Assessing Urban Ecology in City of
Kuwait
Mohammad Anwar Alattar (1741107)
2018
This dissertation is submitted as part of a MSc degree in Geography at King’s College London
KING’S COLLEGE LONDON
DEPARTMENT OF GEOGRAPHY
MA/MSc DISSERTATION
I, Mohammad Anwar Alattar hereby declare (a) that this
Dissertation is my own original work and that all source
material used is acknowledged therein; (b) that it has been
specially prepared for a degree of King’s College London; and
(c) that it does not contain any material that has been or will be
submitted to the Examiners of this or any other university, or
any material that has been or will be submitted for any other
examination.
This Dissertation is 9915 words.
Date: Aug 22, 2018
I
ABSTRACT
Given the numerus benefits that urban ecology can provide to urban settlements and the
environmental vulnerability of Kuwait, this study aims to assess urban ecology in the City of Kuwait.
The objective of this study to assess the change in UGSs from 1992 to 2016; and assess urban fauna
by surveying 38 different locations in April 2018 using a passive acoustic monitor. The results
revealed that, apart from the drop between 2004-2010, the area of UGSs expanded, reaching 128 km2
in 2016. In addition, the complexity increased throughout the period while isolation decreased.
Spatially, unlike the number of UGSs which were observed to rise towards the centre of different
areas, the size was found to be higher at the boundaries. Using acoustic indices, the functionality of
UGSs was assessed, and it was shown that the existence of bioacoustic communities in UGSs is
greater than non-green spaces. However, more data is needed to thoroughly investigate the underlying
acoustic index patterns of UGSs.
II
TABLE OF CONTENTS
ABSTRACT ....................................................................................................................................... II
TABLE OF CONTENTS................................................................................................................. III
LIST OF FIGURES .......................................................................................................................... V
LIST OF TABLES ........................................................................................................................... VI
INTRODUCTION .............................................................................................................................. 1
1.1.
Country Profile .................................................................................................................. 2
1.2.
Statement of the Problem................................................................................................... 3
1.3.
Area of Interest .................................................................................................................. 3
1.4.
Significance of Study......................................................................................................... 4
1.5.
Aims and Objectives .......................................................................................................... 4
1.6.
Research Questions ............................................................................................................ 4
LITERATURE REVIEW.................................................................................................................. 5
2.1.
Kuwait Natural Environment ............................................................................................. 5
2.1.1.
Wildlife....................................................................................................................... 5
2.1.2.
Vegetation .................................................................................................................. 5
2.2.
Kuwait Built Environment ................................................................................................. 7
2.3.
Landscape Metrics ............................................................................................................. 8
2.4.
Bioacoustics ..................................................................................................................... 11
2.4.1.
Acoustic Indices ........................................................................................................... 12
METHODOLOGY........................................................................................................................... 14
3.1
Remote Sensing ............................................................................................................... 14
3.1.1.
Data Acquisition ....................................................................................................... 14
3.1.2.
Spectral Vegetation Index ........................................................................................ 15
3.1.3.
Dataset Classification ............................................................................................... 16
3.1.4.
Accuracy Assessment ............................................................................................... 17
3.1.5.
Change Detection ..................................................................................................... 19
3.1.6.
Landscape Metrics .................................................................................................... 19
3.1.7.
Gradient Analysis ..................................................................................................... 20
3.2
Acoustic Monitoring ........................................................................................................ 21
3.2.1.
Equipment ................................................................................................................ 21
3.2.2.
Sampling................................................................................................................... 21
3.2.3.
Acoustic Indices and Analysis ................................................................................. 22
RESULT AND DISCUSSION......................................................................................................... 23
4.1.
Results.............................................................................................................................. 23
4.1.1.
Landscape Metrics Analysis ..................................................................................... 23
4.1.1.1. UGSs Change Detection ..................................................................................... 23
4.1.1.2. Synoptic characteristics of UGSs ....................................................................... 24
III
4.1.1.3. UGSs Change Detection ..................................................................................... 25
4.1.2.
Acoustic Indices Analysis ........................................................................................ 27
4.1.2.1. Illustration of Acoustic Indices .......................................................................... 27
4.1.2.2. Assessing UGS Functionality............................................................................. 30
4.1.2.3. Acoustic Indices Pattern ..................................................................................... 33
4.2.
Discussion ........................................................................................................................ 34
4.2.1.
Flora Assessment ...................................................................................................... 34
4.2.2.
Fauna Assessment .................................................................................................... 36
CONCLUSION................................................................................................................................. 38
5.1.
Managerial Implications .................................................................................................. 39
5.2.
Limitations ....................................................................................................................... 39
5.3.
Direction for Future Research ......................................................................................... 39
BIBLIOGRAPHY ............................................................................................................................ 40
IV
LIST OF FIGURES
Figure 1. Overview of AOI. ................................................................................................................. 3
Figure 2. Kuwait vegetation map. ........................................................................................................ 6
Figure 3. New proposed city locations in Kuwait according to Kuwait municipality ......................... 8
Figure 4. Landscape elements. ............................................................................................................. 9
Figure 5. Landscape metric categories ............................................................................................... 10
Figure 6. The soundscape ecology elements conceptual model. ....................................................... 11
Figure 7. Schematic representation of ANH. ..................................................................................... 12
Figure 8. Map derived from SAVI, representing vegetation features in Kuwait. ............................. 16
Figure 9. Linear SVM. ....................................................................................................................... 16
Figure 10. Area of interest after applying the SVM method. ............................................................ 17
Figure 11. Kuwait City downtown using true and false colours. ....................................................... 18
Figure 12. AOI and the designed transects. ....................................................................................... 20
Figure 13. Sampled locations using a passive acoustic sensor (AudioMoth). ................................... 21
Figure 14. UGSs change detection map derived from the SAVI differencing method for the years
1992-2016. ......................................................................................................................................... 23
Figure 15. Gradient analysis of selected metrices with west to east transect from 1992 to 2016. .... 26
Figure 16. Gradient analysis of selected metrices with north to south transect from 1992 to 2016. . 27
Figure 17. Spectrograms of recordings with high and low NDSI values. ......................................... 28
Figure 18. Spectrograms of recordings with high and low ACI values. ............................................ 29
Figure 19. Spectrograms of recordings with high and low BI values. ............................................... 30
Figure 20. Combined NDSI values for all UGS locations. ................................................................ 33
Figure 21. Kuwait city growth between the years 1957-1995. .......................................................... 35
Figure 22. Comparison between two different shape complexities. .................................................. 36
V
LIST OF TABLES
Table 1. UGS potential benefits. .......................................................................................................... 1
Table 2. Satellite image dataset attributes. ......................................................................................... 14
Table 3. Accuracy assessment for the 1992, 2004 and 2012 datasets. .............................................. 18
Table 4. Summary of landscape metrics. ........................................................................................... 19
Table 5. Summary of acoustic indices. .............................................................................................. 22
Table 6.Change detection results derived from the SAVI differencing method for the years 19922016 (Area in km2). ............................................................................................................................ 23
Table 7. Landscape metrics results from 1992 to 2016. .................................................................... 25
Table 8. Shapiro-Wilk normality test results. ................................................................................... 31
Table 9. One-sample T-Test (Test value= -0.75). .............................................................................. 32
VI
INTRODUCTION
Globally, the accelerated rate of urbanisation has adversely affected the quality of life of individuals
living in the city. City dwellers are surrounded by masses of concrete and are deprived from natural
environments and their benefits. Additionally, urban area structures cause a vulnerability to certain
environmental problems, such as surface runoff, the urban heat island effect and different types of
pollution (Göbel et al., 2007; Leal Filho et al., 2017; Moudon, 2009). Currently, urban areas are
occupied by 50% of the world population and by 2050, this is predicted to increase to 70% (United
Nations, 2012). Thereby, it is essential to alleviate the challenges faced in urban settlements in order
to make such places more liveable and sustainable which refer to collective of life quality and
wellbeing indicators of its current inhabitants without causing a pressure on future generation
(Johnstone et al., 2012).
Urban ecology refers to the study of the distribution and abundance of living organisms and
their interaction with each other within urban terrestrial areas where humans are abundant and make
changes to the environment. This field has an impact on the quality of life and liveability (Xu, 2013).
Any outdoor areas that contain a remarkable amount of vegetation and are surrounded by urban areas
are denoted as urban green spaces (UGSs) (WHO, 2016; Kong and Nakagoshi, 2006). An UGS can
take many forms, such as conventional parks, linear parks, green roofs, green walls and green
corridors. These areas can be conventional, such as zoos, gardens and parks green corridors; or
unconventional, such as urban green roofs (Peng and Jim, 2013) and greenways, e.g. the High line,
an abandoned rail in New York (Wolch et al., 2014). Numerous studies have proven that UGSs have
many potential benefits that can help urban environment restoration.
Table 1 shows selected studies and their findings with regards to UGS benefits. Nevertheless,
UGSs that are not maintained can be accompanied by downsides, such as converting into a potential
space for illegal activities, or even leading to the reduction of nearby propriety values (Harnik et al.,
2017).
Category
Table 1. UGS potential benefits.
Finding
Location
Increase propriety values
Socioeconomical
Reduce urban heat island effect
Improve social inclusion and community
attachemnt
Deter crime
1
Study
Jinan City, China
Los Angeles County,
California, USA
Singapore and Kuala
Lumpur, Malaysia
KwaZulu-Natal Province,
South Africa
Yan’an City, China
(Kong et al., 2007)
Zurich, Switzerland
(Seeland et al., 2009)
Chicago, Illinois, USA
(Kuo and Sullivan, 2001)
(Harnik et al., 2017)
(Sorensen et al., 1997)
(Odindi et al., 2015)
(Zhang et al., 2017)
Provide recreational activities and attract
tourists
Reduce heating and cooling energy
consumption
Reduce air pollution
Environmental
Sequester carbon
Reduce rainwater runoff
Detent dust
Host threatened species
Promote phyiscal health
Mental and
physical
wellbing
Improve mental health
Reduce noise
New York, USA
Mexico City, Mexico
(Appleseed, 2015)
(Smit et al., 1996)
Chicago, Illinois, USA
(McPherson et al., 1994)
Taipei, Taiwan
Kumasi, Ghana
Sejong City, South Korea
Beijing, China
Ningbo, China
Various cities in Australia
Aydın, Turkey
Chicago, Illinois, USA
Chicago, Illinois, USA
Netherlands
Stockholm and Göteborg,
Sweden
(Liu and Shen, 2014)
(Nero et al., 2017)
(Lee et al., n.d.)
(Zhang et al., 2015)
(Ye, 2011)
(Ives et al., 2016)
(Akpinar, 2016)
(Fan et al., 2011)
(Fan et al., 2011)
(Maas et al., 2009)
(Gidlöf-Gunnarsson and
Öhrström, 2007)
Although urban areas constitute less than 0.5% of the entire Earth’s total land area (Schneider
et al., 2009), due to the abundance of food and absence of predators in urban areas (Adams et al.,
2006), some UGSs have become a natural habitat for many endemic, native species. Even endangered
species can be found in urban areas, such as the song thrush Turdus philomelos in Essex, England,
U.K. and the peninsula spider orchide Caladenia thysanochila in Melbourne, Victoria, Australia
(Ives et al., 2016). However, in some cases, developing urban areas (urbanizing) can lead to habitat
loss and species extinction (Czech et al., 2000).
Kuwait is currently encountering many environmental challenges, such as the deforestation
of rangelands; the scarcity of conservation areas; unregulated hunting and industrial and oil pollution
(EPA, 1998). All previous studies have suggested that Kuwait has a fragile environment. Moreover,
catastrophic environmental consequences also occurred due to the invasion (2 August 1990), and the
occupation and liberation (28 February 1991) of Kuwait. In addition, Iraq military ignited over 700
oil wells during their retreat, which were subsequently capped on 6 November 1991 (Young, 2003).
Given the existing issues in Kuwait, it is vital to assess its urban ecology.
1.1.Country Profile
The State of Kuwait lies between the latitudes 28°33′ and 30°05′ and longitudes 46°33′ and 48°30′ in
the northwest of the Arabian Gulf corner, bordering the Kingdom of Saudi Arabia to the south and
Iraq to the north. Administratively, the capital of Kuwait is Kuwait City. The country is portioned
into six governorates (Al Asimah, Hawalli, Farwaniya, Mubarak Al-Kabeer, Ahmadi and Jahra), each
divided further into areas, additionally. In addition to this, the country has nine islands. From herein,
the term “City of Kuwait” is used to represent the urban area which includes Kuwait City (the capital)
and the surrounding areas, as many literatures use both terms interchangeably to refer to the capital.
Kuwait is economically one of the top global oil producers. In terms of topography, it
encapsulates an area of 17,818 km2 with a gradual elevation rise from the Arabian Gulf in the
2
direction of the western corner, reaching 284 m above mean sea level (Al-Sarawi et al., 2006). It is
located in an arid zone, in which the average summer and winter temperatures are 43°C and 15°C
respectively. The rainfall is considered to be low, ranging from 35 mm to 242 mm, occurring between
October and late April; whereas evaporation is high, varying from 3.1 to 21.6 mm d-1 (Almedeij,
2014).
1.2.Statement of the Problem
Kuwait does not have a rich natural and biodiversity heritage. Floods (Al-Dousari et al., 2007), dust
storms, deteriorating natural resources and biodiversity and many environmental issues are
intensifying as a consequences of human activities (EPA, 1998). Furthermore, all types of human
interferences, from disturbances, buildings and pavements, which exist prominently in urban
settlements, lead to heat retention (Leal Filho et al., 2017). Most importantly, Kuwait has experienced
an environmental tragedy throughout the course of the invasion of Kuwait in 1990. However, the
impact on urban ecology, which affects the life quality and liveability of the city, has not yet been
studied.
1.3.Area of Interest
The area of interest (AOI) has been delineated by digitizing all types of areas, making main roads the
boundaries. Remote areas with getaway houses were excluded from the linear shape extraction along
the southern coast. Figure 1 shows an overview of the AOI, which has an area of 801 km2,
representing 4.5% of the total area of Kuwait.
Figure 1. Overview of AOI. The AOI was delineated on a basemap derived from Esri (2018) to
represent urban areas and its surroundings using main roads as the boundary.
3
1.4.Significance of Study
Assessing urban ecology provides an indicator of how healthy a city is from an environmental
perspective, as well as its resilience to environmental stress. Decision-makers and city planners will
recognize the intensity of the urban challenges impacting the urban ecosystem, with recent research
showing that human interaction with the environment improves quality of life, liveability, well-being
and social cohesion (Soulsbury and White, 2015).
1.5. Aims and Objectives
The main aim of this dissertation is to assess the urban ecology in the City of Kuwait by studying the
flora and fauna within the boundaries of an urban settlement (see Figure 1). The objectives of this
research are as follows:
1- To assess the flora by studying changes in UGSs using satellite images and the Soil-Adjusted
Vegetation Index (SAVI) to extract vegetation and subsequently perform the following
techniques. Firstly, change detection to evaluate any alterations in UGSs from 1992, the year
follow the liberation from Iraq invasion up until 2016. Secondly, the comprehensive analysis
of landscape metrics from 1992 to 2016 with a six-year gap to evaluate UGSs in terms of
disturbance, fragmentation, abundance and other indices. Thirdly, gradient analysis to infer
the spatio-temporal changes in UGSs using north-south and west-south transects.
2- To assess the fauna, passive acoustic monitoring will be applied to UGSs, in order to infer
various acoustic indices and to draw conclusions related to the functionality of UGSs and their
underlying acoustic indices patterns using inferential statistics
1.6.Research Questions
Taking the previous research objectives into account. The research questions were formulated as
follows:
1- How have UGSs changed from 1992 to 2016?
2- How have UGSs changed along the north-south transect and west-east transect?
3- Do bioacoustic communities exist in UGSs more than in non-green spaces?
4- What are, if any, the underlying patterns of UGS acoustic indices?
4
LITERATURE REVIEW
2.1.Kuwait Natural Environment
Climatological factors, rainfall scarcity, the prevalence of northwest wind which induce
sedimentation transportation and erosion, all play a crucial role in inducing deforestation and
eventually, land degradation (Al-Awadhi et al., 2003). Additionally, the excessive grazing of
rangelands, desert camping, gravel and sand overexploitation and off-road vehicles are the main
causes of land degradation in Kuwait (Misak et al., 2002). Kuwait has witness environmental
catastrophe due to the Gulf War because of heavy military vehicle and tank movements, mine fields,
the digging of bunkers and over 700 oil well fires (Khordagui and Al-Ajmi, 1993). Khordagui (1991)
classified the environmental damage from the warfare into three groups: (1) air pollution that occurred
from the burning of oil wells; (2) marine pollution caused by oil spills in the Arabian gulf; (3) land
surface disruption and contamination. Furthermore, a remarkable decline in species richness is caused
by human disturbances and war machinery (Zaman and Al-Sdirawi, 1993). Brown and Schoknecht
(2001) reported that off-road vehicles are mainly responsible for ecosystem degradation and potential
vegetation recovery can only occur in microsites.
2.1.1. Wildlife
Kuwait hosts 28 mammalian, 40 reptilian and 300 bird species (Omar et al., 2009). Marine wildlife,
which is out of the research scope, has been studied intensively, e.g. Saburova et al., (2009) AlZaidan et al., (2013) and Al-Mohanna et al., (2014). In contrast, land biodiversity has only been
limitedly studied, yet a lot of studies discuss land biodiversity in the light of the Gulf War impact. AlHouty (2009) attributed the extinction of the dung beetle (Scarabaeus sacer) to soil compaction, as
the compactness prevents this species from burrowing, mating and storing food. In addition, the jewel
beetle (Juloides distincta) has relocated and disappeared from many locations because of the loss of
perennial shrub, which provides shelter for this species. Furthermore, oil spills have also led to
dramatic consequences, e.g. Alsdirawi (1991) states that oil lakes were confused by some avian
species and insect as water surfaces and ended up getting trapped. Al-Hashem et al. (2007) found out
that sand lizards (Acanthodactylus scutellatus) in oil polluted areas have a noticeable behavior, more
specifically, an early emergence during the daytime, rapid eating habits and a shorter basking period
in comparison to those in clearer areas. Omar and Roy (2010) projected future climate change
conditions for Kuwait and predicted that these will cause a loss in biodiversity and more extreme
events, such as drought and dust storms.
2.1.2. Vegetation
Kuwait has 256 species of annuals, 83 species of herbaceous perennials, 34 species of shrubs and
under shrubs and one tree species (Omar et al., 2009). Several studies have been conducted on
5
vegetation in Kuwait. Halwagy and Halwagy (1974) emphasized that vegetation in Kuwait consist of
undershurbs, perennial herbs and ephemerals, and are mainly effected by rainfall, with landforms
and biotic factors having less of an influence. Unlike the mainland, Abbadi and El-Sheikh (2002)
indicated that Failaka Island vegetation distribution has the best correlation with soil factors, such as
salinity, sodium, calcium, sand and other soil-related factors. A. S. Omar et al., (2001) conducted a
reconnaissance field survey in all areas excluding urban, restricted and agricultural areas. As seen in
Figure 2, it was found that bare land covers the most of the country, in terms of vegetation
Stipagrostietum genus represents 39% of the vegetation, followed by Cyperetum (27%) and
Haloxyletum (23%), while the remaining is made up of different genera.
Several studies have discussed spectral vegetation extraction in Kuwait. Kwarteng (1998)
suggested that because of the location of Kuwait, the Normalized Difference Vegetation Index
(NDVI) should be used with caution and it is recommended to be used in the spring season where the
vegetation density at its highest. Almutairi et al. (2013) found that the soil-adjusted vegetation index
(SAVI), with a soil factor of 0.9 (R2 = 0.97), had a greater correlation than NDVI (R2 = 0.66) to the
percentage of vegetation pixels.
Figure 2. Kuwait vegetation map. Bare land covers most of the country. In terms of vegetation
coverage, Stipagrostietum genus represents 39%, followed by Cyperetum (27%) and Haloxyletum
(23%), while the remaining is made up of different genera. Adopted from S. A. S. Omar et al.
(2001)
6
Up to the author’s knowledge, just one study has been carried out investigating the one UGS
in the City of Kuwait by Abdullah (2015). The same study also conducted fieldwork to investigate
urban public parks and found out that several were mis-listed in the Public Authority of Agriculture
Affairs and Fish Resources (PAAF), the official authority responsible for public parks and
landscaping planting
In the current study, the UGSs in city of Kuwait were geometrically compared between 19822014, with an approximately 5-year interval, using Landsat 5, 7 Enhance Thematic Mapper plus
(ETM+) and 8. The study spatially investigated the UGSs by employing buffering techniques and
revealed that in many areas, both boulevards and cross-streets hindered the accessibility of UGSs.
2.2.Kuwait Built Environment
The 1950s’, the built environment was described as a walled waterfront town containing a bulk of
barren areas and open sandy fields with numerous urban settlements along the road intersections. A
few years after the first shipment of crude oil in 1946, Kuwait experienced a dramatic economic,
social and demographic transformation and adopted and implemented master plans. Prior to that year,
the economy was depressed. In the 1980s, the open sandy fields were filled southwardly with more
settlements toward the Kuwait International Airport (Ibrahim, 1982). Caton and Ardalan (2010)
indicated that regionally, unlike other countries in the region, Kuwait is one of the first nations to
urbanize their settlements. In 2017, the population was estimated to have reached 4,082,704;
interestingly, the majority (2,812,508) are Non-Kuwaiti (SCPD, 2016).
Fundamentally, three master plans have been implemented. The First Master Plan in 1951 aimed
to allocate suitable zones for different purposes, such as residential, industrial, commercial and
educational, and provide suitable road systems to suit the traffic conditions. The Second Master Plan
in 1970 intended to address and overcome the challenges that accompanied the rapid expansion. Abdo
(1989, p. 306) assessed the first and second master plans and described the urbanisation until then as
“neither poorly conceived nor ideal”. Finally, in 1997, the Third Master Plan sought to make the
country a business and financial hub (SCPD, 2016). Alghais and Pullar (2018) examined the
feasibility of establishing the new suggested cities, shown in Figure 3, that are proposed by the
Kuwait municipality in response to current trends of urban and population growth. This was done by
implementing an agent-based model to simulate the population distribution, as well as primary data
collected via interviews and survey questionnaire. Results showed that traffic congestion and housing
insufficiency are the main reason to establish new cities, and accordingly, residents are willing to
relocate to overcome these challenges.
7
Figure 3. New proposed city locations in Kuwait according to Kuwait municipality. New cities
are proposed to overcome the main current urban challenges namely, traffic congestion and housing
insufficiency. Adopted from Alghais and Pullar (2018)
With respect to microclimatic conditions in urban areas, Nasrallah et al. (1990) studied the urban
heat island effect in Kuwait using climatic data from three different stations. The study identified
changes in temperature patterns that were associated with urban growth in Kuwait City and
determined a decadal urban heat island between 0.07 and 0.12 °C decade-1. Kwarteng and Small
(2007) calculated surface temperatures using the Landsat thermal bands for the years 1992 and 2001
using Planck’s law and concluded that areas that surrounded by desert have higher temperatures than
residential areas because of the cooling effect that vegetation provides.
2.3.Landscape Metrics
Landscape metrics are tools used to assess the landscape structure and changes using numeric data to
support planning and decision making. As illustrated in
Figure 4, the structure of any landscape consists of four elements as follows; (1) Patch: a polygon
area which is usually less abundant; (2) Corridor: a type of patch that has an elongated shape and
links patches; (3) Matrix: the most dominant element in the landscape, with interconnectivity
features; (4) Mosaic: a group of patches. Landscape change occurs due to either natural or human
interventions. Nowadays, human intervention is noticeable in landscape change. To assess landscape
change dynamics, previous data on the landscape is required for comparisons (Gkyer, 2013).
8
Figure 4. Landscape elements. (1) Patch: a polygon area which is usually less abundant; (2)
Corridor: a type of patch that has an elongated shape and links patches; (3) Matrix: the most
dominant element in the landscape, with interconnectivity features; (4) Mosaic: a group of patches.
Adopted from FISRWG (1998).
These metrics evaluate both the landscape composition and landscape configuration (also
referred to as spatial configuration) as follows; (1) landscape composition quantifies the non-spatial
characteristics of patches within the landscape, more specifically variety and abundance, by
determining patch characteristics, such as patch rate, richness, evenness and diversity; (2) landscape
configuration quantifies the spatial characteristics of the patch within the landscape, such as shape,
size, connectivity and distribution. A visualisation of landscape structure by Silva et al. (2014) is
shown in Figure 5, the authors also categorised landscape structure can as follows:
•
Shape irregularity metrics, which measure the extent to which the patches/landscapes have a
regular or complex shape.
•
Fragmentation metrics, which measure the extent to which patches are aggregated or
fragmented.
•
Diversity metrics, which measure the extent to which shapes are distributed.
9
Figure 5. Landscape metric categories. (A) Shape irregularity: Measures the extent to which
patches/landscapes have a regular or complex shape. (B) Fragmentation: Measures the extent to
which patches are aggregated or fragmented. (C) Diversity: Measures the extent to which shapes
are distributed. Adopted from Silva et al. (2014).
Many studies have demonstrated the correlation between biodiversity and landscape structure.
To exemplify, Brown and Kodric-Brown (1977) coined the term “rescue effect” to describe the
processes of individuals of a certain species immigrating from a patch with high density to a
neighboring low density area, in order to reduce the risk of extinction. Thus, the distance between
patches plays a remarkable role in species distribution. In addition, Öckinger et al., (2012) reported
that butterfly richness was found to be positively correlated with patch area and negatively with
fragmentation. Walz (2011) mentioned that a connected habitat is usually associated with more
species because of the ease of to inhabit an area. Furthermore, Fearer (1999) reported that ruffed
grouse (Bonasa umbellus) were found in higher densities in habitats with regular shapes. Maier
et al. (2005) reported a positive association between the density of the female moose (Alces alces L.)
and patch richness.
Accordingly, to assess the life quality in urban areas, many studies use landscape metrics
(LM) to evaluate UGSs from various prospective. He et al. (2012) employed LM to investigate
human preferences for UGSs in Hanoi, Vietnam and found out that connectivity and size variability
contribute positively to the satisfaction of residents. Rafiee et al. (2009) quantified changes in UGS
patterns using LM derived from satellite images from 1986 and 2006 in Mashhad, Iran and
10
investigated life quality using UGSs as a proxy. Sun Caige et al. (2016) studied UGSs in Zhuhai,
China and pointed out that the center of the city exhibits fragmentation. The authors suggested in
establishing green corridors to gain more ecological benefits.
2.4.Bioacoustics
Sound is a vibration that propagates through a medium, causing the molecules to pulse outward or
collide and thus creating waves which can be perceived by a receiver, such as a person or animal.
Sound has the following four attributes; intensity (loudness), which is measured in decibels (dB),
frequency, periodicity and duration (Villanueva-Rivera et al., 2011). Many animal populations use
acoustics for various purposes, such as communication, socializing, echolocation and sexual display
(Bradbury and Vehrencamp, 1998).
Soundscape ecology refers to the interaction of three elements in any given landscape, namely
anthrophony (sounds emanating from human-made objects), geophony (sounds emanating from
geophysical sources such as wind or thunder) and biophony (sounds emanating from living
organisms, such as bird vocalisations). Figure 6 shows a conceptual model for these elements. It can
be inferred that each land use has a unique combination of the elements. Anthrophonic sounds mostly
dominate urban areas in which humans are mostly present. Due to the existence of human-made
objects, it is predicted that geophonic sounds will be amplified, for instance, raindrops hitting a car
(Bryan C. Pijanowski et al., 2011). Furthermore, Ortega (2012) found that anthropogenic noise
pollution can alter the behaviour of some avian species, such as foraging and reactional behaviour.
The main focus of this research is bioacoustics, which is the field studying the sound produced and/or
perceived by animals (Ozga, 2017), as some animals produce infrasound or ultrasound, which are
lower and higher than the sounds which are able to be heard by humans, respectively (Farina, 2013).
Figure 6. The soundscape ecology elements conceptual model. Dashed line illustrates a predicted
pattern that can occur in which rain sounds will be amplified by hitting human made objects.
11
Soundscape ecology elements vary across various land use types. Urban areas are dominated by
both stationary and moving human-made objects. Adopted from Bryan C. Pijanowski et al. (2011)
By employing passive acoustic sensors, a biodiversity survey becomes possible in locations
where other monitoring devices are restricted, and to survey species that are visually challenging to
survey (MacSwiney G et al., 2008). Following this, the obtained data can be represented in a spectrum
that consists of frequency and time, and can be used to calculate bioacoustic indices or identify
species, both manually and using machine learning techniques (Lomolino et al., 2015; Chesmore and
Ohya, 2004). These indices are based on the acoustic niche hypothesis (ANH) proposed by Krause,
(1987), which states that since the sound spectrum is limited, species that use the same frequency
minimize acoustic competition by avoiding temporal overlapping. Figure 7 illustrates ANH, whereby
different species inhabiting African tropical forests (a to g) tend to partition frequency to avoid
spectral and temporal overlapping (Farina, 2014).
Figure 7. Schematic representation of ANH. The spectrogram depicts the vocal behaviour of
different species (a to g) inhabiting African tropical forests. Species avoid spectral and temporal
overlapping to reduce the masking effect. Adopted from Farina (2014).
2.4.1. Acoustic Indices
Kasten et al. (2012) introduced the normalized difference soundscape index (NDSI), which computes
the ratio of anthrophonic to biophonic sounds between the range [-1 (pure anthrophonic) to +1 (pure
biophonic)] by assuming that anthrophonic sounds prevail the spectral range between 1 kHz to 2 kHz,
whereas the prevalence of biophonic sounds is between 2 kHz to 11 kHz . The authors also indicated
that this index can be utilized as a filter for the obtained acoustic recordings for further analysis.
Similarly, Fairbrass et al. (2017) recommended eliminating the bias caused from anthrophonic sounds
prior to any further analysis
12
Farina and Morri (2008) proposed the Acoustic Complexity Index (ACI) to measure the
absolute intensity, assuming that biophonic sounds have fluctuated intensity values, unlike
anthrophony sounds which are characterized by stable intensity values. ACI aims to monitor fauna
dynamics by identifying alterations in vocalizing communities. Sueur et al. (2014) elucidated that the
ACI of any bioacoustic community will increase with the increase of species and number of
individuals within the species contributing to this community. Hartigan (2017) used the ACI to assess
marine biodiversity in five different locations in the cost of Cape Hatteras in North Carolina from
October 2013 to August 2015 and determined the time in which species where at their highest active
level. In this study, ACI was found to be at its highest in the time of whale migration.
Species diversity and evenness can be estimated using the Acoustic Diversity Index (ADI)
and the Acoustic Evennes Index (AEI) respectively, as suggested by Bryan C Pijanowski et al. (2011).
Both indices utilize the same original data, by calculating the proportion of the divided spectrogram
into bins (default = 1 kHz) with an amplitude greater than a predefined threshold (default – 50 dBFS).
Subsequently, for ADI, Shannon’s index will be applied, whereas for AEI, the Gini index will be
applied, The Gini index is a statistical dispersion measurement to determine inequality among
frequency distribution values. Originally, the index was proposed by Gini (1936) to measure a
nation’s income inequality. Shannon’s index takes into account both species abundance and evenness
(Kapley et al., 2007). Gasc et al. (2015) used a bird assemblages simulator and confirmed that the
ADI can be used as a species diversity proxy.
The bioacoustics index (BIO), which is proposed by Boelman et al. (2007), measures avian
abundance. BIO calculates the area under the spectrum curve within the range between 2 kHz to 8
kHz. The authors utilized the index to distinguish between exotic and native avian species.
13
METHODOLOGY
In this research, three main approaches were applied to assess urban ecology in the City of Kuwait.
To assess flora, remote sensing (RS) was utilised to prepare UGS data from 1992, a year from the
liberation from the Iraq invasion, to 2016, with a six-year gap due to the unavailability of data for
some years and to have a consistent time interval. To assess fauna, passive acoustic monitoring was
employed in UGSs and non-green spaces, such as sandlots, to obtain species information. In addition,
geographic information system (GIS) was intensively employed for further data analysis and the
presentation of both assessments.
3.1 Remote Sensing
3.1.1. Data Acquisition
A total of 10 Surface Reflectance Level-2 data products were downloaded from the USGS Earth
Resources Observation and Science website (https://espa.cr.usgs.gov/). Landsat 4-5 Thematic
Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were processed
through the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). In addition,
images from the Landsat 8 Operational Land Imager (OLI) were processed using the Landsat Surface
Reflectance Code (LaSRC). Surface reflectance was derived using LEDAPS and LaSRC, so that
atmospheric attributes, sun angle, and the sensor view angles were all accounted for (U.S. Geological
Survey, 2018a; U.S. Geological Survey, 2018b).
For each year, the AOI is represented by consecutive scenes. All datasets were cloud-free,
with a 30 m resolution and a scene size of 170 km × 185 km. Table 2 shows the attributes of the
datasets. All images used were acquired within the summer time to avoid clouds that might cover the
AOI. Landsat 7 ETM+ images lost 22% of data due to the Scan Line Corrector (SLC) failure (USGS,
2018). To compensate for this data loss, the Landsat gapfill extension for ENVI, with the Delaunay
triangulation technique, was used (O’Connor, 2015). This technique is considered a non-reference
technique and is originally derived from land surveying, whereby each missing pixel value can be
interpolated with a triangle calculated from two pixels surrounding the missing pixel (Li et al., 2017).
Year
1992
1998
2004
2010
Table 2. Satellite image dataset attributes.
Path Row Acquisition Date
Satellite
165
40
1992/8/28
Landsat 5 TM
166
40
1992/8/3
165
40
1998/6/10
Landsat 5 TM
166
40
1998/6/1
165
40
2004/09/6
Landsat 7 ETM+
166
40
2004/9/29
165
40
2010/9/7
Landsat 7 ETM+
166
40
2010/8/29
14
2016
165
166
40
40
2016/9/15
2016/9/22
Landsat 8 OLI
3.1.2. Spectral Vegetation Index
For areas attributed with sparse vegetation, it is recommended to use the soil adjusted vegetation
index (SAVI) to account for soil and rock reflectance (Barati et al., 2011). This index was proposed
by Huete (1988) by modifying the Normalized Difference Vegetation Index (NDVI) to eliminate soil
brightness variation. As suggested by Almutairi et al. (2013), using SAVI with a soil factor of L =
0.9 is the most suitable index to extract vegetation in Kuwait. Using Eq. (1) will produce a new
dataset based on Landsat 5 TM images acquired on 28 August 1992 [path = 165, row = 40] and 3
August 1992 [path = 166, row = 40], in which pixels are assigned new values proportional to the
density of the vegetation (Ma, 2013). Figure 8 shows a map derived from SAVI using the
aforementioned datasets.
𝑆𝐴𝑉𝐼 =
𝜌𝑁𝐼𝑅 − 𝜌𝑅𝐸𝐷
∙ (1 + 𝐿).
𝜌𝑁𝐼𝑅 + 𝜌𝑅𝐸𝐷 + 𝐿
(1)
Where 𝜌 denotes the spectral band reflectance; for Landsat 5 TM and Landsat 7 ETM+, NIR
corresponds to band 4 and RED to band 3, and for Landsat 8 OLI, NIR corresponds to band 5 and
RED to band 4. 𝐿 represents the soil factor.
15
Figure 8. Map derived from SAVI, representing vegetation features in Kuwait. Using a
Landsat 5 TM [path = 165, row = 40] image acquired on 28 August 1992 and [path = 166, row =
40] an image acquired on 3 August 1992, at a 30 m spatial resolution.
3.1.3. Dataset Classification
Following this, the AOI was cropped from each dataset. A supervised classification method, the
support vector machine (SVM) algorithm, was used to classify each dataset into two classes, namely,
vegetation and non-vegetation. Firstly, training data was set to identify vegetation and non-vegetation
(such as roads, water and buildings). As illustrated in Figure 9, the basic principle behind this
classification method is to identify the optimal hyperplane, either linear or non-linear, which has the
maximum margin width between the support vectors boundaries. The greater the margin, the more
accurate the classification method (Pradhan, 2012). An example of a map derived from the SVM
classification of Landsat 5 TM data is illustrated in Figure 10.
Figure 9. Linear SVM. The optimal hyperplane maximises the margin width between the support
vectors boundaries. Adapted from Burges (1998).
16
Figure 10. Area of interest after applying the SVM method. Derived from Landsat 5 TM [path =
165, row = 40] images acquired on 28 August 1992 and [path = 166, row = 40] images acquired on
3 August 1992 at a 30 m spatial resolution.
3.1.4. Accuracy Assessment
In order to validate the classification results, false colour images with a display adjustment for the
years 1992, 2004 and 2016 were used as reference images for each year to calculate the observed
vegetation area by digitizing and then comparing against the vegetation area derived from the SVM
classification using root mean square error (RSME). The false colours aim to make the visual
discrimination between vegetation and other objects by displaying green spaces in red colour. An
example of images from1992 are shown in Figure 11. For both the Landsat 5 TM and 7 ETM+
images, the false colour combination was set with bands 4, 3 and 2, whereas that of Landsat 8 OLI
was set using the combination of bands 5, 3 and 2.
17
Figure 11. Kuwait City downtown using true and false colours. Derived from Landsat 5 TM
[path = 165, row = 40] images acquired on 28 August 1992 at a 30 m spatial resolution. (A) True
colour using visible bands (R= band 3, G = band 2, B = band 1). (B) False colour using NIR and
visible bands (R = band 4, G = band 3, B = band 1) with display adjustment.
The RSME calculates the residual of the difference between the measured vegetation area and
the classified vegetation area. Table 3 shows the observed and classified vegetation area, using Eq.
(2). The RSME was calculated for 1992, 2004 and 2012, and found to be 0.23 km2, indicating that
almost 8 pixels were misclassified.
Table 3. Accuracy assessment for the 1992, 2004 and 2012 datasets.
Year
1992
2004
2012
Observed Vegetation Area
30.26 km2
89.07 km2
128.75 km2
Classified Vegetation Area
29.97 km2
88.84 km2
128.50 km2
∑𝑛𝑖=1(𝑋𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑠,𝑖 − 𝑋𝑚𝑜𝑑𝑒𝑙,𝑖 )2
√
𝑅𝑀𝑆𝐸 =
.
𝑛
Difference
0.17 km2
0.13 km2
0.1 km2
(2)
Where Xmeasures and Xmodel denote the observed vegetation area and classified vegetation area
respectively, and n is the number of datasets used in the accuracy assessment.
18
3.1.5. Change Detection
Change detection is a common technique in RS and GIS used to compare two classified images to
provide descriptive data. In the assessment of UGSs, this technique will indicate where UGS loss and
gain has occurred. The Kuwait administrative divisions dataset was overlaid onto the change
detection map to assess each governorate in order to provide insight on each governorate UGS status
from 1992 to 2016.
3.1.6. Landscape Metrics
The R software packages ‘SDMTools’ developed by VanDerWal et al. (2014) and
‘LandscapeMetrices’ developed by Marchesan (2017) were used to assess the spatial pattern changes
throughout 1992 to 2016 by calculating landscape metrics. These metrics have an impact on the
ecological processes, as explained previously in Section 2.2. Since some of these metrics are highly
or perfectly correlated, and in order to eliminate redundancy, shape irregularity, fragmentation,
connectivity, as well as area and density, will be evaluated using the metrics shown in Table 4. In
this study, landscape refers to the delineated urban area, which consists of two classes; UGSs,
representing the class of interest, and non-vegetation areas. Finally, each class is formed by a mosaic
of patches, each of which is an individual UGS.
Table 4. Summary of landscape metrics.
Category
Landscape metrics
- Normalised Landscape Shape Index (NLSI): The normalised patch edge to
total area ratio, in which the edge length total is compared to a same sized
standard shape (square) (McGarigal and Marks, 1995).
Shape
- Mean Shape Index (MSI): Refers to shape index, which is all landscape
irregularity
patch perimeters divided by the square root of their area, divided by the number
of patches in the class (McGarigal and Marks, 1995).
- Area Weighted Mean Shape Index (AWMSI): The mean shape weighted by
patch area for each class (McGarigal and Marks, 1995).
- Euclidean Mean Nearest Neighbour Distance (EMNND): Measures
interspersion the mean of -edge-to-edge distance between each patch in the
class (McGarigal and Marks, 1995).
Fragmentation - Number of Patches (NP): Number of patches in the class (McGarigal and
Marks, 1995).
- Mean Patch Size (MPS): The area of landscape divided by the number of
patches in the class (McGarigal and Marks, 1995).
- Patch Cohesion Index (COHESION): Measures the physical connectivity of
Connectivity
the class (Gustafson, 1998).
- Largest Patch Index (LPI): Measures the dominance by quantifying the total
landscape area comprised by the largest patch (McGarigal and Marks, 1995).
Area and
density
- Class Area (CA): Measures landscape composition based on the landscape
area comprised by the class (McGarigal and Marks, 1995).
19
- Percent of Landscape (PLAND): Equals the proportion of the total landscape
area occupied by the class (McGarigal and Marks, 1995).
3.1.7. Gradient Analysis
To investigate the spatio-temporal changes caused by urbanisation on UGSs, gradient analysis was
been integrated with selected landscape metrics. Initially, a moving window analysis provided by
FRAGSTATS (version 4.2.1) was executed to the entire landscape of 1992 and 2016 to calculate the
selected landscape metrics. In this analysis, spatio-temporal changes in terms of abundance,
interspersion, connectivity and shape irregularity using NP, EMNND, COHESION and MPS, were
evaluated. The moving window with a size of 2.5 km × 2.5 km divided the AOI into sub landscapes.
As seen in Figure 12, two transects were selected, spanning from north to south and west to east,
making major roads the pivots of the transect. Finally, a graph representing the change in the value
of each of the indices for the years 1992 and 2016 was derived.
Figure 12. AOI and the designed transects. The north-south and west-east transects were
investigated through the gradient analysis.
20
3.2 Acoustic Monitoring
3.2.1. Equipment
Eight passive acoustic monitors (AudioMoth) were deployed in this study. AudioMoth is a low-cost
open-source acoustic sensor, manufactured for the terrestrial environment in order to capture both
audible range and ultrasound (up to 192 kHz). The device records the uncompressed audio onto a
Kingston 16 GB micro SD card. All devices were sealed in plastic bags to prevent water damage.
3.2.2. Sampling
As seen in Figure 13, a total of 38 sites were surveyed, of which 36 are UGSs and 2 are non-green
spaces features (sandlots), from 1st of April to 30th of April, corresponding to the period of avian
passage through Kuwait (Pope and Zogaris, 2012). The non-green sites have been sampled to serve
as a controlled location. The locations were selected randomly across the six governorates.
The devices were configured to record for 1 minute and sleep for 10 minutes throughout the
day with a frequency of 192 kHz , producing 130 files each day. Each device was installed in each
location for almost two days to account for species that are characterized by less vocal activities, as
suggested by Browning et al., (2017).
Figure 13. Sampled locations using a passive acoustic sensor (AudioMoth). Location of 38
surveyed sites along date scale and park name.
21
3.2.3. Acoustic Indices and Analysis
Acoustic indices were calculated using the ‘Soundecology’ R package, developed by VillanuevaRivera et al. (2018). These indices are powerful tools used to analyse large volumes of data and
determine ecological inferences. Table 5 shows the acoustic indices that will be utilized in this study.
Table 5. Summary of acoustic indices.
Index
Normalised Difference
Soundscape Index (NDSI)
Acoustic Complexity Index
(ACI)
Acoustic Diversity Index (ADI)
Acoustic Evenness Index (AEI)
Bioacoustic Index (BI)
Description
Estimates the ratio of human-generated (anthrophony) to biological
(biophony) acoustic components. The index ranges between -1,
representing pure anthropogenic, to +1, representing pure biophonic
sounds (Kasten et al., 2012).
Measures fauna dynamics by calculating the absolute difference between
two adjacent intensity values that have been extrapolated from dividing
the spectrogram into timesteps and frequency bins (Pieretti et al., 2011).
Measures species diversity by dividing the spectrogram into bins (default
1 kHz steps), then applying Shannon’s Index to the proportion of the bins
that have an amplitude greater than the threshold (default -50 dBFS)
(Bryan C Pijanowski et al., 2011).
Measures species evenness by dividing the spectrogram into bins (default
1 kHz steps), then applying the Gini Index to the proportion of the bins
that have an amplitude greater than the threshold (default -50 dBFS)
(Bryan C Pijanowski et al., 2011).
Measures avian abundance by calculating the area under the frequency
between 2-8 kHz (Boelman et al., 2007).
After obtaining the aforementioned acoustic indices, a T-test analysis was used to compare the
acoustic means between the sites to assess their functionalities. Additionally, factor analysis was
applied to identify the underlying biodiversity patterns among the UGSs.
22
RESULT AND DISCUSSION
4.1.Results
4.1.1. Landscape Metrics Analysis
4.1.1.1. UGSs Change Detection
The change detection map is depicted in Figure 14. Overall, change detection analysis showed a
massive gain in UGSs. During the period of 1992-2016, the area of UGSs expanded by 112.43 km2,
whereas an area of 12.92 km2 remained and 12.94 km2 was lost. By considering each administrative
division, Al Asimah Governorate (Capital) maintained 4.17 km2 of the UGSs, and also witnessed a
further reduction of 4.00 km2 of their UGS area, making it the governorate with the greatest UGS
area maintained and lost. Ahmadi Governorate underwent the most gain in comparison to other
divisions, with its UGS area expanding by 23.22 km2. Detailed results of the analysis are shown in
Table 6.
Figure 14. UGSs change detection map derived from the SAVI differencing method for the
years 1992-2016.
Table 6.Change detection results derived from the SAVI differencing method for the years
1992-2016 (Area in km2).
Administrative division
Jahra
Al Asimah (The Capital)
Farwaniya
Hawalli
No change
1.07 km2
4.17 km2
1.36 km2
2.49 km2
Decrease
1.69 km2
4.00 km2
0.98 km2
2.71 km2
23
Increase
17.67 km2
21.83 km2
13.27 km2
17.81 km2
Non-green features
116.72 km2
99.87 km2
100.13 km2
60.71 km2
Mubarak Al-Kabeer
Ahmadi
Grand Total
1.12 km2
2.71 km2
12.92 km2
1.05 km2
2.49 km2
12.94 km2
18.63 km2
23.22 km2
112.43 km2
81.48 km2
202.71 km2
661.62 km2
4.1.1.2. Synoptic characteristics of UGSs
A study of the synoptic characteristics of the landscape metrics of UGSs over the area of interest
provides general information on UGSs from 1992 to 2016. Detailed results of UGS landscape metrics
are provided in Table 7.
In 1992, there were 4,796 UGS patches covering 29.97 km2 (3.74% of the total urban area),
with an average area of 0.62 ha. These numbers increase dramatically after six years; there were 7,203
UGS patches with an average area of 1.03 ha, whereas their coverage area almost tripled (74.47 km2),
constituting 9.29% of the urban area. The number of UGS patches decreased in 2004 and 2010,
reaching 5,799 and 4,814 respectively. Nevertheless, the total area occupied by UGSs fluctuated in
2004 and 2010, reaching 88.84 km2 (11.09%) and 59.12 km2 (11.09%), respectively. In 2004, the
UGS size was found to be the largest (1.33 ha) among the other years. In 2016, the number of UGS
patches reached 9,770, covering 128.50 km2, the equivalent of almost a quarter (16.04%) of the total
urban area, with an average size of 1.31 ha.
LPI, which represents the proportion of the largest UGS patch from the total area, was found
to have a similar trend to CA, with LPI increasing between 1992-2004 from 0.15% to 0.64%, reaching
a fall in 2001 to cover just 0.34% of the total area, followed by a peak (2.47%) in 2016. However,
MSI reached a minimum in 1992 (1.27) and a maximum in 2004 (1.25), whereas in 1998, 2010 and
2016, MSI was given as 1.23. The interpretation of this is that large numbers of UGS patches are
represented by one pixel with a value of 1.
EMNND examines UGS interspersion by measuring the mean nearest neighbourhood UGS
patch distance. Greater EMNND values represent more dispersion between patches. EMNND was
found to be greatest in 2010, where the mean distance was 113.26 m, and lowest in 2016, at 88.72 m.
The results suggest that UGS were least isolated in 2016.
AWMSI compares the shape of each patch to a square, which is considered to be the simplest shape,
with 1 representing the shape of a square (Chefaoui, 2014). AWMSI was found to have a similar trend
to CA, reaching a minimum (2.55) in 1992 and a maximum (10.02) in 2016, suggesting that new
UGSs were introduced, forming larger UGS patches.
NLSI is a measurement of aggregation and ranges between 0 and 1, where 1 corresponds to
maximum disaggregation. NLSI was found be the highest in 1992 (0.43), declined until 2004,
reaching 0.29, and subsequently slowly increased to 0.33 in 2016. An additional indication of
aggregation is COHESION, an index of the homogeneity of the landscape. COHESION measures the
physical connectivity of UGS patches; the more aggregated UGS patches, the greater the index value,
24
indicating increasing landscape homogeneity (Wang and Ellis, 2005). The trend for this index was
found to inversely echo the EMNND index. The connectivity of UGS patches increased throughout
1992 to 2004, from 82.50% to 94.29%. Following this, in 2010, the connectivity retreated slightly to
90.39%, and reached its maximum in 2016 (96.85%).
Table 7. Landscape metrics results from 1992 to 2016.
Metrics
NP
CA (km2)
PLAND
LPI
EMNND (m)
MPS (ha)
MSI
AWMSI
NLSI
COHESION (%)
1992
4,796
29.97
3.74%
0.15%
109.38
0.62
1.17
2.39
0.42
82.50%
1998
7,203
74.47
9.29%
0.48%
94.07
1.03
1.23
3.55
0.35
90.25%
2004
5,799
88.84
11.09%
0.64%
99.36
1.53
1.25
5.10
0.29
94.29%
2010
4,814
59.12
7.38%
0.34%
113.26
1.22
1.23
3.23
0.31
90.39%
2016
9,770
128.50
16.04%
2.47%
88.72
1.31
1.23
10.02
0.33
96.85%
4.1.1.3. UGSs Change Detection
The results of the gradient analysis are shown in Figure 15 and Figure 16. For the west-east transect,
as illustrated in Figure 15a, the value of NP in 2016 was remarkably higher than in 1992 and consists
of peaks representing the centre of areas. For both years, NP reached its maximum at 25 km eastwards.
In 1992, as illustrated in Figure 15b, patch isolation was noticeably higher than in 2016, with a peak
at 20 km towards east as EMNND reached 700 m, whereas in 2016 the distance between patches
almost remained constant in the west-east transect. The connectivity, which is given by COHESION
and depicted in Figure 15c, fluctuated along the west-east transect in 1992, while in 2016 the
fluctuation shrunk and faded out towards the end of the transect. MPS has a similar, but shifted pattern
to NP, as can be seen from Figure 15d, consisting of waves in which the peaks represent boundaries
of the area,
25
250
1992
(A) NP
1992
(B) EMNND (m)
800
2016
2016
700
200
600
500
150
400
100
300
200
50
100
0
0
0
10
20
30
40
0
10
Distance (km)
20
30
40
Distance (km)
1992
(C) COHESION (%)
100
90
80
70
60
50
40
30
20
10
0
1992
(D) MPS (ha)
3
2016
2016
2.5
2
1.5
1
0.5
0
0
10
20
30
40
0
Distance (km)
10
20
30
40
Distance (km)
Figure 15. Gradient analysis of selected metrices with west to east transect from 1992 to 2016.
(A) Represents NP, measuring UGS abundance. (B) Represents, measuring UGS interspersion. (C)
Represents COHESION, measuring UGS physical connectivity. (D) Represents MPS, measuring
UGS average area.
For the north-south transect, as shown in Figure 16a, the peak of NP shifted by 2.5 km from
the year 1992 to 2016 due to the development of Kuwait City. As with the west-east transect, NP in
2016 consisted of several peaks signifying the centres of areas; whereas in 1992 a similar pattern with
lower magnitude was observed. Similarly, the distance between patches (EMNND), which is shown
in Figure 16b, oscillated in 1992 and almost remained constant in 2016, with the exception of a small
fluctuation 37 km towards the south. Figure 16c shows that the connectivity fluctuated notably in
1992 and converged in the year 2016, at 10 km towards south, with a major drop 20 km towards
south. In 2016, the connectivity of the patches remained higher than in 1992, with a depression
between 35-40 km towards south. Finally, Figure 16d indicates that MPS shows a similar peak in
both years 10 km towards south, suggesting that areas around Kuwait City maintained their UGS
unfragmented. In addition, a peak is observed in 1992, 38 km towards south, reaching 4 ha. In 2016,
MPS showed a similar pattern to NP due to higher values for boundaries and lower for the centre due
to the abundance of one-pixel patches.
26
(A) NP
(B) EMNND (m)
1992
200
180
160
140
120
100
80
60
40
20
0
2016
0
10
20
30
40
50
2016
0
10
Distance (km)
1992
(C) COHESION (%)
20
30
30
40
50
40
1992
(D) MPS (ha)
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
2016
10
20
Distance (km)
100
90
80
70
60
50
40
30
20
10
0
0
1992
2000
1800
1600
1400
1200
1000
800
600
400
200
0
50
2016
0
Distance (km)
10
20
30
40
50
Distance (km)
Figure 16. Gradient analysis of selected metrices with north to south transect from 1992 to
2016. (A) Represents NP, measuring UGS abundance. (B) Represents, measuring UGS
interspersion. (C) Represents COHESION, measuring UGS physical connectivity. (D) Represents
MPS, measuring UGS average area.
4.1.2. Acoustic Indices Analysis
4.1.2.1. Illustration of Acoustic Indices
Prior to the fauna assessment, all recordings with high anthrophonic interferences were excluded from
the calculation of the ACI, ADI, AEI and BI indices using NDSI. Figure 17a shows that anthrophonic
sounds prevail at the lower end of the spectrogram, between a frequency of 1 to 2 kHz, resulting in
negative NDSI values, while biophonic sounds prevail the range frequency between 2 to 8 kHz
resulting in positive NDSI values as in Figure 17b. To reduce the bias of anthrophonic sounds that
might occur in other indices, only recordings with an NDSI equal or greater to 0.50 were included in
the other index calculations. A spectrogram of a recoding with an NDSI value of 0.50 is shown in
Figure 17c.
27
Figure 17. Spectrograms of recordings with high and low NDSI values. (A) Represents
5AC1848C.wav1 file, with an NDSI value of -0.94, the recoding is dominated by anthrophonic
sounds (call of prayer broadcasting). (B) Represents 5AD9CA10.wav2 file, with an NDSI value
of 0.95, the recoding is dominated by biophonic sounds (bird vocalisation). (C) Represents
5AE023B0.wav3 file, with an NDSI value of 0.50, the recoding is mainly dominated by
biophonic sounds with the existence of anthrophonic sounds.
The AIC and BI indices are dependent on the measured frequencies, unlike ADI and AEI,
whereby further biological indices, namely, Shannon’s index and Gini index respectively, are applied.
To exemplify AIC, which returns the sum of the absolute value of each of the two adjacent intensity
frequency values, Figure 18a shows a spectrogram of a recording in which the bioacoustics
1
URL: https://www.dropbox.com/s/2q98z0znawm8k05/5AC1848C.WAV?dl=0
URL: https://www.dropbox.com/s/rn9ii3rglgoi8ct/5AD9CA10.WAV?dl=0
3
URL: https://www.dropbox.com/s/s90ck8sffdmufcq/5AE023B0.WAV?dl=0
2
28
community utilizes higher frequencies bands reaching beyond 60 kHz. In this case, an ACI value of
3056.67 was determined. Figure 18b shows a spectrogram of a recording in which the bioacoustics
community utilizes relative lower frequencies under 15 kHz, with ACI value of 0.5. For BI, almost a
continuous occupation of frequency bands between 2 and 8 kHz can be seen in Figure 19a, resulting
in a BI value of 3.98, unlike Figure 19b, in which these frequency bands have been utilized less,
resulting in a BI of 0.06.
Figure 18. Spectrograms of recordings with high and low ACI values. (A) Represents
5AD15E48.WAV4 file, with an ACI value of 3056.67, the recording has a bioacoustics
community using various frequency bands reaching beyond 60 kHz, resulting in a high ACI
value. (B) Represents 5ADC06A4.wav5 file, with an AIC value of 0.50, the recording has
acoustic communities that occupy a lower frequency band, under 15 kHz, resulting in a low AIC
value.
4
5
URL: https://www.dropbox.com/s/ekjer62okqb1ase/5AD15E48.WAV?dl=0
URL: https://www.dropbox.com/s/mjpe1fax5904jpv/5ADC06A4.WAV?dl=0
29
Figure 19. Spectrograms of recordings with high and low BI values. (A) Represents
5AD3449C.WAV6 file, with a BI value of 3.98 the recording has a bioacoustics community using
various frequency bands between 2 and 8 kHz, resulting in a relatively high BI value. (B)
Represents 5AC5C844.wav7 file, with a BI value of 0.06, the recording has a bioacoustics
community that poorly occupies the frequency bands between 2 and 8 kHz, resulting in a low BI
value.
4.1.2.2. Assessing UGS Functionality
To evaluate UGS functionality, the NDSI mean is compared between each UGS and non-green spaces
(sandlots). The average NDSI of non-green spaces was found as -0.75, suggesting high anthrophonic
penetration and poor existence of bioacoustic communities. To assess the functionality of UGSs in
terms of supporting biodiversity, an average NDSI higher than -0.78 for UGSs is assumed, as
bioacoustic communities are more active in UGSs compared to non-green spaces.
Firstly, the Shapiro-Wilk Normality Test was performed to determine whether the UGS NDSI
data is normally distributed. As can be seen in Table 8, all UGS NDSI values were found to be
following a normal distribution, with the exception of location #24. Therefore, all locations except
#24 NDSI will be included in a one-sample t-test to determine whether UGS have an NDSI mean
different to -0.75. The results of the test are shown in Table 9, revealing that all UGS NDSI means
6
7
URL: https://www.dropbox.com/s/jcycchqg60zv9dv/5AD3449C.WAV?dl=0
URL: https://www.dropbox.com/s/n84lcc19gfezo3x/5AC5C844.WAV?dl=0
30
are significantly different to -0.75. It can be inferred from the results that the UGS NDSI means are
greater than -0.75, with location #17 having the highest mean difference, suggesting that biophonic
sounds are more pronounced in this location as the mean difference was found to be 1.13.
Table 8. Shapiro-Wilk normality test results.
Location # Statistic df
Sig.
Location # Statistic df
Sig.
***
1
0.88
118
0
19
0.84
118
0 ***
***
2
0.87
118
0
20
0.95
118
0.001 **
3
0.83
118
0 ***
21
0.89
118
0 ***
±
4
Excluded
22
0.90
118
0 ***
5
0.93
118
0 ***
23
0.75
118
0 ***
**
6
0.96
118 0.008
24
0.98
118
0.196
***
7
0.63
118
0
25
0.84
118
0 ***
***
8
0.87
118
0
26
0.76
118
0 ***
9
Excluded ±
27
0.93
118
0 ***
***
10
0.66
118
0
28
0.91
118
0 ***
***
11
0.86
118
0
29
0.72
118
0 ***
12
0.92
118
0 ***
30
0.82
118
0 ***
***
13
0.88
118
0
31
0.90
118
0 ***
14
0.91
118
0 ***
32
0.92
118
0 ***
***
15
0.93
118
0
33
0.92
118
0 ***
16
0.87
118
0 ***
34
0.88
118
0 ***
***
17
0.89
118
0
35
0.90
118
0 ***
18
0.87
118
0 ***
36
0.59
118
0 ***
Where: Statistic denotes W value; df denotes degree of freedom; Sig. denotes p-value;
± Excluded because it represents non-green space location (sandlot); ** Statistical
significance at the .01 level; *** Statistical significance at the .001 level.
31
Table 9. One-sample T-Test (Test value= -0.75).
Location
t
df
Sig.
Mean
Difference
1
2
3
5
6
7
8
10
11
12
13
13
14
15
16
17
18
13.37
17.93
9.79
13.99
14.96
21.17
14.86
6.28
10.97
18.44
4.10
23.71
16.31
18.06
15.94
36.60
9.83
147
147
133
136
117
136
177
142
139
177
139
177
177
149
161
177
126
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0.30
0.86
0.34
0.28
0.30
0.53
0.29
0.39
0.31
0.57
0.08
0.40
0.77
0.67
0.64
1.13
0.41
95% CI of the
Difference
Lower
Upper
0.25
0.76
0.27
0.24
0.26
0.48
0.25
0.27
0.25
0.51
0.04
0.37
0.68
0.59
0.56
1.07
0.33
0.34
0.95
0.41
0.32
0.34
0.58
0.32
0.51
0.36
0.63
0.11
0.43
0.87
0.74
0.72
1.19
0.49
Location
t
df
Sig.
Mean
Difference
19
20
21
22
25
26
27
28
29
30
31
32
33
34
35
36
11.08
19.66
20.84
14.00
12.67
3.69
25.16
24.93
3.43
11.24
15.20
18.39
14.59
16.86
12.03
8.25
140.00
122.00
146.00
139.00
139.00
140.00
177.00
177.00
177.00
139.00
137.00
140.00
135.00
140.00
141.00
136.00
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0***
0.48
0.70
0.90
0.54
0.59
0.05
0.92
0.89
0.08
0.23
0.65
0.55
0.63
0.78
0.32
0.33
95% CI of the
Difference
Lower Upper
0.40
0.63
0.81
0.46
0.50
0.02
0.84
0.82
0.03
0.19
0.57
0.49
0.54
0.69
0.27
0.25
Where: t denotes t-value; df denotes degree of freedom; Sig. denotes P-value; CI denotes confidence interval; *** Statistical
significance at the .001 level.
32
0.57
0.78
0.99
0.62
0.69
0.07
0.99
0.96
0.13
0.27
0.74
0.61
0.72
0.87
0.37
0.41
The combined NDSI values of all UGS sites is plotted in Figure 20. The date scale of the
location is shown in Figure 13. Overall, two high biophonic acoustic signal periods can be observed,
from 0200 to 1600, up until 15th of April, with the remaining of the month from 0000 to 0300.
Additionally, since the UGSs are located in residential areas, it can be observed that during weekdays,
biophonic signals are more pronounced than on weekends. In places that have consecutively been
monitored for two days, a pattern of biophonic activities can be detected, such as location 10, which
was monitored from the 9th to 13th of April.
Figure 20. Combined NDSI values for all UGS locations. Data from 1st of April to 31st of April
2018, the average NDSI values for each hour were combined to produce the figure.
4.1.2.3. Acoustic Indices Pattern
Expletory Factor Analysis is a data reduction method used to construct new latent variables that can
be used to identify underlying patterns according to the correlation of the five observed variables:
NDSI, ACI, ADI, AEI and BI. However, prior to the EFA execution, the Kaiser-Meyer-Olkin test
was ran to determine the suitability of the observed variables for EFA, with the derived value of 0.38
33
suggesting the suitability as nonacceptable. Kaiser (1974) stated that the closer the value to 1, the
more suitable and < 0.5 as unacceptable. This was confirmed by Bartlett's Test of Sphericity, which
was found to be greater 0.05, suggesting the unsuitability of the data (Chen, 2012). It can be concluded
that the provided data and sample size are not adequate for EFA.
4.2.Discussion
In this study, urban ecology in the City of Kuwait was assessed by carrying out a flora and fauna
assessment, aiming to provide a clear picture of the current situation of urban ecology. To assess
flora, an integrated application of remote sensing and GIS was used, which encompasses change
detection, landscape metrics and gradient analysis. This innovative approach is widely used in UGS
assessments. Passive acoustic monitors were deployed to compute acoustic indices that summarise
the spectral and temporal characteristic for each recording and its relationships to the fauna features.
This study posed forward four questions and sought their answers using the aforementioned
methods. The first question sought to determine the detailed changes in UGS characteristics in terms
of shape abundance, area, irregularity, isolation and connectivity from 1992, which is a year after the
Kuwait liberation to 2016. The second question aimed to identify the overall spatio-temporal changes
in UGSs. The third question asked whether UGSs provide a habitat to urban fauna. Lastly, the fourth
question attempted to identify the correlation of acoustic indices in UGSs.
4.2.1. Flora Assessment
How have UGSs changed from 1992 to 2016?
UGSs were assessed mainly through change detection and by quantifying spatial characteristics using
landscape metrics. Change detection revealed that from 1992 to 2016, the AOI gained 112.4 km2 of
UGS. The greatest gain was found in Ahmadi Governorate (23.22 km2), while Al Asimah (The
Capital) maintained and lost the most UGS throughout the years. This finding can be attributed to the
urban expansion, as can be seen in Figure 21, where Ahmadi Governorate recently sprawled and Al
Asimah (The Capital), the core of Kuwait, maintained the green belt which has existed since the 70s
and has the greatest number of public parks (Abdullah, 2015).
34
Figure 21. Kuwait City growth between the years 1957-1995. Adopted from Abdullah (2015).
The number of UGSs and the area they occupied increased dramatically throughout the years,
with the exception of the depression witnessed in 2010. It was found that 2016 experienced the largest
area of UGSs in comparison to other years, as well as the mean size of UGSs, which almost doubled,
reaching 1.31 ha. These findings suggest that UGSs are growing in all aspects. Comparing the
percentage of UGSs with neighbouring cities, in Mashhad Iran UGSs were found to cover 23.5% of
the entire city in 1986, falling to 7.7% in 2006 (Rafiee et al., 2009). Whereas in Jeddah in the Kingdom
of Saudi Arabia, in 2014, UGSs covered just 0.01% for almost half of the district, and only 10% of
the districts had a coverage range between 5-10% (Khalil, 2014). This suggest that the status of
Kuwait UGSs is fairly acceptable in comparison to these cities, however it can be improved.
It was found that the isolation between UGSs is shrinking, with the Euclidian mean distance
between UGSs calculated at 88.72 m. As suggested by Iqbal )2012), this is a positive indicator as
isolated UGSs are more susceptible to vandalism and different types of crime and are more difficult
to maintain. In terms of fragmentation, NLSI and COHESION indicated that UGSs recently are more
aggregated. This gives the impression that the recent urban growth is “planned” as it takes into
account UGSs, and there is no evidence of unplanned urban growth, which usually results in UGS
fragmentation and habitat loss (Yu et al., 2017).
Shape irregularity indices elucidate that the shape of UGSs divert from a square due to the
introduction of the usage of non-green surfaces within these units, such as paved tracks, water
fountains, water bodies, and other facilities. Figure 22 illustrates the difference between two parks.
Figure 22a has a more complex shape because of the walking track and basketball court, whereas
Figure 22b has a more regular shape due to the lack of such facilities. Collectively, landscape metric
35
results indicate that flora in Kuwait has improved from the year after the Kuwait liberation (1992),
with a drop in 2010, and a subsequent recovery. However, the loss in 2010 is understudied and no
study has been found to discuss any issues occurring in that period.
Figure 22. Comparison between two different shape complexities. (A) Sha’ab Park, which has a
wide walking track and basketball court. (B) Shuwaikh Park, which has more of a regular shape.
Derived from Esri (2018).
How have UGSs changed along the north-south transect and west-east transect?
A gradient analysis was undertaken along two transects; the north-south and west-east. A pronounced
spatio-temporal change can be depicted in this analysis, where the number of UGSs oscillated along
both transects. Similarly, UGS size has a similar pattern, with a shift in the peak, representing the
edges of the area. These findings reveal areas more abundant with UGSs, with the number tending to
lower at the edges. Yet the size of UGSs is larger, as usually roads are vegetated for landscaping and
visual purposes, unlike 24 years ago. In 2016, UGSs are more aggregated, as well as more connected.
From the west-east transect profile, it can be inferred that the industrial area has the least amount of
UGSs, as with the Jahra area, located in the farthest western part of AOI, with the average UGS size
diminishing from 2.5 ha to 1 ha. Whereas from the north-south transect profile, the peak of the number
of UGSs shifted by almost 5 to the south, due to developing Kuwait Capital, with the southern areas
in Mubarak Alkabeer Governorate having the least amount of UGSs and connectivity between them.
4.2.2. Fauna Assessment
Are there more UGSs compared to non-green spaces?
Many studies indicate that UGSs can provide a habitat for wildlife and it is known that they act as a
“wildlife corridor”, which provides a linkage between rural areas and urban areas (Rouquette et al.,
2013). Here, the acoustic method was deployed to assess the habitat provisions of UGSs. The acoustic
index, NDSI, was used to investigate the dominance of biophonic sounds, which is a proxy for
acoustic communities. Firstly, the NDSI baseline was determined by averaging the index for the
36
sandlot locations, found as -0.75, suggesting a high anthrophonic dominance. By comparing the mean
of all UGSs, the NDSI values were found to be greater than -0.75. This could be interpreted as UGSs
providing a habitat for various wildlife. Thus, according to the obtained data, there is statistically
significant evidence that UGSs have more biophonic sources than sandlots.
Most importantly, it was found that location #17 had the highest biophonic ratio. This location
needs to be further investigated in terms of terrain morphology, types of vegetation, and management
practices, such as mowing regimes, in order to implement the findings to other UGSs to enhance their
habitat provision (Farinha-Marques et al., 2015; Wastian et al., 2016).
What are, if any, the underlying patterns of UGS acoustic indices?
Acoustic indices for recordings with an NDSI value of 0.5 or more were averaged for each location
and assessed using KMO and Bartlett's test. The test revealed that the data is not adequate for factor
analysis due to low variations, as the KMO value was less than 0.5 and Bartlett's test p-value was
more than 0.05. This can be interpreted as no correlation existing between the acoustic indices. It
should be noted that this study has encountered two main challenges. Two devices were stolen, and
high anthrophonic signal penetration caused a loss of data.
37
CONCLUSION
Urban settlements around the world are constantly facing environmental challenges and are the
vulnerable to the risk posed from climate change. Kuwait has undergone environmental tragedies due
to the warfare of invasion and liberation and the development of the city, and is still exposed to
various environmental stress as a consequence of human activities and the geographic location
(Young, 2003). UGSs have been proven to provide numerous benefits that can adapt and mitigate the
environmental challenges as well as provide many social and health benefits. Given the current
environmental condition in Kuwait, managing UGSs will help offset the loss of nature and increase
the city resilience to environmental changes and climate change.
In this study, urban ecology in the City of Kuwait was assessed to investigate how resilient
the city is to these changes and assess the livability of the city. RS and GIS were integrated to examine
the change in UGSs from 1992 to 2016, with a six-year gap. Passive acoustic monitors were deployed
to examine urban wildlife by sampling 38 locations across the six Kuwait governorates from
1/April/2018 to 31/April/2018.
UGSs in Kuwait expanded notably to reach 128 km2 in 2016. Change detection analysis
indicted that Ahmadi governorate UGSs grew the most, while Al Asimah (the capital) has the most
maintained area of UGSs. The results of the landscape metrics revealed that from 1992 to 2016, the
number of UGSs grew, adopted more of a complex shape, were more aggregated, less isolated and
relatively less physically connected. This suggests that recent urban development has considered
UGSs and recent UGSs has various facilities rather than just solid green space. North-south and westeast transects were detected through gradient analysis. The analysis showed that UGSs are increasing
in number in the center of the areas, yet are becoming smaller in size. Overall, an upgrade in quality
and quantity of UGSs can be seen. Additionally, the functionality of UGSs was examined and it was
found that biophonic signals in UGSs are greater than non-green spaces, suggesting that bioacoustic
communities inhabit UGSs more than non-green spaces. However, the unsuitability of acoustic data
stemming from the time limitation of this study made the investigation of underlaying patterns of
acoustic indices statistically unsound.
It can be concluded that, through this assessment that the development of the Kuwait City is
following planned urbanisation, which takes into consideration urban ecology, and is heading towards
a built environment that is integrated with nature, providing a more pleasant, livable and sustainable
setting for the city.
38
5.1. Managerial Implications
Future cities that will address the current urban challenges in Kuwait, such as traffic congestion and
housing shortages, should consider applying similar PAAF schemes in maintaining UGSs (Alghais
and Pullar, 2018). To further optimize UGSs, it is essential to understand their current situation. UGSs
have recently thrived, suggesting the PAAF efforts are effective towards providing UGSs for the city,
in addition to the visual enhancement and landscape purposes benefits that they provide. Thus,
practitioners should examine the schemes that have been applied between 2010 and 2016 and apply
them repeatedly with further enhancements. Moreover, constantly monitoring UGS conditions is
important to determine reasons for deterioration, as observed in 2010. No publications have been
published examining the depression with relation to UGSs.
5.2. Limitations
The major limitations of this study is linked to the passive acoustic monitors. Due to high
anthrophonic signals and in order to avoid bias in calculating acoustic indices, many recordings
were excluded. Additionally, two devices were stolen, causing further data loss.
5.3. Direction for Future Research
Future research can make advancements in flora assessments using Unmanned Arial Vehicles
(UAVs) to obtain high resolution data, which will yield more accurate results. To exemplify, the
Oktokopter UAV has a resolution of 1 cm with a visible camera, 3 cm with a multispectral camera
and 10 cm with a thermal infrared camera (Turner et al., 2014).
In regards to fauna assessments, the deployment of passive acoustic monitors has many
advantages, such as the size, cost of the devices and their durable deployed in the field (Browning et
al., 2017). However, researchers should take into consideration the obstacles that occurred when these
monitors were used to collect the data. Additionally, the high penetration of information and
communication technology in Kuwait suggests the high potential of Volunteered Geographic
Information which describes the employment of spatial data with contributions from volunteers
(CAIT, 2016). Platforms such as iNaturalist8 can suggest more locations to be sampled, in which
users witness unique and endangered species in all around the world, including Kuwait (Böhm and
Popescu, 2016). Hence, these locations should be investigated and included in future researches.
8
https://www.inaturalist.org/
39
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