Geospatial Analysis of Land Use/Cover Dynamics in Awka Metropolis, Nigeria: a Sub-Pixel Approach

Journal of Geography, Environment and Earth Science International

11(4): 1-19, 2017; Article no.JGEESI.35209 ISSN: 2454-7352

Geospatial Analysis of Land Use/Cover Dynamics in Awka Metropolis, Nigeria: A Sub-pixel Approach

S. D. Musa1, S. U. Onwuka2 and P. S. U. Eneche1*

1Department of Geography and Environmental Studies, Kogi State University, Anyigba, Nigeria. 2Department of Environmental Management, Nnamdi Azikiwe University, Awka, Nigeria.

Authors’ contributions

This work was carried out in collaboration between all authors. Author SDM designed the study and the first draft of the manuscript. Author SUO managed the analyses and managed the literature searches. Author PSUE performed the geospatial mapping and geostatistical analysis, wrote the protocol, and prepared the final manuscript. All authors read and approved the final manuscript.

Article Information

DOI: 10.9734/JGEESI/2017/35209 Editor(s): (1) Kaveh Ostad-Ali-Askari, Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Iran. Reviewers: (1) Angela Terumi Fushita, Federal University of São Carlos, Brazil. (2) Nurhan Kocan, Bartin University, Turkey. Complete Peer review History: http://www.sciencedomain.org/review-history/20747

Received 30th June 2017 rd Original Research Article Accepted 23 August 2017 Published 30th August 2017

ABSTRACT

This study aimed at characterizing the urban Land Use/Cover (LU/C) types and their spatio- temporal changes in Awka Metropolis, Anambra State from a sub-pixel perspective. The study made use of Landsat satellite imageries for three epochs (1986, 2001 and 2016) covering a total of 30 years. The Ridd Model of Vegetation (V), Impervious surfaces (I), Soil (S) and Water (W) was employed by applying the Linear Spectral Mixture Analysis (LSMA) to characterize satellite image fractions for each epoch. Cellular Automata Markov (Ca-Markov) chain and the Land Change Modeller (LCM) were used to predict future LU/C for the year 2031 and the transition of each LU/C categories between 2016 and 2031, respectively. ArcGIS 10.5 and Idrisi Selva software were used for the analyses. The findings of this study indicated that vegetation reduced over the years from 181.79 sq.km in 1986 to 110.89 sq.km in 2016 while impervious surface on the other hand increased from 16.79 sq.km in 1986 to 73.34 sq.km in 2016. Areas classified as soil experienced an increase from 26.15 sq.km to 36.519 sq.km within the same period while (exposed) water fractions increased from 0.961 sq.km in 1986 to 2.748 sq.km in 2016. The prediction analysis performed revealed that by the year 2031, Awka Metropolis will be reduced to about 88.20 sq.km of vegetation; impervious surfaces is expected to increase by an additional 17.780 sq.km in 2031; soil

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*Corresponding author: E-mail: [email protected];

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cover also predicted to increase to 42.75 sq.km in 2031. The transition map produced in this study (between 2016 and 2031) did not only locate areas expected to transform from each LU/C category to another or areas where they may persist but also indicated that the transition of vegetation to impervious surface was most pronounced than any other category of LU/C. LU/C changes of this nature have been held as a principal cause of Urban Heat Islands (UHIs), high urban surface temperature and a major proponent of climate change. The study therefore recommends the use of sub-pixel approach in characterizing LU/C fractions especially when the level of objectivity is highly needed and/or in the modelling of non-linear and chaotic environmental phenomena, e.g. Land Surface Temperature (LST), soil moisture, erosion and flood vulnerability, etc.

Keywords: Linear Spectral Mixture Analysis; Cellular Automata; Markov Chain; maximum likelihood algorithm.

1. INTRODUCTION expansion at the detriment of the environment and other ecosystem services that abound [5,6]. Urbanization is a process of shift from rural to This has become the scenario in Awka, since it urban areas in which an increasing proportion of was named the capital of Anambra State [7]. It an entire population live in cities as well as can however be expected that with such suburb of cities [1]. According to Trivedi, Sareen, increases in population sizes, much land has and & Dhyani [2], urbanization is now driving the still will be converted into residential and economy of most nations causing them to yearn commercial uses, amongst others [8], hence, a for increased urbanization. However, the rate of serious need for objective assessment. urbanization in developing countries has been noted for its spontaneity. For instance, in Nigeria, Land use/cover (LU/C) changes, especially the population growth has been spectacular, moving attendant conversion of greenery into built-up from a growth rate of about 2.8% per annum to surfaces has been held as being the principal about 5.8% per annum, with more than 60% of cause of high urban surface temperatures, urban her population projected to reside in urban heat islands (UHIs) and a major proponent of centres by year 2025 [3]. With this increasing climate change [9,10,11,12]. The devegetation or population, migration and function of urban areas fragmentation of urban greeneries such as parks as caused by urbanization, land use is affected also inhibits atmospheric cooling due to and this in turn affects land cover as well. horizontal air circulation generated by the Consequently, agriculture or primary forested temperature gradient between vegetated and land and grassland is replaced by the urban urbanized areas thereby resulting in the landscape characterised by growing impervious development of cool island spots due to surfaces such as roads, sidewalks, parking lots, advection. On the other hand, due to the high rooftops etc. [3]. built-up cover of land at the core and the often narrow arrangement of buildings forming Land cover according to Rawat and Kumar [4] canyons inhibits the escape of the reflected refers to the physical characteristics of earth’s radiation from most of the urban surface, thus surface which are expressed in vegetation, are absorbed by the building. This is a prime water, soil and other physical features of land reason for the high urban heat island effects while land use simply refers to the actual use to (UHIEs) experienced in different cities of the which land is used by humans and their habitat – world. usually with accent on the functional role of land for economic activities. Although as suggested There exist several models often adopted for by Rawat and Kumar [4], land use affects land LU/C studies, however, one of the most cover and that changes in land cover affects land quantitative adopted continuum-based approach use as well. used for LU/C studies is the Ridd’s Model, otherwise known as the V-I-S model. This model According to UN-Habitat [5], Anambra State with is based on the assumption that the urban fabric a growth rate of 2.21% per annum and 60% of is complex and heterogeneous and that the her population residing in urban centres, has urban land cover (as tele-connected as it can be) been noted as the third most urbanized state in is a linear combination of three (3) biophysical Nigeria. The state has also experienced rapid components: Vegetation, Impervious Surface population increase and attendant urban and Soil, hence the name, V-I-S Model [13].

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Proposed as a fundamental theory, the V-I-S 2. METHODOLOGY model was developed to simplify the quantification of different surface components 2.1 Description of the Study Area which are not often times orthometrically or spatially separated in satellite imageries [14,15]. Awka is the administrative capital of Anambra The components of the VIS models as adopted State. It is located absolutely between Latitude in this study are represented as a range of 6°06'N and 6°16'N, and Longitude 7°01'E and values where features or pixels can be shown as 7°10'E of the Greenwich Meridian [7]. See Figure a set of combination of the components within 1a. It is majorly an annex of two Local certain thresholds. Thus, it has been recognized Government Areas (Awka North and South) as that the spatial varying character of land cover as shown in Fig. 1b. For the purpose of this study, shown in the study of Ridd [13], can be better however, the delineation of Awka Metropolis was described by probability surfaces [16]. In other achieved by gridding the whole State using a 5 words, each pixel is allowed to have a “class km by 5 km system, from which a 3 x 3 grid member” probability rather than a single class blocks were then used to demarcate the urban label and the result of this operation has been area of Awka as seen in Fig. 1b. The towns in idealized by Eastman [17] in his soft Awka Metropolis (as delineated) include classification basis. This is said to offer more Amawbia, Enugwu Agidi, Enugwu-Ukwu, Isiagu, meaningful information to planners in better Amansea, Ifite, Nawfia, Okpuno, Nibo, etc. See understanding land use patterns and changes the Maps in Fig. 1c. Although there are local over time. Hence, from this basis, land use variations, Awka town according to Ezenwaji, classification could then be based on the degree Phil-Eze, Otti, & Eduputa [22], has an average of membership rather than just a member and elevation of 99m. Meanwhile, Rivers that drain again, such a perspective can be more objective the area, Awka, are Haba, Obizi and Obibia in relating land use/cover to other surface Rivers in the South, Obizi Okpuno River in the phenomena. For instance, a residential or built- North, Idemili River in the South and Mamu River up area can consist of houses, green spaces, in the East [22]. The soil types that characterize footpaths and small water bodies as different Awka town are loamy, clay and fine white sands. members for which different end-members can It is also characterized by lateritic; red to be obtained [16]. brownish soil, poorly cemented and with moderate permeability, making it highly Urban areas have complex morphologies vis-à- susceptible to be flooded [23]. vis their LU/C spatial expression which is also not fixed but mixed and dynamic too. As such, a Awka town has been noticing a gradual increase more objective LU/C classification such as the in population; experiencing one of the fastest VIS-Water model submitted by Ridd [13], Zemba population growths in the country (UN-HABITAT) [15] and Abubakar [18], Mróz & Sobieraj [19] and [5]. The town had grown from a population of Ahmed, Kamruzzaman, Zhu, & Rahman [11]. 11,243 in 1953, 40,725 in 1963, and 70,568 in This study therefore examined the LU/C changes 1978 to 141,262 in 1983 [24]. However, the in Awka, the capital of Anambra State and one of population of Awka town as at the last published the major urban centres in the state at a sub- national population census conducted in Nigeria pixel level to elucidate the direction and pattern in 2006 stood at 301,657 [25], Eni [26] made an of change between 1986, 2001 and 2016 (30 estimation of 375, 000 persons for 2010. In years) in-line with the submissions of Ridd [13], Awka, land use exhibits a dual character deriving Yang, Xian, & Klaver [20] Zhang, Odeh, & Han from its two major components – the first, a new [10], Zhao, Deng, Feng, & Zhao [21], etc. and town grafted onto the old city separated by the also the application of different land change expressway and the older part reflecting the models in analysing and predicting the future urban elements peculiar to a typical traditional LU/C fractions from the observed changes. This Igbo settlement, usually known for its location of result is to add a higher level of objectivity in a palace and market square at the centre of the modelling and predicting LU/C types in the study town, providing ample open spaces for area to aid environmental managers in recreation, religious, economic and socio-cultural understanding the past, present and future activities. Present day land use of Awka is dynamics of urbanization in Awka Metropolis. distributed over residential, industrial, This can go a long way in mitigating the negative commercial, administrative and agricultural impacts of rapid urbanisation on the environment types. and also guide relevant policy formulation.

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Fig. 1a: Nigeria showing Awka; Fig. 1b: Anambra showing the gridded portion of Awka Metropolis in yellow; Fig. 1c: Awka Metropolis (Right Hand Side)

2.2 Research Design 2.3 Data Sources

This study adopted both experimental and survey This study made use of primary and secondary designs to achieve its objectives. The data. Primary data involved detailed experimental design involved the acquisition, reconnaissance survey and groundtruth data processing and analysis of remotely sensed acquisition via the use of Global Positioning satellite imageries and other related dataset in a System (GPS) with which spatial and aspatial GIS laboratory. On the other hand, the survey data campaign was made possible. As for the design employed in the study was for the secondary data required for the study, medium purpose of groundtruthing and/or validation of all resolution Landsat TM/ETM+ and TIRS satellite analytical results and datasets obtained in the imageries of the 1986, 2001 and 2016 covering study. The analytical workflow of the study is the study area (i.e. Path 188 and Row 56) were shown in Fig. 2 and explained succinctly in the downloaded online from the United States succeeding sub-sections. Geological Survey (USGS) website.

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Fig. 2. Workflow of the study

2.4 Image Pre-processing study. Some of the algorithms used are the Destripe, Pan-sharpen and ATMOSC algorithms, The satellite imageries obtained (for the purpose where necessary. See Weng, Lu and Schubring of characterizing land use/land cover) were pre- [27]. IDRISI Selva and ArcGIS 10.5 software processed where necessary (e.g. for were used. atmospheric correction, image destriping, etc.) to ensure the use of highly accurate data for 2.5 Land Use/Cover (LU/C) Classification analysis. This could be due to atmospheric effects on the satellite sensor (as at the time of The soft and hard classification algorithms were image acquisition) or as caused by variable used to characterize LU/C fractions in the study detector output in scanner imagery, beyond the area. The (broad) LU/C scheme adopted for the researchers control. The IDRISI software was study in the view of Ridd [13] is shown in used to pre-process all satellite data used in the Table 1.

Table 1. Details of land use classification scheme adopted for the study

S/No. Land use Description type 1. Impervious Also known as built-up area and comprises of all infrastructure – residential, Area (I) commercial, mixed use and industrial areas, villages, settlements, road network, pavements, and man-made structures. 2. Water Comprises of river, permanent open water, lakes, ponds, canals, Body (W) permanent/seasonal wetlands, low-lying area, marshy land and swamps 3. Vegetation Trees, natural vegetation, mixed forest, gardens, parks and playgrounds, (V) grassland, vegetated lands, agricultural lands and crop fields 4. Soil (S) Fallow land, earth and sand land in-filling, erosion sites, construction sites, developed land, excavation sites, open space, bare soils and the remaining land cover types.

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Table 2. Digital numbers (DNs) used for endmember signature (EndSig) development

Satellite Land use/cover Digital number (watts/(meter squared*ster*µm) sensor/year Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 Landsat 4 [1986] Vegetation 78.420 32.380 30.690 54.400 NA* NA* 23.420 Impervious surface 86.340 40.040 47.760 56.270 NA* NA* 66.920 Soil 81.820 37.140 44.550 55.360 NA* NA* 63.300 Water 80.270 35.200 34.430 43.150 NA* NA* 18.950 Landsat 7 [2001] Vegetation 88.610 67.580 60.720 67.470 NA* NA* 43.610 Impervious surface 96.810 80.420 91.960 58.020 NA* NA* 88.510 Soil 94.310 79.490 97.880 67.660 NA* NA* 118.260 Water 88.840 70.600 64.940 48.740 NA* NA* 28.520 Landsat 8 [2016] Vegetation NA 10913.119 10443.923 9858.328 15266.154 11674.880 NA Impervious surface NA 11523.414 11255.023 11577.455 13648.145 14232.504 NA Soil NA 11252.351 11058.128 11744.409 15054.843 16664.252 NA Water NA 10961.553 10573.234 10064.064 13548.468 10176.043 NA *Not applicable

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First, all the acquired Landsat satellite imageries generate the Markov probability statistic which were separated into identifiable end-member served as input in the Cellular Automata (Ca- classes using their respective mean digital Markov) operation that was used to predict future numbers and spectral signatures for each band, LU/C for a future time, t3. In general, in the which is also based on the VIS-W model Markovian process, the future state of a system approach. To achieve this, pure endmember in time t2 can be predicted based on the signatures for different image bands were used immediate preceding state, time t1. Hence, to develop endmember fractions per LU/C according to Eastman (2012) [16], if X[k] is a category using the Linear Spectral Mixture Markov chain with the states {x1, x2, x3, ···}, then Analysis (LSMA) as put forward by Eastman [16] the probability of transition from the state i to the and Zemba [15]), see Table 2. state j in one-time instant can be expressed as:

Thus, from Table 2, the four (4) image endmembers that were generated per year are (2) recognizable LU/C materials that have homogeneous spectral properties all over the If Markov chain therefore has a finite number of images. As mentioned earlier, the LSMA n, assumes that the spectrum measured by a states, i.e. transition probability matrix can still be defined as follows: sensor is a linear combination of the spectra of all components within such a pixel which can be explained using the mathematical model given in equation 1.

= ∑ + (1)

Where: i = number of spectral bands (3) k = number of end-members First, the Markov Chain model was used to Ri = the spectral reflectance of band i of a pixel that contains one or more end-members produce related probability change matrices and then, CA tool was used to consider the Fk = the proportion of end-member k within the pixel composition of associations of pixels based on concept of proximity (i.e. regions closer to R = the known spectral reflectance of end- ik existing areas of the same class will have a member k within the pixel in band i E = the error for band i or remainder between higher propensity to change to a different class) i and then was used to project the future state of measured and modelled DN (band residual) such cells. It focuses mainly on the local To solve fk, the following conditions were interactions of cells (or image pixels) with their satisfied: distinct temporal and spatial coupling features and the powerful computing capability of space. i. Selected end-members were independent of The CA model can however be expressed as each other, follows: ii. The number of end-members were not larger than the spectral bands used, and S(t, t + 1) = f (S(t), N) (4) iii. Selected spectral bands were not highly correlated. Where S = set of limited and discrete cellular states Hence, the degree of membership of each image N = the cellular field pixel to a particular endmember fraction, for t and t +1 = difference in times and instance water, could be expressed in absolute terms and not generalized completely. However, f = transformation rule of cellular states in local final endmember fractions where further space. subjected to a supervised, hard classification method - the maximum likelihood algorithm. Thus, the hardened LU/C maps of 2001 and 2016 were used to first determine the different 2.6 Land Use/Cover Prediction cell transition rules before applying a CA contiguity filter of 5 x 5 filter and the number of The hardened VIS-W LU/C maps obtained for iterations required to predict LU/C for 2031. A the most recent times, i.e. t1 and t2 were used to period of 15 years was used based on an equal

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interval scenario and in-view of adopting a more built-up areas increased over the years as well. current LU/C consumption trend, the 2001 and This is due to urban sprawling, particularly after 2016 maps were used for the prediction. the christening of Awka as the capital of Anambra State in 1991 [7]. Hence, from visual 2.7 Transition Mapping of Thermal Zones observation of the maps presented in Fig. 3, it is evident that there exist a negative relationship Transition mapping was adopted to aid a more between the vegetation and built-up end-member in-depth view and depiction of the transition of fractions. each LU/C categories into other categories as predicted or envisaged to undergo change or Also, the results of the LSMA for Soil and Water persist between 2016 and 2031. This comprised end-member fractions per study epochs are of sixteen (16) transition zones, namely: presented in Fig. 4. As presented, the sub-maps for soil did not maintain a definite spatial i. Persistent Vegetation degradation/aggradation pattern over the years ii. Impervious surface to vegetation but they revealed areas that had higher degrees iii. Soil to Vegetation of soils for each study year. The areas with iv. Water to Vegetation higher degrees of soil membership are identified v. Vegetation to Impervious surface to be very close to areas of higher degrees of vi. Persistent Impervious surface impervious surface end-member fractions. On vii. Soil to Impervious surface the other hand, water end-member fractions, with viii. Water to Impervious surface higher degrees or intensities were identified more ix. Vegetation to soil randomly in 1986 but gradually disappeared in x. Impervious surface to Soil other sub-maps of 2001 and 2016. However, the xi. Persistent Soil linear pattern of the river in the region can be xii. Water to Soil easily identified at the western axis of the sub- xiii. Vegetation to Water map, and it shows also the rate at which water xiv. Impervious surface to Water end-members varied spatially over the years, xv. Soil to Water especially for other LU/C pixels in the study area. xvi. Persistent Water 3.2 Land Use/Cover Classification The Land Change Modeller (LCM) tool in IDRISI software was applied for this purpose to In order to categorize the LU/C fractions the characterize the hardened LU/C maps for all the hardening operation was performed. The four years: their respective spatial coverages, net end-members’ (VIS-W) pixels were analysed and change, gain and loss statistics. All these were each pixel was reassigned to the class that had computed accordingly and presented the highest presence or degree of membership. appropriately using graphs and maps. As shown on the sub-maps in Fig. 5 and the bar graph in Fig. 6, vegetation reduced over the 3. RESULTS AND DISCUSSION years from 181.179 sq.km (80.52%) in 1986 to 151.10 sq.km (67.16%) in 2001 and to 110.89 3.1 Linear Spectral Mixture Analysis sq.km (49.29%) in 2016. Impervious surface category on the contrary, increased from 16.704 The result of the LSMA for Vegetation and sq.km (7.42%) in 1986 – before the creation of Impervious surfaces is presented for the different Anambra State, to 36.96 sq.km (16.43%) in study epochs in Fig. 3. The sub-maps show a 2001, ten (10) years after the creation of the gradual degradation of areas characterized by state, and finally increased to about 73.34 sq.km high membership to vegetation (shown as (32.60%) in 2016. Areas classified as soils were purple), while other areas low in degree of about 26.156 sq.km (11.62%) in 1986 but membership of vegetation (green to yellow) increased to about 35.619 sq.km (15.83%) in increased over the years. This shows to a large 2001 and to about 38.015 sq.km (16.90%) in extent the increased rate of deforestation as 2016. From the map shown in Fig. 3, soil unlike caused by increasing urbanization in the study other categories was not located in same areas area. In confirmation, however, built-up or over the years as such areas could express impervious surfaces also showed increases from areas of intense farmland cultivation or as a 1986, 2001 and in 2016. Also, other areas that result of land consumption from other categories, appeared to be low in degree of membership to or from their contribution. Lastly, the areas classified as water fractions increased from

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0.961 sq.km (0.42%) in 1986 to 1.320 sq.km more or less negligible with a slight increase (0.59%) in 2001 and 2.748 sq.km (1.22%) in observed in the three different time periods. This 2016. The results therefore revealed the high increase however can be attributed to the spontaneity of urbanisation and the degradation reduction in the riparian canopy along the river and/or transition of vegetal cover into other LU/C course - from North to South. This can be types. attributed to the farming activities going on along/around these areas. As gleaned from Fig. 5, the change in water cover in the study area in the different epochs is

Fig. 3. LSMA result of unmixed land use/cover maps of Awka: Top LHS, Middle LHS and Bottom LHS – Vegetation endmember fractions of 1986, 2001 & 2016, respectively; Top RHS, Middle RHS and Bottom RHS – Impervious surface endmember fractions of 1986, 2001 & 2016

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Fig. 4. LSMA result of unmixed land use/cover maps of Awka: Top LHS, Middle LHS and Bottom LHS – Soil endmember fractions of 1986, 2001 & 2016, respectively; Top RHS, Middle RHS and Bottom RHS – Water endmember fractions of 1986, 2001 & 2016, respectively

3.2.1 Magnitude of Change of the LU/C 2.68 sq.km per year for the epochs considered in Categories the study. For impervious surface, a higher magnitude of change of about 36.960 sq.km at The dynamics of the LU/C categories were an annual change rate of 2.43 sq.km (243ha) analysed further for each period under between 2001 and 2016 can also be observed investigation and the findings are presented in from the table above. It therefore reveals clearly Table 3. that the magnitude of deforestation and/or devegetation and the proliferation of urban built- From Table 3, vegetation had the highest up spaces in Awka was much more intensified magnitude of change of -40.208 sq.km between more in recent times. In contrast however, the 2001 and 2016 with an annual rate of change of - magnitude of change of soil cover was more

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pronounced in the period 1986 – 2001 with about of 1.89 sq.km; soil cover increased with about 9.464 sq.km and an annual increase of about 11.859 sq.km and a mean annual increase of 0.63 sq.km (63ha) per year -within the period. 0.40 sq.km (40ha) per year; while changes in the Generally, between 1986 to 2016 (30 years), exposure of water covers increased with a vegetation was observed to have reduced by magnitude of 1.787 sq.km and an annual rate of 70.286 sq.km with a mean annual change of - change of 0.06 sq.km (6ha) per year. All these 2.34 sq.km; impervious surfaces increased with gains and losses per LU/C categories have been over 56.641 sq.km and a mean annual increase presented graphically in Fig. 7.

Fig. 5. LU/C categories of Awka (Top: 1986; Middle: 2001; & Bottom:2016)

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Fig. 6. LU/C categories of Awka (1986, 2001 & 2016)

Fig. 7. LU/C dynamics of Awka Metropolis between 1986 - 2001, 2001 - 2016 and 1986 - 2016

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Table 3. Summary of multi-temporal land use/cover change detection in Awka metropolis

Land use/cover categories 2016 2001 Change [2016-2001] 2001 1986 Change Change [2001-1986] 2016-1986] Area Area Mag. Freq. Area Area (Sq.km) Mag. Freq. Mag. Freq. (Sq.km) (Sq.km) (Sq.km) Vegetation (V) 110.9 151.1 -40.2 -2.7 151.1 181.2 -30.1 -2.0 -70.3 -2.3 Impervious surface (I) 73.3 37.0 36.4 2.4 37.0 16.7 20.3 1.4 56.6 1.9 Soil (S) 38.0 35.6 2.4 0.2 35.6 26.2 9.5 0.6 11.9 0.4 Water (W) 2.7 1.3 1.4 0.1 1.3 1.0 0.4 0.0 1.8 0.1 Total 225 225 225 225

Table 4. Markov Chain LU/C probability matrix for Awka, 2031

Given Probability of changing to Vegetation Impervious surface Soil Water Vegetation 0.6625 0.2146 0.1115 0.0114 Impervious surface 0.0715 0.6958 0.2265 0.0061 Soil 0.2068 0.4250 0.3585 0.0096 Water 0.5842 0.0518 0.0164 0.3476

Table 5. LU/C categories of Awka between 2016 and 2031

S/No. LU/C categories 2016 2031 Change [2031-2016] Area (Sq.km) Area (Sq.km) Magnitude Annual Frequency 1 Vegetation (V) 110.893 88.200 -22.693 -1.51 2 Impervious surface (I) 73.345 91.125 17.780 1.19 3 Soil (S) 38.015 42.750 4.735 0.32 4 Water (W) 2.748 2.925 0.177 0.01 Total 225 225

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Fig. 8. Predicted LU/C map of Awka metropolis (2031)

3.2.2 Prediction of future LU/C 22.693 sq.km of vegetation at a frequency of - 1.51 sq.km (-151 ha) per year. This rate is lesser This study used the Ca-Markov simulation tool to than that of the past periods considered in this predict future LU/C categories for the year 2031 study (i.e. 1986 – 2001, 2001 – 2016 and 1986 – and the result of the Markov Chain probability 2016) and may be linked directly or indirectly to matrix of Awka from the year 2031 are shown in decreasing urbanisation rates. The rate observed Table 4. for vegetation change is closely followed by impervious surface categories that further The result of the Markov chain analysis as increased with a magnitude of 17.780 sq.km presented in Table 2 shows a very high (91.125 sq.km) in 2031, with a frequency of 1.19 probability of vegetal cover, impervious surfaces sq.km (119ha) per year. Soil cover is also and extent of water bodies to be retained in predicted to increase from 38.015sq.km in 2016 2031. In contrast, soil fractions, displayed a to 42.750 sq.km in 2031 and translates into a probability of being converted or changed to magnitude of 4.735sq.km and about 0.32sq.km impervious surfaces. This is an indication that in (32ha) per year. However, from the Markov 2031, only a slight modification in LU/C will occur probability matrix of Awka (Table 4), there is a since all other LU/C categories apart from soil high probability (0.425) of the soil cover to be are envisaged to be more or less unchanged, converted to impervious surfaces, thus, still except for the occurrence of any extraneous shows a high propensity of the projected soil event which are obviously beyond the scope of cover to however be converted to impervious this study. The predicted LU/C map of Awka is surfaces than other categories. shown in Fig. 8, while the magnitude of change for the various LU/C categories in the study area The result of the LCM gives also a graphical from 2016 to 2031 is also presented in Table 5. representation of the gains and losses, net change and contribution to net change for each Table 5 reveals that in 2031, Awka Metropolis LU/C category between 2016 and 2031. See will be characterized by 88.20 sq.km down from Figs. 9 and 10. the 110.893 sq.km obtained in 2016 losing about

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Fig. 9. Gains and losses and net change graphs of LU/C classes of Awka between 2016 and 2031

Fig. 10. Contribution to net change in each LU/C class in Awka between 2016 and 2031

The graphs in Figs. 9 and 10 go a long way to converted into other categories or persist as the reveal future change in LU/C categories. Most of case may be. the losses experienced in Awka is seen in Vegetal makeup, although, other vagaries exist From Fig. 11, transition of vegetation to within each LU/C category. The Net change in impervious surface is most pronounced and is LU/C is predicted to increase more for expected to occur at the outer bounds of built-up impervious surface, followed by Soil fractions. areas (i.e. cellular sprawling). The map also Meanwhile, the greatest contribution to the Net shows areas of persistent built-up or impervious change in impervious surface is vegetation and cover (in red) which still remains the most vice versa; to net change in soil is vegetation dominant of all other categories. To objectively also but in the case of water fractions, vegetation account for all other categories, Table 6 is is expected to reduce and consequently expose presented as it reveals the transition area more water cover, impervious surfaces and coverages of the sixteen categories as described (invariably) soil are expected to increase to hitherto. occupy or hide more water fractions – come 2031. The most pronounced transition as presented in Table 6 was the transition from vegetation into 3.2.3 Transition and Predicted LU/C Map of impervious surfaces in the study area. Between the study area 1986 and 2016, 48.15 sq.km of vegetation changed into impervious surfaces, with 18.45 The transition map in Fig. 10 shows clearly areas sq.km of vegetation expected to be converted where different categories of LU/C will likely be into impervious surfaces by 2031. Also, as

Musa et al.; JGEESI, 11(4): 1-19, 2017; Article no.JGEESI.35209

gleaned from Table 6, 24.75 sq.km of vegetation label gives the LSMA method a more quantitative experienced a transition into soil between 1986 perspective in handling dynamics of LU/Cs and and 2016 with a further 4.95 sq.km expected to thus, handy to environmental managers. Hence, change between 2016 and 2031, this transition from this basis LU/C classification could then be may be related to an increasing demand for based on the degree of membership rather than agricultural land at the fringes of the Metropolis. just a member and again, such a perspective can Similarly, 13.27 sq.km of soil experienced a be more objective in relating land cover to other transition to impervious surfaces between 1986 surface phenomena. This is said to offer more and 2016, with 4.275 sq.km expected to change meaningful information to urban planners and into impervious surfaces between 2016 and policy makers in better understanding land use 2031. Thus, a lower rate of conversion is patterns and changes over time. however expected between 2016 and 2031 vis-à- vis the result obtained for 2001 – 2016 epoch. The spontaneity of urbanization in the selected Therefore, although rate of urbanization in Awka town, Awka vis-à-vis the degradation and/or is to be on the increase, a more reduced rate is transition of vegetal cover into other LU/C envisaged. categories, and particularly the proliferation of impervious surfaces is remarkably high. From the foregoing, GIS remains a powerful tool Therefore, it is solemn to note that LU/C that made the sub-pixel characterization of LU/C, modification is a result of the present and their miniaturization and possible prediction in predicted rates of rapid urban population this study. In other words, the fact that each pixel increase and a call for more rigorous methods in is allowed to have a “class member” probability tracking and predicting their spatio-temporal or value rather than a single class (generalized) patterns.

Fig. 11. LU/C transition map of Awka metropolis between 2016 and 2031

Musa et al.; JGEESI, 11(4): 1-19, 2017; Article no.JGEESI.35209

Table 6. Land use/cover transition categories of Awka

S/No. Land use transition categories Transition (2016 – 2031) Area Annual rate (Sq.km) (Sq.km) 1 Persistent Vegetation 87.075 5.805 2 Impervious surface to vegetation 0.225 0.015 3 Soil to Vegetation 0.450 0.03 4 Water to Vegetation 0.450 0.03 5 Vegetation to Impervious surface 18.450 1.23 6 Persistent Impervious surface 68.175 4.545 7 Soil to Impervious surface 4.275 0.285 8 Water to Impervious surface 0.000 0 9 Vegetation to soil 4.950 0.33 10 Impervious surface to Soil 4.725 0.315 11 Persistent Soil 32.850 2.19 12 Water to Soil 0.000 0 13 Vegetation to Water 0.225 0.015 14 Impervious surface to Water 0.225 0.015 15 Soil to Water 0.225 0.015 16 Persistent Water 2.250 0.15 Total 225

4. CONCLUSION and recommended for environmental experts who intend to adopt a more objective approach in This study ascertained the extent and magnitude quantifying or characterizing the urban landscape of LU/C change in Awka Metropolis in Anambra parameters, particularly, LU/C for the modelling State using a continuum-based approach. The of other urban phenomena. For instance, in the result of the LSMA performed for different LU/C modelling of LST as applied by Maduako, Yun types indicated a decline in the areas covered by and Patrick [29]. It is further recommended that vegetation, an increase in impervious surfaces or environmental planners ought to adopt such built-up areas while soil fractions and water approaches to further increase the objectivity of bodies were noted to fluctuate slightly and their studies and models developed in remain almost uniform over the three epochs understanding the non-linear and chaotic nature considered (i.e. 1986, 2001 and 2016). The LU/C of urban systems. maps generated in this study indicated that vegetation reduced from 80.52% in 1986 to COMPETING INTERESTS 67.16% in 2001 and a further decrease to 49.29% in 2016. Impervious surfaces increased Authors have declared that no competing from 7.42% in 1986 to 32.60% in 2016. A interests exist. prediction analysis performed revealed that by the year 2031, Awka Metropolis will be REFERENCES characterised by 88.20 sq.km of vegetation, down from the 110.893 sq. km recorded in 2016. 1. Nnebue CC, Adinma ED, Sidney-Nnebue An increase in impervious surface will also be QN. Urbanization and health: An overview. observed at an annual rate of 119 ha per year. Orient Journal of Medicine. 2014;26(2):1-8. Although a slower rate of urbanization is expected in the future, the grave detriments of 2. Trivedi JK, Sareen H, Dhyani M,. Rapid vegetal loss in the face of ravishing built-up area urbanization - its impact on mental health: cannot be overlooked. In fact, it has been A South Asian perspective. Indian Journal reported that the attendant conversion of of Psychiatry. 2008;50(3):161-165. greenery into built-up surface has been held as 3. Alkali JL. Planning sustainable urban being the principle cause of high urban surface growth in Nigeria: Challenges and temperature, surface urban heat islands (SUHI’s) strategies. A paper presented at the and a major proponent of climate change as Conference on Planning Sustainable reported by Mallick [28]. Smart geospatial tools Urban Growth and Sustainable were used in this study and should be upheld Architecture held at ECOSOG Chambers,

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