical oil 
spill data were gathered from joint investigation reports 
by Shell Plc, a major oil and gas company operating in 
the study area. The data source provided the specific 
URL for accessing spill incident data on Shell’s website. 
This information serves as a crucial dataset for the study, 
providing valuable insights into the changing landscape 
and environmental conditions over time.

Figure 2: Research methodology flowchart

Table 1: The Adopted Data and their Attributes
S/N Data Source Year Resolution/Scale Relevance
1 Landsat Oli/Tm United States Geological 

Survey (USGS)
2003 30 m For remote sensing 

indices calculation2013
2023

2 Administrative 
map

Rivers state ministry of  
lands and survey.

2023 1:17000 Extract the boundary 
of  the study area

3 Coordinates of  
oil spill points in 
the study area

Shell Plc. Yearly Spill 
Incident Data

2023 - For validation of  OSI 
results2022

2021
2020
2019
2012



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For this study, the various Landsat-7 ETM + and -8 OLI 
and TIRS spectral bands such as, the red band, green 
band, NIR band (near-infrared), SWIR band (short-wave 
infrared) and thermal band (for LST calculation) were 
used in calculating NDVI, NDWI, LST, and NDBI. 
The required bands were staked into a single image 
file using ArcGIS 10.5 software. The quality of  data is 
paramount to ensure the reliability and accuracy of  the 
study findings. Therefore, several steps were undertaken 
to ensure data quality; atmospheric correction and 
radiometric calibration were applied to the Landsat 
imagery to account for atmospheric interference and 
sensor sensitivity differences. These corrections were 
essential for accurately representing surface features and 
conditions. In addition, validations of  the results were 
conducted by comparing the calculated indices with 
known oil spill incident data. This validation process 
helped verify the accuracy of  the oil spill index map 
generated from the integration of  various remote sensing 
indices. 
The data pre-processing involves a series of  steps aimed 
at preparing the data for analysis and deriving meaningful 
insights. The pre-processing steps include radiometric 
calibration, atmospheric correction, masking, and 
geometric correction. 
The processes involve calculating various indices, 
including Normalized Difference Vegetation Index 
(NDVI), Normalized Difference Water Index (NDWI), 
Normalized Difference Built-up Index (NDBI) and 
Land Surface Temperature (LST). These indices were 
calculated using appropriate spectral bands and formulas. 
The calculations are as follows:

NDVI Calculation
NDVI was calculated with the formula:
NDVI=(NIR+Red)/(NIR−Red)              (1) 
Where: NIR is the near-infrared band.
Red is the red band.
NDVI values typically range from -1 to 1, with higher 
values indicating denser and healthier vegetation

NDWI Calculation
NDWI was calculated with the formula: 
NDWI=(Green+NIR)/(Green−NIR)             (2)
Where: NIR is the near-infrared band.
Green is the green band.
NDWI values typically range from -1 to 1, with higher 
values indicating water bodies and lower values indicating 
non-water features.

NDBI Calculation
NDBI was calculated with the formula: 
NDBI=(SWIR+NIR)/(SWIR−NIR)              (3)
Where: SWIR is the short-wave infrared band.
NIR is the near-infrared band.
NDBI values typically range from -1 to 1, with higher 

values indicating built-up areas and lower values indicating 
non-built-up areas.

LST Calculation
The thermal infrared bands were used for LST calculation. 
Landsat 8 and Landsat 7 imagery from 2023, 2013, and 
2003 respectively were used to retrieve LST. The thermal 
infrared bands (Band 10 for Landsat 8 and Band 6 for 
Landsat 7) were converted to top-of-atmosphere (TOA) 
during pre-processing. This involved converting the 
digital numbers to TOA spectral radiance using sensor 
radiometric calibration coefficients.

Conversion to Brightness Temperature
The TOA spectral radiance values were further processed 
to obtain brightness temperature (T) using Planck’s Law 
and the thermal band wavelength. The conversion from 
radiance to brightness temperature was represented by 
the equation:
K2/ln(K1/Lλ )                (4)                      
Where:

• T is the brightness temperature,
• K2 and K1 are band-specific thermal conversion 

constants,
• Lλ is the TOA spectral radiance.

Deriving Land Surface Emissivity (LSE)
The calculation of  land surface emissivity (LSE) is critical 
to LST retrieval and was performed using the following 
equations:
Pv=((NDVI-NDVImin)/(NDVImax-NDVImin))2      (5)
Subsequently, the land surface emissivity (e) was calculated 
using 
e=0.004×Pv+0.986               (6)
This step is crucial in refining the accuracy of  LST retrieval.

Conversion of  LST from Kelvin to Degree Celsius
After the emissivity-corrected land surface temperatures 
were estimated in degrees Kelvin, the values were 
converted to degrees Celsius for easy comprehension. 
This conversion was achieved using the relation 
LST(°C)=LST(K)-273.15               (7)

Reclassification and Weight Assignment 
Reclassification and weight assignment are crucial steps 
in data processing, aimed at standardizing scales and 
assigning weights to different indices. This involves 
converting the continuous values of  the indices into 
discrete classes and categories. These classes (NDVI, 
NDWI, NDBI, and LST) were assigned ranks ranging 
from 1 to 5 (low to high) based on their sensitivity to 
oil spills, with higher ranks indicating greater sensitivity. 
Weight assignment was done using Analytical Hierarchy 
Process (AHP) method, which compares the importance 
of  different criteria relative to each other. This comparison 
was made using Saaty’s 9-point scale (Figure 3)



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As part of  the AHP process, the raw comparison matrix 
was normalized to derive ratio scale priority vectors. 
Normalization was achieved by dividing each value in 
a criterion’s column by the sum of  the column. This 
converted the comparisons to a proportional ratio scale 
while maintaining their relativity. The normalization 
process was applied to each column of  the comparison 
matrix. It enabled priority vectors to be extracted that 
represented the relative weights or priorities of  each 
criterion proportional to the others.
The normalized comparison matrix retained the original 
criteria relationships while transforming values to a 0 to 
1 scale suitable for quantifying the relative importance or 
weight of  each factor in the suitability analysis.
Formula: 

• Nij is the normalized value for the comparison 
between criterion i and criterion j.

• n is the number of  criteria.

Consistency Table
The consistency of  judgments in the AHP comparison 
matrix was evaluated to ensure logical, high-quality criteria 
weights. Consistency was measured by first calculating a 
consistency index (CI) for the matrix. CI indicates the 
level of  consistency in the comparisons, with lower values 
being more consistent.
CI was compared to a random index (RI) based on the 
number of  criteria, to derive a consistency ratio (CR). 

Saaty established that CR should be less than 0.1 to 
indicate acceptable consistency.
A consistency table was generated showing the CI, RI 
and CR values. If  CR exceeded 0.1, the most inconsistent 
judgments were identified and the comparisons were 
revised to improve consistency.
This consistency evaluation ensured that irrational or 
random comparisons did not propagate into the final 
AHP weights. Logical, high-quality judgments translated 
to reliable, data-driven criteria priorities for the suitability 
analysis. The consistency table provided quantitative 
verification that the AHP process produced a consistent, 
robust weighting of  factors for the oil spill index map.
∑n

(j=1) (Nij * Weightj )                (8)
• Weighted Sum Value i: Weighted sum for criterion i.
• Nij: Normalized value for the pairwise comparison 

between criteria i and j.
• Weight j: Weight for criterion j.
• n: Number of  criteria.

principal eigenvalue (λmax) =

                 (9)

Figure 3: Saaty scale for various elements comparison
Source: Saaty (1980)

C.I = (λmax-n)/(n-1)              (10)
C.I: Consistency Index.
λmax: Principal eigenvalue of  the matrix.
n: Number of  criteria.
Formula:  C.R = (C.I)/(R.I)               (11)

Table 2: Random index matrix of  the same dimension (Saaty 1980)
"Number of  criteria" 2 3 4 5 J 7 8 9 10 11
R1 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.5

Integration of  Indices and Validation
This involves combining the normalized indices using a 
weighted summation approach to create a composite Oil 
Spill Index (OSI) as seen in (equation 12). Each index 

was multiplied by its corresponding weight, and the 
results were summed to obtain the OSI. This composite 
index represents the oil spill potential for different years, 
providing valuable insights into areas prone to oil spill.



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OSI= wNDVI × NDVI + wNDWI × NDWI + wLST × 
LST + w NDBI × NDBI               (12)                 
Where:
wNDVI, wNDWI, wLST, and wNDBI are the weights 
assigned to each index.
NDVI, NDWI, LST, and NDBI are the normalized 
values of  NDVI, NDWI, LST, and NDBI, respectively.
The OSI was validated against historical oil spill incident 
points obtained from Shell Plc. This validation process 
ensures spatial alignment between the oil spill index map 
and historical oil spill incident data, helping verify the 
accuracy of  the index in predicting and identifying areas 
prone to oil spills. 

RESULT AND DISCUSSION 
Results Analysis 
The results of  NDVI, NDWI, LST, and NDBI for the 
years 2003, 2013 and 2023 were presented and analysed. 
The study further presented the result of  the AHP 
weighed indices based on their sensitivity to oil spills and 
integrated them into a comprehensive oil spill index. In 

addition, the validation of  the oil spill index was done 
against historical oil spill incident data.  

Normalized Difference Vegetative Index (NDVI)
Figures 4, 5, and 6 present the Normalized Difference 
Vegetation Index (NDVI) maps for the years 2003, 2013, 
and 2023. While figure 6 and 8 are the charts revealing 
notable trends in vegetation health and density. In 2003, 
NDVI values ranged from -0.12 to 0.68, expanding to 
-0.19 to 0.76 in 2013, and narrowing to -0.17 to 0.63 in 
2023.
Low NDVI values suggest sparse or stressed vegetation, 
while high values indicate dense and healthy vegetation. 
Consistent dense vegetation was observed in the western 
part of  the study area across all three years, contrasting 
with a decline in vegetation cover in the eastern part, 
which happens to be the part where most oil spill incidents 
occurred in the study area, particularly between 2013 and 
2023. Analysis shows a general decrease in NDVI from 
2003 to 2023, emphasizing the environmental impacts of  
oil spills.

Figure 4: 2003 Normalize Difference Vegetative Index (NDVI)

Figure 5: 2013 Normalize Difference Vegetative Index (NDVI)



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Normalized Difference Water Index (NDWI)
The NDWI maps in Figure 9, 10, and 11 show changes 
in water content for 2003, 2013 and 2023. The NDWI 
ranges vary over the years: from -0.56 to 0.19 in 2003, 
-0.64 to 0.31 in 2013, and -0.52 to 0.29 in 2023. Lower 

values indicate less water, while higher values suggest 
more water or moist vegetation. Figure 12 and 13 shows 
the NDWI chart. Fluctuations in NDWI may result from 
water contamination due to oil spills.

Figure 6: 2023 Normalize Difference Vegetative Index (NDVI)

Figure 7: Changes in NDVI between Years 2003 and 2013 Figure 8: Changes in NDVI between Year2003 and 2023

Figure 9: 2003 Normalize Difference Water Index (NDWI)



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Figure 10: 2013 Normalize Difference Water Index (NDWI)

Figure 11: 2023 Normalize Difference Water Index (NDWI)

Figure 12: Changes in NDWI between Years 2003 and 
2013

Figure 13: Changes in NDWI between Years 2003 and 
2023

Normalized Difference Built-Up Index (NDBI)
Figure 14, 15, and 16 depict the Normalized Difference 
Built-up Index (NDBI) maps for the years 2003, 2013, 
and 2023, respectively. In 2003, NDBI values ranged 
from -0.48 to 0.39, followed by -0.48 to 0.29 in 2013, and 
-0.44 to 0.31 in 2023. High NDBI values indicate a high 

concentration of  built-up areas. Figure 17 and 18 shows 
the NDBI chart. Conversely, areas with lower NDBI 
values represent a lower concentration of  built-up areas. 
The NDBI value ranges provide valuable insights into 
changes in built-up areas and their sensitivity to oil spills 
over time.



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Figure 14: 2003 Normalize Difference Built-Up Index (NDBI)

Figure 15: 2013 Normalize Difference Built-Up Index (NDBI)

Figure 16: 2023 Normalize Difference Built-Up Index (NDBI)



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Figure 17: Changes in NDBI between Years 2003 and 
2013

Figure 18: Changes in NDBI between Years 2003 and 
2023

Land Surface Temperature (LST)
Figure 19, 20, and 21 illustrate the Land Surface 
Temperature (LST) variations across the study area for 
the years 2003, 2013, and 2023. In 2003, LST values 
ranged from 17.63 to 31.78°C, expanding to 20.07 to 
34.21°C in 2013, and then narrowing to 18.52 to 30.4°C 
in 2023. Built-up areas consistently exhibit high LST 
values throughout the study period, indicating urban heat 

island effects. Figure 22 and 23 shows the LST charts. 
Conversely, areas with dense vegetation and water bodies 
show lower LST values due to shading and evaporative 
cooling effects. Analysis of  oil spill points reveals an 
increase in LST values for the affected areas in both 
2013 and 2023, suggesting potential impacts of  oil 
contamination on surface temperatures.

Figure 19: 2003 Land Surface Temperature (LST)

Figure 20: 2013 Land Surface Temperature (LST)



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Figure 21: 2023 Land Surface Temperature (LST)

Figure 22: Changes in LST for Years 2003 and 2013 Figure 23: Changes in LST for Years 2003 and 2023

Analytical Hierarchy Process (AHP)
The AHP results show NDVI as most influential with 
a weight of  0.52, followed by NDWI and LST, each at 
0.20, and NDBI at 0.08. NDVI’s prominence underscores 
vegetation’s role in oil spill impacts. NDWI and LST carry 
equal weight, acknowledging their joint importance. NDBI, 

representing built-up areas, receives the lowest weight, 
reflecting its lesser impact compared to vegetation, water, 
and temperature. However, NDBI’s inclusion is still crucial 
for addressing urban vulnerabilities to oil spills. The AHP 
matrix table, the normalized matrix table, and consistency 
table is depicted in Table III, IV, and V respectively.

Table 3: AHP Matrix Table
PAIRWISE NDVI NDWI NDBI LST
NDVI 1 3 5 3 3.00
NDWI 1/3 1 3 1 1.33
NDBI 1/5 1/3 1 1/3 0.47
LST 1/3 1 3 1 1.33

1.87 5.33 12.00 5.33
Source : Author

Table 4: Normalized Matrix
NORMALIZED NDVI NDWI NDBI LST WEIGHT
NDVI 0.54 0.56 0.42 0.56 0.52
NDWI 0.18 0.19 0.25 0.19 0.20
NDBI 0.11 0.06 0.08 0.06 0.08
LST 0.18 0.19 0.25 0.19 0.20
SUM 1.00

Source : Author



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Table 5: Consistency Table
CONSISTENCY NDVI NDWI NDBI LST WEIGHTED SUM
NDVI 0.52 0.60 0.39 0.60 2.12 4.08
NDWI 0.17 0.20 0.24 0.20 0.81 4.04
NDBI 0.10 0.07 0.08 0.07 0.32 4.02
LST 0.17 0.20 0.24 0.20 0.81 4.04

Source : Author

Reclassification Result Based on the Assigned 
Weights
Since LST has a different measurement range and units 

from NDVI, NDWI and NDBI reclassification was done to 
harmonize all the factors by converting them into common 
classes or categories. The reclassification maps provide a 

Figure 24: 2003 Reclassified Maps of  NDVI, NDWI, NDBI and LST

Figure 25: 2013 Reclassified Maps of  NDVI, NDWI, NDBI and LST



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Figure 26: 2023 Reclassified Maps of  NDVI, NDWI, NDBI and LST

comprehensive overview of  the spatial distribution of  
these key indices within the study area. Each index was 
carefully categorized into five distinct levels, ranging 
from 1 to 5, based on their respective value ranges which 
represent very low to very high. The reclassification results 
are presented in maps as shown in Figure 24 to 26.
 
Oil Spill Index Map 
The observed trends in the oil spill index (Figure 27 to 
31) between the years 2003 to 2013 and 2003 to 2023 

reveal significant changes in oil spill susceptibility over 
time. The chart depicting the oil spill index between 2003 
and 2013 shows a mix of  increases and a little decrease in 
oil spill susceptibility across different regions. The chart 
illustrating the oil spill index trend from 2003 to 2023 
indicates a substantial increase in the oil spill index over 
the two-decade period. This drastic rise in the oil spill 
index suggests a worsening of  environmental conditions 
or an escalation in factors contributing to oil spill risks 
across the study area.

Figure 27: 2003 Oil Spill Index Map



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Am. J. Geo Spat. Technol. 3(1) 51-68, 2024

Figure 28: 2013 Oil Spill Index Map 

Figure 29: 2023 Oil Spill Index Map 

Figure 30: Chart Showing Changes in OSI between Year 2003 and 2013



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Validation of  Oil Spill Index 
The finding of  higher and increased oil spill index values 
corresponding to the locations of  historical oil spill 
incidents provides valuable validation of  the effectiveness 
of  the analysis of  the oil spill index performed through 
the integration of  AHP-weighted NDVI, NDWI, NDBI, 
and LST. The tables VI to VII show the OSI of  the 
points before the oil spill incident (2003) and after the 
spill incident (2013 and 2023) 

Validation of  Oil Spill Index in 2003 and 2013
In 2003, the OSI values ranged from 1.04 to 3.48 
across different points, indicating varying degrees of  
oil contamination before the spill incident. After the 
incident in 2013, there was a noticeable increase in OSI, 
with values ranging from 3.08 to 4.72 at the spill points. 
This suggests a worsening of  the oil spill’s impact on the 
affected areas. 

Table 6: 2003 and 2013 validation Result
Fid Shape Date Community Latitude Longitude OSI 2003 OSI 2013
0 Point 17th-25th Aug. 2012 Eneka 4.917683 7.028221 2.76 3.28
1 Point  Eneka 4.91769 7.028196 2.76 3.28
2 Point  Eneka 4.917718 7.02823 2.76 3.28
3 Point  Eneka 4.917679 7.028259 2.76 3.28
4 Point  Eneka 4.917641 7.028215 2.76 3.28
5 Point 23rd-26th July 2012 Eneka 4.896433 7.053776 2.28 4.72
6 Point  Eneka 4.896405 7.053804 3.27 4.72
7 Point  Eneka 4.896489 7.053832 3.28 3.68
8 Point  Eneka 4.896489 7.053777 2.54 3.68
9 Point  Eneka 4.89635 7.053804 3.48 4.66
10 Point  Eneka 4.89635 7.05386 3.28 4.72
11 Point  Eneka 4.896295 7.053887 3.28 4.72
12 Point  Eneka 4.896294 7.053832 3.28 4.72
13 Point  Eneka 4.896295 7.053915 3.28 4.72
14 Point  Eneka 4.896267 7.053389 3.28 3.68
15 Point  Eneka 4.896239 7.053915 3.28 4.72
16 Point  Eneka 4.896267 7.053943 3.28 4.72
17 Point  Eneka 4.896239 7.053944 3.28 4.72
18 Point  Eneka 4.896017 7.054221 3.08 3.16
19 Point  Eneka 4.895739 7.05461 3.28 4.04
20 Point 27th -29th July 2012 Atali 4.88704 7.06475 1.04 3.64
21 Point  Atali 4.88746 7.064469 1.04 3.64
22 Point  Atali 4.887405 7.064527 1.04 3.64
23 Point  Atali 4.887099 7.064721 1.04 3.64
24 Point  Atali 4.887183 7.064693 1.04 3.64

Figure 31: Chart Showing Changes in OSI of  Year 2003 and 2023



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Am. J. Geo Spat. Technol. 3(1) 51-68, 2024

Validation of  Oil Spill Index in 2003 and 2023
The 2003 and 2023 OSI validation table provides the 
Oil Spill Index (OSI) values of  various points before 
and after an oil spill incident, specifically comparing the 
OSI values from 2003 to those in 2023. In 2003, the OSI 
values varied across different points, ranging from 1.64 

to 3.72, indicating varying degrees of  oil contamination 
before the spill incident.
After the incident in 2023, there is a noticeable change 
in OSI values, with values ranging from 3.2 to 4.84. This 
suggests a potential increase in the impact of  the oil spill 
on the affected areas over the years.

Table 7: 2003 and 2023 validation Result
Fid Shape Date Community Latitude Longitude OSI 2003 OSI 2023
1 Point 19th - 20th Dec. 2022 Rumukwurushi 4.86697 7.08829 1.72 4.44
2 Point  Rumukwurushi 4.86692 7.08836 1.72 4.44
3 Point  Rumukwurushi 4.86688 7.08829 1.72 4.44
4 Point  Rumukwurushi 4.86697 7.08812 1.72 3.4
5 Point  Rumukwurushi 4.86705 7.08805 1.72 3.4
6 Point  Rumukwurushi 4.86713 7.0881 1.64 3.4
7 Point  Rumukwurushi 4.86658 7.08869 1.72 4.28
8 Point  Rumukwurushi 4.86664 7.08865 1.72 4.28
9 Point 27th -30th Sept. 2022 Eneka 4.89337 7.05743 3.08 3.6
10 Point Eneka 4.89336 7.0574 3.08 3.6
11 Point Eneka 4.89347 7.05734 3.08 3.6
12 Point Eneka 4.89343 7.05743 3.08 3.6
13 Point Eneka 4.89339 7.05746 3.08 3.6
14 Point 3rd - 10th Jan. 2023 Rumukwurushi 4.86027 7.06392 3.76 3.56
15 Point  Rumukwurushi 4.8603 7.06391 3.76 3.56
16 Point  Rumukwurushi 4.86018 7.06394 3.76 3.56
17 Point 3rd-4th Dec 2021 Eneka 4.91514 7.01834 3.6 4.84
18 Point  Eneka 4.91501 7.01815 2.72 3.2
19 Point  Eneka 4.91509 7.01813 3 4.84
20 Point  Eneka 4.91511 7.01842 3.6 4.84
21 Point 26th-27th Feb 2020 Atali 4.88889 7.06267 1.72 4.64
22 Point  Atali 4.88891 7.06235 1.72 3.6
23 Point  Atali 4.88931 7.06243 1.64 4.64
24 Point  Eneka 4.89971 7.01572 3.92 3.96
25 Point  Eneka 4.8997 7.01571 3.92 3.96
26 Point  Eneka 4.89971 7.01564 3.92 3.96
27 Point  Eneka 4.89968 7.01564 3.12 3.72
28 Point  Eneka 4.89966 7.01568 3.12 3.72
29 Point  Eneka 4.89965 7.01568 3.12 3.72
30 Point  Eneka 4.89968 7.01562 3.12 3.72
31 Point  Rumuewhara 4.87912 7.04243 2.04 4.44
32 Point  Rumuewhara 4.87908 7.04239 2.04 4.44
33 Point 2nd May 2020 Elelenwo 4.848219 7.076442 3.12 4.64
34 Point  Elelenwo 4.848222 7.076459 3.12 4.64
35 Point  Elelenwo 4.84844 7.076592 3.12 4.64
36 Point  Elelenwo 4.848459 7.076565 3.12 4.64
37 Point  Elelenwo 4.848456 7.076481 3.12 4.64
38 Point  Elelenwo 4.848299 7.076389 3.12 4.64
39 Point  Elelenwo 4.848203 7.076414 3.92 3.92
40 Point  Rumuowha 4.899843 7.015256 3.12 3.56



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Am. J. Geo Spat. Technol. 3(1) 51-68, 2024

41 Point  Rumuowha 4.899748 7.015351 3.12 3.56
42 Point  Rumuowha 4.89955 7.01582 3.12 3.92
43 Point 15th -20th June 2020 Rumuokwurusi 4.86997 7.08475 1.72 3.28
44 Point  Rumuokwurusi 4.86995 7.08482 1.72 3.28

CONCLUSION
The study comprehensively analyzed the impact of  oil 
spills on the study area’s environment by examining the 
temporal dynamics of  key indices (NDVI, NDWI, LST, 
and NDBI) and integrating them to create an Oil Spill 
Index (OSI) map. To determine the relative significance 
of  each criterion, the Analytical Hierarchy Process (AHP) 
method was employed, assigning weights to the indices. 
NDVI emerged as the most influential criterion, followed 
by NDWI and LST, while NDBI had the lowest weight. 
This weighting system highlighted NDVI’s predominant 
role in expressing the extent and severity of  oil spills in 
the area. Upon reclassification and integration of  the 
indices, the generated OSI map revealed a concerning 
trend of  increasing oil spill susceptibility from 2003 to 
2023. The validation of  the OSI maps against historical 
oil spill records further affirmed their accuracy and 
predictive capability in identifying areas prone to oil 
spills. Through meticulous examination spanning from 
2003 to 2023, significant changes in index ranges were 
observed, indicating dynamic environmental shifts. For 
instance, OSI values for 2003 and 2013, indicated a 
range of  1.04 to 3.48 for 2003, with a noticeable increase 
observed in 2013, expanding the range to 3.08 to 4.72. 
Similarly, a comparison of  OSI values between 2003 and 
2023 indicated OSI values, ranging from 1.64 to 3.92 in 
2003, then in 2023 after the oil spill incidents, there were 
notable changes in the OSI values, with the range shifting 
to 3.2 to 4.84. This escalation in the OSI values suggests 
worsening environmental conditions and heightened oil 
spill risks across the study area. 
Finally, the study emphasizes the critical importance of  
ongoing monitoring and mitigation efforts in addressing 
the evolving environmental challenges posed by oil spills. 
By providing comprehensive insights into the temporal 
dynamics and spatial distribution of  oil spill impacts, the 
research serves as a valuable resource for policymakers 
and stakeholders involved in environmental management 
and disaster response initiatives.

RECOMMENDATION
Based on the findings and conclusions drawn from 
this research, this study is recommended as valuable 
information for monitoring and assessing the 
environmental impact of  oil spills as it shows the 
likelihood of  the study area being damaged by oil spills 
as the year progresses. In addition, while the current 
research focuses on the integration of  environmental 
indices, future studies should consider incorporating 
socio-economic factors to provide a more holistic 
understanding of  oil spill risks. This may include factors 
such as population density, infrastructure vulnerability, 

economic activities, and community resilience, which play 
a significant role in shaping the impacts and responses to 
oil spills.

Acknowledgement 
I am grateful to the Petroleum Technology Development 
Fund (PTDF) for generously funding my academic 
program, which includes this research.

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