Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding

Urban Climate 31 (2020) 100545

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Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding

T

⁎

Asfaw Mohameda,b, , Hailu Workua a b

Ethiopian Institute of Architecture, Building Construction and City Development, Addis Ababa University, Addis Ababa, Po. Box. 518, Ethiopia Hawassa University, Department of Geography and Environmental studies, Hawassa, Ethiopia

A R T IC LE I N F O

ABS TRA CT

Keywords: CA-Markov model Multi-criteria AHP method LULC GIS Addis Ababa

Efficient Land Use and Land Cover (LULC) monitoring and management require awareness of previous dynamics, current trends, and predictions of future developments. Understanding such an urban dynamics is, thus, necessary to deliberate a proper urban growth management approach. The study is aimed to simulate the LULC dynamics and develop a scenario-based LULC prediction for sustainable urban growth planning and management in the case of Addis Ababa and the surrounding area. The research employed a hybrid Cellular Automata, Markov chain (CAMarkov) and Multi-criteria Analytical Hierarchy Process (AHP) modeling approach. Accordingly, the research depicted continuous historical increment of Built-up spaces by consuming other ecologically valuable LULC classes. The quantitative measures of landscape metrics confirmed the benefit of Ecologically Sensitive Scenario (ESS) modeling as compared to Business As Usual Scenario (BAUS) as it keeps the dynamism of the city region more sustainable. ESS modeling enables an urban system to grow into a better way by making built-up augmentation relatively mild and controlling water bodies, forests and cultivated land losses. Therefore, this scenariobased simulation of the LULC dynamics providing decision-making options for those who strive for sustainable urban growth planning and management not only in the study region but also other similar cities.

1. Introduction Land-use/Cover (LULC) change has received a growing concern from managers and planners who involved in issues of urban and environmental sustainable development (Nouri et al. 2014). Besides, analysis of LULC dynamics has got current concern in the academic community as one third to one half of the world's land surface were converted into anthropogenic activities (Schindler 2009). Particularly to Africa, in the last 25 years large amounts of natural resources of the sub-Saharan region have changed to other land-uses which were induced by human activities and natural factors such as drought, civil unrest and rapid population growth (Brink and Eva 2009). The massive LULC changes in the region had a clear impact on the nearby environment. For instance, Midekisa et al. (2017) studied the LULC change of the Africa continent between 2000 and Ezquiaga Arquitectura and Territorio 2015 and showed a high declining trend in biomass class and a large increment of man-made impervious surfaces. The highest urbanization

⁎ Corresponding author at: Ethiopian Institute of Architecture, Building Construction and City Development, Addis Ababa University, Addis Ababa, Po. Box. 518, Ethiopia. E-mail addresses: [email protected] (A. Mohamed), [email protected] (H. Worku).

https://doi.org/10.1016/j.uclim.2019.100545 Received 29 May 2019; Received in revised form 24 August 2019; Accepted 4 October 2019 2212-0955/ © 2019 Elsevier B.V. All rights reserved.

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rate of the continent (World Bank 2015) accompanied by such LULC dynamics exacerbated the acceleration of the overall urban growth and associated environmental footprint. In line with this, Ethiopia is one among the least urbanized nations, even in the sub-Saharan standard (Tsegaye 2010). This poor experience of urbanization is not only a drawback but also an opportunity to plan or re-plan in a present context with a minimum crisis. The rapid urbanization in the country is also related to the inadequate quality of life, poor physical and environmental assets and increasing socioeconomic inequalities (Fay 2005; Hove et al. 2013). Currently, the country's, urban growth appears more conspicuous than ever, and Addis Ababa and the surrounding urban centers continue to make a prominent contribution (CSA, 2013) by which this region has gone through different forms of urban irregularities. For instance, the uncontrolled growth in the region led to the deteriorating livelihood of households in the hinterlands (Feyera and Terefe 2011; Leulsegged et al. 2011). Furthermore, the cities' physical expansion causes for the degradation of the natural support system and irreversible damage and loss of vital ecosystem functions (Tadesse 2010). Some pressures are beyond its population size and an actual built-up as it has a large ecological footprint in catering to the urban economic need for energy and construction material resources. Generally, the sustainability of urban planning and management endeavors in this study region are lack of proper approach to monitor the present and upcoming LULC dynamics, rural-urban land conversion scheme and clarify interregional urban land development strategy. Simulating the urban LULC dynamics is not a common practice in the urban planning process of Ethiopian, in general, the study region in particular. The city governance system reacts when the problem actually occurred and they ad-hocly endorses the instrument which can't manage the real growth. Therefore, in practice built-up increase has not yet been led by plans and all the planning process is simply chasing after the city growth and expansion. In fact, the attainment of any planning and management scheme determined by the ability to understand the existing and upcoming phenomena, and simulation modeling has an important approach to have an abstraction of this interwoven reality and its future trend. After the advent of various modeling techniques in GIS and remote sensing technologies, studies were made applying the methods to analyze LULC dynamics and help to devise urban growth monitoring policy and strategy (Deep and Saklani 2014). Various LULC simulation approaches were developed as an important planners' toolkit to evaluate existing and the future condition of the urban structure. Although the modeling techniques have a wide variety in their conceptual basis, the Geo-simulation approaches have got more attention now in relation to the development of GIS and remote sensing technologies. Studies also tried to address the problem using various types of Geo-simulation techniques like Markov chain (Muller and Middleton 1994) cellular automata (Yeh and Li 2001) and Multi-agent system (Schindler 2009). Therefore, the Ethiopian urban centers, particularly the study area (Addis Ababa and the Surrounding Oromiya Special Zone) growth and consequent environmental dynamics require thorough examination of the existing and the future trend of the planning process. This research, hence focused to simulate the urban LULC dynamics for a sustainable planning and management, and urban-rural synergy. Methodologically, to uphold the multiple urban growth driving factors and to take the advantages of the varieties of simulation technique, the research hybridized cellular automata, Markov chain, and Multi-criteria AHP simulation techniques. The research, thus, forwarded a choice of approaches for sustainable urban LULC and environmental planning and management by comparing the result of BAUS and ESS modeling. 2. Study area and research methods 2.1. Study area Addis Ababa and the surrounding area is found in the center of the country. The geographical coordinates of the region are between 8°56′N to 9°32′N latitude and 38°25′E to 39°07′E longitude. The total study area is 5444 sq.km (see Fig. 1). It incorporates the capital Addis Ababa and surrounding areas (six districts of Oromia Special Zone). Currently, Addis Ababa city cannot horizontally expand further unless their demands move to the surrounding area. That is why the small towns around have grown at an alarming rate. For example, Burayu, Sebeta, and Dukem-Gelan are urbanizing more than the country average. The city LULC analysis cannot, therefore, be complete unless it covers the surrounding areas as the city growth blurred their boundaries and they affected by any stresses of the city core. 2.2. Materials and methods The study used a hybrid LULC dynamics simulation approach which integrates the Cellular automata, Markov chain, and multicriteria AHP techniques. The modeling process required well prepared LULC map of the area, thus, the map preparation and the selected simulation approaches explained in the next sections. 2.2.1. LULC data sources and classification To prepare the base maps for analysis and applying the different methods to achieve the research objectives, the study used the Landsat images from the United State Geological Survey (USGS) official website. Several studies were made in the urban LULC change analysis and prediction using Landsat data such as Ahmed and Ahmed (2012), Karimi et al. (2018) and Zhang et al. (2018). Table 1 presented the main and ancillary data type, source and collection techniques. The collected data were evaluated in the preprocessing stage as it is in proper condition. Lu and Weng (2004) showed the significance of temporal, spatial and radiometric corrections for the multi-temporal change detection study, as it controls the success and failure of it. Before the classification work, the research made geometric and spectral correction of the images pertaining to 1:50,000 topo-sheets and other vector data, to select the proper band combination and extract correct end-members representing the 2

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Fig. 1. Location of Addis Ababa and the surrounding area. Table 1 Presentation of data type, source and collection technique. Data type and materials

Data source

Method of data collection and uses

Topographical sheet Landsat ETM+ 2005

EMA USGS online

Landsat TM 2011

USGS online

Landsat OLI 2015

USGS online

DEM

USGS online

30 m Point elevation data

Addis Ababa City administration

Highway, railway, local motorway, major rivers and protected areas featuredata The city and District boundary Ground survey field attributes (Training)

Addis Ababa City, major towns of the special zones Oromia town administration Addis Ababa and the surrounding Oromia integrated planning office Field measurement and observation

Purchase (with 1:50000) Download (with row and path number of 168 by 54) Download(with row and path number of 168 by 54) Download (with row and path number of 168 by 54) 30 m resolution for the region elevation and slope map for topographic correction of the elevation data Edited and rasterize to develop proximity map Gathered from concerned offices , GPS measurement, and checklist

Ancillary data Queckbird image 2006 Orthophotos 2011 Google Earth image 2015

Ethiopian Mapping Authority (EMA) Addis Ababa City administration Online sources

Purchase Gathered from concerned offices Download

region. Indeed, all the main and ancillary images, highway, motorway, railway and the study area shapefiles were brought into the same coordinate system. In the next step, the images were cut off along the boundary of the study region. Besides, the study area covers with a high rugged terrain thus the images must be corrected topographically. These correction was conducted to reduce the high error of the image. The 30 m grid point elevation topo map of Addis Ababa and GPS data were used for reproducing the DEM of the region with 30 m resolution and a slope map was extracted through the process of gradient filtering. In addition, the image visualization was enhanced using false color composites and histogram balancing to get a wider range of spectral values and to select perfect training samples. After obtaining data and performing all the preprocessing steps, processing of images for generating the LULC maps of 2005, 2011 and 2015 was carried out. Indeed, these years have been selected since the LULC's dynamism at this 3

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moment was faster than ever (Asfaw and Hailu 2019). The next step is classifying the image to produce LULC maps. Accordingly, the research first applied unsupervised classification based on indices (for water bodies, vegetation cover, bare land and built-up) to have a brief picture of the region. Consequently, the study produced the LULC map of the area using a maximum likelihood algorithm supervised classification. In order to handle the classification, it took sixty ground truths (training samples) of each type of LULC. The current training samples were selected from the field GPS point measurement and for 2005 and 2011 a very high–resolution Google Earth image, Quick Bird image of 2006 and the orthophoto of 2011 were used. In the next step, the training areas were created based on the collected reference and ancillary data the areas were defined for each LULC class in the image. The training area for similar land cover types was digitized as polygons around the spots based on the selected color composite. Thus, the spectral signature for each type of land cover type was created by analyzing the pixels of the training sites. This study thus identified six main LULC types. These are water bodies, forest, mixed woodland, cultivated land, bare land and urban built-up. Some of the isolated pixels were finally filtered and generalized in the most common neighboring class. Accordingly, this research applied a confusion matrix technique to assess the accuracy of the classification. So, the producer, user, overall accuracy, and the Kappa coefficients were calculated to check the image classification and signature selection dependability. 2.2.2. Markov chain approach Markov chain is a sequence of random values whose likelihood at a time interval relies on the value of the number at the previous time. A tract of land theoretically may alter from one class of LULC, one of the other type, whenever. Markov chain analysis, therefore, applies matrices that denote all the multi-directional LULC dynamics between all the mutually exclusive LULC types (Ye and Bai 2008). Currently, the relevance of Markov chain analysis widely used in LULC dynamics modeling because of its ability to quantify not only the states of conversion between LULC types, but also the rate of conversion among each type (Sang et al. 2011). In a Markov chain, the probability of the next state is only dependent upon the current state. Hence, a Markov model for predicting landuse change can be represented mathematically as follows in Eq. (1) to compute the transition probability matrix (Nasehi et al. 2018) (See Table 2).

P1n ⎡ P…11 … … … ⎤ Pij ≥ 0, ⎢ ⎥ P Pnn ⎦ … ⎣ n1

n

∑ j=1

Pij = 1, i = 1, …, …, n (1)

And Predictions of the future state probabilities can be calculated by Eq. (2).

Lt + 1 = Pij ∗ Lt

(2)

2.2.3. Cellular automata approach Cellular Automata (CA) is discrete dynamic systems in which the condition of each cell at time t + 1 is governed by the situation of its neighboring cells at a time based on the pre-defined transition procedures. CA is a method that can simulate the temporal and spatial dynamics or evolution of things in two dimensions (Ye and Bai 2008). CA model used both global and local activities. Although it attained by local computation CA can simulate global changes. In global models, it is assumed that all variables are the same. Infect, global models cannot be expressed very effectively in a specific location (Yang et al. 2016). The Local analysis uses as the mutual evaluation between the different situations of cells within a neighborhood considering the interaction intensity of the cell that decreases with the increase of the distance between the cells, and the extent of the area encompasses in the locality. The result is the LULC change of the central cell. The land use competition intensity expresses the central and neighboring cells relationship (Hua 2017). Currently, the CA approach has been increasingly applied to model the urban growth, because of its ability to fit the complex spatial nature using simple and effective rules (Nouri et al. 2014). The representation of CA model discrete dynamic system consists four elements, these are the discrete lattice or the physical environment (L), the set of possible states in each ith cell of the lattice at time step t (ε), the neighborhood of a cell automaton defined as all cells fallen within a radius around the actual cell (ℵ), and the local transition rule (δ). Ahmed and Ahmed (2012) assessed these four elements and the formula to be computed in the processes (Eq. (3)).

i1,i2 ℵs r = ⎧ < r⎫ ⎨ ⎬ dist (i1,i2 ) ⎩ ⎭

(3)

Table 2 Land cover classes used in the study. LULC type

Characteristics

Water bodies Forest land cover Mixed woodland Cultivated land Bare land The built-up cover

Reservoirs and dams. Essentially forest patches. Woody grassland and shrubs. Complex cultivation patterns, Crops and harvested areas. Dried vegetation, quarries, and exposed rocks. All forms of urban fabrics, Roads, parking lots and buildings.

4

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Where dist(i1,i2)= (i12 + i 22) ; ℵsr= the relative index of all neighbors of a particular cell (Eq. (4))

δ:

|ℵ|

∑

→

∑ : Uj∈ℵi (t ) σj (t ) → σi (t + 1)

(4)

The equation shows that the condition of the ith cell at the next time step t + 1 is computed by δ based on the condition of all the cells in its neighborhood at the current time t. in this case,ℵi(t)refers to the related neighborhood with ith cell at time t; |ℵ|represents the number of cells in the neighborhood. The local transition rule is set by a rule table providing the sizes of Σ and ℵ, then the total ℵ number of possible rules denoted byΣΣ . Each of the Σℵpossible configurations of a cell's neighborhood is mapped to the number of possible conditions a cell can be in. Accordingly, considering the ordered set of all the conditions of all cells collectively at time step t, refers a CA's global arrangement that con be expressed in Eq. (5) Maerivoet and Moor (2006):

∁ (t ) = UjϵL σj (t )

(5)

In the next step, applying the local transition rule to all the cells in the CA's lattice, the next configuration of the CA can be computed by its induced global map (Eq. (6)).

G: ΣL → ΣL: ∁ (t ) → ∁ (t + 1)

(6)

Though the model follows aforementioned steps and equations to follow and compute in the process, the concise and standard CA measured through Eq. (7).

S t + 1 = f (S t , N )

(7)

Where, S = the set of all possible states of the cellular automata; N = a neighborhood of all cells providing input values for the function f and f = a transition function that defines the change of the state from t to t + 1. 2.2.4. Multi-criteria evaluation and analytic hierarchy process (AHP) The third component of this hybrid simulation approach is a multi-criteria evaluation of the LULC dynamics driving factors. Therefore, to produce transition potential maps of the model, multi-criteria evaluation (MCE), analytic hierarchy process (AHP), and fuzzy membership function were applied. The AHP method was applied to determine the weights of driving factors with the use of pairwise evaluations (Saaty 1980; Memarian et al. 2012 and Rimal et al., 2018). The AHP technique, thus, enables weighting of landcover transition potential with reference to a set of potential maps, which includes the constraint factors. The transition potential maps indicate the ability of a pixel to alter from one type to another or remain unchanged. The situation of the different driving factors determines a kind of transition potential maps the region have. Driving factors define the extent of suitability the given LULC type based on the criteria. The weighted score of the factors may boost or decline the suitability of the given alternate variables. Although different methods were applied to standardize the multicriteria evaluation this study used fuzzy logic, which provides a broader range of membership functions than other methods of standardization (Myint and Wang 2006). IDRISI multi-criteria and multiple objective environment was used to execute fuzzy standardization, and used different fuzzy membership function shape and type these are sigmoidal, J-shaped and linear membership function as shown in Fig. 2. The Sigmoidal Membership function Fig. 2 shows a monotonically decreasing with curves on the upper left and lower right. The J-Shaped function is also quite common, although in most cases it would seem that a sigmoidal function while it approaches 0 but only reaches it at infinity. The figure shows the J-shaped functions and the positions of the climax points. Besides, the fuzzy membership linear function uses a linear function between the user-specified minimum and maximum values. Anything below the minimum will be assigned a 0 (definitely not a member) and anything above the maximum a 1 (definitely a member). The study thus considers six driving factors and four constraints that determine the LULC dynamics of the region. These are

Fig. 2. Basic fuzzy membership functions: (a) sigmoidal, (b) J-shaped and (c) linear function. 5

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Fig. 3. Raster layers of driver variables as the continuous values: (a) Elevation DEM, (b) Slope, (c) Distance from Highway, (d) Distance from Motorway, (e) Distance Railway and (f) Urban centers.

elevation, slope, distance from highways, local motorways, railways, and distance urban growth centers (Fig. 3). Besides, the four constraint factors include the riversides, protected area other waterbodies and major forest patches. The riverside considers the 15 m distance either side of the river and the protected area. The 15 m riverside buffering distance is based on the reference of the country urban planning guideline (MUDC's Ministry of Urban Development and construction Urban Planning Sanitation and Beautification Bureau, 2012). The protected area also includes the green spaces and parks protected by Addis Ababa city administration and the major forest patches at least to keep the forest cover as it is in 2015 and waterbodies include reservoirs of water supply dams found in the Oromia special zone surrounding Addis Ababa. The multi-criteria evaluation of the driving factors relationship functions critical values was determined by the overall biophysical characteristics of the region, expertise's interview and researchers' personal experience. For example, the region elevation as presented by DEM ranges around 1400–3400 m.a.s.l. and areas, having an elevation > 2800 m.a.s.l. is taken as less suitable. Therefore, these margins enables to determine the first and the second control point at which suitability begins to rise sharply to the point at which suitability infliction levels in a monotonously decreasing sigmoidal membership function. Similarly, the highway, motorway, and railway characterized by the same relationship function with a different control point. After a consultation with urban planners an area < 15% slope taken as a highly suitable place, in fact, suitability may not as such higher in places having 0 or slope very near to 0. The J-shaped function thus accommodates best the slope factors as it never reaches zero with the monotonously decreasing pattern. Distances from urban centers, on the other hand, using a linear distance decay function. Consequently, the weights of each factor were generated using a pairwise comparison method of the Analytical Hierarchy Process (AHP) with a consistency value of 0.02. The pairwise comparison data pertaining to urban growth factors was based on the data obtained through a set of interview made with urban planning expertise in the Addis Ababa city administration. Table 3 show that the driving factors membership function and the weight of each factors.

2.2.5. Cellular automata and Markov chain model This study thus integrates the aforementioned approaches to recompense ones weakness by the strength of another's. For instance, Table 3 Driving factors Fuzzy membership function and weight enginevector. Driving factor

Method of standardization

Membership function

First control point

Second control point

Weights engine-vector

Elevation Slope Distance highway Distance motorway Distance railway Urban centers

Fuzzy-DEM Fuzzy-Slope Fuzzy-Distance Highway Fuzzy-Distance Motorway Fuzzy-Distance Railway Fuzzy-Urban centers

Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing

1400 m 0.5% 5000 m 3000 m 5000 m 5000 m

2800 m 25% 10,000 m 5000 m 10,000 m 10,000 m

0.1314 0.2093 0.1637 0.1637 0.1466 0.1853

Sigmoidal J-shaped Sigmoidal Sigmoidal Sigmoidal Linear

6

Fig. 4. Analytical framework (Kn – K no, Kl – K location, Ks – K standard).

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one inherent problem with Markov chain is that it provides no sense of geography. The transition probabilities may be accurate on per category basis, but there is no knowledge of the spatial distribution of occurrences within each land use category. We used CA to add spatial character to the model (Ye and Bai 2008). In addition, the multi-criteria evaluation helps to incorporate assumptions and scenarios that are specific to the study to manage the automation by driving factors. IDRISI software program integrates the Markov chain and cellular automata approaches and referred to as CA-Markov (Araya and Cabral 2010). The validation of the LULC simulation considered the simulated and the actual 2015 LULC maps then IDRISI environment validate tool measured the K for no information (Kno), K-location and K-standard. Evaluation of the simulated and the actual LULC maps by Kappa Index of Agreement approach is widely applied to validate LULC change predictions (Subedi et al. 2013; Mishra and Rai 2016; Parsa et al. 2016). Before, simulating future LULC maps, the 2005 and 2011 LULC maps of the area were produced to predict 2015 LULC map. Models with accuracies > 80% are considered very strong predictive tools (Araya and Cabral 2010; Keshtkar and Voigt 2015) thus, this results approved that the CA–Markov model is a reliable and powerful tool for predicting LULC changes. After this validation, the model was run redundantly (about 20 times) to get the best adjustment of the parameter function (Soares-filho and Coutinho 2002). Fig. 4 demonstrated the overall analytical framework of the study. Consequently, the study set scenarios to simulate the LULC dynamics considering the BAUS and ESS modeling. Different studies (Behera et al. 2012; Vaz et al. 2012; Hamad et al. 2018; Kamusoko et al. 2011) were produced their own specific scenario under the CA-Markov modeling environment. In this study BAUS considers the existing LULC change as it is observed in the temporal dimension 2005–2011 and 2015. In fact, the scenario considers LULC maps of the given time dimension, elevation, slope and proximity maps (highway, local motorway, railway, and urban centers). However, in the case of ESS, the LULC dynamics deliberates the protection of ecologically valuable area like the forest, protected city green spaces and water bodies (reservoirs and riversides), in addition to all the biophysical and socioeconomic driving factors that are mentioned in the BAUS simulation. Furthermore, the scenario made agriculture to keep growing by incorporating the probability of cultivated land dynamics and cultivated land suitability map in a driving factor collection. Table 4 depicts the driving factors consider under BAUS and ESS. Furthermore, spatial metrics were useful to measure urban structures and quantify the spatial characteristics of patches, class areas, and the landscape as a whole (Herold et al. 2002 and Araya and Cabral 2010). The study, thus, used these landscape metrics to evaluate the LULC patterns and compare the two scenarios. Accordingly, the dynamics of the landscape were measured and analyzed using the FRAGSTATS tool by taking some of the major metrics stated in Table 5. 3. Result and discussion 3.1. Results The result of this study first deal on the LULC classification and a change detection evaluation outputs then it also present the output of the scenario based LULC simulation analysis. 3.1.1. LULC classification and change analysis Sustainable urban land use planning and management need a good understanding of the present and future LULC dynamism. Fig. 5 showed the Addis Ababa LULC configuration and the surrounding city region in a distinct temporal dimension. Table 6 also illustrates the producers, users, overall accuracies and kappa statistics of the various LULC classes in the Addis Ababa and the surrounding urban region maps of different periods. Accordingly, the assessment Portrayed 86%, 87and 87% overall accuracy and 0.83, 0.84 and 0.84 overall kappa coefficient for 2005, 2011 and 2015 maps, respectively. The LULC change analysis showed that the tradeoff between the six categories of LULCs (Fig. 5). Table 7 shows the LULC classes of the selected time dimension and it portrayed that the percentage share of the built-up was continuously grown from 3.7% in 2005 to 5.7% in 2011 and 7% in 2015. During these periods, water bodies, mixed woodland, and bare land were in continuous decline, while cultivated and forest land cover types have shown a varying pattern. Concurrently, Fig. 6 demonstrates the gains and losses of the different LULC type throughout the given period of investigation. In Table 4 Consideration of factors for simulating LULC dynamics under different scenarios. Factor type

Input dataa

Business-as-usual scenario (BAUS)

Ecologically sensitive scenario (ESS)

Driving factors

LULC map of Elevation Slope Distance to Highway Distance to local motorways Distance to railway Distance to urban centers Waterbodies Protected urban green space Forest areas Riversides

The same LULC map 1400–2800 m 0.5–25% 5000–10,000 m 3000–5000 m 5000–10,000 m 5000–10,000 m Not restricted Restrict as it is Not restricted Not restricted

The same LULC map 1400–2800 m 0.5–15% 3000–5000 m 1000–3000 m 3000–5000 m 5000–10,000 m Maintain at least as it is in 2015 Restrict as it is Maintain at least as it is in 2015 Restrict 15 m buffering

Growth restriction

a

Indicates input data consideration under each scenario. 8

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Table 5 Landscape metrics used in the study. Landscape metric

Acronyms

Class area

CA

Number of patches Largest patch index

NP LPI

Edge density

ED

Fractal index distribution

FRAC_MN

Euclidean nearest neighbor distance distribution

ENN_MN

Contagion index

CONTAG

Equation n

∑ j = 1 aij

Range

(

1 10000

CA ≥ 1, without limit.

)

NP ≥ 1, without limit. 0 < LPI ≦ 100

Ni n max aij i=j (100) A ∑m e ik k=1 A

ED ≥ 0, without limit. 1 ≤ FRAC_MN ≤ 2

⎛ 2lnpij ⎞ ∑nj = 1 ⎜ lnaij ⎟ ⎝ ⎠ ni ei hij = min ei

⎡ ⎢ ⎢1 + ⎢ ⎢ ⎣

ENN_MN > 0, without limit.

gik gik ⎞⎤ ⎤ ⎤ ⎡ ⎛ ⎡ ∑im= 1 ∑m k = 1 ⎢pi − ∑m g ⎥ − ⎢ln ⎜pi − ∑m g ⎟ ⎥ ⎥ ⎢ k = 1 ik ⎠ ⎥ k = 1 ik ⎥ ⎢ ⎣ ⎝ ⎦⎥ (100) 2 ln (m) ⎥

0 ≥ CONTAG ≦ 100

⎥ ⎦

Fig. 5. The Land use-land cover map of the study area by the period of investigation. Table 6 Accuracy assessment of the 2005, 2011 and 2015 LULC maps. LULC type

Water Forest Mixed woodland cultivated land Bare land Built-up Overall accuracy Overall kappa statistics

2005

2011

2015

PAa

UAa

PA

UA

PA

UA

100 93.3 73.3 80.0 70.0 96.7 86% 0.83

100 94.9 73.3 72.7 79.2 96.7

100.0 98.3 76.7 80.0 76.7 90.0 87% 0.84

100 96.7 83.6 77.4 74.2 90.0

100 95 73.3 81.7 78.3 91.7 87% 0.84

100 96.6 77.2 80.3 78.3 87.3

Bold signifies the emphasis of the final result. a PA = producers accuracy UA = users accuracy.

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Table 7 Temporal pattern of land use areas in Addis Ababa and the surrounding.

Water Forest Mixed woodland Cultivated land Bare land Built-up

2005

%

2011

%

2015

%

18.5 375.9 2157.5 1442.3 1221.2 198.6

0.3 6.9 39.9 26.6 22.6 3.7

17.6 417.3 1525.2 1939.2 1207.6 307

0.3 7.7 28.2 35.8 22.3 5.7

10.2 405.9 1496.7 1933.1 1190.2 379.4

0.2 7.5 27.6 35.7 22.0 7.0

2005_2011 500 0 -500 -1000 Water

Forest

Mixed woodland

Culvated land

Bare land

Built-up

2011_2015

80 60 40 20 0 -20 -40 Water

Forest

Mixed Culvated Bare land woodland land

Built-up

2005_2015 500 0 -500 -1000 Water

Forest

Mixed Culvated Bare land Built-up woodland land

Fig. 6. Gains and losses in sq.km of LULC by type in different time periods. Dark and light gray tons of the bar representing of gain and loss respectively.

a time dimension from 2005 to 2011, built-up gained 108 sq.km. During the period 2011–2015, however, the only LULC type that increases in the region is built-up, gaining about 72 sq km area. In the whole period of investigation (2005–2015), about 180 sq.km area are transferred to built-up by consuming water bodies, mixed woodland, cultivated land and bare land. 3.1.2. The region LULC dynamics simulation and future prediction the selected scenarios At this stage the research prepare the LULC and the driving factors data and execute the simulation, and first it has to compute the Markov transition probability. The Markov transitional probability matrix accounts the number of pixels projected to alter from one particular category of LULC to another. Table 8 shows the probability of the interchange from one LULC type to another that helps to predict the future Dynamism numerically. Before executing the simulation model for the future target years of 2025 and 2035, the validity of the model was pre-tested based 10

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Table 8 Markov transition probability matrixes for 2005–2011, 2011–2015 and 2005–2015.

2005–2011

2011–2015

2005–2015

CA-Markov

Water

Forest

Mixed woodland

Cultivated land

Bare land

Built-up

Water Forest Mixed woodland Cultivated land Bare land Built-up Water Forest Mixed woodland Cultivated land Bare land Built-up Water Forest Mixed woodland Cultivated land Bare land Built-up

0.8226 0.0000 0.0000 0.0000 0.0001 0.0000 0.2094 0.0000 0.0000 0.0000 0.0001 0.0000 0.0453 0.0001 0.0001 0.0002 0.0003 0.0003

0.0033 0.8126 0.0334 0.0000 0.0000 0.0000 0.0002 0.7572 0.0111 0.0019 0.0013 0.0021 0.0126 0.5034 0.0369 0.0227 0.0220 0.0090

0.0004 0.1869 0.6522 0.0326 0.0691 0.1410 0.0029 0.0750 0.4818 0.3763 0.0372 0.0472 0.1865 0.1216 0.2722 0.3141 0.2732 0.1117

0.0001 0.0000 0.3003 0.8129 0.0000 0.0000 0.0406 0.0120 0.3686 0.5682 0.2225 0.0941 0.3762 0.1311 0.3659 0.3357 0.4273 0.1983

0.1735 0.0005 0.0000 0.0589 0.8292 0.0097 0.7447 0.0065 0.0453 0.0353 0.7254 0.0199 0.3474 0.1121 0.2089 0.2584 0.2207 0.1110

0.0000 0.0000 0.0140 0.0957 0.1016 0.8493 0.0022 0.1493 0.0932 0.0182 0.0135 0.8367 0.0320 0.1317 0.1160 0.0688 0.0566 0.5697

on the predicted and actual LULC of 2015. The time dimension of future LULC prediction used to maintain 10 years interval to follow the periodic master plan review culture of the city administration. The changes over the period of 2005–2011 used to compute the projected 2015 LULC configuration. The indices of K-standard, K-no and K-location were used to assess the accuracy of the model. The IDRISI validate tool generated 0.8570, 0.8729 and 0.9211 as values of K-standard, K-no, and K-location, respectively. Having this validation result, the study simulated future LULC dynamics of Addis Ababa and the surrounding city region based on BAUS and ESS. The hybrid CA-Markov model was used to predict LULC changes following the trends of the dynamics that have been experienced in 2005, 2011 and 2015 years. Fig. 7 shows the predicted LULC maps of 2015, 2025 and 2035 under both BAUS and ESS. Table 9 shows the extent of LULC types in 2015, 2025 and 2035 under the stated two scenarios. As shown in the table, the trends of the LULC dynamics are differently predicted in a BAUS and ESS modeling. Under BAUS the water bodies, forest and cultivated land cover show a continuous decline in their proportion of area coverage. However, the ESS modeling made water bodies and forest loss restricted, and cultivated land loss minimum in the proportion at least in a period of 2025 and 2035. Concerning the built-up cover, both scenarios have reviled the same increasing trend throughout the period of investigation. Nevertheless, the ESS modeling predicted built-up percentage share is a bit lower than the BAUS modeling. Fig. 8 demonstrated the trend and amount of area gain and loses of each LULC type under the aforementioned two scenarios. Results show that built-up gains an area continuously though the magnitude of area gain relatively lower in the ESS, particularly in the 2025–2035 time dimension. In the case of BAUS, the amount of area gain reaches up to 123sq.km, however, in the case of ESS the amount of area gain was limited around 92sq.km. Comparison of the LULC classes (water bodies, forest and cultivated) that are restricted under the ESS modeling, they have lost large area under the BAUS modeling. For example, forest diminishes about 54sq.km area in 2015–2025 as it was 18sq.km in the case of ESS. In a time dimension 2025–2035 the ESS modeling effectively restrict the water bodies and forest and keeps cultivated land decline minimum. Indeed, the water bodies remains the same and the loss of forest and cultivated land also limited around 1.1 and 1.2 sq.km, respectively, which was about 2.5 sq.km for the water body, 9 sq.km for forest and 58 sq.km for cultivated land in the case of BAUS. Concerning bare land though it gained area in 2015–2025 time dimension the amount is somehow lower in the case of ESS modeling than BAUS that accounts 72 and 79 sq.km, respectively. In the period 2025–2035, it can lose an area in both scenarios while the magnitude was higher in the case of ESS. 3.1.3. Landscape metrics based comparison of the LULC dynamics scenarios The analysis extends further using some selected landscape metrics to examine the pattern of LULC dynamics and re-affirm the ecological advantages of the ESS over BAUS. Fig. 9 showed the comparison of the selected LULC types using the selected five landscape metrics. Concerning built-up landscape measure, the trend of NP in the BAUS modeling showed a fast decline while it is getting lower in ESS modeling. On the other hand, LPI continuously increased in both cases although the magnitude is relatively modest in the ESS. In the case of BAUS, ED showed a fast decline and yet FRAC_MN depicted a continuous increase. The ESS modeling depicted a slower decline of ED with nearly constant FRAC_MN. A fast increase of ENN_MN particularly the first time dimension (2015–2025) in the BAUS represents the increase in distance among the built-up patches. The magnitude was relatively lower in the case of ESS, although it showed a growing trend, especially in the second period (2025–2035). Forest and cultivated land cover landscape metrics were interpreted in a similar way. The first metric CA referred to the total area of the selected LULC types the areal dynamics of the LULC and their interchange have already discussed in the aforementioned section. Forest NP value in BAUS is very low and it has a decreasing pattern, whereas in the ESS modeling it is relatively higher though it has the declining pattern too. On the other hand, the cultivated land NP metrics showed a fast decline in the first period (2015–2025) of BAUS modeling, whilst the ESS modeling maintained almost constant. 11

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Fig. 7. The simulated LULC map under BAUS and ESS. Table 9 Area statistics of the predicted LULC classes by different scenario and period of investigation. BAUS

ESS

2015

Water Forest Mixed woodland Cultivated land Bare land Built-up

2025

2035

2015

2025

2035

A.sq.km

%

A.sq.km

%

A.sq.km

%

A.sq.km

%

A.sq.km

%

A.sq.km

%

15.5 396.7 1487.8 1923.8 1123.2 475.1

0.3 7.3 27.4 35.5 20.7 8.8

4.9 342.7 1453.2 1898.1 1202.0 520.7

0.1 6.3 26.8 35.0 22.2 9.6

2.4 333.4 1434.8 1839.7 1166.6 644.6

0.0 6.1 26.5 33.9 21.5 11.9

17.2 396.6 1521.7 1950.9 1095.3 430.4

0.3 7.3 28.1 36.0 20.2 8.0

9.4 377.8 1430.2 1924.3 1167.8 506.5

0.2 7.0 26.4 35.5 21.6 9.4

9.5 376.7 1404.3 1923.1 1103.1 598.7

0.2 7.0 25.9 35.5 20.4 11.1

Forest landscape LPI metrics showed continuous decline in both the two scenarios while the extent of decline is very little in the ESS. Cultivated land LPI, on the other hand, first increased significantly and later it decline slowly in BAUS whereas in the ESS modeling it continuously declines. Forest and cultivated land similarly have shown a continuous decline of ED in the BAUS modeling as the magnitude of ED value slowly decreased in the case of ESS. Whereas the ED value of the cultivated land is controlled and remains constant. Regarding ENN_MN the trend of the forest is not shown significant difference throughout the period of investigation while it shows a small increment in the time dimension 2015–2025 in BAUS. In the case of ESS it shows almost no change, yet the value of ENN_MN in this scenario is higher. The research then took in to consider contagion (CONTAG) as a landscape level measure to see a mosaic of the city region as a 12

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BAUS 200

Area in Squ. Km

150 100 50 0 -50 -100 2015_2025

2025_2035

2015_2035

ESS

Area in Squ.Km

200 150 100 50 0 -50 -100 -150 2015_2025

2025_2035

2015_2035

Time Dimension

Water Cultivated land

Forest Bare land

Mixed woodland Built-up

Fig. 8. BAUS and ESS scenario LULC Gains and losses (sq.km) by different time dimension.

whole. This evaluation considered an assumption of the landscapes consisting of patches of the relatively large, less fragmented cover is described by a low CONTAG index. Regarding the temporal trend of each scenario, the BAUS modeling shows a continuous increase of CONTAG. For instance, in 2015–2025 it increases by about 18 and in 2025–2035 the value appears very small which is about 0.5. In the case of ESS, it has shown a declining pattern, which decreased by 0.9 in the first time dimension. In the next period, it starts to increase with a small value (0.4). 3.2. Discussion 3.2.1. Implication of hybridized CA-Markov and AHP approach in Addis Ababa and the surrounding Hybridizing the LULC prediction models is helpful to recompense the weakness of the different approach previous studies also highlighted the benefit of an amalgam of different simulation methods (Ahmed and Ahmed 2012; Subedi et al. 2013; Hamdy et al. 2016; Jafari et al. 2016; Omar et al. 2014 and Zhang et al. 2018). CA-Markov as the main modeling platform highly applied in different contexts and these studies have recognized the potential of this model in producing precise results. Besides, the models may integrate the multi-criteria AHP method (Omar et al. 2014), multiple linear regression (Seto et al., 2011), logistic regression (Hamdy et al. 2016) and artificial intelligence (artificial neural network) (Grekousis et al. 2013) to incorporate the driving factors of the LULC dynamics. Addis Ababa and the surrounding city region existing LULC change detection computation has shown a strong interchange of the different LULC categories. As it is shown in the result section built-up has continuously grown by consuming ecologically valuable natural landscapes. In the assessment of the simulated LULC dynamics built-up also continue to grow with the expense of the loss of those ecologically valuable environments (water bodies, forest, mixed woodland, and cultivated land). Though forest and cultivated land have shown some increment in the first time dimension (2005–2011) they diminished again in the period 2011–2015. At the same time, the future LULC dynamics also reveal that the continuous decline of the water bodies, forest, mixed woodland, and cultivated land in both the BAUS and ESS modeling. In fact, the ESS modeling restricts the water bodies and forests from other land cover envision and limits at least in the extent as they are in the 2015 (the last year of the existing LULC dynamic evaluation). This area requires not only the method of delineating area closure, but also landscape-based rehabilitation program for green 13

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Fig. 9. The landscape pattern for the specified LULC classes by the given scenarios (The dark color represents BAUS and the light gray reflects the ESS modeling and columns represented the selected LULC classes). 14

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Fig. 10. The contribution of different land cover type for built-up in the LULC transformation (The broken lines represents the BAUS and the solid line represents the ESS modeling).

development. In the case of cultivated land, it is subjected to be occupied by built-up as they favored to similar bio-physical suitability area and cultivated land of the region is more of subsistence type and found around their settlement quarter. Actually, in the ESS modeling, the built-up growth is somehow redirected into bare lands, particularly in the 2025–2035 time dimension. Fig. 10 also shows the contribution of different LULC classes for the projected built-up cover. In the case of BAUS the contribution of forest, mixed woodland and the cultivated land was higher whereas in the case of ESS the cultivated land and bare land contributes high for the area increases of built-up. As it is identified in the result section, the amount of bare land area gain is lower in the ESS case than the BAUS. This shows the redirecting of the built-up area gain source in the second scenario. Fig. 9 depicts the bare land contribution, which is getting higher magnitude in ESS, particularly 2025–2035 time dimension. Even in 2015–2025 time dimension the contribution bare land in the case of ESS is almost equal to the 2025–2035 of BAUS's. 3.2.2. The inference of landscape metrics and scenarios comparison The discussion also take in to consider the inferences of landscape metrics to evaluate the scenarios. NP and LPI are a typical measure of aggregation or disaggregation (Araya and Cabral 2010), for instance, the built-up landscape continuous declining of NP and increasing LPI reflects a trend of disaggregation or the presence of isolation, fragmentation discontinuous growth of urban structure. The continuous increase of LPI, in addition, indicated the tendency of built-up growth around the old historical urban centers (Remmel and Csillag 2003). ED and FRAC_MN again imply the landscape fragmentation and the decline ED with continuous increase of FRAC_MN has reflected a more contained built-up growth pattern with increasing fragmentation within the patch. In fact, FRAC_MN value has been often a bit higher than 1, which reflect a moderate shape complexity prevailing in the given landscape (Araya and Cabral 2010). ENN_MN also represents distance among the built-up patches that means it is the less suitable status for integration as the increase of distances between patches. This evaluation also confirmed that the growth of less fragmented and more contained built-up structure in the case of ESS than BAUS. NP, LPI, ED and ENN_MN in a class level landscape metrics analysis of forest and cultivated land cover are an important measures to show a number of ecological processes. Therefore, the continuous decline of forest landscape LPI indicates the condition of a dispersed pattern in the two scenarios while the extent is very little in the case of ESS. At the same time, cultivated land LPI value increase show that the trend of the cultivated land expansion around the preexisted farmlands (Remmel and Csillag 2003). Forest and cultivated land ED in the BAUS modeling that reflect the diminishing pattern of shape complexity. In the case of ESS the level of shape complexity has maintained more and remains constant. The pattern of forest and cultivated land ENN_MN is also confirms the growing fragmentation in the BAUS modeling as it shows contained and almost no change in the ESS modeling. This indicates that the function of the landscape and the ecological process have deteriorated over time, although comparatively more controlled patterns have been observed in the case of ESS. The CONTAG assessment the study area affirms the extent fragmentation at the overall landscape level. According to Herold et al. (2002), if a landscape is dominated by a relatively greater number of small or highly fragmented patches, the CONTAG index appears higher. As it is shown in the result section, both a given scenarios' CONTAG value is a bit less than that of the median of the standard. However the value is smaller in the ESS modeling. 4. Conclusion The existing and predicted LULC dynamics of Addis Ababa and the surrounding city region is highly characterized by the continuous growth of built-up cover with the deterioration of other ecologically valuable LULC classes. The result also depicted the rapid growth of built-up, which accounts 3.7% in 2005, 5.7% in 2011 and 7.0% in 2015. The real trend of rapidly increasing built-up expenses to degrade or destroy other LULCs classes such as water bodies, forest patches and cultivated land. It is then helpful for 15

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decision makers and planners to learn about the urban growth pattern and to deliberate a certain planning scheme and compare a choice of urban growth options. Alongside, the research designed two distinct situations. The first one is what if scenario or BAUS modeling that predicts relatively an aggressive built-up growth with unlimited transformation of the waterbody, forest cultivated land and other LULC types. However, the ESS modeling approach enables to control the built-up growth relatively better way and constrained the waterbodies, forest and cultivated land. Moreover, the landscape metrics also computed to measure the LULC dynamics in the given scenarios and confirms the advantage of ESS modeling over BAUS. Generally, well-structured scenario based LULC prediction enables to guide the urban growth in a more sustainable manner. Therefore, the hybrid CA-Markov and multi-criteria AHP technique of the city region LULC dynamics prediction and the choice of scenarios are helpful to monitor the city and the hinterlands status by making appropriate decision on the city region LULC and environmental dynamics in Ethiopia in particular and some similar African countries' urban system striving to monitor their urban growth and ecological potential. Future related studies should be fulfilled by an agent-based modeling technique in the study region to compare the result of urban expansion and the LULC dynamics trend. In addition, other driving factors should be incorporated in a modeling process such as population dynamics, per capita income, and land market demand and supply patterns. Declaration of Competing Interest The authors declare no conflict of interest. Acknowledgement We gratefully acknowledge the financial support from Addis Ababa University Office of the Director of Research under the thematic research title “Resource efficiency, environmental quality and sustainability of urban areas in Ethiopia”, Grant no. TR/11/ 2013. References Ahmed, B., Ahmed, R., 2012. Modeling urban land cover growth dynamics using multi-temporal satellite images: a case study of Dhaka, Bangladesh. ISPRS Int. J. Geo Inf. 1 (1), 3–31. https://doi.org/10.3390/ijgi1010003. Araya, Y.H., Cabral, P., 2010. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sens. 2 (6), 1549–1563. https://doi.org/ 10.3390/rs2061549. Asfaw, M., Hailu, W., 2019. Quantification of the land use / land cover dynamics and the degree of urban growth goodness for sustainable urban land use planning in Addis Ababa and the surrounding Oromia special zone. J. Urban Manage. 8 (1), 145–158. https://doi.org/10.1016/j.jum.2018.11.002. Behera, M.D., Borate, S.N., Panda, S.N., Behera, P.R., Roy, P.S., 2012. Modelling and analyzing the watershed dynamics using Cellular Automata (CA)-Markov model A geo-information based approach. J. Earth Syst. Sci. 121 (4), 1011–1024. https://doi.org/10.1007/s12040-012-0207-5. Brink, A.B., Eva, H., 2009. Monitoring 25 years of land cover change dynamics in Africa : a sample based remote sensing approach. Appl. Geogr. 29, 501–512. https:// doi.org/10.1016/j.apgeog.2008.10.004. CSA, 2013. Population Projection of Ethiopia for All Regions At Wereda Level from 2014 – 2017. Central Statistical Agency, Addis Ababa. Deep, S., Saklani, A., 2014. Urban sprawl modeling using cellular automata. Egypt. J. Remote Sens. Space Sci. 17 (2), 179–187. https://doi.org/10.1016/j.ejrs.2014. 07.001. Ezquiaga Arquitectura, S.Y., Territorio, S.L., 2015. The experience of Latin America and the caribbean in urbanization. In: Knowledge Sharing Forum on Development Experiences: Comparative Experiences of Korea and Latin America and the Caribbean, pp. 28. March. Retrieved from. http://www.iadb.org. Fay, M., 2005. In: Fay, M. (Ed.), The Urban Poor in Latin America. The World Bank Retrieved from. http://siteresources.worldbank.org/INTLACREGTOPURBDEV/ Home/20843636/UrbanPoorinLA.pdf. Feyera, Senbeta, Terefe, Degefa, 2011. Urbanization and changing livelihood: the case of farmers' displacement in the expansion of Addis Ababa. In: AssefaHailemariam, C.H. (Ed.), The Demographic Transition and Development in Africa: The Unique Case of Ethiopia. London. Springer Science & Business Media, New York. Grekousis, G., Manetos, P., Photis, Y.N., 2013. Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities 30 (1), 193–203. https://doi.org/10.1016/j.cities.2012.03.006. Hamad, R., Balzter, H., Kolo, K., 2018. Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustain. (Switzerland) 10 (10), 1–23. https://doi.org/10.3390/su10103421. Hamdy, O., Zhao, S., Osman, T., Eid, M.A.S.Y.Y., 2016. Applying a hybrid model of Markov chain and logistic regression to identify future urban sprawl in Abouelreesh, Aswan: a case study. Geosciences 6 (4), 43. https://doi.org/10.3390/geosciences6040043. Herold, M., Scepan, J., Clarke, K.C., 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ. Plan. A 34 (8), 1443–1458. https://doi.org/10.1068/a3496. Hove, M., Ngwerume, E.T., Muchemwa, C., 2013. The urban crisis in Sub-Saharan Africa: sustainable development a threat to human security and. Stability 2 (1), 1–14. https://doi.org/10.5334/sta.ap. Hua, A.K., 2017. Application of CA-Markov model and land use/land cover changes in Malacca river watershed, Malaysia. Appl. Ecol. Environ. Res. 15 (4), 605–622. https://doi.org/10.15666/aeer/1504_605622. Jafari, M., Majedi, H., Monavari, S.M., Alesheikh, A.A., Zarkesh, M.K., 2016. Dynamic simulation of urban expansion based on cellular automata and logistic regression model: case study of the Hyrcanian region of Iran. Sustain. (Switzerland) 8 (8). https://doi.org/10.3390/su8080810. Kamusoko, C., Oono, K., Nakazawa, A., Wada, Y., Nakada, R., Hosokawa, T., Homsysavath, K., 2011. Spatial simulation modelling of future forest cover change scenarios in Luangprabang province, Lao PDR. Forests 2 (3), 707–729. https://doi.org/10.3390/f2030707. Karimi, H., Jafarnezhad, J., Khaledi, J., Ahmadi, P., 2018. Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran. Arab. J. Geosci. 11 (19). https://doi.org/10.1007/s12517-018-3940-5. Keshtkar, H., Voigt, W., 2015. A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Model. Earth Syst. Environ. 2 (1), 1–13. https://doi.org/10.1007/ s40808-015-0068-4. Leulsegged, K., Gete, Z., Dawit, A., Fitsum, H., H, A., 2011. Impact of Urbanization of Addis Ababa Eity on Peri-Urban Environment and Livelihood Paper Presented for 10th International Conference on Ethiopia Economy. pp. 1–30. Lu, D., Weng, Q., 2004. Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogramm. Eng. Remote. Sens. 70 (9),

16

Urban Climate 31 (2020) 100545

A. Mohamed and H. Worku

1053–1062. https://doi.org/10.14358/PERS.70.9.1053. Maerivoet, S., Moor, B.De., 2006. Katholieke Universiteit Leuven Cellular Automata Models of Road Traffic. vol. 22. pp. 2002–2006. Memarian, H., Kumar Balasundram, S., Bin Talib, J., Teh Boon Sung, C., MohdSood, A., Abbaspour, K., 2012. Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia. J. Geogr. Inf. Syst. 04 (06), 542–554. https://doi.org/10.4236/jgis.2012.46059. Midekisa, A., Holl, F., Savory, D.J., Andrade-pacheco, R., Gething, W., Bennett, A., Sturrock, H.J.W., 2017. Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLOSONE 12 (9), 1–15. https://doi.org/10.1371/journal.pone.0184926. Mishra, V.N., Rai, P.K., 2016. A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab. J. Geosci. 9 (4). https://doi.org/10.1007/s12517-015-2138-3. MUDC's Ministry of Urban Development and construction Urban Planning Sanitation and Beautification Bureau, 2012. Structure Plan Manual (Revised Version). Ministry of Urban Development and construction, Addis Ababa. Muller, M.R., Middleton, J., 1994. A Markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landsc. Ecol. 9 (2), 151–157. https://doi.org/ 10.1007/BF00124382. Myint, S.W., Wang, L., 2006. Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Can. J. Remote. Sens. 32 (6), 390–404. https://doi.org/10.5589/m06-032. Nasehi, S., Imanpournamin, A., Salehi, E., 2018. Simulation of land cover changes in urban area using CA-MARKOV model (case study: zone 2 in Tehran, Iran). Model. Earth Syst. Environ. 5 (1), 193–202. https://doi.org/10.1007/s40808-018-0527-9. Nouri, J., Gharagozlou, A., Arjmandi, R., Faryadi, S., Adl, M., 2014. Predicting urban land use changes using a CA–Markov model. Arab. J. Sci. Eng. 39 (7), 5565–5573. https://doi.org/10.1007/s13369-014-1119-2. Omar, N.Q., Sanusi, S.A.M., Hussin, W.M.W., Samat, N., Mohammed, K.S., 2014. Markov-CA model using analytical hierarchy process and multiregression technique. IOP Conf. Ser. 20 (1). https://doi.org/10.1088/1755-1315/20/1/012008. Parsa, A., Yavar, V., Nejadi, A., Athare, 2016. Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. Model. Earth Syst. Environ. 2 (4), 1–13. https://doi.org/10.1007/s40808-016-0227-2. Remmel, T.K., Csillag, F., 2003. When are two landscape pattern indices significantly different? J. Geogr. Syst. 5 (4), 331–351. https://doi.org/10.1007/s10109-0030116-x. Rimal, B., Zhang, L., Keshtkar, H., Haack, B.N., Rijal, S., Zhang, P., 2018. Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov chain. ISPRS Int. J. Geo-Inf. 7 (4), 154. https://doi.org/10.3390/ijgi7040154. Saaty, T.L., 1980. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill International Book Company. Sang, L., Zhang, C., Yang, J., Zhu, D., Yun, W., 2011. Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Math. Comput. Model. 54 (3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019. Schindler, J., 2009. A Multi-Agent System for Simulating Land-Use and Land-Cover Change in the Atankwidi Catchment of Upper East Ghana. Dissertation. University of Bonn. university of Bonn. Seto, K.C., Fragkias, M., Burak Güneralp, M.K.R., 2011. A meta-analysis of global urban land expansion. Posone 6 (8), 1–9. https://doi.org/10.1371/Citation. Soares-filho, B.S., Coutinho, G., 2002. DINAMICA—A Stochastic Cellular Automata Model Designed. PDF. vol. 154. pp. 217–235. https://doi.org/10.1016/S03043800(02)00059-5. Subedi, P., Subedi, K., Thapa, B., 2013. Application of a hybrid cellular automaton – Markov (CA-Markov) model in land-use change prediction: a case study of saddle creek drainage basin, florida. Appl. Ecol. Environ. Sci. 1 (6), 126–132. https://doi.org/10.12691/aees-1-6-5. Tadesse, Amera, 2010. Review of the urban environment in Ethiopia in 2008. In: Edwards, S. (Ed.), Ethiopian Environmnet Review. Forum for Environment, Addis Ababa, pp. 61–78. Tsegaye, T., 2010. Urbanization in Ethiopia: Study on Growth, Patterns, Functions and Alternative Policy Strategy. Stockholm University, Stockholm. Vaz, E. de N., Nijkamp, P., Painho, M., Caetano, M., 2012. A multi-scenario forecast of urban change: a study on urban growth in the Algarve. Landsc. Urban Plan. 104 (2), 201–211. https://doi.org/10.1016/j.landurbplan.2011.10.007. World Bank, 2015. Stocktaking of the Housing Sector in Sub-Saharan Africa Summary Report. (Washington DC). Yang, J., Chen, F., Xie, P., Su, J., Ge, Q., 2016. A local land use competition cellular automata model and its application. ISPRS Int. J. Geo Inf. 5 (7), 106. https://doi. org/10.3390/ijgi5070106. Ye, B., Bai, Z., 2008. COMPUT COMPUT TECHNOL AGR-2009-Simulating Land Use Cover Changes of Nenjiang County Based on CA-Markov Model. pdf. 1. pp. 321–329. Yeh, A.G.-O., Li, X., 2001. Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogramm. Eng. Remote. Sens. 67 (1), 83–90 (https://doi.org/Cited By (since 1996) 58\rExport Date 23 November 2011). Zhang, P., Rijal, S., Keshtkar, H., Rimal, B., Zhang, L., Haack, B., 2018. Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov chain. ISPRS Int. J. Geo Inf. 7 (4), 154. https://doi.org/10.3390/ijgi7040154.

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