AcademicPres
Olokeogun OS et al. (2020)
Notulae Scientia Biologicae 12(4):959-971
DOI:10.15835/12410808
Research Article
Notulae Scientia Biologicae
Geospatial analysis-based approach for assessing urban forests under
the influence of different human settlement
extents in Ibadan city, Nigeria
Oluwayemisi S. OLOKEOGUN1*, Abiodun O. OLADOYE2,
Oluwatoyin O. AKINTOLA1
1
Federal College of Forestry, Department of Forestry Technology, P.M.B. 5054, Ibadan, Oyo State,
Nigeria; olokeogunoluwayemisi@gmail.com (*corresponding author); toyinakintola73@gmail.com
2
Federal University of Agriculture, Department of Forestry and Wildlife Management, Abeokuta, Nigeria; segun11us@yahoo.com
Abstract
Urban forests are an essential component of urban areas as they provide many environmental and social
services that contribute to the quality of life in cities. Urban forests in most cities of Nigeria are gradually
becoming bitty as a result of urbanization activities, thereby posing adverse effects. In this study, we assessed
the changes in the urban forests cover under the influence of different human settlement (HS) extents across
the urban area of Ibadan city using remotely sensed data. The pattern of change(s) in the urban forests cover
over 20 years were examined by analysing and manipulating Landsat and Sentinel-2 datasets using Google Earth
Engine, ArcGIS 10.1, and Erdas 2014 software. The extents of human settlement (for the year 2000, 2005,
2010, 2015, and 2020) were extracted (from Landsat datasets), analysed, and mapped to evaluate the status of
the urban forests cover under different human settlement extents. The result reveals a substantial land cover
changes within the urban area of Ibadan. The urban forest cover decreased from 24.14% to 7.99%. Also, there
is a significant decrease in the urban forests cover as a result of a substantial increase in human settlement extent
(102,806 to 122,572 pixels). The study provides an opportunity to map the status of urban forest cover and
extents of HS in a developing city using remotely sensed data and applications of GIS tools.
Keywords: GIS; human settlement; remote sensing; remotely sensed data; urban forests
Introduction
Urban forests are ecosystems characterized by the presence of trees and other vegetation in association
with the people and their developments (Nowak et al., 2001; Adelusi et al., 2002). They are also considered as
the sum of all woody and associated vegetation in and around dense human settlements from small
communities to large metropolitan cities (Gann, 2003). According to Clark et al. (1997), a sustainable urban
forest is defined as the naturally occurring and planted trees in cities, managed to provide the inhabitants with
a continuing level of economic, social, environmental, and ecological benefits today and into the future.
Comprehensively, urban forests are viewed as trees on the land that fulfil the requirements of forest and other
wooded lands except that the area is less than 0.5 ha (Larinde, 2010).
Received: 28 Aug 2020. Received in revised form: 12 Nov 2020. Accepted: 07 Dec 2020. Published online: 21 Dec 2020.
Olokeogun OS et al. (2020). Not Sci Biol 12(4):959-971
Urban forest significantly influences the sustainability and environmental quality of a city. A large,
healthy urban forest can increase local urban air quality and mitigate carbon dioxide emissions (McPherson
and Rowntree, 2016; Nowak et al., 2007); decrease temperature elevated by the urban heat island (UHI) effect
and decrease associated energy costs (McPherson and Simpson, 2001); increase city walkability (Wolf, 2008);
provide stormwater retention services including decreased peak flow and increased water quality (McPherson,
2006); contribute to economic prosperity through increased job opportunities and increased retail sales in
urban areas with trees (Wolf, 2005a, 2005b); as well as many other benefits that increase the overall quality of
the urban environment and the quality of life for urban residents (McPherson, 2006).
Recently, urban forests within Ibadan city are gradually becoming more fragmented due to urbanization
causing ripple effects such as the expansion of human settlement, increased infrastructural development,
change in the city’s landscape pattern, and design. The impact of their continuing disappearance is manifested
through increased UHI effect, changes in microclimate, and rainfall pattern. Also, the city lacks proper and
comprehensive knowledge of the status and performance of its urban forests thereby making it difficult to
preserve and enhance it. Therefore, there is a need for an evaluation of these urban forests. We assess here the
changes in the landscape pattern occurring over assessment years as it relates to the extents of human
settlement.
Assessment of changes in the landscape pattern using remotely sensed data and geographic information
system (GIS) help to depict, quantify, and map, among other factors, the change in landform (on a large scale)
from permeable to impermeable surfaces with urban development (Booth et al., 1989; Booth, 1990; Masek et
al., 2000; Klemas, 2001; Hayden, 2004; Lunetta et al., 2004; Kulash, 2009; Tan et al., 2010; Banai and
DePriest, 2014; Chen and Guinness, 2014; Kumar et al., 2019; Savita et al., 2019). Urban forest managers and
city planners require this information to direct future patterns of growth and green space development and also
to prepare an effective and efficient urban forest management plan.
How degraded are these urban forests as a result of urbanization (in terms of human settlement extent)
pressure is one of the key questions for forest policymakers and city planners? To address this question, here we
assess the impact of the trend in human settlement spatial extent on urban forests cover across the urban area
of Ibadan city in the south-west region of Nigeria using remotely sensed data. We focused more on the
exploitation of Landsat data potentials in assessing urban forest cover and also in extracting consistent human
settlement extent layers at a 30m spatial posting and time series. The specific objectives of the study are to (1)
delineate and classify the urban forests using Landsat and Sentinel datasets to understand the pattern of change
over 20 years (at an interval of 5 years) and; (2) extract human settlement extent from Landsat data (at an
interval of 5 years, for 20 years) and evaluate the status of the urban forests under different human settlement
extents.
Materials and Methods
Study area
The study was conducted in the urban area of Ibadan city, an ancient city of the Nigerian western region
that has witnessed rapid urbanization in the recent past era. Ibadan is the capital city of Oyo state in Nigeria
having it extent between latitude 7° 2' N - 7° 44' N and longitudes longitude 3° 30' E - 4° 9' E (Figure 1). It is
located at a distance of about 120 km East of the border with the Republic of Benin in the forest zone, close to
the boundary between the forest and the Savanna. The city is naturally drained by four rivers (Ona river,
Ogbere river, Kudeti river, and Ogunpa river) with many tributaries: It covers an area of 3,080 sq. km.
The elevation of the city ranges from 150 m - 275 m above sea level. The climate of the city is a tropical
wet and dry climate with a lengthy wet season and relatively constant temperatures throughout the year. The
wet season runs from March through October, though August seems somewhat of a lull in precipitation, while
November to February forms the city’s dry season, during which it experiences the typical West African
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harmattan. It receives a mean total rainfall of 1420.06 mm, falling in approximately 109 days. There are two
peaks for rainfall, June and September. The mean minimum and maximum temperatures are 21.42 °C and
26.46 °C, while the relative humidity is 74.55%.
Figure 1. Location map of the study area, Urban area of Ibadan (showing a satellite image) in Oyo State,
Nigeria
Geologically, the study area lies within the basement complex rocks of metamorphic origin of the
Precambrian age (Figure 2). The rocks are divided into two groups; quartzite of the meta-sedimentary series
and the migmatite complex consisting of banded gneiss, augen gneiss and magnetite; and other ones include
pegmatite, quartz, aplite, diorite, amphibolites and zenoliths (Amanambu, 2015). The rock types are a major
factor controlling the characteristics of the groundwater resource in the study area. Basement complex rocks
(consisting of metamorphic and igneous rock types) are fairly low in groundwater yield when compared with
sedimentary rock areas to the south
lbadan urban area (Figure 3) had four forest reserves (Popoola and Ajewole, 2001). Alalubosa forest
reserve (constituted in 1916), a land area of 308.53 ha destroyed and converted to residential quarters and
'Aiesinloye' market. On the other hand, Oke Aremo reserve (constituted in 1935) with a total land area of
57.67 ha, also de-reserved with greater part ceded for the development of the new King's (Olubadan) palace
and associated projects. Ogunpa dam forest reserve (constituted in 1931, but later declared a game reserve in
1952) over an area of 81.27 ha, also destroyed and larger portion of it has given way for the construction of the
cultural center, Premier hotel, and public schools. Eleyele forest reserve (acquired in 1941 and formally
constituted a reserve in 1956) with an area of 325.2 ha, is gradually been converted into a residential area.
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Figure 2. Geological map of the urban area of Ibadan (After Amanambu, 2015)
Figure 3. Map of urban area of Ibadan (showing a satellite image covering Challenge/Olomi Area),
Nigeria: (a) year 2000; (b) year 2020
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Data source and analytical tools used
In this study, remotely sensed data (satellite images) were primarily used. Multispectral satellites
(Landsat-5, 7 & 8, and Sentinel-2) datasets were extracted using Google Earth Engine (GEE). The GEE
(https://earthengine.google.com/) provides planetary-scale geospatial analysis through large scale cloud
computing to extract various archived geospatial layers to perform scientific analysis (Gorelick et al., 2017;
olokeogun and Kumar 2020). The manipulation, processing, and handling of the remotely sensed data involved
the use of GEE, ArcGIS and ERDAS EMAGINE software as shown in Figure 4.
Figure 4. Schematic representation of steps involved in the study
Delineation and classification of the urban forest using remotely sensed images
The GEE was used for creating a cloud-free composite image for five time periods (2000, 2005, 2010,
2015, and 2020) by utilizes imagery from Landsat-7, Landsat-8, and Sentinel-2 datasets. Methodology, as
suggested by Zhu et al. (2015) was adopted for the extraction of composite images in an automated manner
that incorporates a sophisticated cloud and shadow masking algorithm; an algorithm based on the spectral and
thermal properties of clouds. It finds pixels that are bright and cold but do not share the spectral properties of
snow. Further description can be referred to in the work done by Huang et al. (2010), Goodwin et al. (2013),
Housman et al. (2015), and Tsai et al. (2018).
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Olokeogun OS et al. (2020). Not Sci Biol 12(4):959-971
The script for obtaining cloud-free image composite for the period 2000, 2005, 2015, and 2020 was
implemented in GEE. The images were saved in Google drive and later downloaded from the drive for its
manipulation and handling in ArcGIS and ERDAS EMAGINE software. The boundary indicating the extent
of the study area was extracted from an existing topographic map sheet covering the area using ArcGIS software.
Furthermore, the downloaded images were imported into ArcGIS software and utilized with the extracted
boundary to define the area of interest (AOI) for each period.
The images of the defined AOI for each period were used to develop land cover maps for the years 2000,
2010, 2015, and 2020 in ERDAS software using supervised classification with the Maximum Likelihood
Classifier. Dominant categories of land cover, viz., Built-up area, urban forest, water bodies, and open space
were mapped (Table 1). A change matrix to see categorical changes of one land cover type in 2000 to another
cover type in the years 2005, 2010, 2015, and 2020 was done using ERDAS. This thus provides an estimate of
proportionate land cover changes from one category to another for five (5) years.
Table 1. Land cover classification scheme and their general description
Classes
Description
Built-up area
Residential, commercial, industrial, facilities and settlement
Urban forest
Evergreen forest and mixed forests with a higher density of trees; including mangrove, sparse
vegetation, etc. and all types of crops.
Water bodies
Areas covered by water such as rivers, ponds, lagoons, dams, and waterlogged areas.
Open space
Open land and non-vegetated land
Extraction of human settlement extent using remotely sensed images
The human settlement (HS) extent for each period (2000, 2005, 2010, 2015, and 2020) were extracted
from Landsat data using GEE based on a processing chain referred to as spectral-based analysis (coupled with a
spatial regularization) as suggested by Trianni et al. (2014). The approach for the extraction requires spatial
and spectral processing. The processing chain consists of four steps; pre-processing and selection of the Landsat
scene, computation of the Normalized Difference Spectral Vector (NDSV), classification, and spatial based
post-processing. The graphical representation of the steps regarding the processing chain can be found in Figure
4.
Landsat 7 scene (for 2000, 2005, 2010) and Landsat 8 scene (for 2015 and 2020) were selected and
scripts for creating variable representing a single image (for each scene) and for obtaining cloud-free images
were implemented in GEE. The NDSV was used to detect urban area pixels from the obtained cloud-free
images. Urban areas exhibit an NDSV spectral signature that is flat across all bands. Support Vector Machine
(SVM) classifier was then utilized to characterize the HS. SVM classifier is a non-parametric classifier
developed for hyperspectral data and able to manage high-dimensional spaces. Also, morphological operators
aimed at getting rid of isolated pixels and at improving the homogeneity of the extracted settlements
concerning their spatial distribution was applied as a post-processing step. Furthermore, the images of the
extracted HS extent were exported and downloaded from Google drive for its manipulation and handling in
ArcGIS software. The AOI shapefile and the downloaded images were used to define the extent of HS (for the
year 2000, 2005, 2010, 2015, and 2020) within the study area. In addition, to further reveal the relationship
between urban forest pattern of change and extent of HS pattern of change, the percentage change of urban
forest and extent of HS in 2000-2005, 2005-2010, 2010-2015, and 2015-2020 were calculated.
Results
Land cover pattern between 2000 and 2020
The land cover (of the urban area of Ibadan city) mapped for the corresponding years of 2000, 2005,
2010, 2015, and 2020 is presented in Figure 5. Four prevailing categories of land cover identified in the study
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area are water bodies, built-up area, urban forest, and open space. A substantial change in the alteration of one
land cover category into another was observed during the comparison years. The water bodies, urban forest,
and open space classes noticeably decreased from 0.31% to 0.01%, 24.13% to 7.99%, and 14.14% to 7.86%
respectively while the built-up area class increased significantly from 61.42% to 84.14%. The distribution of
land cover within the urban area of Ibadan city during the years 2000, 2005, 2010, 2015, and 2020 is depicted
in Table 2.
Water Bodies with the lowest land cover (0.31%) in year 2000, decreased to 0.22%, 0.18%, 0.13%. 0.01%
in the year 2005, 2010, 2015 and 2020 respectively. Built-up area with the largest land cover (61.42%) in the
year 2000, decreased to 53.51% in the year 2005, then increased to 67.71%, 74.32%, and 84.14% in the year
2010, 2015 and 2020 respectively. Furthermore, Urban forest with 24.13% land cover in the year 2000,
decreased to 23.67% in the year 2005 but increased to 27.34% in the year 2010, and then decreased to 18.46%
and 7.99% in the year 2015 and 2020 respectively, while Open Space with 14.14% land cover in the year 2000,
increased to 22.61% in the year 2005, but decreased drastically to 4.77% in the year 2010, and then increased
to 7.09% and 7.86% in the year 2015 and 2020 respectively.
Figure 5. The land cover map of the urban area of Ibadan city, Nigeria: (a) year 2000; (b) year 2005; (c)
year 2010 (d) year 2015 (e) year 2020
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Table 2. Land cover distribution of the urban area of Ibadan during the year 2000, 2005, 2010, 2015, and
2020
Land
cover class
Water
bodies
Built up
area
Urban
forest
Open
space
Total
2000
2005
2010
2015
Percentage
change
2020
A
(Km2)
P
(%)
A
(Km2)
P
(%)
A
(Km2)
P
(%)
A
(Km2)
P
(%)
A
(Km2)
P
(%)
(2000-2020)
0.46
0.31
0.29
0.22
0.25
0.18
0.18
0.13
0.02
0.01
- 95.65
77.83
61.42
72.75
53.51
92.06
67.71
101.04
74.32
114.4
84.14
+ 46.99
36.36
24.13
32.18
23.67
37.17
27.34
25.1
18.46
10.86
7.99
- 70.13
21.31
14.14
30.74
22.61
6.48
4.77
9.64
7.09
10.68
7.86
- 49.88
135.96
100
135.96
100
135.96
100
135.96
100
135.96
100
Note: Area (A) and Percentage (P)
Evaluation of urban forests cover under different human settlement extent between 2000 and 2020
The extent of HS within the urban area of Ibadan city generated for the corresponding years of 2000,
2005, 2010, 2015, and 2020 is presented in Figure 6. A significant increase in the extent was detected during
the comparison years. The extent of HS during the years 2000, 2005, 2010, 2015, and 2020 with their
corresponding urban forest cover for the urban area of Ibadan is presented in Table 3. The urban area of Ibadan
had 24.14% urban forest cover under 102,806 pixels of HS extent in the year 2000, 23.67% urban forest cover
under 80,833 pixels of HS extent in the year 2005, 27.34% urban forest cover under 102,290 pixels of HS
extent in the year 2010, 18.46% urban forest cover under 112,271 pixels of HS extent in the year 2015, and
7.99% urban forest cover under 122,572 pixels of HS extent in the year 2020.
The pattern of change between urban forest cover and extent of HS at 5 years interval (2000–2005,
2005-2010, 2010-2015, and 2015-2020) for the urban area of Ibadan is presented in Figure 7. The pattern of
changes in the urban forest and the extent of HS are both positive and negative. From 2000 to 2005, the
negative change accounted for 1.91% and 21.37% in urban forest and extent of HS respectively; from 2005 to
2010, the positive change accounted for 15.50% and 26.54% in urban forest and extent of HS respectively;
form 2010 to 2015, the negative change accounted for 32.48% (in the urban forest) while the positive change
accounted for 9.76% (in the extent of HS); and from 2015 to 2020, the negative change accounted for 56.72%
(in the urban forest) while the positive change accounted for 9.18% (in the extent of HS).
Table 3. Urban forest and human settlement extent for the urban area of Ibadan during the year 2000,
2005, 2010, 2015, and 2020
Time period
Urban forest
Human settlement extent
(Year)
(%)
(No of pixels)
2000
24.13
102806
2005
23.67
80833
2010
27.34
102290
2015
18.46
112271
2020
7.99
122572
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Figure 6. Reference GIS layer of human settlement extent extracted for the urban area of Ibadan city,
Nigeria: (a) year 2000; (b) year 2005; (c) year 2010 (d) year 2015 (e) year 2020
40.00
26.54
30.00
15.50
20.00
9.76
9.18
2010 - 2015
2015 - 2020
Prcentage Change
10.00
0.00
-10.00
2000 - 2005
2005 - 2010
-1.91
-20.00
-30.00
-21.37
-32.48
-40.00
-50.00
-60.00
-56.72
-70.00
Urban forest (% Change)
Human settlement extent (% Change)
Figure 7. Pattern of change (in percentage) between urban forest and human settlement extent at 5 years
interval for the urban area of Ibadan
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Discussion
The results revealed the past and current land cover patterns of the urban area of Ibadan city, HS extents,
and urban forests cover under the influence of different HS extent. The water bodies, urban forest, and open
space classes decreased while the built-up area class increased over twenty years. This implies a substantial
redistribution of the landscape pattern. As reported by Asmat et al. (2012), and Noor and Rosni, (2013), a
developing/growing city is usually characterized by a continuous restructuring of landscape pattern. The builtup area class decrease from 61.42% to 53.51% between the year 2000 and 2005, but later continued to increase
till the year 2020 with 84.14% (Table 2). The decrease between the years 2000 and 2005 is traceable to urban
restructuring embarked upon by the then Oyo State government between the years 2004 and 2006 as reported
by Akingbogun et al. (2012). Also, the changes can be largely attributed to anthropogenic activities such as
urbanization activities (urban development and human settlement expansion, including road constructions,
demolition, and construction of residential/non-residential buildings) and unstable government policy.
According to Asmat et al. (2012), and Banai and DePriest, (2014), indicators of urbanization include not only
the following, human settlement, land development, population, mix of residential and commercial land use,
and multi-modal mobility options.
Furthermore, urban forest class decreased from 24.13% to 23.67% between the years 2000 and 2005,
increased to 27.34% in the year 2010, and then continued to decrease to 18.46% and 7.99% in the year 2015
and 2020 respectively (Table 2). The decrease between the year 2000 and 2005 implies the occurrence of forest
destruction, which affirm the report of Popoola and Ajewole (2001), and Agbola et al. (2012) concerning the
trends of urban forest deforestation and its consequences in Ibadan. The increase witnessed in the year 2010
can be linked to the impact of the afforestation program implemented within the city in the year 2006, as
reported by Akingbogun et al. (2012). The observed continued decrease in the year 2015 and 2020, indicates
major loss which could be attributed to rapid urbanization which has led to a massive conversion of
vegetation/forested area to residential and non-residential areas. Several studies reported that swift
urbanization has momentous impact causing many unforeseen consequences including loss of natural resources
(such as forests), loss of prime farmland, increased environmental pollution, and many other physical, social
and economic effect (Burchell and Shad, 1999; Sierra, 2001; Adelusi et al., 2002; Hasse and Lathrop, 2003;
Noor and Rosni, 2013). Also, the decrease might have a great adverse influence on the quality of life within the
city as reported by Popoola and Ajewole (2001). According to Kuchelmeister (2000), the conversion of forests
and farmland for urban development can reduce water-permeable areas, upset natural drainage patterns, and
cause serious flooding.
Also, HS extent increased from 102,806 to 122,572 pixels between the years 2000 and 2020 (Table 3).
This suggests that the urban area of Ibadan city experienced urban expansion pointing to urbanization. Several
studies agreed that urban expansion is an important indicator of urbanization (Nasser and Paul, 2001; Yeh and
Xia, 2001; Weng, 2002; Sudhira et al., 2003; Jain, 2008; Ibrahim et al., 2009; Kulash, 2009; Li, 2009; Mohd
Noor et al., 2012;). Also, the urban forest cover was 24.14% under 102,806 pixels of HS extent in the year 2000,
urban forest cover and HS extent decreased in the year 2005, both increased in the year 2010, however, in the
year 2015 and 2020, urban forest cover decreased while HS extent increased (Table 3).
Conclusions
This research focused on examining the status of urban forests under the influence of different HS
extents within the urban area of Ibadan city. The findings revealed considerable changes in landscape patterns
within the urban area of Ibadan city for over twenty years (2000 - 2020). Furthermore, there is a significant
decrease in the urban forests cover as a result of a substantial increase in HS extent. The study demonstrates the
successful application of remote sensing and GIS tools for the acquisition of relevant information that can be
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Olokeogun OS et al. (2020). Not Sci Biol 12(4):959-971
used for mapping the status of urban forests cover and extents of HS within cities. The mapping is therefore
important for proper, effective, and efficient landscape management. The study would be useful for urban
forest managers and city planners who are involved in urban growth and green space development.
Authors’ Contributions
Conceptualization: OS and AO; Data curation: OS; Formal analysis: OS; Investigation: OS, AO, and
OO; Methodology: OS and AO; Project administration: AO and OO; Resources: OS, AO, and OO; 12
Software: OS; Supervision: AO and OO; Validation: OS, AO, and OO; Visualization: OS, AO, and
OO; Writing - original draft: OS; Writing - review and editing: OS, AO, and OO. All authors read and
approved the final manuscript.
Acknowledgements
We want to thank the Department of Forestry and Wildlife Management, Federal University of
Agriculture, Abeokuta, Nigeria, and the Department of Forestry Technology, Federal College of Forestry,
Ibadan, Nigeria for co-hosting this research and providing initial training.
Conflict of Interests
The authors declare that there are no conflicts of interest related to this article.
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