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American Journal of  
Geospatial Technology (AJGT)

GIS and Artificial Intelligence Application in Smart Forest Ecosystem Sustainability 
Evaluation of  Olokemeji Forest Reserve, Ogun State, Nigeria

Victor Ayodele Ijaware1*

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

DOI: https://doi.org/10.54536/ajgt.v3i1.2621
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: March 01, 2024

Accepted: March 28, 2024

Published: April 02, 2024

The increase in human population over the years has accelerated growth in anthropogenic 
activities, which have led to the conversion of  forest reserves to other land uses. In the se-
quel, it has become imperative for researchers to focus on the mapping of  forest reserves 
through the use of  GIS and Artificial Intelligence (AI) with time-efficient, automated, and 
low-cost methods to preserve the existing forest reserve and its sustainability evaluation 
implementation. This research aimed at utilizing GIS and artificial intelligence applications 
in smart forest ecosystem sustainability evaluation of  Olokemeji forest reserve, Ogun State, 
with the following objectives: (i.) assessment of  the current state of  the forest ecosystem.
(ii.) identify potential threats and risks to the study area and (iii.) develop sustainable man-
agement strategies for its conservation and preservation. In pursuance of  this, GIS and AI 
were deployed in this study to assess the spatial characteristics of  the forest ecosystem in 
Olokemeji forest reserve. Landsat imagery, ground coordinates, and a research questionnaire 
were the major data used. Object-based classification, Normalized Difference Vegetation 
Index (NDVI), and Land Use Land Cover in ArcGIS 10.2 software was deployed in data 
generation and analysis. The results showed that in 2013, about 1657.115 ha of  the study 
area was occupied by dense forest cover while in 2023, it decreased to 1188.060 ha, with a 
difference of  about 469.055 ha. By implementing smart forest monitoring and evaluation 
systems that use artificial intelligence, the government and commercial groups should set 
regulations focused on reducing the escalating risks to forest reserves.

Keywords
Geographic Information Systems 
(GIS), Artificial Intelligence 
(AI), Convolution Neural 
Network (CNN), Normalized 
Difference Vegetation Index 
(NDVI), Satellite Imagery

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

INTRODUCTION
Globally, enhancing human well-being and the reduction 
of  greenhouse gases (GHGs), forests offer a variety of  
services and activities (Kyere-Boateng & Marek, 2021). 
The benefits of  forests help communities and individuals 
who rely on them for their livelihoods to thrive. However, 
Deforestation and Forest Degradation (D&FD) are a twin 
environmental crisis that threatens the world’s forests, 
leading to widespread loss of  forest biodiversity and 
ecosystem services and eventual forest cover loss. Ghana 
in West Africa is susceptible to D&FD brought on by 
humans, just like any other tropical forest in developing 
nations (Kyere-Boateng & Marek, 2021; Amoah & 
Korle, 2020; Oyediji & Adenika, 2022; Morgan, 2022). 
Forests also play a crucial role in maintaining ecological 
balance, regulating climate, conserving biodiversity, and 
providing various ecosystem services. However, due to 
deforestation, illegal logging, and climate change, forest 
ecosystems face significant challenges. To ensure forest 
long-term sustainability, it is imperative to develop 
effective monitoring and evaluation mechanisms (Zhang 
et al., 2020). Smart forest ecosystems are essential to 
maintaining the sustainability and wellbeing of  earth 
planet. The combination of  Geographic Information 
Systems (GIS) with Artificial Intelligence (AI) can offer 
useful tools and insights for assessing and monitoring the 
sustainability of  these ecosystems (González-Vélez et al., 
2021). With the help of  this combination, complicated 
spatial and non-spatial data may be gathered, analyzed, 

and interpreted, enabling well-informed decision-making 
and pro-active management techniques. (Corbett and 
Mellouli, 2017). Geographically referenced data are 
collected, managed, analyzed, and presented using GIS 
technology. Through the use of  GIS, numerous sorts 
of  information, including human activity, soil quality, 
biodiversity, forest cover, and vegetation patterns, can be 
combined and represented in a spatial context (Salem, 
2023). Spatial awareness is essential for evaluating the state 
and functionality of  forest ecosystems and identifying 
regions that need conservation efforts or intervention. On 
the other hand, artificial intelligence improves the evaluation 
process by utilizing sophisticated algorithms and machine-
learning strategies Nivanya (2024). Using historical and 
real-time data, AI may be used to evaluate massive datasets, 
spot patterns and trends, and make predictions (Jahani, et 
al., 2016). With the help of  this capability, forest managers 
may better understand the ecological dynamics, spot 
irregularities or disturbances, and predict changes to the 
ecosystem in the future. (Holzinger et al., 2023). 
Some identified challenges in the implementation of  
smart forest ecosystem sustainability evaluation using 
GIS and Artificial Intelligence (AI) include:

(i) High cost of  and resource-intensive nature of  
conventional forest monitoring methods, which are often 
prohibitive.

(ii) Reliability of  data and findings vary significantly, 
affecting the overall effectiveness of  the ecosystem 
assessment.



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(iii) The lack of  a comprehensive implementation 
strategy for these technologies means that their potential 
is not fully realized, and their integration into existing 
systems is slow.

(iv) Moreover, there is often resistance to adopting 
new technologies due to lack of  understanding or trust 
in their capabilities, coupled with regulatory and policy 
challenges that hinder widescale adoption. Addressing 
these challenges is crucial to making informed decisions 
for sustainable forest management and unlocking the 
potential of  GIS and AI capabilities. 
Therefore, the goal of  this research is to carryout GIS 
and artificial intelligence applications in smart forest 
ecosystem sustainability evaluation within Olokemeji 
forest reserve, Ogun state, Nigeria, with a view to 
enable data-driven decision-making, promote sustainable 
forest management practices, and contribute to the 
conservation and preservation of  forest ecosystems for 
future generations. The following research questions 
enable the research aim to be achieved:

(i) what is the current state of  the forest ecosystem? 
(ii) what are the potential threats and risks to the forest 

ecosystem?
(iii) What are the sustainable management strategies 

required for the development of  conservation and 
preservation of  smart forest ecosystem sustainability 
evaluation implementation using GIS and Artificial 
Intelligence?
Realizing the full potential of  GIS and AI in smart forest 
ecosystem sustainability evaluation in combination with 
remote sensing techniques (Pei, 2021) requires addressing 
these issues and limits. Overcoming these obstacles 
will make it possible to create trustworthy and efficient 
decision-making tools, which will result in more informed 
and sustainable forest management practices.

LITERATURE REVIEW 
The array of  studies reviewed for the current research 
highlights significant advancements and challenges in the 
field of  Smart Forest Ecosystem Sustainability Evaluation, 
particularly utilizing GIS and AI. Zhang et al., (2020) 
focused on optimal grain size for landscape analysis, 
finding 30-60m as the ideal range, while Nitoslawski et al., 
(2019) emphasized the importance of  integrating urban 
forests into smart city planning, highlighting the role of  
digital infrastructure in urban green spaces management. 
In exploring applications of  machine learning and AI in 
forestry ecology, Liu et al. (2018) and Gonzalez-Velez et al. 
(2021) provide significant contributions.
The former examined various machine-learning 
techniques in forest ecology, discussing their potential 
and limitations, while the latter developed a synthetic 
intelligence framework for predicting wildlife-vehicle 
collision hotspots, endorsing the Random Forest 

algorithms as the most effective.  Afroz et al., (2022) 
study assessed changes in land use and land cover 
(LULC) patterns and forest cover in the Dinajpur district 
of  Bangladesh over the periods 1989–1999, 1999–2010, 
2010–2020, and 1989-2020, They revealed that the 
majority of  forest land was converted to homestead and 
crop/fallow land, resulting in a substantial reduction 
of  forest area. The research of  Jahani et al., (2016) 
introduced an optimized model for predicting forest 
degradation, while Yin et al., (2021) proposed an AI-
assisted planning framework for the terrestrial ecosystem 
restoration, however, both focused on modeling and 
planning for ecosystem sustainability and restoration 
as well as highlighting the promising role of  advanced 
computational models in forest management. Aroge et 
al.,(2023)  identified optimal locations for new primary 
health care facilities in the Ado Local Government Area 
using a novel method integrating GIS, MCDA, and AHP.  
The findings revealed that only 10% of  the land mass 
was most suitable, while 53% was suitable, and 37% was 
unsuitable for primary health care facilities.
Collectively, these studies provide a rich tapestry of  
methodologies, findings, and insights, yet none have 
specifically focused on Smart Forest Ecosystem 
Sustainability Evaluation using GIS, Remote Sensing 
techniques, and AI concurrently in the research area, 
which is the gap that is filled in this study. 

MATERIALS AND METHODS
Study Area 
The study site is between the boundary of  the lowland 
rainforest and the derived savanna ecological zone 
(Figure 1). The site is also referred to as the wooded 
grassland within the 7,100 ha of  Olokemeji forest reserve 
(Hopkins, 1956 ). It is within Abeokuta North Local 
Government Area (LGA) of  Ogun State Southwestern 
Nigeria and located between Latitude 7º 25´N & 7º 
39´N and Longitude 3º 32´E & 3º44´E. According to 
(Ogunleye et al., 2004) it occupies about 59km2 and has 
a population of  6, 379,500 based on the 2023 population 
projection (Brinkhoff, 2022). The terrain configuration 
of  the forest reserves area is characterized by hills and 
valleys with elevation values varying from 90m to the 
lowest, while the highest point is about 140m (msl). 
However, a quartzite ridge occupying the western side 
of  study area conspicuously towered above 240m (Agbo-
Adediran et al., 2016). The annual rainfall varies from 
1200mm to 1300mm and used to occur from March 
to November, while the month-by-month temperature 
ranges from 280C to 360C (Hopkins,1965). The drainage 
pattern of  the research area is dendritic and form part of  
the median channel of  River Ogun draining nearly all the 
Precambrian basement complex rocks of  Southwestern 
Nigeria (Ogundele & Odewunmi, 2012; Wilson, 1922).



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collection was supported by hardware and software tools. 
Similarly, the Secondary data consisted of, Landsat 7 
Enhanced Thematic Mapper Plus (ETM+) imageries for 
2013 and Landsat 8 imageries (OLI-2/TIRS-2) for 2023, 
sourced from the United States Geological Survey at 30m 
resolution, supplemented by Google Earth Imagery.
A comprehensive approach was taken to process both 
primary and secondary data, and the adoption of  a mixed-
methods approach facilitated the thorough evaluation of  
the forest ecosystem’s sustainability using Geographic 
Information Systems (GIS) and Artificial Intelligence (Pei, 
2021). The data underwent a coordinate transformation 

Figure 2: Flowchart of  research methodology

Data Collection and Processing 
The flowchart of  the research methodology is as shown 
in Figure 2
The data used in this study are in two parts which include 
the primary and the secondary data. The primary data 
involves the data that was acquired directly from the field 
which include the coordinates of  some selected points 
within the study area, and the 210 detailed socioeconomic 
survey responses collected from the local population. 
Specifically, random sampling techniques were the 
method used in distributing the questionnaire, and it was 
tested before the actual field survey exercise. The data 

Figure 1: Study Area



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Am. J. Geo Spat. Technol. 3(1) 9-16, 2024

to align different sources within the Universal Tranverse 
Mercator (UTM) system, zone 31N, which was essential 
for integrating the datasets in a common spatial language. 
Georeferencing attached real-world coordinates to 
satellite images using Ground Control Points, a step 
critical for accurate spatial analysis within the ArcMap 
software environment. Raster data from satellite imageries 
were converted into vector format, which allowed for 
the digitization of  features enhancing the spatial analysis 
capability. This vectorization process was pivotal in 
creating a detailed and interactive study area map. 
Central to the AI component of  the study was the use 
of  Convolutional Neural Networks (CNNs) for land 
cover classification. The CNNs preprocessing involved 
several steps to prepare the satellite imagery for analysis. 
Resampling ensured uniform spatial resolution across 
images, while band selection targeted the spectral 
bands most relevant to identifying different land cover 
types. Data augmentation techniques like rotation and 
scaling generated additional training data, enhancing 
the robustness of  the CNN model against variability in 
input data. The model training phase involved feeding 
the preprocessed data into CNN, designed with an input 
layer to match the image dimensions and spectral bands 
and an output layer corresponding to the various land 
cover classes. A suitable loss function and optimization 
algorithm were selected to iteratively adjust the model’s 
weights, with the aim to minimize classification errors. 
Validation of  the model was a crucial step in ensuring its 
accuracy and generalizability. The model’s performance 
was assessed using a separate validation dataset, which 

was not part of  the training set. Metrics such as accuracy, 
precision, recall, and the F1-score provided quantitative 
measures of  the model’s efficacy. This phase also helped 
in fine-tuning the model by adjusting hyperparameters 
to prevent overfitting and to improve its predictive 
capabilities. Change detection analysis complemented the 
AI classification, utilizing NDVI calculations to provide 
insights into vegetation health and density changes over-
time. This analysis, along with the AI-derived classifications, 
were synthesized into detailed maps using ArcGIS 10.2.
Maintaining data quality was a crucial aspect of  the 
research. Ground truthing was carried out to check the 
conformance of  the secondary data to the primary data. 
Also, it was ensured that the secondary data used were 
up-to-date, accurate, and properly validated to ensure 
good data quality. 
The implementation of  these advanced data processing 
techniques ensured the generation of  reliable and 
actionable insights for the conversation and management 
of  the forest reserve.

RESULT AND DISCUSSION 
The result’s analysis is categorized into three sections: 
land cover changes, Normalized Difference Vegetation 
Index (NDVI), and Questionnaire analysis. 

Land Cover Changes
The table 1 shows the land cover analysis of   the wooded 
grassland of  Olokemeji forest reserve area while Figure 
2 shows the corresponding land cover map of  the study 
area. The classification was mainly Forest area and Grass 

Figure 2: Land cover map of  the study area for 2013 and 2023



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Am. J. Geo Spat. Technol. 3(1) 9-16, 2024

Table 1: Land cover analysis of  Olokemeji forest reserve area
Class  Area (ha)(2013)   % Area (2023) %
Forest area 1657.115  99.44 1188.060  71.29
Grass land 9.349   0.56 478.404  28.71 
Total 1666.464  100  1666.464 100  

land. It indicates that in 2013, forest area covers 1657.115 
Hectares (99.44%), and Grass land covers 9.349 Hectares 
(0.56%), While in 2023, forest area covers 1188.060 
Hectares (71.29%), and Grass land covers 478.126 
Hectares (28.71%). This corroborates with the work done 
by (Aigbokhan, et al., 2022) which indicated a declining 
trend for forest cover due to habitat loss from uncontrolled 
incursion into the forest reserve area for daily survival. 
Specifically, their study revealed that forests covered about 
20,338 ha (26%), 13440 ha (17%) and 10427 ha (13%) in 
the years 2001, 2011 and 2021 respectively.

Normalized Difference Vegetation Index (NDVI)
Essentially, according to Figure 3, the study area has 
transformed from being densely and moderately 
vegetated with NDVI values (0.122684978 minimum 
to 0.449542463 maximum) in the year 2013 to being 
sparsely and moderately vegetated with NDVI values 
(0.111201495 minimum to 0.296176106 maximum) in 
year 2023. The implication of  this is that the vegetation 
health and density in the study area have declined 
significantly from 2013 to 2023.

Figure 3: Normalized Difference Vegetation Index for the year 2013 and 2023

Figure 4: Normalized Difference Vegetation Index for the year 2013 and 2023



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Questionnaire Analysis 
The demographic data of  the respondents are presented 
in figure 5. Considering  the 210 respondents, 76.2% were 
males. All the respondents were 18years and above. Most of  
the participants were first Degree holders while 57.1% and 

38.1% are Master’s Degree holders. The respondent was 
purposely selected because of  the nature of  the research 
which focus on GIS and artificial intelligence application 
in smart forest ecosystem sustainability evaluation and the 
unwavering conviction that its mainly a male occupation.

The analysis of  the sustainability evaluation and 
conservation awareness data of  the respondents 
displayed as  Figure 6 shows that out of  the 210 
respondents, 66.7% have visited the study area, while 
50% and 25% ‘strongly agree’ and ‘agree’ respectively that 
the conservation and preservation of  forest ecosystems 
are important for the well-being of  current and future 
generations. Furthermore, 52.4% ‘strongly’ believe that 
data-driven decision-making is essential for sustainable 
forest management practices, with 28.6% of  respondents 
believing on average.
The current status assessment data of  the study area 

(figure 7) revealed that 52.4% claimed they are aware of  
the potential threats and risks forest ecosystems face, 
such as deforestation, habitat loss, and climate change. 
Also, 57.1% believe that GIS and Artificial Intelligence 
technologies can effectively contribute to the evaluation 
and conservation of  forest ecosystems. Similarly, among 
the 210 respondents, 55% think it is ‘very important’ 
to develop sustainable management strategies for 
the conservation and preservation while 30% of  the 
respondents also think it is ‘important’ to develop 
sustainable management strategies for the conservation 
and preservation of  the Olokemeji Forest Reserve.

Figure 5: Image showing the Demographic data of  the respondents in the study area

Figure 6: Image showing the Sustainability Evaluation and Conservation Awareness data



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CONCLUSION 
The study has effectively demonstrated the pivotal 
role of  Geographic Information Systems (GIS) and 
Artificial Intelligence (AI) in the sustainable management 
of  forest ecosystems. Through a rigorous analysis of  
the Olokemeji Forest Reserve, the study has revealed 
concerning trends in land cover change, with a notable 
reduction in forest area over the past decade. These 
findings are in line with those presented by Aigbokhan 
et al. (2022), which underscore the dynamic and often 
precarious nature of  forest ecosystems. The application 
of  Convolutional Neural Networks (CNNs) in land cover 
classification, coupled with the Normalized Difference 
Vegetation Index (NDVI) for assessing vegetation 
health, has provided compelling evidence of  the forest’s 
declining health. Such technological advancements in data 
processing and analysis have proven crucial in identifying 
subtle and overt changes within the ecosystem. Also, the 
integration of  local community perspectives, gleaned 
from questionnaire responses, has enriched the study’s 
insights, emphasizing the necessity of  conservation 
efforts. Finally, the research articulates a clear and urgent 
call for the implementation of  smart forest ecosystem 
monitoring systems that harness the power of  AI and 
GIS. These systems provide a comprehensive and 
nuanced understanding of  ecosystem dynamics, essential 
for the development of  informed conservation strategies. 
The study’s comprehensive methodology demonstrates 
the value of  integrating advanced technologies in 
environmental management and conservation efforts 
which may be adopted in other forest reserves in Nigeria.

RECOMMENDATION
The study recommends the implementation of  smart 
forest monitoring and evaluation systems that use artificial 
intelligence. Also, the Government and commercial 
groups should set regulations focused on reducing the 

escalating risks to forest reserves. Systematic tree felling 
techniques should be introduced as a matter of  urgency, 
and continuous tree planting campaigns should be 
conducted to mitigate the effects of  climate change and 
maintain healthy forest health for overall sustainable and 
revenue-driven management.

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