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American Journal of  
Geospatial Technology (AJGT)

An in-depth analysis of  the Terrorists Activities on Land Use and Land Cover (LULC) 
Change of  Sambisa Forest of  Nigeria

Ukah, C.1*, Ejaro, S. P.2, Makwe, E.2, Ahmad, H. A2

Volume 4 Issue 1, Year 2025
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v4i1.4665
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: March 03, 2025

Accepted: April 08, 2025

Published: September 16, 2025

This study analyzed terrorists activities on land use and land cover change of  Sambisa 
Forest of  Nigeria. Land use and land cover change was employed to determine the rate 
of  change in Sambisa Forest as a result of  Boko Haram activities. GIS and remote sensing 
was employed in the course of  this study. Secondary types and sources of  data were used 
and they include data on the vegetation cover of  Sambisa Forest before and during the 
emergence of  Boko Haram and data on the land use/land cover. The data for the vegetation 
analysis of  this study were obtained using satellite imageries of  the study area from 1994, 
1999, 2004, 2009, 2014 and 2019. The Landsat images of  each year was independently 
classified with supervised classification technique. Each analysis carried out was subjected 
to different formular for deducing each of  the variables. Maximum likelihood classifier 
algorithm was applied to classify the LULC types in QGIS 3.8.2 software. The result from 
satellite imagery analysis for land use land cover of  Sambisa forest for the year 1994 revealed 
that marshland constitutes 13.4% amounting to 63.4km2 on the satellite image. Vegetation 
measurement on the satellite image covers an area of  222.38km2 representing 46.9%. Bare 
surface covers an area of  84.01km2 representing 17.7% of  the imagery. Built-up accounts for 
21.2% covering an area of  100.6km2, such settlements include; Masba, Dalwa, Dalori, Bama, 
Konduga, Tateri, Kuka Kowa, among others. Water (lakes, ponds, streams, etc.) amounted 
to 0.76% covering an area of  3.61km2. The year 2009 witnessed the beginning of  the vicious 
cycle of  violence starting from Borno State, around the Sambisa forest northern corridor of  
Konduga, Bama Dalori and ultimately Maiduguri. From the analysis, the result experienced 
a fluctuating dip and rise with water bodies accounting for 0.66% of  the total landcover 
with an area coverage of  3.12km2 comparing this result with that of  2004 which was 0.5% 
(2.17km2), this simply means that there was a slight increase by 0.16% which accounted 
for the rise. Bare surface cover in the area was 20.6% representing 97.6km2 precisely areas 
around Konduga. Marshland in the area accounted for 19.1% representing 90.7km2. There 
was an increase in the vegetation of  the study from 104.13 the previous year to 201.5 in 
2009. Land use land cover analysis for 2019 yielded another interesting result leaving little 
to doubt from the previous years. From the analysis, Water occupied 0.39% representing 
1.86km2, vegetation within the area had 11.61% which is a drop from the 28.5% in the 
previous year. Marshland covered 32.7%, translating to 155km2; Bare surface accounted 
for 32.1%, covering an area of  152.10km2, while Built-up had a drastic increase of  23.2%, 
representing 110.3km2 of  the total area. This is a clear indication of  Boko Haram activities 
and Military counter insurgency on Sambisa forest. From the findings, it is recommended 
that the military should stop Boko Haram activities and force them out of  the forest and the 
area will regenerate and the soil remediated.

Keywords
Boko Haram, Land Cover, Land 
Use, Military Activities, Sambisa 
Forest, Vegetation

1 Department of  Environmental Management, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria.
2 Department of  Geography and Environmental Management, University of  Abuja, Federal Capital Territory, Nigeria.
* Corresponding author’s e-mail: nomsostainless@gmail.com, c.ukah@unizik.edu.ng

INTRODUCTION
Using a land change approach, land use represents the 
interaction between humans and their environment and is 
used as a conceptual platform upon which to determine 
both the causes and consequence of  land use change 
and to investigate the influence and potential success of  
land management decisions. Analyzing land use change 
requires a time series of  land use maps and remote 
sensing classification is the primary tool used to acquire 
such data. There are many advantages of  using remote 
sensing classification tools to map land use such as, it 
allows efficient access to otherwise inaccessible or remote 
locations and it provides time series information at a 
scale meaningful for regional decision making. Szabo et 
al. (2016) opined that remote sensing techniques provide 
a great possibility to analyze the environmental processes 

in local or global scale. Satellite images provide a wide 
range of  possibilities of  monitoring the environment in 
a fast way, especially on areas which are unavailable for a 
field survey due to the topography, dense vegetation or 
other local factors.
Terrorist activity is a particularly dangerous form of  
criminal activity that poses a threat to all mankind, along 
with environmental disasters and wars. Global terrorism 
is a rapidly growing threat to world security and increases 
the risk of  bioterrorism (Green et al., 2019). There are 
environmental implications of  wars, terrorists’ activities 
and the use of  bombs on the environment. Indirect 
impacts are many and varied and are often long lasting 
than the direct impacts. They include the construction 
of  various camps although ephemeral, camps cause 
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personnel and vehicles and the disposal of  waste materials 
(Ukah et al., 2021). War also inevitably entails destruction, 
resulting in widespread toxic substances, dead wildlife, 
and an atmosphere chocked with fumes. McCarthy 
(2022) noted that there three (3) key facts about how war 
impacts the climate crisis and the environment. Firstly, 
militaries consume enormous amounts of  fossil fuels, 
which contributes directly to global warming. Secondly, 
bombings and other methods of  modern warfare directly 
harm wildlife and biodiversity. And lastly, pollution of  the 
environment.
LULC was employed to determine the rate change in 
Sambisa forest as a result of  Boko Haram activities. Sambisa 
forest is one of  the ungoverned spaces in Nigeria. Igboin 
(2021) opined that the concept of  ungoverned spaces is 
sly and controversial. This is because many definitions 
proffered to encapsulate its contents have either failed 
woefully or succeeded only partially. Taylor (2016) noted 
that ungoverned space is a place where the state or central 
government is unable or unwilling to extend control, 
effectively govern, or influence the local population, 
and where a provincial, local, tribal or autonomous 
government does not fully or effectively govern, due 
to inadequate governance capacity, or restrictive norms 
of  behaviour. Ungoverned area should be assumed to 
include under-governed, ill-governed, contested, and 
exploitable areas. Contested spaces are safe havens for 
non-state actors that have come to occupy them. Safe 
haven can be regarded as a space for operational activity, 
spaces where non-state actors can galvanize and mobilise 
forces for expansionist purposes. From there, they are 
able to establish themselves, consolidate, plan, organise, 
fundraise, recruit, train and operate. Boko Haram is 
contesting space with the government of  Nigeria with 
the aim to institute its own kind of  rule and form of  
Islam (a sharia governed space) as a forestate to what it 
probably plans to extend to other parts of  the country. 
Its reference to a caliphate expressly underscores that 
it wants an exclusive rule based on sharia and Islamic 
Law. Since 2009, Boko Haram has not only continued to 
contest space with the Nigerian government but has also 
taken over some spaces where it has raised its flag and 
declared independence. Unfortunately, Sambisa forest 
happens to be one of  those places occupied by Boko 
Haram due to its rugged terrain, room to hide or receive 
supplies, and low population density with Maiduguri close 
enough to allow necessary interaction with the outside 
world. It was based on this premise that this study tends 
to analyze the impact of  Boko Haram activities on the 
LULC of  Sambisa forest.

LITERATURE REVIEW
Norris and Grol-Prokopczyk (2018) argued that the 
definition of  terrorism is a highly-contested and 
politicized process, and the term terrorism is applied in 
highly inconsistent ways. For example, left-wing activists 
contest their labelling as terrorists for minor crimes, while 
right-wing extremists who commit murder to advance 

ideology are rarely labelled as terrorists (Norris, 2016, 
2017). Terrorism is the illicit way of  hostility carried out 
to accumulate ransom, bring down a government, get the 
release of  hostages, ensure total breakdown of  economic 
activities or penalize unbelievers of  religion and lots 
more (Ball et al., 2013). Terrorism is ultimately a form 
of  psychological warfare that relies on fear to achieve its 
ends (Lutz & Lutz, 2017). This is evident in the situation 
faced by communities living within and around Sambisa 
forest of  Nigeria. Terrorism restricts the environment 
for the people to enjoy their fundamental rights and 
freedoms. The environmental destruction that can be 
legitimately labeled terrorism is when the act or threat 
breaches national and or international laws governing the 
disruption of  the environment during peace time or war 
time. It could also be when the act or threat exhibits the 
fundamental characteristics of  terrorism (i.e. the act or 
threat of  violence has specific objectives and the violence 
is aimed at a symbolic target).
There is a significant relationship between terrorism and 
forest areas. Umar and Naibbi (2015) stated that forests, 
grasslands, deserts and swamps provide different logistic 
and vulnerability potentials to states. For instance, while 
bare ground favours offensive, forests favour defence. 
This is the strategy that the Vietnamese understood 
and made the tactical use of  their rainforest during the 
USA-Vietnam war in the 1960s – 70s. Vietnamese used 
the techniques of  strike and run in to the bush till they 
frustrated the most powerful-heavily armed USA soldiers 
despite the defoliation of  about 16,200km2 of  the 
Vietnam forest land by the USA armies. Studies on the 
impacts of  defoliants estimated that ten percent of  trees 
sprayed were not only defoliated but also killed by a single 
application of  Agent Orange, with a particularly strong 
effect on sensitive and ecologically important mangrove 
forests along the Mekong Delta (Aju & Aju, 2018). On the 
same hand, the Indonesian military repeatedly bombed 
and napalmed forested areas in East Timor where 
independence guerrillas took shelter, with devastating 
impacts to the environment. In Darfur, Sudan, trees were 
deliberately destroyed by militia in an attempt to sever 
community ties and reduce possibilities for resettlement 
in the area. On the other hand, barren land of  the Azawad 
region in Mali provided an easy terrain for the French and 
African Union forces to locate and destroy the military 
vehicles of  the Tuareg rebels that cut-out Northern Mali 
in 2013.
Varga et al. (2015) noted that satellite images provide 
a wide range of  possibilities of  monitoring the 
environment in a fast way, especially on areas which are 
unavailable for a field survey due to the topography, 
dense vegetation or other local factors. Xue and Su (2017) 
stated that vegetation indices (Vis) obtained from remote 
sensing-based canopies are quite simple and effective 
algorithms for quantitative and qualitative evaluations of  
vegetation cover, vigour, and growth dynamics, among 
other applications. Vegetation indices (Vis) are now 
indispensable tools in land cover classification, climate 



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and land use change detection, drought monitoring and 
habitat loss, etc. Adelaja and George (2019) opined that 
an increase in the intensity of  terrorist attacks results 
in an increase in the amount of  land owned due to 
the abandonment of  farms by neighbours and family 
members, increases the percentage of  land left fallow, 
increases the average size of  plots farmed, increases 
the average distance between plots farmed and the 
homestead, discourages mono cropping and encourages 
mixed. Farmers’ expectations about the values of  their 
lands also decrease with exposure to terrorism.  

MATERIALS AND METHODS
The study area
The Sambisa Forest is one of  the several forests that 
constitute the governed space in Nigeria geography 
(Ukah et al., 2025). Yet it remained a subject of  no 
interest to both the leadership and the citizenry until the 
media shone a searchlight on the Boko Haram insurgency 
in the country. The Sambisa forest is located at the north-
eastern tip of  the west Sudanian Savanna and the southern 
boundary of  the Sahel Acacia Savanna about 60 km south 
east of  Maiduguri the capital of  the state of  Borno. It 
covers an area of  474km2 (Figure 1). It is administered by 
the Local government areas of  Nigeria of  Askira/Uba in 
the south, by Damboa in the southwest, and by Konduga 

and Jere in the west. The name of  the forest comes from 
the village of  Sambisa which is on the border with Gwoza 
in the East.
The Gwoza hills in the East have peaks of  1,300 
meters above sea level and form part of  the Mandara 
Mountains range along the Cameroon-Nigeria border. 
Seasonal streams drain the forest into the Yedseram 
and the Ngadda Rivers. Some of  the Sambisa tributaries 
are linked to Balmo forest stretching from Bauchi State 
through to Jigawa State. The forest consists of  a mixture 
of  open woodland and sections of  very dense vegetation 
of  short trees about two metres high and thorny bushes, 
with a height of  0.5 - 1 metre which are difficult to 
penetrate. Major trees and bushes in the forest include 
acacia, date palm (dabiinuuwaa), terminalia, tallow, red 
bushwillow (baa ruwaanaa), rubber, tamarind (busai), wild 
black plum (dinyaa), jackalberry, monkey bread, birch, 
mesquite, baobab (bambuu), shea tree (ciri), rubber vine 
(ciiwoo), whitehorn (damaagiram) (Birdlife International, 
2015). The climate is hot, dry and wet with minimum 
temperatures of  about 21.50C between December and 
February, a maximum of  about 480C in May with average 
temperatures of  about 28 – 290C. The dry season is from 
November to May and the wet season is between May 
and September/October with annual rainfall of  about 
190mm.

Figure 1: Map of  Sambisa Forest
Source: Author, 2020



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MATERIALS AND METHODS
This section comprised of  the methods and techniques 
that were adopted to generate data for the study as well as 
the methods used to analyse the data. Remote sensing was 
employed in the course of  this study. Secondary types and 
sources of  data were used in this study and they include 
data on the vegetation cover of  Sambisa forest before and 
during the emergence of  Boko Haram and data on the 
land use/land cover. The data for the vegetation analysis 
of  this study were obtained using satellite imageries of  
the study area from 1994, 1999, 2004, 2009, 2014 and 
2019. They were used to assess the rate of  change in Land 
Use Land Cover (LULC). GIS was used in the course of  
this analyses with Landsat 8 OL1 imagery and Landsat 
7 ETM. Multi-temporal Landsat satellite images of  the 
study area were freely downloaded from the USGS’ 
Earth Explorer portal. The imageries were downloaded 
in the months of  June, July and August as they will give 
a better view or understanding of  the vegetation (dense 
vegetation period). Except for the thermal infrared, all 
the visible and infrared spectral bands were included in 
the image classification. Pre-processing of  the images 
such as atmospheric and geometric corrections, suitable 
band selection, sub-setting, layer stacking and image 
enhancements were applied before performing the 

classification of  the remote sensing data.
To obtain surface reflectance data, Landsat images were 
atmospherically corrected using SCP Plugin software 
tools. Spatial enhancement technique was also applied in 
order to improve the image quality. The Landsat images 
of  each year was independently classified with supervised 
classification technique. Each analysis carried out was 
subjected to different formular for deducing each of  the 
variables. For land use land cover, maximum likelihood 
classifier algorithm was applied to classify the LULC types 
in QGIS 3.8.2 software. More so, accuracy assessment was 
also carried out to evaluate the dependability of  extracted 
information from the classification. The accuracy of  
the classification results was tested using the data for 
validation. Lastly, the post classification comparison 
was done using separately classified Landsat images and 
then a comparison was made for the LULC maps. The 
bands used were band 1, 2, 3, 4, 5 and 7 for Landsat 4 - 
5TM, and Landsat 7ETM+. While for Landsat 8OLI, the 
bands used were band 2, 3, 4, 5, 6 and 7. Five major image 
classifications were employed which include; Water, 
Vegetation, Bare surface, Marshland and Built-up areas.
Table 1 shows the data type and sources while the 
materials and methods for the analyses is shown on Table 
2.

Table 1: Data used for the Analyses

Data Type Source Year of  Acquisition Month Spatial Resolution 
Landsat 4 and 5 TM Earth Explorer 1994 August 30m
Landsat 4 and 5 TM Earth Explorer 1999 August 30m 
Landsat 7 ETM+ Earth Explorer 2004 July 30m
Landsat 7 ETM+ USGS Glovis 2009 June 30m
Landsat 8 OLI/TIR USGS Glovis 2014 August 30m
Landsat 8 OLI/TIR USGS Glovis 2019 August 30m
Shuttle Radar Topographic 
Mission (SRTM)

USGS Glovis 2019 30m

Table 2: Materials used for the Analyses

Material Type Uses
Hardware Computer PC, GPS, etc. Data processing, Classification and Analysis
Software QGIS 3.8.0 LTR Map Presentation, Land Cover Change Modeller LCCM to determine 

change detection between 1994, 1999, 2004, 2009, 2014 and 2019 and 
Classification Report to determine Area cover in Square Kilometres 
and Percentage.

UTM Coordinate Converter For coordinate conversion from Universe Transverse Mercator to 
Geographic coordinate for area AOI clipping.

Change Detection Analysis
Starting from previously described dataset of  Landsat 
classified imageries, the process of  digital change detection 
developed has allowed for the determination and description 
of  changes in landcover between five fundamental intervals 
of  1994 – 1999, 1999 – 2004, 2004 – 2009, 2009 – 2014 and 
2014 – 2019. There are many methods of  change detection 
available and each has variations depending on the imagery 

type, final purpose for the change image, and the type 
of  change to be detected. In this case, the methodology 
followed has been the post-classification comparison. Such 
approach allows for the determination of  the difference 
between independently classified images from each of  the 
dates in question and it is the only method in which ‘from’ 
and ‘to’ classes can be calculated for each changed pixel. 
Hence, comparing each classified map with the successive, 



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it has been possible to determine the changes in land use 
land cover at different years from 1994 to 2019. QGIS 3.8 
Post classification algorithm has been used to analyse and 
integrate LULC maps and extract the GIS layers describing 
changes and dynamics of  land cover. Subsequently, 
in order to perform the accuracy assessment on the 
change detection, procedures and approach proposed by 

Congnalton and Macleod (2009) have been followed in 
which the error matrix normally used for the single date 
classification is purposively modified.

RESULTS AND DISCUSSION
The summary of  the LULC from the year 1994 to 2019 
is shown on Table 3.

Table 3: LULC distribution from the year 1994–2019

LULC category 1994 (km2) 1999 (km2) 2004 (km2) 2009 (km2) 2014 (km2) 2019 (km2)
Water 3.61 2.5 2.17 3.12 1.8 1.86
Vegetation 222.38 103.6 104.13 201.5 135.0 55.04
Bare surface 84.01 93.9 93.7 97.6 169.0 152.10
Marshland 63.4 181.0 178.3 90.7 93.62 155.0
Built-up 100.6 93.0 95.7                                                      81.08 74.58 110.0
Total 474 474 474 474 474 474

Land use land cover of  Sambisa forest for the year 1994
As at the year 1994, Water bodies including lakes, pond, 
streams and other smaller natural impoundment of  water 
occupies an area of  about 3.61km2 representing 0.76% of  
the total landmass of  Sambisa forest and vegetation covers 

46.9% amounting to 222.38km2. Bare surface covers an 
area of  84.01km2 representing 17.72%, marshland covers 
an area of  63.4km2 representing 13.38%. While Built-up 
area constitutes 21.22% representing 100.6km2 as could 
be seen on Figure 2.

Land use land cover of  Sambisa forest for the year 
1999
The Landsat classification of  Sambisa forest for the year 
1999 is shown in Figure 2. The result from the analysis 
of  the imagery for the year 1999 reveals that water 
constituted 0.53% amounting to 2.5km2, vegetation had 
an area coverage of  103.6km2 representing 21.9% of  the 
satellite imagery, bare surface occupied an area of  93.9km2 

representing 19.8%, marshland had an area coverage of  
181km representing 38.2% while built-up constituted 
19.6% amounting to 93.0km2.

Land use and land cover of  Sambisa forest for the 
year 2004
Figure 2 revealed the result from satellite imagery analysis 
for the land use and land cover of  Sambisa forest for 

Figure 2: Land use land cover of  the study area from the 1994 – 2019.



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the year 2004. From the analysis, water accounted for 
0.46% covering 2.17km2, vegetation covered an area of  
104.13km2 representing 21.97%, bare surface covered 
an area of  93.7km2 representing 19.77%, marshland 
accounted for 37.6% covering an area of  178.3km2 while 
built-up area accounted for 20.2% covering an area of  
95.7km.

Land use land cover of  Sambisa forest for the year 
2009
The result from satellite imagery analysis of  the land use 
land cover of  Sambisa forest for the year 2009 is shown 
in Figure 2. From the analysis, Water constituted 0.66% 
representing 3.12km2, Vegetation constituted 42.5% 
representing 201.5km2, Bare surface recorded 20.6% 
amounting to 97.6km2, Marshland constituted 19.1% 
representing 90.7km2 while Built-up constituted 17.1% 
representing 81.08km2 on the ground.

Land use and land cover of  Sambisa forest for the 
year 2014
Figure 2 show that satellite imagery analysis for land use 
land cover of  Sambisa forest for the year 2014. From the 
analysis, Water occupied an area of  1.8km2 constituting 
0.38% of  the total area, Vegetation accounted for 28.5% 
representing 135km2, Bare surface accounted for 35.7% 
representing 169km2, Marshland constituted 19.8% 
amounting 93.62km2 while Built-up recorded an area 
coverage of  74.58km representing 15.7% of  the total area 
of  Sambisa forest.

Land use and land cover of  Sambisa forest for the 
year 2019
Figure 2 present the satellite imagery analysis for land use 
land cover of  Sambisa forest tor the year 2019. From Table 
5.14, Water constituted 0.39% amounting to 1.86km2 of  
the study area, Vegetation occupied an area of  55.04km2 
accounting for 11.6%, Bare surface accounted for 32.1% 
and area coverage of  152.10km2 of  the total study area. 
Marshland accounted for 32.7% representing 155km2 
while Built-up constituted 23.2% representing 110.3km2.

Discussion of  findings from LULC of  Sambisa 
forest
The environmental implications of  Boko Haram activities 
on land use land cover were discussed in this section. The 
removal of  vegetation coupled with soil excavation either 
by anthropogenic activities or by natural ways increases 
the potential for soil erosion, and reduces water.

Land use land cover of  1994
The result from satellite imagery analysis for land use land 
cover of  Sambisa forest for the year 1994 revealed that 
marshland constitutes 13.4% amounting to 63.4km2 on 
the satellite image. Marshlands are areas with soil mixed 
with water and vegetation, often represented as slight dark 
on the satellite image. Vegetation measurement on the 
satellite image covers an area of  222.38km2 representing 

46.9%. It is noteworthy that the vegetation within this 
zone is a mixture of  Sudan and Sahel savannah which 
are characterized by short shrub and trees. One of  the 
major factors measured in the course of  this study was 
the soil of  the study area which could serve the potential 
of  agricultural purpose especially as it relates to crop 
growth and other anthropogenic support. Bare surface 
covers an area of  84.01km2 representing 17.7% of  the 
imagery. This shows that agricultural activities especially 
farming takes place in the area. The reason for this may 
not be farfetched owing to the presence of  water bodies 
around the forest and the presence of  marshland which 
supports certain rare agricultural products that are grown 
in the Sahelian region. On this note, agricultural activities 
should thrive in this area for a good reason. It also 
accounts for why certain agrarian settlements might be 
found in and around the forest area and this brings about 
built-up areas as one of  the major classifications. Built-up 
in this area could be villages and small farm settlements 
located in and out of  the forest. Built-up accounts for 
21.2% covering an area of  100.6km2, such settlements 
include; Masba, Dalwa, Dalori, Bama, Konduga, Tateri, 
Kuka Kowa, among others. Water (lakes, ponds, streams, 
etc.) amounted to 0.76% covering an area of  3.61km2. 
One of  the major reasons for the stream availability in the 
area was the presence of  vegetal cover which prevented 
the loss of  water bodies via evapotranspiration in the 
forest. On the northern axis of  the map is the state 
capital Maiduguri from where some of  these streams 
take their rise from and flow onward into the forested 
region. During this period, Boko Haram insurgency has 
not emerged.

Land use land cover of  1999
The 1999 imagery analysis revealed another slight 
deviation with a strong follow-up from the previous 
years. Most of  the deviations came from systemic 
reduction in the water bodies compared to imagery 
analysis of  the year 1994. Compared to 1994 imagery 
analysis, water bodies ranging from lake, stream and 
natural ponds fell from 0.76% to 0.53% resulting to the 
reduction in area coverage from 3.61km2 to 2.5km2. This 
significant reduction in water bodies can be attributed 
to first, the increase in the rate of  desertification or the 
encroachment of  the Sahara Desert into the state via the 
north east corridor. This assertion can be deduced from 
the alarming increase in the bare surface analysis which 
can be seen as at 1994, it was 17.7% covering an area 
of  84.01km2 but as at 1999, it had increased to 19.8% 
covering an area of  93.9km2. Although, this increase can 
also be tied to the fact that there was significant increase 
in anthropogenic activities ranging from farming, hunting 
and significant decrease in waterbodies in the area. Mzuza 
et al. (2019) in their work found out that rapid population 
growth and increase in gross domestic product (GDP) are 
identified as the major drivers of  deforestation and forest 
degradation due to clearing of  vast fields for agriculture, 
land expansion for urban settlement, and cutting down 



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of  trees for fuelwood. Hurt et al., (2006) also noted that 
human activities have altered 42% - 68% of  the global 
land surface by transforming natural vegetation into 
crops, pastures and woods for harvesting from the years 
1700 to 2000. Added to this, the agricultural practices 
which were supported five years ago (1994) could be 
seriously impeded. It is worthy to note that at this time, 
Boko Haram insurgency was not in existence neither 
was it active during this period but there were issues 
bothering on the encroachment of  the Sahara Desert in 
which one of  the United Nation Convention to Combat 
Desertification initiative was the planting of  the gum 
Arabic trees to slow and stop the encroachment of  desert. 
From the analysis also, marshland increased from 13.4% 
to 38.2% leading to an increase from 13.4km2 to 181km2. 
One of  the implications of  this result is the increment in 
the agro-viability of  these lands as marshland can serve as 
a great potential agricultural land for cultivation of  crops 
such as rice and this also means an increase in the level of  
aquatic fauna existing in the area. 
In the same vein, anthropogenic activities at this period 
also translated to a decrease in the number of  built-
up areas from 21.2% representing 100.6km2 to 19.2% 
representing 93km2 on the ground with a decrease of  
2%. This particular result has two implications. Firstly, 
from the standpoint of  the “push and pull” factor which 
implies that due to the arid nature of  this area, people 
tend to migrate from dry zones to more favourable zone 
where their daily economic activities can be supported 
such as farming. The result by default includes clearing 
of  farmlands for farming and building of  shelters. These 
anthropogenic activities could spark up an exacerbation 
of  desert encroachment. Secondly, the soil in the area 
hence with water, irrigation farming could possibly be 
carried out in the dry season with limited number of  
crops to be grown such as carrot, pepper, onions, among 
others. Tsendbazar et al. (2018) stated that the problems 
of  land use land cover changes are global and serious in 
many developing countries where increasing population 
has resulted into excessive pressure on natural resources. 
Significant population increase, migration and accelerated 
socioeconomic activities have intensified environmental 
changes over the centuries. 

Land use land cover of  2004
The satellite imagery analysis of  2004 revealed another 
progression in the analysis of  the classified variables 
for the land use land cover. The result shows that water 
constituted 0.46% covering an area of  2.17km2, which 
is another reduction from the previous year analysed 
(1999) that yielded 0.5% at 2.5km2. The rate of  this 
reduction would be examined later in this analysis but it is 
noteworthy that climate change was a major contributor 
to this factor as one of  its attendant effect is the drastic 
reduction of  water bodies around the semi-arid areas 
and attendant flood in the coastal areas. Anthropogenic 
activities due to the demand for water happened to be 
another factor that contributed to the steady reduction of  

the water bodies in the study area with the fact that most 
of  the streams are seasonal in nature. The reduction in the 
water bodies brought about a shift in the built-up areas 
as most of  the built-up areas are agrarian communities 
whose farming is dependent on irrigation farming due to 
inadequate rainfall. In comparison with the result from 
the year 1999, the built-up area shows 20.2% on an area 
of  95.7km2 as previously compared to 19.6% covering 
an area of  93km2 of  the study area. Marshland reduction 
also could be a contributory factor in the drastic increase 
of  the population in the study area as compared with the 
year 1999. Marshland accounted for 38.2% covering an 
area of  181km2 in 1999 but reduced to 37.6% representing 
178.3km2 in 2004. 
Vegetal cover within the area showed a variation of  
21.97% covering an area of  104.13km2 as against the 
previous 21.9% covering an area of  103.6km2. One of  
the reasons for this increase is probably the aggressive 
campaign launched by the then Federal Government 
and the United Nations as part of  effort geared towards 
reducing the Sahara Desert encroachment through 
aggressive tree planting campaign in Nigeria especially 
in the Sudano-Sahelian region. Soil has been a major 
factor that attract population as ready arable land can 
be cultivated for food production either subsistent or 
commercially in this regard. Bare surface of  the imagery 
analysed stood at 19.8% covering an area of  93.7km2. 
Also, Boko Haram insurgency has not emanated during 
this era. 

Land use land cover of  2009
It is important to note at this point that the Boko Haram 
insurgency gained momentum by 2009 after the death 
of  its leader and founder Mohammed Yususf. Before 
this time, the areas around the Sambisa forest was not a 
trouble spot even though information gathered was that 
the Boko Haram sect had withdrawn to the interior parts 
of  Borno State from where they had been steadily gaining 
religious advantage and proceeded to launch attack on 
various government parastatals in Nigeria as a whole. The 
year 2009 witnessed the beginning of  the vicious cycle of  
violence starting from Borno State, around the Sambisa 
forest northern corridor of  Konduga, Bama Dalori and 
ultimately Maiduguri. The impact of  their activities was 
not felt from the humanitarian point of  view alone but 
also from the environmental point of  view which brought 
in a sporadic increase in the number of  anthropogenic 
activities within these areas from both side of  the 
government force and Boko Haram militias who sought 
to declare Islamic caliphate over that area in alliance with 
the internationally recognised terror organisation (Islamic 
State and Levante (ISIL). The drivers of  LULC change in 
the region include anthropogenic (such as economic and 
demographic) and environmental factors (Tran et al., 2015 
as cited in Spruce et al., 2020). Both negative and positive 
forest change drivers occur, which can collectively affect 
the amount of  agricultural expansion and contraction 
as well as the amount of  deforestation and afforestation 



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(Imai et al., 2018). The decrease in the amount of  forest 
covered area may have a negative influence on the whole 
earth’s environment (Modzelewska et al., 2017). From 
the analysis, the result experienced a fluctuating dip and 
rise with water bodies accounting for 0.66% of  the total 
landcover with an area coverage of  3.12km2 comparing 
this result with that of  2004 which was 0.5% (2.17km2), 
this simply means that there was a slight increase by 
0.16% which accounted for the rise. This was buttressed 
further by the fact that built-up areas as stated on the 
land use land cover map showed a decrease as residents 
fled from their homes as a result of  the emergence of  
Boko Haram activities. The few settlements during this 
era were mostly military camps who were carrying out 
counter insurgency operation to root out the militant 
residents in the area. Resulting violence account for built-
up area or settlements around that area to be overrun 
by Boko Haram militant who later declared the area of  
Dalwa and Konduga as part of  the Islamic caliphate. Bare 
surface cover in the area was 20.6% representing 97.6km2 
precisely areas around Konduga. Marshland in the area 
accounted for 19.1% representing 90.7km2. There was 
an increase in the vegetation of  the study from 104.13 
the previous year to 201.5 in 2009. The sharp increase 
in vegetation could be as a result of  the United Nations 
Convention to Combat Desertification the previous years. 

Land use land cover of  2014
The result from the imagery analysis for the year 
2014 witnessed a drastic shift environmentally and 
anthropogenically especially as it affects human habitations 
and land cover within the study area. It should be asserted 
that the Boko Haram crisis witnessed a massive upscale 
in 2014 which made several Nigerians affrighted for their 
safety. It would also be recalled that it was from 2014 
the militant group declared fully their allegiance to the 
international renowned terrorist group, Islamic State of  
Iraq and the Levante. In May 2013, Nigerian government 
forces launched an offensive attack in the Borno region in 
an attempt to dislodge Boko Haram fighters after a state 
of  emergency was declared on the 14th of  May, 2013. All 
the activities of  Boko Haram in the area had an attendant 
effect on the environmental structure of  the Sambisa 
forest added to the fact that the upsurge in violence 
brought about the use of  modern weaponry such as 
bombs, anti-craft gun and other sophisticated weapon 
which could pose catastrophic effect on the environment. 
Military operations in the area would definitely bring 
about the clearing and destruction of  vegetation thereby 
reversing the gains made in the aggressive afforestation 
approach especially with weapons such as Armoured car, 
Tanks, among others. Specific reduction was experienced 
in the amount of  water bodies in the area as percentage 
coverage for water reduced to 0.38% covering an area of  
1.8km within the study area. There was a massive decrease 
in vegetation of  the area as vegetation fell to 28.5%. Thus, 
virtually the gains made through the federal government 
afforestation program around the forest environs was 

completely lost as a result of  intense military operations 
in this area. This reduction in the amount of  vegetal cover 
gave rise to an increase in the Built-up of  the area as both 
the military and Boko Haram insurgents were trying to 
gain dominance of  the area.
Ahmad (2012) made it clear that vegetation is profoundly 
impacted by anthropogenic activities, particularly 
trampling. The ultimate effect of  trampling is a reduction 
in amount of  vegetation, often resulting in complete loss 
of  vegetation cover. Soil in the area was 40% covering 
189km2 of  the total area of  coverage. It should be noted 
that there is no way farming would have taken place 
in this area during military operation which involves 
clearing and recovering lost areas to Boko Haram 
militants. A careful examination of  2014 imagery would 
give an understanding that vegetation in the area started 
decreasing especially around the water bodies where 
ordinarily vegetation would thrive. It is pertinent to note 
that as water bodies and vegetation experience steady 
reduction, there would be a progressive desertification in 
the area over the space of  5 years. This could serve as an 
advantage for the military conducting military operations 
from both the air and land which involves dropping of  
bombs and artillery shelling of  militant positions and 
hideouts in and around the forest. But this operation is 
quite a catastrophic disadvantage to the ecosystem as 
flora and fauna composition within the area are almost on 
extinct leaving just bare land and the desert to consume 
the area. Marshland had 19.8%, another reduction 
by 0.7% from the previous year 2009 imagery. This 
reduction also signalled the alarming rate at which vegetal 
cover are being depleted seriously and giving space for 
the Sahara Desert to encroach on the remaining arable 
land for agriculture. 

Land use land cover of  2019
Land use land cover analysis for 2019 yielded another 
interesting result leaving little to doubt from the 
previous years. From the analysis, Water occupied 0.39% 
representing 1.86km2, vegetation within the area had 
11.61% which is a drop from the 28.5% in the previous 
year. Marshland covered 32.7% translating to 155km2, 
Bare surface accounted for 32.1% covering an area of  
152.10km2 while Built-up had a drastic increase of  23.2% 
representing 110.3km2 of  the total area. This is a clear 
indication of  Boko Haram activities and Military counter 
insurgency on Sambisa forest. Aju and Aju (2018) noted 
that the war in the north-eastern parts of  the country 
by the Boko Haram insurgents is not just a war on the 
socio-political, socioeconomic and socio-cultural life of  
the people but also an environmental war. They stressed 
that since the emergence of  Boko Haram insurgency, 
the Sambisa has never been the same and will never be 
the same again even when the militants are wiped out 
totally from the forest. A study on woody cover change 
in Colombia between 2001 and 2010 showed that the 
impact of  illegally armed groups has reduced forest cover, 
particularly in areas rich in gold and lands appropriate for 



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cattle grazing (Sanchez-Cuervo & Aide, 2013). It was 
also revealed that aerial application of  Agent Orange and 
other herbicides during the Vietnam War, defoliated 14% 
of  the country’s forest cover and over 50% of  its coastal 
mangroves. The illustration was shown in a study of  the 
effects of  high explosive munitions (bombs and shells), 
chemical anti-plant agents (herbicides), and heavy land 
clearing tractors (Rome plows) as employed by the USA 
in South Vietnam during the Second Indochina War of  
1961 – 1975 for the purpose of  extending large-scale area 
denial (Negasi et al., 2017).
From the result of  land use land cover for the year 2019, 
it was very obvious that if  nothing is done to checkmate 
the activities of  Boko Haram on Sambisa forest or to 
bring to an end the issue of  insurgency, time shall come 
when there will be no Sambisa forest. This implies that 
there is need for environmental monitoring of  the forest 
to avoid future environmental problems. The works of  
Bell et al. (2018) opined that land use and management 

practices are important factors in determining the 
extent of  soil erosion. Good vegetation cover promotes 
infiltration of  water into the ground and soil retention, 
while deforestation results into increased runoff  
than infiltration occurring during periods of  more 
precipitation (Alexakis et al., 2014). Increased runoff  
consequently leads to stronger soil erosion usually in 
areas with poor vegetation cover (Bell et al., 2018). 
Netzer et al. (2019) stated that extensive conversion of  
forests to permanent crops or other non-forests can 
change the runoff  characteristics within a sub-basin. The 
land cover changes occur naturally in a progressive and 
gradual way, however, it may be rapid and abrupt due to 
anthropogenic activities.

Change detection analysis
Remotely sensed data processed and elaborated can be 
useful in change detection task to monitor the land cover 
changes over the years at different times. 

Table 4: Change detection analysis

LULC 
Category

Area diff  b/w 
1994 – 1999 
(km2)

Area diff  
b/w 1999 – 
2004 (km2)

Area diff  
b/w 2004 – 
2009 (km2)

Area diff  
b/w 2009 – 
2014 (km2)

Area diff  b/w 
2014 – 2019 
(km2)

Area diff  
b/w 1994 – 
2019 (km)

Water 1.11 0.33 0.95 1.32 0.06 1.75
Vegetation 118.78 0.53 97.37 66.5 79.96 167.34
Bare surface 9.89 0.2 3.9 71.4 16.9 68.09
Marshland 117.6 2.7 87.6 2.92 61.38 91.6
Built-up 7.6 -2.7 14.62 6.5 35.42 9.4
Total 474 474 474 474 474 474

Thus, starting from previously described dataset of  
Landsat classified imageries, the process of  digital change 
detection developed has allowed for the determination 
and description of  changes in land cover between five 
fundamental intervals of  1994 – 1999, 1999 – 2004, 2004 
– 2009, 2009 – 2014 and 2014 – 2019, with the addition 
of  1994 – 2019.

Change detection analysis of  Sambisa forest from 
the year 1994 – 1999
Figure 3 shows the change variable from 1994 – 1999 
with the white patch on the change detection map 
representing the unchanged area over the years while 
others were coded using colour code of  the same 
variables in the land use land cover maps to identify 
the changes that have taken place over the years. From 
the change detection map of  1994 – 1999, Vegetation 
changed to Water, Vegetation changed to Bare surface, 
Marshland changed to built-up while Built-up changed 
to bare surface.

Change detection analysis of  Sambisa forest from 
the year 1999 – 2004
The changed variables from 1999 – 2004 with the white 
patches on the change detection map representing 
the unchanged areas over the years. From the change 

detection map (Figure 3), Vegetation changed to bare 
surface, Bare surface changed to marshland, Marshland 
changed to built-up and from built-up to bare surface 
while Built-up changed to bare surface.

Change detection analysis of  Sambisa forest from 
the year 2004 – 2009
Figure 3 shows the variable from 2004 – 2009 with the 
white patches on the change detection map representing 
the unchanged area over the years while others were 
coded using colour code of  the same variables in the land 
use land cover map to identify the changes that have taken 
place over the years. From the map, Vegetation changed 
to marshland, Marshland changed to bare surface, Bare 
surface changed to built-up while Built-up changed to 
vegetation and water.

Change detection analysis of  Sambisa forest from 
the year 2009 - 2014
The change variables from the year 2009 – 2014 with the 
white patches on the change detection map representing 
the unchanged area over the years while others were 
coded using colour code of  the same variables in the 
land use land cover map to identify the changes that have 
taken place over the years. From the map (Figure 3), Bare 
surface changed to water, Vegetation changed to bare 



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surface, Built-up changed to marshland while Marshland 
changed to built-up.

Change detection analysis of  Sambisa forest from 
the year 2014 – 2019
The changed variables from 2014 – 2019 with the white 
patches on the change detection map representing the 

unchanged areas over the years while others were coded 
using colour code of  same variables in the land use land 
cover map to identify the changes that have taken place 
over the years. From the map (Figure 3), Bare surface 
changed to vegetation, Marshland changed to vegetation, 
Vegetation changed to built-up while Built-up changed to 
bare surface. 

Figure 3: Change detection map from the year 1994 to 2019

Change detection graphical plot
Figure 4 shows the graphical plot of  the rate of  changes 
in the study area from the year 1994 – 2019. From the 
result, the rate of  individual variables is not static as 
factors responsible for depletion are often changing. 
Anthropogenic activities would always be the game 

changing factors responsible for the depletion. This 
implies that a reduction or increase in Anthropogenic 
activities would always result to either addition or 
subtraction in the quantities or values of  environmental 
variables.



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Figure 4: Graphical plot showing the rate of  changes in environmental variables from 1994 – 2019 
Source: Author’s work (2020)

From the Figure, water was on a steady decline over the 
years with the highest as 9% from 1994 – 1999, while a 
sharp decline occurred from 1.11km2 to 0.33km2 which 
later increased to 0.93km2, then to 1.28km2 and later with 
a drastic fall to 0.04km2 from 2014 - 2019. This gives a 
vivid representation of  the nature of  changes over the 
years with highest at 1.28km2 and the lowest at 0.04km2. 
When observed the factorial years in which there was a 
dip in water bodies size was the same period when the 
insurgency in the study area scaled up. Also, it might be 
an indication that not only the insurgency affects the 
environment of  the study area but also the study area 
itself  being in the Sudano-Sahelian region happens to 
have problems of  desert encroachment. 
Vegetation change rate from 1994 – 1999 was not 
reasonable. From 1999 – 2004, there was massive decrease 
which scaled to 94.4km2 and with a sharp increase to 
177.5km2 from 2004 - 2009 which brought about plunging 
result to 146.5km2 and ultimately to 100km2. 
Built-up areas are direct result of  anthropogenic activities 
which brings about interference in the natural ecosystem. 
From the result displayed on the graphical plot, 1994 – 
1999 had 63.6km2 which slide down to 58.7km2 between 
1999 – 2004, with an upward increase of  94.6km2 from 
2004 – 2009, it later decreased to 72.9km2 and 36.3km2 
between 2014 – 2019. It is noteworthy that 2014 - 2019 
was the height of  the insurgency with the government 
fierce attempt at recapturing the area from the terrorists.  
Soil had its lowest value from 1999 – 2004 at 0.2km2, with 
112.8km2 as the highest from 2014 – 2019. The serious 

increase experienced from 2014 – 2019 was as a result of  
consistent anthropogenic activities alongside the Sahara 
Desert encroachment. 
Marshland had initial increase from 1994 – 1999 at 
327.6km2, and a drastic reduction to 152.7km2. It later 
reduced to 87.6km2 but fell drastically to 17.1km2 with a 
massive increase of  177.4km2 which suggested that the 
impact of  the war waged on the insurgency in the area 
has had a tremendous effect on the marshland, bringing 
about some bit of  stability for flora and fauna to flourish 
in the area.

Time series analysis
In the study of  the land use land cover data gathered from 
imageries analysed and the values derived are subtracted 
from one point to another (example, 1994 – 1999) while 
all component of  water, vegetation, soil, marshland and 
built-up areas are subtracted from previous years and 
applied to Microsoft excel to analyse using the time series 
method. A forward intercept of  0.5 representing a five 
year forward intercept was adopted for all the interval 
years done, the result yielded quite a number of  results 
as can be seen on Figure 5. This shows that the year 
2009 – 2014 witnessed the high fluctuation in behaviour 
as ecosystem disturbance and imbalance were witnessed, 
which was a direct result of  the Boko Haram activities 
in the area. While 2014 – 2019 time series graph showed 
signs of  high shift indicating that as long as the insurgency 
operation continues, it’s only a matter of  time before the 
entire environment will be completely disturbed.



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Figure 5: Time series graphical plot from the year 1994 – 2019

Figure 6: Time series graph analysis for 1994 – 1999

Time series analysis for the year 1994 – 1999
Time series projection between 1994 – 1999 shows the 
polynomial forward intercept graph revealing a gradual 
high value in marshland areas being the highest. Forward 
intercept of  0.5 was adopted thus projecting the possible 
scenario of  what to expect between 1999 – 2004. From 

the polynomial graph on Figure 6, if  the change rate was 
held at 17.193, the expected rate of  change in the area 
would be at 0.2147. This could be tagged as reasonable 
limit given that severe anthropogenic factors do not 
impair the rate of  change within the environment.

Time series analysis for the year 1999 – 2004
The year 1999 – 2004 revealed a number of  time series 
changes which are quite interesting. There were reductions 

in values of  the parameters as can be seen on Fig. 7 when 
compared with that of  1994 - 1999. 

Figure 7: Time series graph analysis for 1999 – 2004



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Figure 8: Time series graph analysis for 2004 – 2009

Time series analysis for the year 2004 – 2009
The year 2004 – 2009 shows there was a slight change 
as changes for vegetation shot up in the area. Although, 
the change rate was measured at 0.0662, it can be 
asserted that the changes that took place in the area 
based on all the five variables measured were expected 

to grow at 0.0662. Thus, showing an indication that the 
Boko Haram activities in the area began to have minute 
effect on the environment even though its effect was 
not as pronounced as it was in the upcoming years. This 
illustration is shown on Fig. 8.

Time series analysis for 2009 – 2014
The year 2009 – 2014 shows a drastic change which 
occurred during this period and change rate revealing 
a steady decline in the various environmental variables 
that were available in the area. Time series graphical 
intercept was pitched at 0.1992 which was clear dive from 
previous 0.0662. This shows that vegetation loss was on 

the increase powered by the insurgency activities as soil 
and marshland were a steady decline which was evident 
as at that time as the federal forces battled the militant 
to retake the area from them. It is projected that if  this 
continues, then the rate of  change in the environment 
would be at 0.1992 which translate to a 59.474 on the 
graphical scale. This is shown on Figure 9.

Figure 9: Time series graph analysis for 2009 – 2014

Time series analysis for the year 2014 – 2019
The year 2014 – 2019 brought about a major change in 
the area of  the study as military operations in the Sambisa 
forest intensify due to the federal government renewed 
effort at capturing back the area. A lot of  game changing 
weapons were deployed to the area as the Airforce 
bombarded the environment with the ground forces 
advancing columns and deploying heavy weapons such as 
tanks and artillery. After the recapturing of  the forest areas 

as put by the federal government, the graphical time series 
indicator showed an upward movement thus, revealing at 
the rate of  0.7938 which reveals gradual steady though 
minute recovery in the area. This shows that even though 
the area seems to be recovering from the destruction of  
the ecosystem, it might likely take some years before a 
full recovery is made as long as the ecosystem stability is 
maintained. This is shown graphically on Fig 10



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Figure 10: Time series graph analysis for 2014 – 2019

Time series analysis for the year 2019 – 2024
Fig. 11 shows the Time series analysis for the year 2019 
– 2024. From the result when compared with 2014 – 
2019, it reveals that there will be a drastic decrease in 
water, vegetation, soil and marshland but with a slight 
increase in built-up. This implies that with the serious 

fight by the military to counter Boko Haram activities on 
Sambisa forest of  Nigeria, there will be a total decline of  
vegetation of  the forest. With the predicted decrease in 
vegetation, soil and marshland as they have regressed to 
the negative, desertification/desert encroachment might 
set in.

Figure 11: Time series graph analysis for 2019 – 2024

CONCLUSION
This study looked at the in-depth analysis of  the terrorists 
activities on land use and land cover (LULC) change of  
Sambisa Forest of  Nigeria. GIS and remote sensing were 
employed in the course of  this study. Secondary types and 
sources of  data were used in this study and they include 
data on the vegetation cover of  Sambisa forest before and 
during the emergence of  Boko Haram and data on the 
land use/land cover. The data for the vegetation analysis 
of  this study were obtained using satellite imageries of  the 
study area from 1994, 1999, 2004, 2009, 2014 and 2019.
In line with the findings of  the study, the following 
suggestions are made in order to put an end or to 
ameliorate the impacts of  Boko Haram activities on the 
Sambisa forest of  Nigeria: 

I. The military should stop Boko Haram activities and 
force them out of  the forest and the area will regenerate 

and the soil remediated. 
II. After recommendation I above is achieved, employ 

and empower forest guards to control activities and bring 
some form of  governance in the forest and game reserve. 
By so doing, the vegetation indices as well as the soil of  
the forest will be conserved.

III. Northern youths as well as the children should be 
encouraged to go to school to avoid brainwashing and 
evil manipulation from the extremists as they will be 
educated enough to know the environmental implication 
of  involving themselves with terrorism and thereby act 
as agents of  conservation for the vegetation and soil of  
Sambisa forest. 

IV. Awareness campaign should be organised in order 
to enlighten the communities within and around Sambisa 
forest on the need to conserve the forest to circumvent 
the dangers of  desert encroachment or desertification.



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