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Citation: Ansah, C.E.; Abu, I.-O.;
Kleemann, J.; Mahmoud, M.I.; Thiel,
M. Environmental Contamination of
a Biodiversity Hotspot—Action
Needed for Nature Conservation in
the Niger Delta, Nigeria.
Sustainability 2022,14, 14256.
https://doi.org/10.3390/
su142114256
Academic Editor: Sharif
Ahmed Mukul
Received: 27 September 2022
Accepted: 25 October 2022
Published: 1 November 2022
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4.0/).
sustainability
Article
Environmental Contamination of a Biodiversity
Hotspot— Action Needed for Nature Conservation
in the Niger Delta, Nigeria
Christabel Edena Ansah 1,2 , Itohan-Osa Abu 1, Janina Kleemann 3, * , Mahmoud Ibrahim Mahmoud 4
and Michael Thiel 1
1Institute for Geography and Geology, Department of Remote Sensing, Julius-Maximilians-University
Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany
2Remote Sensing Group, Institute of Computer Science, University of Osnabrück, Wachsbleiche 27,
49090 Osnabrück, Germany
3
Institute for Geosciences and Geography, Department of Sustainable Landscape Development, Martin Luther
University Halle-Wittenberg, Von-Seckendorff-Platz 4, 06120 Halle (Saale), Germany
4
National Oil Spill Detection and Response Agency (NOSDRA), NAIC Building, 5th Floor, Plot 590, Zone AO,
Central Business District, Abuja 900211, Nigeria
*Correspondence: janina.kleemann@geo.uni-halle.de
Abstract:
The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting
many endemic and endangered species. Therefore, its conservation should be of highest priority.
However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large
extent. In particular, oil spills threaten the biodiversity, ecosystem services and local people. Remote
sensing can support the detection of spills and their potential impact when accessibility on site is
difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover
as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land
cover types and protected areas could be threatened in the Niger Delta due to oil spills. The results
showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index and the Soil
Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover
than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills
also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest
and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong
conservation measures are needed even though security issues hamper the monitoring and control.
Keywords:
nature conservation; NDVI; pollution; remote sensing; species; vegetation indices; oil spill
1. Introduction
The Niger Delta is one of the largest wetlands in the world with huge biodiversity [
1
,
2
].
It has the largest river delta and mangrove extension in Africa [
3
–
5
]. The International
Union for Conservation of Nature (IUCN) rated this region as one of the highest conser-
vation priority areas in West Africa [
5
–
8
]. In contrast, it has also become one of the most
vulnerable biodiversity hotspots in Africa. It has lost large portions of its protected areas
over the years due to overexploitation, deforestation and pollution [
3
,
5
,
9
,
10
]. Oil explo-
ration has become a curse rather than a blessing because of the numerous adverse effects on
people and nature resulting from oil production. Large amounts of oil were discovered in
1956 under the Delta surface [
5
,
11
]. In 2020, Nigeria’s crude oil reserves were estimated to
be 36.9 billion barrels, making it the second-largest reserve in Africa after Libya [
12
,
13
]. In
2015, Nigeria was the world’s fourth-largest exporter of liquefied natural gas and Africa
´
s
major oil producer [
14
–
16
]. Even though about 86% of the country’s exports and foreign
exchange earnings come from oil production, it constitutes only about 10% of the country’s
gross domestic product (GDP) [
17
]. Therefore, the infrastructural development is low.
Sustainability 2022,14, 14256. https://doi.org/10.3390/su142114256 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 14256 2 of 21
The Niger Delta is facing conflicts between oil-producing communities and oil companies
or the government with ethnic, economic and environmental concerns resulting from oil
production [
18
]. The main environmental concerns in the region are gas flaring and oil
spills. The gas flaring releases harmful gases such as sulfur dioxide that causes acid rain
and breathing problems in the affected areas [
18
]. Oil spills contaminate soil, water, flora
and fauna. Between 2007 and 2015, almost 90 million liters of oil were spilled in the Niger
Delta and more than 1000 km
2
of the surface has been polluted [
19
]. In many selected
areas, the soil was polluted to a depth of more than 5 m and the mangroves were destroyed.
The study also revealed that restored spill sites remained extremely contaminated [
20
].
According to Obida et al. [
19
], about 1160 km
2
of, mainly, broadleaved forest, mangroves
and croplands have been affected by oil spills between 2007 and 2015. Sabotage (theft) was
the primary cause of oil spills in the Niger Delta ([19]; see also Appendix A, Figure A2).
The severe pollution not only causes difficulties for flora and fauna but also for local
people. Approximately 565,000 people are at high exposure to oil spills because they live in
close proximity [
19
]. The majority of the residents are directly dependent on the (polluted)
environment by farming, fishing and other resource usages [
9
]. In addition, sources of
potable water are polluted. Crude oil contains benzene, toluene, ethylbenzene and xylenes
(BTEX), among others. Prolonged exposure to BTEX can affect the liver, kidney and blood
system. For instance, long-term exposure to benzene, which evaporates quickly into the
air, reduces the red blood cells and leads to anemia. Its short-term symptoms include
headaches, irregular heartbeats, convulsion and skin irritations [
21
,
22
]. Furthermore, a
higher child mortality rate was detected in the Niger Delta region [23].
Remote sensing can support the detection, mapping and monitoring of oil spills. In
most cases, remote sensing is used to detect oil spills on water surfaces [
24
–
26
], as, for
example, assessing the impact of one of the largest oil spills in history—BP’s Deepwater
Horizon spill in 2010 [
27
,
28
]. The emissivity property of oil makes identification of oil
easier on water surfaces. In contrast to active (radar) remote sensing data, passive (optical)
remote sensing, visible, infrared and thermal infrared bands can be used to detect oil on
the surface of water. A study by De Kerf et al. [
29
] detected oil spill on the surface of
water using a combination of the visible bands from an unmanned aerial vehicle (UAV)
and thermal infrared band. The thermal infrared images were able to detect oil spill on
the water because the emissivity of oil is greater than water. The oil absorbs visible light
and re-radiates some of that light in the thermal infrared spectrum which makes it appear
warmer than water [
30
,
31
]. Xing et al. [
28
] also detected oil spills of the Deepwater Horizon
(Gulf of Mexico) using Landsat thermal infrared imagery. From their research, thermal
images of an oil slick usually show hot slicks when the oil absorbs the heat during the
day. The high emissivity of oil as a result of the sun heating the oil on the surface of water
enabled the detection of oil slicks since it was warmer than the surrounding water surface.
The use of radar imagery (active remote sensing) to monitor oil spills is also possible
because oil slicks reduce the surface roughness on water and appear darker in the radar
imagery. Brekke and Solberg [
32
] concluded in their research that the use of Synthetic
Aperture Radar (SAR) was the most applicable when it comes to oil-spill detection due to
its wide coverage and ability to capture data despite the weather. It can penetrate clouds
and capture data both during the day and at night. Hyperspectral sensors have also been
used by some authors to explore the spectral signature of oil-polluted environments [
33
–
35
].
Furthermore, the spectral signature of vegetation is dependent on the physiological state of
the vegetation at a particular time. Stressed vegetation gives a different spectral signature
in comparison to healthy vegetation due to the reduction in the leaf chlorophyll content and
its absorption properties [
36
,
37
]. Therefore, vegetation indices have been used by various
authors to detect oil spills (e.g., [38–40]).
Our objective was to detect the land use/land cover types and protected areas that
have been affected by oil spills between 2016–2020 in the Niger Delta. In addition, we
estimated the potentially affected species indirectly by location information by IUCN. We
Sustainability 2022,14, 14256 3 of 21
used remote sensing techniques in combination with vegetation indices to identify polluted
surfaces. The subordinated research questions were:
•Which vegetation indices are most suitable to detect oil spills in the Niger Delta?
•Where are the hotspots (accumulation) of oil spills located in the Niger Delta?
•
Which species, land cover types and protected areas/ecosystems are most threatened
by oil spills in the Niger Delta?
2. Materials and Methods
2.1. Study Area
The Niger Delta is located in southern Nigeria along a 450-km coastline of the Gulf of
Guinea and covers approx. 7.5% of Nigeria’s surface [
41
]. The Niger Delta is highly populated.
In our study area (Figure 1), approx. 43 million people were living in 2020 [
42
]. The most
densely populated state is Imo State with 600–800 people/km
2
. Therefore, high pressure on
the environment is caused by urbanization, agriculture and resource extraction [3].
Sustainability 2022, 14, x FOR PEER REVIEW 3 of 22
Our objective was to detect the land use/land cover types and protected areas that
have been affected by oil spills between 2016–2020 in the Niger Delta. In addition, we
estimated the potentially affected species indirectly by location information by IUCN. We
used remote sensing techniques in combination with vegetation indices to identify pol-
luted surfaces. The subordinated research questions were:
• Which vegetation indices are most suitable to detect oil spills in the Niger Delta?
• Where are the hotspots (accumulation) of oil spills located in the Niger Delta?
• Which species, land cover types and protected areas/ecosystems are most threatened
by oil spills in the Niger Delta?
2. Materials and Methods
2.1. Study Area
The Niger Delta is located in southern Nigeria along a 450-km coastline of the Gulf
of Guinea and covers approx. 7.5% of Nigeria’s surface [41]. The Niger Delta is highly
populated. In our study area (Figure 1), approx. 43 million people were living in 2020 [42].
The most densely populated state is Imo State with 600–800 people/km
2
. Therefore, high
pressure on the environment is caused by urbanization, agriculture and resource extrac-
tion [3].
Figure 1. The study area Niger Delta in Nigeria with ecoregions and land use types. The pipelines
are shown in Appendix A, Figure A1.
Seven ecoregions cover the Niger Delta (see Figure 1): Cameroonian Highlands For-
ests; Central African Mangroves; Cross-Niger Transition Forests; Cross-Sanaga-Bioko
Coastal Forests; Guinea Forest-Savanna Mosaic; Niger Delta Swamp Forests; and the Ni-
gerian Lowland Forests. The ecoregion with the largest extent of mangroves is the Central
African Mangroves, which covers about 30–40 km in the south of the Niger Delta and
contains all the mangrove species found in Nigeria. In addition to the loss of mangroves
due to industrialization, urbanization, timber exploitation and agriculture, oil exploitation
is also posing a threat to this region [43]. Located between the biogeographic areas of the
Cross River and the Niger River is the Cross Niger Transition Forests ecoregion. This
ecoregion is confronted with a high agricultural intensity that has caused the removal of
Figure 1.
The study area Niger Delta in Nigeria with ecoregions and land use types. The pipelines
are shown in Appendix A, Figure A1.
Seven ecoregions cover the Niger Delta (see Figure 1): Cameroonian Highlands Forests;
Central African Mangroves; Cross-Niger Transition Forests; Cross-Sanaga-Bioko Coastal
Forests; Guinea Forest-Savanna Mosaic; Niger Delta Swamp Forests; and the Nigerian
Lowland Forests. The ecoregion with the largest extent of mangroves is the Central African
Mangroves, which covers about 30–40 km in the south of the Niger Delta and contains
all the mangrove species found in Nigeria. In addition to the loss of mangroves due to
industrialization, urbanization, timber exploitation and agriculture, oil exploitation is also
posing a threat to this region [
43
]. Located between the biogeographic areas of the Cross
River and the Niger River is the Cross Niger Transition Forests ecoregion. This ecoregion
is confronted with a high agricultural intensity that has caused the removal of most of
the natural tree cover [
44
]. The Cross-Sanaga-Bioko Coastal Forests ecoregion has the
highest number of forest birds and mammal species in Africa [
45
]. The highly threatened
Cross River population of the lowland gorilla is living in this ecoregion in Nigeria and
Cameroon [
46
]. The ecoregion of the Guinean Forest-Savanna is located in the northern part
Sustainability 2022,14, 14256 4 of 21
of the Niger Delta and forms a mosaic of different ecosystem types and, therefore, a high
variety of habitats and species. The main threats to its biodiversity are hunting, agriculture
and deforestation [
47
]. The Niger Delta Swamp Forest is Africa’s largest swamp forest after
the Congo Basin Swamp Forests. For many years, it escaped habitat degradation because
it was less populated and had less socioeconomic activity. It was the discovery of oil that
opened the region for development at the expense of its environment.
The Nigerian Lowland Forest ecoregion remains one of the densely populated areas
with a lot of anthropogenic activities. The high exploitation of the region’s forests has led to
fragmentation and reduced forest connectivity. Almost all reserves were converted to rubber
plantations and crops [
48
]. The Cameroonian Highland Forest is the smallest ecoregion in our
study area and forms part of the Cross River State where no oil spills have been recorded.
Over 90% of the region’s protected areas are nationally designated forest reserves [
49
].
Only a few protected areas have internationally acknowledged protection status. The
Urhonigbe Protected Area is a strict nature reserve according to IUCN (protection category
Ia). Human activities are very limited and the primary aim is the protection of nature.
The Gilli-Gilli Game Reserve has IUCN protection category IV, which is a habitat/species
management area. This category aims to protect specific species or habitats by actively
managing the protected area [
50
]. The Apoi Creek Forest is a lowland swamp-forest
and since 2008 a Ramsar Site (Wetlands of International Importance [
51
])—especially for
the protection of the endemic and endangered Niger Delta Red Colobus Monkey [
52
].
Other internationally designated protected areas are the Oban Biosphere Reserve and
Okwangwo Biosphere Reserve which were assigned as biosphere reserves within the Man
and Biosphere program of UNESCO [
53
,
54
]. The Oban Biosphere Reserve covers the Oban
Forest Reserve, the Cross River National Park and the Obudu Plateau. The Okwangwo
Biosphere Reserve is located north of the Cross River National Park [54].
The oil facilities in the Niger Delta cover an area of about 31,000 km
2
with oil and
gas flow lines stretching over 45,000 km [
7
,
55
]. The Niger Delta has 500 oil fields, with
more than 55% onshore. The remaining oil fields are found less than 500 m in shallow
waters (offshore; [
56
]). Among the oil facilities, there are higher rates of oil spills linked
to the pipelines [
19
]. Pipelines have experienced regular leaks and ruptures since their
invention in the late 19th century [
57
,
58
]. They transport crude oil at a high pressure,
over 1000 pounds per square inch (psi), which can result in oil spills when the pipelines
are not well maintained [
11
]. Furthermore, oil facilities are often attacked and sabotaged
by local and militant groups trying to obtain a share of the oil wealth [
59
]. According
to Onuoha [
59
], over 10% of stolen oil is exported every year in the country. Between
2009 and 2018, Nigeria lost USD38.5 billion due to crude oil theft [
60
]. In addition, some
oil spills were caused by equipment failure, operational/maintenance error and corrosion
after abandonment [
20
,
61
]. In Nigeria, there are serious concerns regarding the age and
condition of pipelines in light of industrial standards and international guidelines [20,62].
It happens that some pipelines (see Figure A1), oil fields and terminals are located within
protected areas. This increases the vulnerability of protected areas being affected by oil spills.
Several species in the Niger Delta are under severe threat. Near-threatened (NT) species had
the highest count with birds being the most threatened species in the region (Appendix A,
Figure A3; [
63
]). Species such as the Mona Monkey (Cercopithecus mona, NT), West African
Black Mud Turtle (Pelusios niger, NT), Niger Delta Red Colobus (Piliocolobus epieni, critically
endangered(CR)), Red-Eared Monkey (Cercopithecus erythrotis, vulnerable (VU)) and Cape
Gannet (Morus capensis, endangered(EN)) have significantly declined over the years.
2.2. Data
We used different datasets for land use/type, biodiversity and oil spills (see Table 1).
The land use data used in this study include the European Space Agency (ESA) Climate
Change Initiative (CCI) Land Cover 20 m Map of Africa 2016 [
64
], a Sentinel-2 prototype
land cover of 20 m, from which the training data for the classification were obtained. A
composite of the datasets at a resolution of 20 m was used for the classification. Due
Sustainability 2022,14, 14256 5 of 21
to its location with high cloud coverage [
65
,
66
], Sentinel-2 MSI, Level-1C and Sentinel-
1 SAR GRD were used for the Niger Delta. Auxiliary variables used to enhance the
classification included WorldPop Global Project Population Data [
67
], Hansen Global
Forest Change v1.8 [
68
] and the 2015 30 m Global Food Security-support Analysis Data
(GFSAD) Cropland Extent of Africa (GFSAD30AFCE v001; [
69
]). Excluding the GFSAD
cropland extent data [70], all the other datasets were obtained from Google Earth Engine.
The ecoregions (RESOLVE Ecoregions; [
71
]) and protected areas (World Database on
Protected Areas; [
49
]) data were also obtained from Google Earth Engine. The RESOLVE
Ecoregions dataset constitutes 846 terrestrial ecoregions of which 7 can be found in the
Niger Delta. With the protected areas, less than 5% of the protected areas in the Niger
Delta have reported IUCN categories. The IUCN classifies protected areas based on their
management objectives, with different levels of restrictions [
50
]. Data on the conservation
status of species were obtained from the IUCN Red List of Threatened Species [
63
]. Location
data of species from the Near-Threatened (NT) to Critically Endangered (CR) were used
for this study the from Global Biodiversity Information Facility (GBIF) [72].
The oil spill incident data used for this study were obtained from the National Oil
Spill Detection and Response Agency (NOSDRA) website [
73
]. Information provided in
the data includes the spill date, when it stopped, reported date, causes of the oil spills,
type of facilities affected, affected states, geographic coordinates and the quantity of the
spill. A total of 2704 oil-spill incidents were used for this study. These are oil spills that
occurred between 2016 and 2020. These are visible on the readily available Google Earth
Pro Landsat imagery.
Table 1. Data used for the study.
Data Resolution Year Data Source
European Space Agency Climate Change
Initiative Land Cover 20 m Map of Africa 20 m 2016 [64]
Sentinel-2 MSI, Level-1C 10 m 2016–2020 [74]
Sentinel-1 Synthetic Aperture Radar Ground
Range Detected 10 m 2016–2020 [75]
WorldPop Global Project Population Data 100 m 2016–2020 [67]
Hansen Global Forest Change v1.8 30 m 2016–2020 [68]
Global Food Security-support Analysis Data
Cropland Extent of Africa 30 m 2015 [69]
RESOLVE Ecoregions - 2017 [71]
World Database on Protected Areas - 2021 [49]
Species occurrence data - 2010–2021 [72]
Oil spill incident data - 2016–2020 [73]
2.3. Methods
The aim was to assess the impact of oil spills on the land cover from 2016–2020 and to
detect the affected biodiversity areas in the Niger Delta by using remote sensing techniques
and vegetation indices. The analyses were completed using Google Earth Engine, R studio,
ArcGIS Pro and QGIS.
2.3.1. Vegetation Indices for Remote Sensing
Vegetation indices (VIs) give information on vegetation cover, vigor and its growth
dynamics [
26
] and can track the changes in the leaf chlorophyll content (LCC) of vegeta-
tion [
37
]. According to Xue and Su [
26
], monitoring vegetation conditions and mapping
land cover changes has increasingly relied on VIs derived from satellite data. Moreover,
many studies have examined vegetation by using individual or grouped spectral bands
(e.g., [
38
–
40
]). The VIs are a mathematical combination of several spectral bands, designed
Sustainability 2022,14, 14256 6 of 21
to maximize sensitivity to the vegetation characteristics [
76
,
77
]. For example, a study
by Mishra et al. [
78
] revealed that stress on salt marshes—caused by an oil spill—led to
a considerable decrease after the oil spill in the biomass and chlorophyll content of the
plants during the growing season of 2010. With remote sensing, the spectral signatures of
vegetation can be assessed and the use of vegetation indices has proved to be a successful
strategy in detecting and assessing the impact of spilt crude-oil on vegetation [
79
]. Another
example is the study by Lassalle et al. [
39
] who employed 14 VIs to monitor oil pollution
in a vegetated industrial site and which had high levels of total petroleum hydrocarbons
(TPH) contamination, stressing the vegetation.
The visible bands are still widely used in oil-spill detection by remote sensing, even
though they have many shortcomings [
24
]. Sensors of this type are poor at detecting oil, as
they find it difficult to separate oil from the normal background interference. In general,
the use of remote sensing to assess vegetation health is based on the following light spectra:
ultraviolet (UV) region (10 to 380 nm); visible spectra, consisting of blue (450–495 nm),
green (495–570 nm) and red (620–750 nm) wavelength, and the near and mid-infrared
(850–1700 nm) bands [
80
,
81
]. Following the approach of Adamu et al. [
82
] who utilized
20 VIs to compare the vegetation health of oil-polluted areas with unpolluted areas, we
selected five VIs for this study: Enhanced Vegetation Index (EVI); Normalized Difference
Vegetation Index (NDVI); Soil-Adjusted Vegetation Index (SAVI); Normalized Green Red
Difference Index (NGRDI) and Green Leaf Index (GLI). EVI and GLI are the two VIs that
expressed the highest differences between polluted and unpolluted sites. NDVI and SAVI
are commonly used in vegetation studies and NGRDI has been integrated to gain new
insights on the usefulness of this index in vegetation pollution studies.
The overview of equations and references is given in Table 2. NDVI is an indicator
for photosynthetic activities in plants and it helps in detecting the health of a plant [
83
–
85
].
It is widely used for vegetation studies due to its responsiveness to green vegetation [
26
].
Nonetheless, it can be biased in sparsely vegetated areas due to its sensitivity to background
variations [
26
,
86
]. In order to address the limitations of the NDVI, EVI was developed,
incorporating both background adjustment and atmospheric resistance concepts [
26
]. It
helps correct the influence of soil and the atmosphere when studying vegetation, where the
constants C1 and C2 are 6 and 7.5, respectively, and L
E
is the soil adjustment parameter,
equivalent to 1. Huete [
87
] also proposed SAVI as an improvement on the sensitivity of
NDVI to background soil. Background variations such as soil color, brightness and moisture
content do not have much of an effect on the SAVI. L
S
is the soil conditioning index, with
the value 0.5. NGRDI is another index for vegetation monitoring [
88
]. However, it is based
on the visible spectral bands, green and red. GLI was initially developed by Louhaichi
et al. [
89
] for the use of the digital RGB camera. The camera was used to monitor wheat
cover using the visible spectral bands: red, green and blue. These indices are broadband
greenness VIs that measure the quantity and vigor of green plants and help to assess the
impact of the oil spills on vegetated areas in the Niger Delta.
Table 2. Vegetation indices used in this study.
Name Abbreviation Equation Reference
Enhanced Vegetation Index EVI 2.5(NIR −Red)/
(NIR + C1·Red −C2·Blue + LE)[90]
Green Leaf Index GLI (2·Gren −Red −Blue)/
(2·Green + Red + Blue) [89]
Normalized Difference
Vegetation Index NDVI (NIR −Red)/(NIR + Red) [88]
Normalized Green Red
Difference Index NGRDI (Green −Red)/(Green + Red) [88]
Soil Adjusted Vegetation Index
SAVI (1 + LS) (NIR −Red)/
(NIR + Red + LS)[87]
Sustainability 2022,14, 14256 7 of 21
2.3.2. Assessing the Impact of Oil Spills on Land Cover
The Sentinel-2 MSI Level-1C image collection for each year was filtered for scenes with
less than 10% cloud cover and then cloud-masked. The other data used were Sentinel-1
Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) yearly median image;
WorldPop Global Project Population Data; Hansen Global Forest Change v1.8 and the
GFSAD Cropland extent data. All data were stacked as bands into the resulting raster
stack for the classification. A pixel-based classification was completed in Google Earth
Engine using Random Forest (RF) classifier and the training data were obtained from ESA
CCI Land Cover 20 m Map of Africa using the stratified random sampling technique: Tree
cover areas (number of training pixels: 450); Shrub cover areas (200); Grassland (300);
Cropland (350); Aquatic vegetation (150); Bare areas (100); Built-up areas (300) and Water
(150). Hyperparameter tuning was used for determining the Number of Trees that resulted
in the highest accuracy.
One of the benefits of using RF is its ability to rank the importance of the variables
used for the prediction. The variable importance of the bands used in the classification
was computed, as well as the confusion matrix, Kappa coefficient, overall, producer and
user accuracy (resubstitution accuracy). After the classification, post-classification using a
moving window (3
×
3 pixel) was completed to reduce the salt and pepper effect in the
final classification maps. Figure 2contains the steps used in obtaining the land use/land
cover (LULC) maps of the Niger Delta, from 2016 to 2020.
Sustainability 2022, 14, x FOR PEER REVIEW 7 of 22
Table 2. Vegetation indices used in this study.
Name Abbre-
viation Equation Reference
Enhanced Vegetation
Index EVI 2.5(NIR − Red)/(NIR + C1·Red − C2·Blue + LE) [90]
Green Leaf Index GLI (2·Gren − Red − Blue)/(2·Green + Red + Blue) [89]
Normalized Difference
Vegetation Index NDVI (NIR − Red)/(NIR + Red) [88]
Normalized Green Red
Difference Index NGRDI (Green − Red)/(Green + Red) [88]
Soil Adjusted Vegeta-
tion Index SAVI (1 + LS) (NIR − Red)/(NIR + Red + LS) [87]
2.3.2. Assessing the Impact of Oil Spills on Land Cover
The Sentinel-2 MSI Level-1C image collection for each year was filtered for scenes
with less than 10% cloud cover and then cloud-masked. The other data used were Senti-
nel-1 Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) yearly median im-
age; WorldPop Global Project Population Data; Hansen Global Forest Change v1.8 and
the GFSAD Cropland extent data. All data were stacked as bands into the resulting raster
stack for the classification. A pixel-based classification was completed in Google Earth
Engine using Random Forest (RF) classifier and the training data were obtained from ESA
CCI Land Cover 20 m Map of Africa using the stratified random sampling technique: Tree
cover areas (number of training pixels: 450); Shrub cover areas (200); Grassland (300);
Cropland (350); Aquatic vegetation (150); Bare areas (100); Built-up areas (300) and Water
(150). Hyperparameter tuning was used for determining the Number of Trees that re-
sulted in the highest accuracy.
One of the benefits of using RF is its ability to rank the importance of the variables
used for the prediction. The variable importance of the bands used in the classification
was computed, as well as the confusion matrix, Kappa coefficient, overall, producer and
user accuracy (resubstitution accuracy). After the classification, post-classification using a
moving window (3 × 3 pixel) was completed to reduce the salt and pepper effect in the
final classification maps. Figure 2 contains the steps used in obtaining the land use/land
cover (LULC) maps of the Niger Delta, from 2016 to 2020.
Figure 2.
Showing the steps used in obtaining the land use/land cover (LULC) maps of the Niger
Delta, from 2016 to 2020. Abbreviations: ESA = European Space Agency; CCI = Climate Change
Initiative; GFSAD = Global Food Security-support Analysis Data; GRD = Ground Range Detected;
SAR = Synthetic Aperture Radar.
After obtaining the LULC maps for each year, land cover information was assigned
to the spill incidences as shown in Figure 3. This provided data on the oil spill-affected
land cover in the region. Then, the oil-spill incidents within vegetation cover areas were
selected (spill sites (SS)). These are tree cover areas, croplands, grassland, scrub cover areas
and aquatic vegetation. An equivalent number of points were also randomly selected in
areas that had not experienced any oil spill. To avoid the non-spill points overlapping with
the spill points, a 30 m buffer around each spill point was clipped from a bounding shape
file of areas where the oil spills occurred. Then, using stratified random sampling, the
non-spill points were generated with a count matching the affected land cover types (NSS;
Appendix A, Figure A4: Comparison of data count within selected SS and NSS). The five
VIs (EVI, NDVI, SAVI, NGRDI and GLI) to assess the impact of oil spills on the vegetation
Sustainability 2022,14, 14256 8 of 21
cover were then calculated. The VIs were assigned to the SS and NSS for each year. A
buffer of 30 m around each SS and NSS point was used for the analysis because oil spills
may migrate from the point of source and affect neighboring surroundings. With 30 m, we
ensured that the spill sides are covered in case of potential errors in the geolocation of the
utilized datasets. The average vegetation indices for each point were then used to generate
a boxplot to compare the performances of the indices with oil-polluted and unpolluted
vegetation cover areas and the significance level of the selected indices were computed.
Sustainability 2022, 14, x FOR PEER REVIEW 8 of 22
Figure 2. Showing the steps used in obtaining the land use/land cover (LULC) maps of the Niger
Delta, from 2016 to 2020. Abbreviations: ESA = European Space Agency; CCI = Climate Change
Initiative; GFSAD = Global Food Security-support Analysis Data; GRD = Ground Range Detected;
SAR = Synthetic Aperture Radar.
After obtaining the LULC maps for each year, land cover information was assigned
to the spill incidences as shown in Figure 3. This provided data on the oil spill-affected
land cover in the region. Then, the oil-spill incidents within vegetation cover areas were
selected (spill sites (SS)). These are tree cover areas, croplands, grassland, scrub cover ar-
eas and aquatic vegetation. An equivalent number of points were also randomly selected
in areas that had not experienced any oil spill. To avoid the non-spill points overlapping
with the spill points, a 30 m buffer around each spill point was clipped from a bounding
shape file of areas where the oil spills occurred. Then, using stratified random sampling,
the non-spill points were generated with a count matching the affected land cover types
(NSS; Appendix A, Figure A4: Comparison of data count within selected SS and NSS).
The five VIs (EVI, NDVI, SAVI, NGRDI and GLI) to assess the impact of oil spills on the
vegetation cover were then calculated. The VIs were assigned to the SS and NSS for each
year. A buffer of 30 m around each SS and NSS point was used for the analysis because
oil spills may migrate from the point of source and affect neighboring surroundings. With
30 m, we ensured that the spill sides are covered in case of potential errors in the geolo-
cation of the utilized datasets. The average vegetation indices for each point were then
used to generate a boxplot to compare the performances of the indices with oil-polluted
and unpolluted vegetation cover areas and the significance level of the selected indices
were computed.
Figure 3. Methodological framework for the impact of oil spills on the land cover using vegetation
indices. Abbreviations: NOSDRA = National Oil Spill Detection and Response Agency; WDPA =
World Database on Protected Areas.
2.3.3. Assessing the Impact of Oil Spills on Biodiversity Areas
Figure 3 shows the framework that was used to detect the affected ecoregions and
protected areas, with spill count, within the oil-spills’ hotspot (high density of oil spills)
and cold spot regions (low density of oil spills) in the Niger Delta. The ecoregion data
were used to assess the oil spills that occurred within each region and to detect the most
impacted ecoregion. Spills within and around protected areas were also assessed, using a
1 km buffer around the boundaries of the protected areas. Spatial join was also used in
this analysis to obtain the spills within and around the protected areas. Getis and Ord’s
Figure 3.
Methodological framework for the impact of oil spills on the land cover using vegetation in-
dices. Abbreviations: NOSDRA = National Oil Spill Detection and Response Agency; WDPA = World
Database on Protected Areas.
2.3.3. Assessing the Impact of Oil Spills on Biodiversity Areas
Figure 3shows the framework that was used to detect the affected ecoregions and
protected areas, with spill count, within the oil-spills’ hotspot (high density of oil spills)
and cold spot regions (low density of oil spills) in the Niger Delta. The ecoregion data
were used to assess the oil spills that occurred within each region and to detect the most
impacted ecoregion. Spills within and around protected areas were also assessed, using
a 1 km buffer around the boundaries of the protected areas. Spatial join was also used in
this analysis to obtain the spills within and around the protected areas. Getis and Ord’s
G∗
i
spatial statistic was used for the hotspot analysis. It was used to analyze spatial association
in relation to distance [
91
,
92
] in ArcGIS Pro: Optimized hotspot analysis (Getis-Ord
G∗
i
). In
addition, Obida et al. [
19
] used this approach to identify the main areas of oil spills in the
Niger Delta in 2007–2015. The Getis-Ord G∗
istatistic used in ArcGIS are given as:
G∗
i=∑n
j=1wij xj−x∑n
j=1wij
Srn∑n
j=1wij −(∑n
j=1wij )2
n−1
where the attribute value for the feature jis x
j
; the spatial weight between feature iand jis
wij; the total number of features is equal to n.
Sustainability 2022,14, 14256 9 of 21
x=∑n
j=1xj
nS=s∑n
j=1x2j
n−x2
This formula generates positive and negative
G∗
i
statistics (Z-value) that give informa-
tion on the spatial clustering of oil spills within the context of neighboring features. For
statistically significant positive Z-values, the larger the Z-value, the more intense the clus-
tering of oil spills (hotspots). Whereas oil-spill cold spot regions are areas with statistically
significant negative Z values, indicating low spatial clustering of oil spills. Values close to
zero imply the oil-spill points are randomly distributed [
93
]. The aggregation method used
in obtaining spill point counts to be used in the analysis was to count incidents within the
hexagon grid. This generates a hexagonal polygon mesh which is positioned over the spill
points to count points within each polygon cell. Ecoregions and affected protected areas
within cold and hotspot regions in the Niger Delta were detected from the results of the
optimized hotspot analysis.
The devastating state of the Niger Delta threatens several species as well as their
habitats. The selection of the species was based on the IUCN Red List of Threatened Species
which is a critical indicator of the health of biodiversity worldwide. The geolocation of
species that are near threatened, vulnerable, endangered and critically endangered in the
Niger Delta was downloaded from the GBIF website [
72
]. Although water bodies are not
excluded when it comes to oil pollution, the assessment of the impact of oil spills on land
cover excluded that of the water class, hence fishes were not part of the species used for
this study. Water bodies were excluded from the core analysis (even though part of the
results in Appendix A, Figure A5) because oil spreads more easily on water surfaces than
on land. Therefore, assessing the impact of oil spills on water surfaces involves taking into
consideration when the oil spilt, the direction of flow of the water and the quantity spilled
since the water will transport the oil on the water surface. This is not feasible considering
the temporal scale of this study.
3. Results
3.1. Vegetation Indices and Land Cover Types Affected by Oil Spills
Figure 4shows the vegetation greenness distribution within the selected spill sites
and non-spill sites. The five vegetation indices were able to distinguish between vegetation
spill sites (SS) and non-spill sites (NSS). The index values of polluted vegetated areas were
lower than unpolluted vegetation (see also Appendix A, Figure A4). This means vegetation
within NSS was greener than that of SS. All vegetation indices were able to distinguish
between SS and NSS. However, three of the indices—EVI, NDVI and SAVI—were very
sensitive to the effects of oil spills on the different vegetation cover areas.
The majority of the oil spills occurred within tree cover areas (848 oil spills) whereas
the least incidence was recorded within shrub cover areas (45 oil spills). Oil spills that
occurred in water were 610, cropland and grassland recorded 367 oil spills each, 201 oil
spills occurred within aquatic vegetation and 266 oil spills occurred within built-up areas
(Appendix A, Figure A5). However, in assessing the impact of oil spills on land cover, only
vegetated areas were selected for this analysis. The results shown in Figure 4show that the
oil spill has stressed the vegetation at the spill sites.
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Figure 4. Vegetation indices showing the effect of oil spills on five vegetation types.
The majority of the oil spills occurred within tree cover areas (848 oil spills) whereas
the least incidence was recorded within shrub cover areas (45 oil spills). Oil spills that
occurred in water were 610, cropland and grassland recorded 367 oil spills each, 201 oil
spills occurred within aquatic vegetation and 266 oil spills occurred within built-up areas
(Appendix A, Figure A5). However, in assessing the impact of oil spills on land cover,
only vegetated areas were selected for this analysis. The results shown in Figure 4 show
that the oil spill has stressed the vegetation at the spill sites.
3.2. Hotspots of Oil Spills and Threatened Biodiversity
The spatial distribution of the oil spills was not random, rather they were clustering
along the pipelines. Amongst the seven ecoregions of the Niger Delta, the one that is
threatened the most by oil spills is the Niger Delta Swamp Forest (Figure 5, oil-spill
hotspot with the highest range of Z-score: 9.62 < Z-score ≤ 17.33). Between 2016 and 2020,
1289 oil spills were recorded in this ecoregion. The Niger River is also located in this oil-
spill hotspot. Furthermore, the Central African Mangroves and the Cross-Niger Transition
Forest are affected by oil spills. The Niger Delta Swamp Forest serves as habitat for some
of the IUCN red list species. The threatened endemic mammals found in this ecoregion
include: Niger Delta Pygmy Hippopotamus (Choeropsis liberiensis, EN); Red-Bellied Mon-
key (Cercopithecus erythrogaster, EN) and Sclater’s Monkey (Cercopithecus sclateri, EN;
IUCN [63]). One of the world’s 25 most endangered primates, the Niger Delta Red Colo-
bus (Procolobus epieni, CR), is also found in this ecoregion. It has a decreasing population
due to threats from hunting and a continual decrease in the extent of its habitat due to
logging. The threats facing this species and others have intensified over the years since
the habitat quality is also hampered by the oil spills. The region which provides habitat
for threatened species such as the West African Manatee (Trichechus senegalensis, VU) rec-
orded the second largest number of oil spills during the study period. Specific species
within the oil-spill hotspot with the highest range of Z-scores (9.62 < Z-score ≤ 17.33) were
the Sclater’s Monkey (Cercopithecus sclateri, EN) and the Hooded Vulture (Necrosyrtes mon-
achus, CR) but due to mobility, species also occur in different oil-spill hotspots (Table 3).
Figure 4. Vegetation indices showing the effect of oil spills on five vegetation types.
3.2. Hotspots of Oil Spills and Threatened Biodiversity
The spatial distribution of the oil spills was not random, rather they were clustering
along the pipelines. Amongst the seven ecoregions of the Niger Delta, the one that is
threatened the most by oil spills is the Niger Delta Swamp Forest (Figure 5, oil-spill hotspot
with the highest range of Z-score: 9.62 < Z-score
≤
17.33). Between 2016 and 2020, 1289 oil
spills were recorded in this ecoregion. The Niger River is also located in this oil-spill hotspot.
Furthermore, the Central African Mangroves and the Cross-Niger Transition Forest are
affected by oil spills. The Niger Delta Swamp Forest serves as habitat for some of the IUCN
red list species. The threatened endemic mammals found in this ecoregion include: Niger
Delta Pygmy Hippopotamus (Choeropsis liberiensis, EN); Red-Bellied Monkey (Cercopithecus
erythrogaster, EN) and Sclater’s Monkey (Cercopithecus sclateri, EN; IUCN [
63
]). One of the
world’s 25 most endangered primates, the Niger Delta Red Colobus (Procolobus epieni, CR),
is also found in this ecoregion. It has a decreasing population due to threats from hunting
and a continual decrease in the extent of its habitat due to logging. The threats facing this
species and others have intensified over the years since the habitat quality is also hampered
by the oil spills. The region which provides habitat for threatened species such as the West
African Manatee (Trichechus senegalensis, VU) recorded the second largest number of oil
spills during the study period. Specific species within the oil-spill hotspot with the highest
range of Z-scores (9.62 < Z-score
≤
17.33) were the Sclater’s Monkey (Cercopithecus sclateri,
EN) and the Hooded Vulture (Necrosyrtes monachus, CR) but due to mobility, species also
occur in different oil-spill hotspots (Table 3).
Due to the fact that some pipelines are located within nature reserves, protected areas
are also affected by the oil spills. From the NOSDRA oil-spill incident data, between 2016
and 2020, 18 protected areas were affected with 322 oil spills within the protected areas
in the Niger Delta. A total of 130 oil spills were also captured within a buffer of 1 km
around protected areas. The most affected protected areas were Apoi Creek Forest and
the Uremure-Yokri Forest Reserve (Figure 6). More than 45 oil spills were detected inside
these two protected areas, respectively. In the Gilli-Gilli Game Reserve (habitat/species
management area by IUCN category), only one oil spill occurred. No spills were detected
in the Oban and Okwangwo Biosphere Reserves.
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Table 3. The endangered species (from GBIF occurrence/point data [72]) within the Niger Delta oil-
spill hotspot regions and according to the International Union for Conservation of Nature (IUCN)
Red List of Threatened Species [63]. Due to mobility, species also occurred in different oil-spill
hotspots. More species could be potentially affected since point data of real occurrence and not po-
tential distribution data were used. Abbreviations: Near threatened (NT); endangered (EN); criti-
cally endangered (CR) species.
Hotspot Regions of Oil Spills
Species names
Highly Clustered Spills
(9.62 < Z-Score ≤ 17.33)
Medium Clustered Spills
(6.34 < Z-Score ≤ 9.62)
Low Clustered Spills
(3.83 < Z-Score ≤ 6.34)
Sclater’s Monkey
(Cercopithecus sclateri, EN)
Calabar Angwantibo
(Arctocebus calabarensis, NT)
Western Red colobus
(Procolobus badius, CR)
Hooded Vulture
(Necrosyrtes monachus, CR)
Putty-Nosed monkey
(Cercopithecus nictitans, NT)
Poto
(Perodicticus potto, NT)
Sclater’s Monkey
(Cercopithecus sclateri, EN)
Red-Bellied Monkey
(Cercopithecus erythrogaster, EN)
Red-Bellied Monkey
(Cercopithecus erythrogaster, EN)
Sclater’s Monkey
(Cercopithecus sclateri, EN)
Hooded Vulture
(Necrosyrtes monachus, CR)
Mona Monkey
(Cercopithecus mona, NT)
Poto
(Perodicticus potto, NT)
Bioko Squirrel Galago
(Sciurocheirus alleni, NT)
Figure 5. Oil spill areas (in red), protected areas (in green), IUCN Red List Species and ecoregions
in the Niger Delta, 2016–2020. Data and sources are shown in Table 1. The specific species that were
found in the oil-spill hotspots are shown in Table 3.
Due to the fact that some pipelines are located within nature reserves, protected areas
are also affected by the oil spills. From the NOSDRA oil-spill incident data, between 2016
and 2020, 18 protected areas were affected with 322 oil spills within the protected areas in
the Niger Delta. A total of 130 oil spills were also captured within a buffer of 1 km around
protected areas. The most affected protected areas were Apoi Creek Forest and the
Uremure-Yokri Forest Reserve (Figure 6). More than 45 oil spills were detected inside
Figure 5.
Oil spill areas (in red), protected areas (in green), IUCN Red List Species and ecoregions in
the Niger Delta, 2016–2020. Data and sources are shown in Table 1. The specific species that were
found in the oil-spill hotspots are shown in Table 3.
Table 3.
The endangered species (from GBIF occurrence/point data [
72
]) within the Niger Delta
oil-spill hotspot regions and according to the International Union for Conservation of Nature (IUCN)
Red List of Threatened Species [
63
]. Due to mobility, species also occurred in different oil-spill
hotspots. More species could be potentially affected since point data of real occurrence and not
potential distribution data were used. Abbreviations: Near threatened (NT); endangered (EN);
critically endangered (CR) species.
Hotspot Regions of Oil Spills
Species names
Highly Clustered Spills
(9.62 < Z-Score ≤17.33)
Medium Clustered Spills
(6.34 < Z-Score ≤9.62)
Low Clustered Spills
(3.83 < Z-Score ≤6.34)
Sclater’s Monkey
(Cercopithecus sclateri, EN)
Calabar Angwantibo
(Arctocebus calabarensis, NT)
Western Red colobus
(Procolobus badius, CR)
Hooded Vulture
(Necrosyrtes monachus, CR)
Putty-Nosed monkey
(Cercopithecus nictitans, NT)
Poto
(Perodicticus potto, NT)
Sclater’s Monkey
(Cercopithecus sclateri, EN)
Red-Bellied Monkey
(Cercopithecus
erythrogaster, EN)
Red-Bellied Monkey
(Cercopithecus erythrogaster, EN)
Sclater’s Monkey
(Cercopithecus sclateri,
EN)
Hooded Vulture
(Necrosyrtes monachus, CR) Mona Monkey
(Cercopithecus mona, NT)
Poto
(Perodicticus potto, NT)
Bioko Squirrel Galago
(Sciurocheirus alleni, NT)
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these two protected areas, respectively. In the Gilli-Gilli Game Reserve (habitat/species
management area by IUCN category), only one oil spill occurred. No spills were detected
in the Oban and Okwangwo Biosphere Reserves.
Figure 6. Oil spills within and around protected areas in the Niger Delta between 2016 and 2020.
4. Discussion
4.1. Performance of Vegetation Indices
According to the National Aeronautics and Space Administration [94] and Rieser et
al. [95], analyzing the absorption and reflection of plants within both the visible and in-
frared wavelengths helps determine the health status of plants. The health status of plants
within oil spill and non-spill areas were assessed using VI and the findings on the perfor-
mance of VI to detect the impact of oil spills revealed that EVI, NDVI and SAVI were more
sensitive to the effects of oil spills on the different vegetation cover areas than the other
tested VI (GLI and NGRDI). These indices include the NIR band in their calculation, which
is an indicator for the health of plants. Unhealthy and stressed plants and in this case oil-
polluted vegetation tend to reflect less NIR which makes their identification with the use
of spectral information easier [96]. The low reflectance of NIR in the oil-polluted (stressed)
vegetation contributed to the SS having lower index values than the NSS. Studies by Bul-
garelli and Djavidnia [97], Pisano et al. [27] and Adamo et al. [98] have proved the im-
portance of NIR in monitoring the health of vegetation. The detection of plant greenness
significantly improves by combining visible and near-infrared bands in calculating VIs
[26].
Oil spill is a controversial topic in the Niger Delta, and backed by the unrest within
the region, a field study would have been impossible. Collecting ground truth data such
as soil samples and biomass which will then be analyzed in the lab would have been a
risk-it-all research as well as capital intensive and time-consuming; in particular, consid-
ering the huge area in question. With remote sensing, the impact of oil spills on vegetation
was assessed for a period of five years over an area of about 70,000 km
2
. Although with
the help of remote sensing vegetation indices, the impact of oil spills on vegetation was
detected, different plants react differently to the stress from oil spills [82]. The quantity of
oil spilt and duration of contact with vegetation till clean-up also plays a role in the extent
of the stress. Including such differences to analyze the impact of oil spills on vegetation in
the future will be beneficial and more detailed. This analysis could however be executed
Figure 6. Oil spills within and around protected areas in the Niger Delta between 2016 and 2020.
4. Discussion
4.1. Performance of Vegetation Indices
According to the National Aeronautics and Space Administration [
94
] and Rieser
et al. [
95
], analyzing the absorption and reflection of plants within both the visible and
infrared wavelengths helps determine the health status of plants. The health status of
plants within oil spill and non-spill areas were assessed using VI and the findings on the
performance of VI to detect the impact of oil spills revealed that EVI, NDVI and SAVI were
more sensitive to the effects of oil spills on the different vegetation cover areas than the
other tested VI (GLI and NGRDI). These indices include the NIR band in their calculation,
which is an indicator for the health of plants. Unhealthy and stressed plants and in this
case oil-polluted vegetation tend to reflect less NIR which makes their identification with
the use of spectral information easier [
96
]. The low reflectance of NIR in the oil-polluted
(stressed) vegetation contributed to the SS having lower index values than the NSS. Studies
by Bulgarelli and Djavidnia [
97
], Pisano et al. [
27
] and Adamo et al. [
98
] have proved the
importance of NIR in monitoring the health of vegetation. The detection of plant greenness
significantly improves by combining visible and near-infrared bands in calculating VIs [
26
].
Oil spill is a controversial topic in the Niger Delta, and backed by the unrest within the
region, a field study would have been impossible. Collecting ground truth data such as soil
samples and biomass which will then be analyzed in the lab would have been a risk-it-all
research as well as capital intensive and time-consuming; in particular, considering the
huge area in question. With remote sensing, the impact of oil spills on vegetation was
assessed for a period of five years over an area of about 70,000 km
2
. Although with the help
of remote sensing vegetation indices, the impact of oil spills on vegetation was detected,
different plants react differently to the stress from oil spills [
82
]. The quantity of oil spilt and
duration of contact with vegetation till clean-up also plays a role in the extent of the stress.
Including such differences to analyze the impact of oil spills on vegetation in the future will
be beneficial and more detailed. This analysis could however be executed on a smaller scale
using remote sensing imagery with a higher resolution, preferably an unmanned aerial
vehicle. The use of hyperspectral imagery instead of optical imagery is also recommended
for detailed identification and quantification of the volume of oil spilt.
4.2. Oil Spills as a Threat for a Biodiversity Hotspot
As the literature has already stated, e.g., [
4
,
5
,
9
,
20
,
58
], oil spills caused high losses
in ecosystems, ecosystem services and biodiversity in the Niger Delta. The majority of
Sustainability 2022,14, 14256 13 of 21
oil spills occurred in forests. Linking this finding to the main cause of oil spills in the
Niger Delta, which is sabotage/theft, it is realistic that the majority of the spills occurred
within tree cover areas. As stated earlier, sabotage is a punishable offence under the
Petroleum Production and Distribution (Anti-Sabotage) Act of 1990. Therefore, tree cover
areas provide a hidden place to facilitate illegal oil refineries.
These spills affect vegetation as proven by this study and if the necessary measures
are not taken, many more ecosystems and habitats for species will be lost in the near future.
Mangroves are especially affected by the pollution caused by oil spills but also deforestation
and land conversion cause a threat. Between 2007 and 2017, approx. 12% of mangrove
cover were lost in the Niger Delta [
99
]. Mangroves provide important ecosystem services
such as flood control, salinity control, purification, food, water and materials, among
others [
9
]. Local communities highly depend on these ecosystem services. For example, the
destruction of mangroves would cause economic losses in the fish catch because mangroves
serve as an important habitat and nursery of different fish species. In addition, mangroves
are relevant in the context of climate change because they can store a lot of carbon [5].
Even though we did not analyze the direct effects on species in this study, it can be said
that wildlife within an oil-polluted ecosystem is at threat because of oil-contaminated food,
animals’ polluted coats and the environment. Oil-contaminated vegetation can be passed on
to different levels of consumers. According to IUCN [
100
], based on the 2013 internal report
of the Shell Petroleum Development Company (SPDC), there were functional wellheads,
pipelines, manifolds and flow stations, amongst others, within the protected areas such
as Apoi Creek Ramsar Site, Olague, Urhonigbe, Ukpe Sobo and Uremure-Yokri Forest
Reserves. To date, some oil facilities are still within protected areas. In general, there are
almost no protected areas in the Niger Delta that are really protected [
101
]. In our analysis,
the Apoi Creek Forest was the reserve with the highest oil-spill occurrence in our study
even though it is a Ramsar site and has an internationally high protection status (strict
nature reserve by IUCN category). The endemic and critically endangered Niger Delta
Red Colobus Monkey (Piliocolobus epieni), living in the Apoi Creek Forest, might be highly
at risk even though the results in Table 3did not show that this species is affected; this
could be related to the fact that we used point data (occurrence of the species) and not the
potential distribution range of species. Between 1994–1997, the population of the Niger
Delta Red Colobus Monkey was less than 10,000 individuals and is now expected to be
only a few hundred [
102
]. Even though many bird species are endangered in the Niger
Delta (Appendix A, Figure A3), they were less reflected in our results as an oil-spill affected
species (Table 3). In contrast to mammals, bird species might be more mobile but they use
the swamp area as an important breeding and feeding ground. For example, the Upper
Orashi Forest Reserve, located in the Niger Delta Swamp Forest and also affected by oil
spills, hosts the Grey Parrot (Psittacus erithacus), the Congo Serpent Eagle (Dryotriorchis
spectabilis) and more than 90 other bird species [
103
]. Some mammal species have just
been discovered in the area (that are new to Nigeria), such as the Black-Fronted Duiker
(Cephalophus nigrifrons), the Pygmy Scaly-Tailed Flying Squirrel (Idiurus sp.) and the Small
Green Squirrel (Paraxerus poensis).
4.3. Implications for Nature Conservation and Environmental Policy
Oil spills were clustered along the pipelines. Furthermore, the oil facilities being
sabotaged depended on the level of accessibility of the facilities to people [
19
,
59
,
104
].
Therefore, highly accessible facilities and regularly sabotaged pipelines can be surveyed
and monitored more often using digital techniques such as the remote oil and gas pipeline
integrity monitoring systems. Oil facilities such as pipelines must be in good condition,
inspected and serviced regularly to help early detection of potential spills. Otherwise,
pipelines could also be replaced with new, concrete-encased pipes sunk 3–4 m underground.
Regarding environmental policy implications, if the contamination that is longstand-
ing in the Niger-Delta region is to be fully addressed, environmental policies such as the
NOSDRA enabling laws need to be revised and amended so that nature conservation
Sustainability 2022,14, 14256 14 of 21
clauses are fully mainstreamed to expand the scope of the agency. This would help to
address remediation, reclamation and restoration aspects of the oil-spill management value-
chain. In addition, the proposed policy update and upgrade need to encourage adoption of
space-dependent technologies such as integrated Remote Sensing and Geographic Infor-
mation Systems to facilitate the continuous mapping and monitoring of the regions with
oil-spill occurrence and impacts. This will enable Nigeria to build on the recommenda-
tions made in the assessment report issued in 2012 by the United Nations Environmental
Programme (UNEP) on the petroleum hydrocarbon pollution in Nigeria with a focus on
the 69 oil-impacted sites in various parts of Ogoniland [
96
], an area currently remediated
under the Hydrocarbon Pollution Remediation Project (HYPREP), which is supervised by
NOSDRA. However, beyond the Ogoniland, the project needs to be expanded to areas
deserving urgent attention reflecting on the data published in the Oil Spill Monitor [
73
]
managed by the lead oil-spill management agency and environmental regulator on oil and
gas pollution matters in the petroleum sector in Nigeria, NOSDRA.
The implication of persistent oil spills in the environment is a challenge to the effec-
tiveness of policies enacted to manage oil spills in Nigeria. Strict measures and control
must be put in place to avoid further oil spills (as well as hunting and deforestation) but
due to security reasons, conservation measures could not be implemented so far [
102
].
Scientific investigations and monitoring of threatened species on site were impossible. In
addition, measures of environmental education and awareness are not conducted [
103
].
The prevailing insecurity for on-site observations poses the need for systematic and remote
initiatives of developing and utilizing up-to-date and accurate map tools showing oil-
sensitive features within oil and gas assets. For example, the utilization of Environmental
Sensitivity Index (ESI, e.g., [
105
]) maps is a proactive tool for smart oil-spill preparedness
and responses that must be instilled and enforced by NOSDRA, the environmental reg-
ulatory agency of the Federal Government of Nigeria for oil companies operating in the
upstream and downstream sectors of the petroleum sector in Nigeria, to ensure environ-
mental sustainability. Beyond oil spills, gas flaring, venting and emissions’ detection and
measurement from the petroleum sector are significant oil and gas pollution that should be
investigated within the context of nature conservation and policy efficiency in the oil and
gas sector and progress can be presented regularly in the Conference of the Parties (COP) of
the Convention on Biological Diversity. This approach will help to amplify environmental
accountability efforts in conservation in oil-producing regions globally.
5. Conclusions
Oil spills have a massive negative impact on local species, ecosystems, ecosystem
services and people. The effects of persistent un-remediated oil-spill sites are measurable
decades after the occurrence of the spill incidence. With the support of remote sensing and
vegetation indices, this study has shown that oil impacts on vegetation and passes through
protected areas, potentially threatening species. The vegetation indices EVI, NDVI and
SAVI are more suitable to identify the impact of oil spills on different vegetation cover than
the other vegetation indices.
The results of this study serve as a wake-up call for the Federal Government of
Nigeria, oil companies and all parties benefiting from the oil production at the expense of
the environment. The identified accumulation of oil spills is located in the heart of the Niger
Delta—the Central African Mangroves and the Niger Delta Swamp Forest. These areas
are of international relevance since they host many endemic and endangered species and
provide important ecosystem services for local livelihoods, e.g., flood protection, climate
regulation and the provision of food, materials and water. Largely unprotected but sensitive
ecosystems in the Niger Delta are at risk but also protected areas, such as the Apoi Creek
Forest and Uremure-Yokri Forest Reserve. We argue that a special remediation, reclamation
and restoration project should be commissioned to conserve nature, halt threats and avert
the long-term implications of oil spills on species, ecosystems and people.
Sustainability 2022,14, 14256 15 of 21
Author Contributions: Conceptualization: C.E.A. and M.T.; methodology: C.E.A., I.-O.A. and M.T.;
software: M.T., validation: C.E.A., I.-O.A. and M.T.; formal analysis: C.E.A., I.-O.A. and M.T.;
investigation: C.E.A., I.-O.A. and M.T.; resources: C.E.A., I.-O.A., J.K. and M.T.; data curation: I.-O.A.
and M.T.; writing—original draft preparation: C.E.A., I.-O.A., J.K., M.I.M. and M.T.; writing—review
and editing: C.E.A., I.-O.A., J.K., M.I.M. and M.T.; visualization: C.E.A. and I.-O.A.; supervision:
M.T., I.-O.A. and J.K. All authors have read and agreed to the published version of the manuscript.
Funding:
Authors acknowledge the support from the German Federal Ministry of Education and
Research (BMBF) via the project carrier at the German Aerospace Center (DLR Projektträger) through
the research project: WASCAL-DE-Coop (FKZ: 01LG1808A).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
Authors acknowledge the support from the German Federal Ministry of Educa-
tion and Research (BMBF) via the project carrier at the German Aerospace Center (DLR Projektträger)
through the research project: WASCAL-DE-Coop (FKZ: 01LG1808A).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Sustainability 2022, 14, x FOR PEER REVIEW 16 of 22
Appendix A
Figure A1. Map of Niger Delta showing the distribution of some oil facilities.
Figure A2. Sankey diagram showing the causes of spills and the facilities affected (own compilation
and representation; NOSDRA data from 2016–2020).
Figure A1. Map of Niger Delta showing the distribution of some oil facilities.
Sustainability 2022,14, 14256 16 of 21
Sustainability 2022, 14, x FOR PEER REVIEW 16 of 22
Appendix A
Figure A1. Map of Niger Delta showing the distribution of some oil facilities.
Figure A2. Sankey diagram showing the causes of spills and the facilities affected (own compilation
and representation; NOSDRA data from 2016–2020).
Figure A2.
Sankey diagram showing the causes of spills and the facilities affected (own compilation
and representation; NOSDRA data from 2016–2020).
Sustainability 2022, 14, x FOR PEER REVIEW 17 of 22
Figure A3. Distribution of near threatened, vulnerable, endangered and critically endangered IUCN
Red List species in the Niger Delta (compilation based on data from IUCN [63] and GBIF [72]).
Figure A4. The performance of the vegetation indices displaying spill and non-spill land cover. All
five vegetation indices were significant at p-value less that 0.0001 (****).
Figure A3.
Distribution of near threatened, vulnerable, endangered and critically endangered IUCN
Red List species in the Niger Delta (compilation based on data from IUCN [63] and GBIF [72]).
Sustainability 2022,14, 14256 17 of 21
Sustainability 2022, 14, x FOR PEER REVIEW 17 of 22
Figure A3. Distribution of near threatened, vulnerable, endangered and critically endangered IUCN
Red List species in the Niger Delta (compilation based on data from IUCN [63] and GBIF [72]).
Figure A4. The performance of the vegetation indices displaying spill and non-spill land cover. All
five vegetation indices were significant at p-value less that 0.0001 (****).
Figure A4.
The performance of the vegetation indices displaying spill and non-spill land cover. All
five vegetation indices were significant at p-value less that 0.0001 (****).
Sustainability 2022, 14, x FOR PEER REVIEW 18 of 22
Figure A5. Land use/land cover impacted by oil spills.
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