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© 2023 by the authors; licensee Asian Online Journal Publishing Group 
 

Asian Review of Environmental and Earth Sciences 
Vol. 10, No. 1, 1-13, 2023 

ISSN(E) 2313-8173 / ISSN(P) 2518-0134 
DOI: 10.20448/arees.v10i1.4407 

© 2023 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Detection of potentially gas flaring related pollution on vegetation cover and its 
health using remotely sensed data in the Niger delta, Nigeria 

 
Barnabas O. Morakinyo1,2,3,4   

Samantha Lavender2,3   

Victor Abbott2   

  
( Corresponding Author) 

 
1Department of Surveying & Geoinformatics, Faculty of Environmental Sciences, BAZE University, Abuja, 
Nigeria. 
2School of Marine Science & Engineering, Faculty of Science & Technology, University of Plymouth, Plymouth, 
UK. 
3Pixalytics Ltd, Plymouth, UK. 
4ARGANS Ltd, Plymouth, UK. 
1,2,3,4Email: barnabas.ojo@bazeuniversity.edu.ng  
2,3Email: Smnthlavender@gmail.com  
2Email: Jamesvictor2021A@gmail.com  

 
Abstract 

Detection of potentially gas flaring-related pollution on vegetation cover using remotely sensed 
data at 11 flaring sites in Rivers State, Nigeria is the emphasis of this research. 21 Landsat 7 

Enhanced Thematic Mapper Plus (ETM+), and 4 Landsat 8 Operational Land Imager and 

Thermal Infrared Sensor (OLI-TIRS) data dated from 21/04/2000 to 05/02/2022 with < 3 % 
cloud cover were used. Normalized Differential Vegetation Index (NDVI) was retrieved from 
corrected Landsat 7 bands (1-4), and Landsat 8 bands (2-5). Corrected thermal band was used for 

the computation of Land Surface Temperature (LST). Change in NDVI (𝛿NDVI450-60)m and LST 

(𝛿LST60-450m) were computed. NDVI values at 60 m from the stack show that as the year increases, 
NDVI values around the stack reduces to almost zero. Linear regression analysis was considered 

for (𝛿NDVI450-60)mN against (𝛿NDVI450-60)mE, (𝛿NDVI450-60)mN against (𝛿NDVI450-60)mS, and 

(𝛿NDVI450-60)mN against (𝛿NDVI450-60)mW. Only (𝛿NDVI450-60)mN against (𝛿NDVI450-60)mW give 

statistically significant results at 99 % confidence level (p-value = 0.0016). (𝛿NDVI450-

60)mN,E,S,W against (𝛿LST60-450)mN,E,S,W were considered and results show positive correlation 
but statistically insignificant. Based on the results of this research, it can be concluded that 
flaring-related pollution can be detected on vegetation cover using Landsat 7 and Landsat 8 data 
in the Niger Delta. 

 
Keywords: Detection, Landsat 7, Landsat 8, Niger delta, Pollution, Remotely sensed data, Vegetation cover. 

 
Citation | Morakinyo, B. O., Lavender, S., & Abbott, V. (2023). 
Detection of potentially gas flaring related pollution on vegetation 
cover and its health using remotely sensed data in the Niger delta, 
Nigeria. Asian Review of Environmental and Earth Sciences, 10(1), 1–
13. 10.20448/arees.v10i1.4407  
History:  
Received: 7 November 2022 
Revised: 21 December 2022 
Accepted: 2 January 2023 
Published: 13 January 2023 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Authors’ Contributions: All authors contributed equally to the conception 
and design of the study. 
Competing Interests: The authors declare that they have no conflict of 
interest. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. 
Ethical: This study followed all ethical practices during writing. 

 

 

Contents 
1. Introduction ......................................................................................................................................................................................... 2 
2. Materials and Methods ...................................................................................................................................................................... 2 
3. Results and Discussion ...................................................................................................................................................................... 5 
4. Conclusion ......................................................................................................................................................................................... 12 
References .............................................................................................................................................................................................. 12 
 

 
 
 

mailto:barnabas.ojo@bazeuniversity.edu.ng
mailto:Smnthlavender@gmail.com
mailto:Jamesvictor2021A@gmail.com
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/arees.v10i1.4407
https://orcid.org/0000-0002-5066-8071
https://orcid.org/0000-0002-5181-9425
https://orcid.org/0000-0003-3972-3102


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Contribution of this paper to the literature 
This study contributes scientific knowledge on detection of potentially gas flaring-related pollution on 
the vegetation cover and its health using Earth Observation (EO) Satellite data. 

 
1. Introduction 

Extraction and processing of crude oil and natural gas in the Niger Delta have unfavourable and harmful effects 
on the environment [1-4]. Oil spillage and gas flaring alter the attributes of the receiving environment, leading to 
its pollution and degradation [1-4]. Among others, gas flaring is responsible for the contamination of vegetation in 
the Niger Delta [5] with destructive impacts on vegetation and agricultural pursuits [6]. For example, almost no 
vegetation grows in the area directly surrounded by the flare as a result of the tremendous Carbon emissions and 
heat releases to the environment; and the acidic nature of the soil potential of Hydrogen (pH) [2].  

Researchers have worked on gas flaring with different aims and methods internationally, for example [7] used 
Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) data to study flared gas volume and emission 
of black carbon globally. The impact of Hydrogen Sulphide (H2S) content and excess air on pollutant discharges at 
the 2 flare sites in Iran using computational methodology with Matrix Laboratory (MATLAB) codes was examined 
by Zoeir, et al. [8]. They stated that the impact of Sulphur III Oxide (SO3) is several times more destructive than 
that of Sulphur II Oxide (SO2) in acidic rains; and that Sulphur III Oxide (SO3) pass through a slow balance with 
water vapour available in the atmosphere and forms Sulphuric acid (H2SO4). H2SO4 is a highly corrosive compound 
within acidic rains. Acidic rain affects soil pH which in turn causes destruction of vegetation and plants. Others 
studies on global gas flares includes atmospheric pollution [9, 10] and estimation of temperatures of vegetation 
fires [11] etc.  

In Nigeria, some studies have been carried out regarding discharge of gas flare activities; and its effects on the 
environment and people. In the Niger Delta, Ndinwa, et al. [6] used questionnaire to study the impacts of flared 
gas on the environment and health of the people of Kwale communities, Ndokwa West Local Government Area. 
Chukwu, et al. [12] and Uyigue and Enujekwu [13] adopted conventional methods by taking measurements on 
site using instruments, and collection of samples to study gas flaring impacts at 2 communities of Mkpanak and 
Iko, Ibeno and Eastern Obolo Local Government Areas respectively, Akwa Ibom State. The influence of flared gas 
on vegetation and water resources for the entire Niger Delta region was reviewed by Seiyaboh and Izah [14]. 
Lawanson, et al. [15]worked on the consequences of gas flaring on the cassava plantations in the Niger Delta. 
Such effects recorded on vegetation and agricultural activities includes deforestation [16] stunted growth of crops 
[6, 12] reduction of soil quality parameters [17] etc. The authors concluded that the comparison between the 
flaring and the control sites used for their researches show that the soil nutrients of the flaring sites were the 
lower. In addition, Chukwu, et al. [12] recorded the influence of flared gas on plants and vegetation as the loss of 
leaves at a distance less than 1 km, withering (1-2 km), stunted growth (3-4 km), and wrinkling leaves (5-6 km) 
respectively from the flare stack. However, none of the existing studies on flared gas and its consequences/impacts 
on vegetation in the Niger Delta has used satellite data.  

Furthermore, many scholars investigated the relationship between the Normalized Differential Vegetation 
Index (NDVI) and Land Surface Temperature (LST) using different satellite data [18-26]. For example, Allam, et 
al. [18] worked on the effect of land surface changes related to urbanization using Landsat 7 Enhanced Thematic 

Mapper Plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) data in 
Sebkha, Oran, Algeria. They focused on the multi-temporal relationship between surface temperature, land use and 
NDVI in the areas affected by salinity which was characterized by a specific geographical space and a fragile 
natural environment. Bindajam, et al. [20] employed Advanced Spaceborne Thermal Emission and Reflection 
Radiometer (ASTER) data for the retrieval of NDVI and LST in the cities of Abha-Khamis-Mushayet, South-
Western province, Saudi Arabia. Also, Shah, et al. [19] used Landsat 8 OLI-TIR data to examine NDVI and LST 
over built-up and vegetative areas from 2013-2020 in Mehar taluka, Dadu, Sindh province, Pakistan. Furthermore, 
Guha, et al. [21] used premonsoon Landsat 5 Thematic Mapper (TM), Landsat 7, and Landsat 8 data for 
monitoring inter-relationship of LST with NDVI in Raipur City, India. Climate, vegetation types, land use, 
urbanization, etc. are factors influencing the correlation between the NDVI and the LST [27, 28]. Generally, the 
results from the above researches stated that NDVI and LST showed opposite trends. 

This paper addressed 2 research questions: (1) Can satellite data be used for detection of gas flaring-related 
pollution on vegetation cover and its health in the Niger Delta? (2) What is the spatial and temporal variability in 
satellite detectable flare related pollution on vegetation cover and its health in the Niger Delta? Due to the above 
stated 2 research questions, the aim of this study is the generation of a Nigeria-focused technique for detection of 
gas flaring-related pollution on vegetation cover and its health using Landsat 7 and Landsat 8 data. Objectives for 
these 2 research questions are (1) Identification of significant gas flaring sites in the Niger Delta; (2) Determination 

of NDVI and LST for the selected sites; (3) Parameterization of flare-related change in NDVI as (𝛿NDVI450-60)m; 
(4) Detection of gas flaring-related pollution on vegetation cover and its health using NDVI data; (5) Evaluation of 
the relationship between the spatial gradient in NDVI and that of the LST around the flare sites in the Niger Delta. 
 

2. Materials and Methods 
2.1. Study Area  

The study was conducted in Rivers State of the Niger Delta Region, Nigeria Figure 1. The selected 11 gas 
flaring sites used for the study are Eleme Petroleum Refinery Companies I and II, Bonny Liquefied Natural Gas 
(LNG) plant, Onne, Rukpokwu, Umurolu, Obigbo, Alua, Umudioga and Chokocho Flow Stations; and Sara oil well 

Figure 1C. All sites are located within Latitude 4° 40′ and 5° 01′ N and Longitude 6° 50′ and 7° 01′ E [1, 2] Figure 

1C. In order to have adequate NDVI and LST data for appropriate analysis, (12 × 12) km area was studied around 
the flare stacks with Landsat 7 and Landsat 8 data.  
 
 



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2.2. Data used and Data Processing 
2 acquired data used for this research are 21 Landsat 7 ETM+ scene dated from 2000 to 2013; and 4 Landsat 8 

OLI-TIRS scene dated from 2018 to 2022 with <3 % cloud cover from the months of January to April, and 
November to December. They were acquired from the United States Geological Survey (USGS) website 
(https://earthexplorer.usgs.gov/). The selected data fall within the dry season in Nigeria. Hence, they are less 

cloudy. Landsat 7 ETM+ images consist of 8 spectral bands with a spatial resolution of 30 m for bands 1-5, and 7. 
The resolution for band 8 (Panchromatic) is 15 m. Landsat 8 OLI-TIRS images consist of 11 spectral bands with 
bands 1-7, and 9 having a spatial resolution of 30 m; and band 8 (Panchromatic) is 15 m. The spatial resolution for 
Landsat 7 band 6 (Thermal infrared), and Landsat 8 bands 10 and 11 (Thermal infrared) are 60 m and 100 m 
respectively but all are re-sampled to 30 m pixels [29]. MATLAB programming codes were used for data 
processing.  

 
 

 
Figure 1. A) Map of Africa showing Nigeria [30]; B) Map of Nigeria [30]; C) Map of Rivers State with the 11 flaring sites examined [30]. 

 
2.3. Landsat Reflectance Retrieval 

The computation of  the spectral reflectance for Landsat 7 ETM+multispectral bands (1-4) and Landsat 8 OLI-
TIRS multispectral bands (2-5) was carried out using Equation 1, Figure 2 which assumes Lambertian surface 
reflectance [31]. 

𝜌p= (𝜋 ×Lλ× d²) ÷ (ESUNλ× Cos𝜃𝑠)                                                     (1) 

Where: 𝜌p = Spectral reflectance. 

L = Surface leaving radiance/ unit solid angle. 

𝝅L = Upwelling radiance. 

d = Earth-Sun distance/astronomical units. 

ESUNλ= Mean solar exoatmospheric irradiances. 

𝜃𝑠= Solar zenith incident angle/°.  
 
2.4. Landsat Normalized Differential Vegetation Index (NDVI)Retrieval 

NDVI is the most regularly used vegetation index is Huang, et al. [32]; Guha, et al. [33]. NDVI is an indicator 
of vegetation that is generally used in the study of vegetation and surface temperature relationship [34]. NDVI 

values are from −1 to +1 [31]. When NDVI value is −, it suggests surfaces like cloud, ocean, ice, water etc. NDVI 
values near zero indicate bare soil, sparse vegetation range from (0-0.01) to (0.1-0.5), and dense, green and healthy 
vegetation is 0.6 above. 

The algorithm for NDVI is:  

NDVI = (NIR − R) / (NIR + R) [18, 21]                                  (2) 
Where:  

NIR = Near Infra-Red reflectance, band 4 for Landsat 7, and band 5 for Landsat 8. 

R = Red reflectance, band 3 for Landsat 7, and band 4 for Landsat 8. 

https://earthexplorer.usgs.gov/


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For this research, atmospheric effects on Landsat data was corrected Figure 2 i.e. the cloud-masked reflectance 
was used for the computation of NDVI with Equation 2. Hence, NDVIs data from 2000 to 2022 was employed for 
detecting and assessing the potentially gas flaring-related pollution on vegetation cover and health.  

Quantitative analysis of a change in vegetation cover and health was evaluated with the computed NDVIs at a 
given time using 2 analyses namely plot of NDVI versus distance from the flare in the North (N), East (E), South 

(S) and West (W) directions; and derivation of change in NDVI as (𝜹NDVI450-60)min the N, E, S and W directions.  
 

2.5. Landsat Land Surface Temperature (LST) Retrieval   
Retrieval of  LST from Landsat data was carried out through radiative transfer equation in 3 stages [21, 35]. 

The 1st stage is the conversion of  Digital Numbers (DN) of  thermal bands to top of  atmosphere (TOA) radiance 
using Equation 3 for Landsat 7 data; and Equation 4 for Landsat 8 images.  

𝐿𝜆 =  ((𝐿𝑀𝐴𝑋𝜆 −  𝐿𝑀𝐼𝑁𝜆)/(𝑄𝐶𝐴𝐿𝑀𝐴𝑋 −  𝑄𝐶𝐴𝐿𝑀𝐼𝑁))  ∗  (𝑄𝐶𝐴𝐿 − 𝑄𝐶𝐴𝐿𝑀𝐼𝑁) + 𝐿𝑀𝐼𝑁𝜆   (3) 
Where: 

Lλ= Spectral radiance (Wm⁻²sr⁻¹µm⁻¹). 

QCAL = Quantized calibrated pixel value in DN. 

LMINλ= Spectral radiance scaled to QCALMIN (Wm⁻²sr⁻¹µm⁻¹). 

LMAXλ= Spectral radiance scaled to QCALMAX (Wm⁻²sr⁻¹µm⁻¹). 

QCALMIN = Minimum quantized calibrated pixel value in DN  = 1. 

QCALMAX = Maximum quantized calibrated pixel value in DN = 255.     

𝐿𝜆  =  𝑀𝐿  ∗ 𝑄𝐶𝐴𝐿  + 𝐴𝐿                     (4) 

Where ML= Band multiplicative rescaling factor; AL= Band additive rescaling factor. ML and AL are available 

in the metadata file of  Landsat 8 data. Lλ and QCAL in (4) are the same as those in (3). 
Conversion of TOA radiance of thermal band to surface-leaving radiance using atmospheric correction tool 

MODerate-Resolution Atmospheric Radiance and Transmittance (MODTRAN 4.1) for the removal of the effects 
of the atmosphere is the 2nd stage [36]. The surface-leaving radiance LT is calculated using Equation 5 [37]: 

𝐿𝑇 =  (𝐿𝜆  − 𝐿𝜇  −  𝜏 (1 −  휀)𝐿𝑑  )/𝜏휀  (5) 

 

Where Lμ, Ld and τ= Upwelling radiance, downwelling radiance, and atmospheric transmission respectively. 

They are atmospheric correction parameters for Landsat thermal band. ε = Emissivity of land covers (LC) type. 

For this study, ε was calculated based on 4 LC (Vegetation, built area, soil and water) types of each site.  
For the 3rd stage, the surface-leaving radiance is converted to LST using Landsat estimate of the Planck curve 

Equation 6 [38] Figure 2: 

LST =
K2

ln((K1/Lλ) + 1)
                            (6) 

Where,  
LST = Land Surface Temperature in Kelvin (K); K1 and K2 = Thermal band calibration constants Table 1. LSTs 

for vegetation, built area, soil and water were retrieved but only LSTs for vegetation was adopted for the analysis. 
 

Table 1. Calibration constants K1 and K2 for Landsat 7 and Landsat 8 data. 

Calibration constants Landsat 7 ETM+ Landsat 8 band 10 Landsat 8 band 11 

K1 (Wm-2sr-1μm-1) 666.09 774.89 480.89 

K2 (K) 1282.71 1321.08 1201.14 
Source: USGS [29]. 

 

 
Figure 2. Flow chart for the methodology adopted. 

 
2.6. NDVI Versus Distance 

At 60 m and 450 m distance from the flare stack, values of NDVIs were recorded Figure 3 in the N, E, S and W 
directions. Also, the range of NDVIs values was computed.   



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2.7. Change in NDVI (𝛿NDVI450-60)m and LST (𝛿LST60-450)m 

Change in NDVI (𝛿NDVI) is the difference in the values of NDVI recorded at 450 m distance from the flare 
stack and that of the values of NDVIs obtained at 60 m from the stack.   

i.e. NDVI450– NDVI60m= (𝛿NDVI450-60)m. 

Figure 3 is the schematic diagram for (𝛿NDVI450-60)m. (𝛿NDVI450-60)m was computed for N, E, S and W 

directions, hence, (𝛿NDVI450-60)mN, (𝛿NDVI450-60)mE, (𝛿NDVI450-60)mS and (𝛿NDVI450-60)mW were obtained. This is 
to help in assessing the vegetation cover and its health in the 4 directions.  
 

 
Figure 3.  Schematic diagram for change in NDVI (𝜹NDVI450-60)m. 

 

For change in LST (𝜹LST60-450)m, it is the difference in the values of LSTs at the 60 m from the flare stack and 
that of values of LST at 450 m from the flare stack [1, 5].  

i.e. LST60− LST450m= (𝛿LST60-450)m. This means that the distance adopted for the computation of (𝛿NDVI450-

60)m is the reverse of the distance chosen for the computation of (𝛿LST60-450)m.  
 

2.8. Linear Regression Analysis 
NDVI-LST relationship has been investigated by researchers using linear regression analysis. Such researchers 

include [18, 19, 39]. In addition, non-linear regression was employed by Cleland, et al. [40]; Sparks and 
Tryjanowski [41]; Dose and Menzel [42] for studying the effects of global change on plant phenology. This study 
adopted linear regression analysis because non-linear regression gives no better results when tested. Hence, 

(𝛿NDVI450-60)m and (𝛿LST60-450)m relationship in the N, E, S and W were examined when no data is zero in order to 

eliminate the effects of uncertainties. When (𝛿NDVI450-60)m= 0, it means that the values of NDVI at both 60 m and 

450 m from the stack are equal, suggesting an uncertain condition. Similarly, when (𝛿LST60-450)m= 0, it means that 
the values of LST at 60 m and 450 m from the stack are equal; suggesting the availability of other heat sources at 
450 m or no burning on the stack at the time of satellite overpass. Table 15 presents r-values and p-values results 

obtained from the analysis. N =total number of each (𝛿NDVI450-60)m’s and (𝛿LST60-450)m’s used, r-values show the 
type of correlation that existed between them, and p-values show whether the results obtained are statistically 

significant or insignificant with apriori 𝛼 = 0.01.   
 

3. Results and Discussion   
3.1. NDVI Versus Distance  

Tables 2-13 present the values of NDVI recorded at 60 m and 450 m from the stack for the 11 sites studied for 
the year 2000, 2011 and 2022 respectively.  
 

Table 2. NDVI retrieved at 60 m and 450 m from the stack (North direction) (2000). 
Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.35 0.74 0.39 
Eleme refinery II 0.18 0.52 0.34 
Onne 0.22 0.75 0.53 
Umurolu 0.22 0.48 0.26 
Bonny LNG 0.20 0.50 0.30 
Alua 0.18 0.76 0.58 
Rukpokwu 0.20 0.68 0.48 
Obigbo 0.19 0.53 0.34 
Chokocho 0.18 0.56 0.38 
Umudioga 0.21 0.71 0.50 
Sara 0.18 0.62 0.44 

 

 

 

 



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Table 3. NDVI retrieved at 60 m and 450 m from the stack (East direction) (2000). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.41 0.78 0.37 
Eleme refinery II 0.48 0.63 0.15 
Onne 0.27 0.77 0.50 
Umurolu 0.36 0.54 0.18 
Bonny LNG 0.41 0.58 0.17 
Alua 0.36 0.74 0.38 
Rukpokwu 0.49 0.75 0.26 
Obigbo 0.35 0.64 0.29 
Chokocho 0.32 0.59 0.27 
Umudioga 0.40 0.75 0.35 
Sara 0.34 0.70 0.36 

 
Table 4. NDVI retrieved at 60 m and 450 m from the stack (South direction) (2000). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.47 0.82 0.35 
Eleme refinery II 0.30 0.60 0.30 
Onne 0.34 0.71 0.37 
Umurolu 0.35 0.56 0.21 
Bonny LNG 0.32 0.58 0.26 
Alua 0.31 0.79 0.48 
Rukpokwu 0.36 0.74 0.38 
Obigbo 0.35 0.59 0.24 
Chokocho 0.25 0.64 0.39 
Umudioga 0.26 0.77 0.51 
Sara 0.21 0.68 0.47 

 
Table 5. NDVI retrieved at 60 m and 450 m from the stack (West direction) (2000). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.38 0.83 0.45 
Eleme refinery II 0.21 0.62 0.41 
Onne 0.25 0.84 0.59 
Umurolu 0.27 0.60 0.33 
Bonny LNG 0.38 0.58 0.20 
Alua 0.36 0.84 0.48 
Rukpokwu 0.37 0.74 0.37 
Obigbo 0.30 0.61 0.31 
Chokocho 0.25 0.69 0.44 
Umudioga 0.24 0.77 0.53 
Sara 0.44 0.73 0.29 

 
Table 6. NDVI retrieved at 60 m and 450 m from the stack (North direction) (2011). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.25 0.68 0.43 
Eleme refinery II 0.10 0.59 0.49 
Onne 0.17 0.71 0.54 
Umurolu 0.18 0.50 0.32 
Bonny LNG 0.16 0.48 0.32 
Alua 0.12 0.79 0.67 
Rukpokwu 0.13 0.62 0.49 
Obigbo 0.14 0.59 0.45 
Chokocho 0.11 0.54 0.43 
Umudioga 0.16 0.73 0.57 
Sara 0.12 0.55 0.43 

 
Table 7. NDVI retrieved at 60 m and 450 m from the stack (East direction) (2011). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.21 0.66 0.36 
Eleme refinery II 0.18 0.57 0.39 
Onne 0.22 0.71 0.49 
Umurolu 0.23 0.52 0.29 
Bonny LNG 0.21 0.45 0.24 
Alua 0.21 0.75 0.58 
Rukpokwu 0.20 0.60 0.40 
Obigbo 0.17 0.57 0.38 
Chokocho 0.19 0.51 0.33 
Umudioga 0.23 0.71 0.50 
Sara 0.15 0.53 0.36 

 
 
 
 



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Table 8. NDVI retrieved at 60 m and 450 m from the stack (South direction) (2011). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.32 0.61 0.29 
Eleme refinery II 0.20 0.50 0.30 
Onne 0.23 0.64 0.41 
Umurolu 0.23 0.45 0.22 
Bonny LNG 0.16 0.43 0.27 
Alua 0.12 0.68 0.56 
Rukpokwu 0.13 0.55 0.42 
Obigbo 0.14 0.69 0.55 
Chokocho 0.11 0.55 0.44 
Umudioga 0.16 0.69 0.53 
Sara 0.12 0.58 0.46 

 
Table 9. NDVI retrieved at 60 m and 450 m from the stack (West direction) (2011). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.25 0.73 0.48 
Eleme refinery II 0.14 0.61 0.47 
Onne 0.19 0.68 0.49 
Umurolu 0.20 0.51 0.31 
Bonny LNG 0.18 0.50 0.32 
Alua 0.14 0.68 0.54 
Rukpokwu 0.16 0.58 0.42 
Obigbo 0.20 0.57 0.37 
Chokocho 0.15 0.53 0.38 
Umudioga 0.17 0.69 0.52 
Sara 0.16 0.52 0.36 

 
Table 10. NDVI retrieved at 60 m and 450 m from the stack (North direction) (2022). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.12 0.70 0.58 
Eleme refinery II 0.12 0.60 0.48 
Onne 0.12 0.71 0.59 
Umurolu 0.13 0.52 0.39 
Bonny LNG 0.14 0.55 0.41 
Alua 0.11 0.68 0.57 
Rukpokwu 0.13 0.71 0.58 
Obigbo 0.12 0.58 0.46 
Chokocho 0.13 0.57 0.44 
Umudioga 0.10 0.65 0.55 
Sara 0.11 0.58 0.47 

 
Table 11.NDVI retrieved at 60 m and 450 m from the stack (East direction) (2022). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.12 0.71 0.59 
Eleme refinery II 0.11 0.59 0.48 
Onne 0.12 0.68 0.56 
Umurolu 0.11 0.53 0.42 
Bonny LNG 0.10 0.57 0.47 
Alua 0.13 0.64 0.51 
Rukpokwu 0.12 0.70 0.58 
Obigbo 0.11 0.60 0.49 
Chokocho 0.10 0.53 0.43 
Umudioga 0.12 0.61 0.49 
Sara 0.10 0.51 0.41 

 
Table 12. NDVI retrieved at 60 m and 450 m from the stack (South direction) (2022). 

Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.10 0.55 0.45 
Eleme refinery II 0.13 0.64 0.51 
Onne 0.08 0.68 0.60 
Umurolu 0.11 0.58 0.47 
Bonny LNG 0.13 0.61 0.48 
Alua 0.11 0.63 0.52 
Rukpokwu 0.12 0.61 0.49 
Obigbo 0.09 0.49 0.40 
Chokocho 0.08 0.50 0.42 
Umudioga 0.08 0.64 0.56 
Sara 0.10 0.60 0.50 

 

 

 



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Table 13. NDVI retrieved at 60 m and 450 m from the stack (West direction) (2022). 
Flaring site NDVI at 60 m NDVI at 450 m (𝜹NDVI450-60)m 

Eleme refinery I 0.11 0.59 0.48 
Eleme refinery II 0.14 0.62 0.48 
Onne 0.13 0.67 0.54 
Umurolu 0.09 0.60 0.51 
Bonny LNG 0.10 0.54 0.44 
Alua 0.12 0.63 0.51 
Rukpokwu 0.11 0.65 0.54 
Obigbo 0.09 0.54 0.45 
Chokocho 0.10 0.57 0.47 
Umudioga 0.12 0.68 0.56 
Sara 0.11 0.61 0.50 

 
Generally, Tables 2-13 show that the values of NDVI at 60 m from the flare stack is much lower. For example 

for the year 2022, NDVIs recorded are the lowest (0.08-0.14). This suggests little or no vegetation cover, bear soil 
etc within the area. However, at 450 m from the stack, the values of NDVI are higher (0.43-0.84) which suggests 
that the vegetation cover is not sparse but green and healthy. Therefore, the results showed that the vegetation 
cover and its health around the flare stack have been negatively affected by the flare leading to the sparse or no 
vegetation cover [21, 43]. NDVI values at 60 m from the flare reduce even to almost zero while LST values are 
higher. This is supported by previous researches [18, 20, 22-26]. This suggest that the vegetation cover is 
unhealthy and polluted, some have died, many are dying, while others have been reduced to almost bear soil as a 
result of the continuous effects of pollution from the flare. However, the NDVI values at 450 m did not follow a 
uniform trend, their values changes. This may be resulted from some external activities apart from gas flaring 
within the area.  
 

3.2. Change in NDVI (𝜹NDVI450-60)m 
Figures 4-6 are the plots for the (𝛿NDVI450-60)mN against (𝛿NDVI450-60)mE, (𝛿NDVI450-60)mN against 

(𝛿NDVI450-60)mS and (𝛿NDVI450-60)mN against (𝛿NDVI450-60)mW for the 11 facilities studied. The number (N) of 

(𝛿NDVI450-60)m used for each of the plot is 348. Data for each facility is identified using the colour of the stack 
height. For exampleEleme Refinery I has the stack height of 50 m and that of Eleme Refinery II is 65 m. Hence, all 
deep red and brown points in these figures are from these 2 refineries.     
 

 
Figure 4. (𝜹NDVI450-60)mN against (𝜹NDVI450-60)mE. 

 

Stack height 



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Figure 5. (𝜹NDVI450-60)mN against (𝜹NDVI450-60)mS. 

 

 
Figure 6. (𝜹NDVI450-60)mN against (𝜹NDVI450-60)mW. 

 

Table 14 presents the results obtained when (𝛿NDVI450-60)mN versus (𝛿NDVI450-60)mE, (𝛿NDVI450-60)mN versus 

(𝛿NDVI450-60)mS and (𝛿NDVI450-60)mN versus (𝛿NDVI450-60)mW relationships are considered. All results show 

positive correlation, but only (𝛿NDVI450-60)mN versus (𝛿NDVI450-60)mW give statistically significant result at 99 % 

confidence level (p-values = 0.0016) whilst the other 2 are not statistically significant.  
 

Table 14. Results for (𝜹NDVI450-60)m against North, East, South and West directions with 𝜶 = 0.01. 

Relationship R-value P-value Type of correlation 

(𝛿NDVI450-60)mN v (𝛿NDVI450-60)mE 0.08 0.11 Positive (+) 

(𝛿NDVI450-60)mN v (𝛿NDVI450-60)mS 0.10 0.07  Positive (+) 

(𝛿NDVI450-60)mN v (𝛿NDVI450-60)mW 0.17 0.00 Positive (+) 

Stack height 

Stack height 



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3.3. Linear Regression Analysis for (𝜹NDVI450-60)m against (𝜹LST60-450)m 

Table 15 presented the results of the analysis of (𝛿NDVI450-60)m against (𝛿LST60-450)m in the N, E, S, and W 

directions. The total number (N) of (𝛿NDVI450-60)m against (𝛿LST60-450)m for each facility used for the analysis in 
the 4 directions, the r-values and p-values obtained in the 4 directions are presented.  
 

Table 15. (𝜹NDVI450-60)m against (𝜹LST60-450)m (when both > 0) with 𝜶 = 0.01. 

Facility 
 

Number (N), 
r-value, 

p-value (North 
direction) 

Number (N), 
r-value, 

p-value (East 
direction) 

Number (N), 
r-value, 

p-value (South 
direction) 

Number (N), 
r-value, 

p-value (West 
direction) 

1: Eleme I 15.0 
0.56 
0.03 

14.0 
-0.22 
0.44 

15.0 
-0.20 
0.45 

13.0 
-0.06 
0.81 

2: Eleme II 17.0 
0.16 
0.53 

19.0 
-0.02 
0.87 

17.0 
0.32 
0.19 

17.0 
0.60 
0.01 

3: Onne 23.0 
-0.13 
0.44 

27.0 
0.13 
0.45 

27.0 
0.14 
0.42 

31.0 
-0.31 
0.07 

4: Umurolu 22.0 
-0.06 
0.79 

29.0 
-0.14 
0.53 

28.0 
-0.24 
0.23 

27.0 
0.03 
0.90 

5: Bonny 15.0 
0.17 
0.62 

24.0 
0.14 
0.65 

20.0 
-0.15 
0.62 

23.0 
0.22 
0.21 

6: Alua 20.0 
0.23 
0.32 

17.0 
-0.18 
0.62 

20.0 
0.02 
0.98 

18.0 
0.15 
0.46 

7: Rukpokwu 30.0 
-0.17 
0.32 

26.0 
-0.27 
0.17 

31.0 
0.42 
0.03 

30.0 
-0.01 
0.98 

8: Obigbo 24.0 
-0.08 
0.72 

18.0 
0.16 
0.46 

20.0 
-0.20 
0.36 

18.0 
-0.37 
0.11 

9: Chokocho 21.0 
-0.20 
0.38 

20.0 
0.27 
0.25 

22.0 
0.16 
0.42 

20.0 
0.29 
0.22 

10: Umudioga 12.0 
-0.04 
0.86 

14.0 
-0.14 
0.33 

16.0 
0.42 
0.06 

18.0 
-0.03 
0.95 

11: Sara 15.0 
-0.09 
0.72 

25.0 
0.17 
0.35 

31.0 
-0.04 
0.83 

29.0 
-0.09 
0.68 

 
In Table 15, Eleme Refineries I and II, Bonny LNG and Alua Flow Station have positive (+) correlation and 

statistically insignificant results in the N direction. Onne, Umurolu, Rukpokwu, Obigbo, Chokocho, Umudioga 

Flow Stations, and Sara oil well show negative (−) r-values and insignificant p-values in the N direction. In the E 

direction, Eleme Refineries I and II; Umurolu, Alua and Umudioga Flow Stations also have − r-values and 
insignificant p-values; and Bonny LNG; Onne, Obigbo, Chokocho Flow Stations; and Sara oil well have a + 
correlation and insignificant results. In the S direction Eleme Refinery, I; Bonny LNG; and Umurolu and Obigbo 

Flow Stations; and Sara oil well give − r-values and insignificant p-values. Furthermore, Eleme Refinery II; Onne, 

Alua, Rukpokwu, Chokocho and Umudioga Flow Stations have + r-values and insignificant p-values. Finally, in 

the W direction, Eleme Refinery I; Onne, Rukpokwu, Obigbo, Umudioga Flow Stations; and Sara oil well show − 
r-values and insignificant p-values. Eleme Refinery II; Bonny LNG; Umurolu, Alua and Chokocho Flow Stations 

show + r-values and insignificant p-values.   

Results in Table 16 are obtained from the linear regression analysis for (𝛿NDVI450-60)m and (𝛿LST60-450)m when 

North, East, South, and West directions were evaluated with 𝛼 = 0.01 for all the 11 facilities processed together.  
 

Table 16. Results obtained for 𝜹NDVI against 𝜹LST with 𝜶 = 0.01. 

Relationship Number r-values p-values Type of correlation 

(𝛿NDVI450-60)mN v (𝛿LST60-450)mN 214 0.00 0.97 Positive (+) 

(𝛿NDVI450-60)mE v (𝛿LST60-450)mE 233 0.09 0.20 Positive (+) 

(𝛿NDVI450-60)mS v (𝛿LST60-450)mS 249 0.03 0.20 Positive (+) 

(𝛿NDVI450-60)mW v (𝛿LST60-450)mW 244 0.10 0.11 Positive (+) 

 

Figures 7-10 show the plots of the (𝛿NDVI450-60)mN versus (𝛿LST60-450)mN, (𝛿NDVI450-60)mE versus (𝛿LST60-

450)mE, (𝛿NDVI450-60)mS versus (𝛿LST60-450)mS, and (𝛿NDVI450-60)mW versus (𝛿LST60-450)mW. The scale bar is an 
arbitrary chosen number for the identification of individual facility. For example, 1 represents Eleme Refinery I, 5 
for Bonny LNG plant and 11 is for Sara Flow Station. 
 



Asian Review of Environmental and Earth Sciences, 2023, 10(1): 1-13 

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Figure 7. (𝜹NDVI450-60)mN against (𝜹LST60-450)mN. 

 

 
Figure 8.(𝜹NDVI450-60)mE against (𝜹LST60-450)mE. 

 

 
Figure 9. (𝜹NDVI450-60)mS against (𝜹LST60-450)mS. 

 

 Facility number 

numbernumber 

Facility number 

Facility number 



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Figure 10. (𝜹NDVI450-60)mW against (𝜹LST60-450)mW. 

 

4. Conclusion 
Only the correlation coefficient for Eleme I in the N direction (0.5558) and Eleme II in the W direction (0.6002) 

show that there is linear interdependence of the 2 variables, (𝛿NDVI450-60)m and  (𝛿LST60-450)m (Table 15). However, 
the rest 9 facilities show non-linear interdependence of the 2 variables in the N, E, S and W. Therefore, due to lack 
of significant correlations no conclusions can be drawn about the effect of the prevailing wind direction (South) in 

the Niger Delta on any of the relationship between (𝛿NDVI450-60)m and (𝛿LST60-450)m. Also, the results in Table 16 

for the 4 relationships (𝛿NDVI450-60)mN against (𝛿LST60-450)mN, (𝛿NDVI450-60)mE against (𝛿LST60-450)mE, 

(𝛿NDVI450-60)mS against (𝛿LST60-450)mS, and (𝛿NDVI450-60)mW against (𝛿LST60-450)mW considered shows positive 
correlation but statistically insignificant results. Based on the results obtained for this research, it can be concluded 
that gas flaring related pollution can be detected on vegetation cover and its health using Landsat 7 and Landsat 8 
remotely sensed data in the Niger Delta. 

Some challenges are encountered in this research, for example, the available Landsat data covers only dry 
season in Nigeria, hence a further research is required to examine and compare the NDVI and LST results for the 
rainy period data too. The recommendations made regarding this research are: (1) Nigerian Government should 
make sure the exorbitant penalty fee is paid by defaulters’ oil and gas companies who refuse to adhere to flaring law 
and regulations; (2) All Nigerian should not lose the focus for the quest of having zero gas flaring in Nigeria.  
 

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