Land use change is the main concern for worldwide environment change and is used by town and country planners to design Eco friendly and sustainable economic growth. Using Nakuru as a major town in Kenya, the research will study urban growth and address the need for urban management tools that can provide perspective scenarios of urban growth. The projecting and modelling of land use change is crucial to the evaluation of subsequent environmental impacts. The latest development of cellular automata (CA) provides a great tool for the dynamic modelling of land use change. The algorithm is designed to simulate the historical growth as a function of local neighbourhood structure of the input data. Transition rules in the algorithm drive the urban growth over time. Spatial and temporal calibration schemes are used to improve the prediction accuracy. Spatially, the model is calibrated on a township basis to take into account the effect of site specific features, while the temporal calibration is set up to adapt the model to the changes in the growth pattern over time. The study will discuss at the end a proposed automatic rule calibration method using genetic algorithm. This research implements the concept of spatial evolution, which is embedded in CA and relates it to land use change study in Nakuru Municipality. The digital land use data of three separate years will be complied and analysed using geographic information systems. The model will be calibrated and tested using time series of urbanised areas derived from remote sensing imageries and project growth to the year 2030 when Kenya aims to achieve Vision 2030. Results obtained will help in comprehensive land use planning and an integrated management of resources to ensure sustainability of land and to achieve social equity, economic efficiency and environmental sustainability.
CHAPTER ONE
INRODUCTION
Urban studies are becoming significant tools for planners knowing that in the year 2015 more than half the global population will be residing in cities, (UNECE, 2003). Suitable urban planning ought to be a top priority for future development but unfortunately sound planning has not taken place in many African cities as heavy rural-urban migration continues to cause cities to expand at uncontrollable rates (Mundia & Aniya, 2007). As a consequence, the urban population in Africa is increasing at a much faster rate than in the rest of the world, contributing to the augmentation of the existing problems (Lavalle et al. 2001). The concentration of population in cities comprises as much as 60% of the total population in most countries. In these immense urban settlements the environmental and social consequences are disastrous (Baredo & Demicheli, 2003).
Cities in Africa such as Nakuru have experienced a fast growth rate of 13 per cent. The growth has been attributed to a number of factors, mainly the opening of the new Naivasha-Nakuru road, which links the town with Nairobi. Post-election violence is said to be one of the contributing factors, since many displaced people from neighbouring towns saw Nakuru as a safe haven.
The main consequences in these cities can be summarised as unsuitable land-use, inadequate transportation systems, pollution, depletion of natural resources, urban sprawl, collapse of public services, proliferation of epidemics, and other negative environmental and social effects. The transforming of surrounding land due to urban expansion and urban dwellers ever-increasing demand for energy, food, goods and other resources is behind the degradation of local and regional environment which is threatening the basic ecosystem services and biodiversity. Problems linked to unsustainable urban development in African cities are many and complicated and requires an integrated approach. Such an integrated urban planning approach needs to recognize and anticipate urban dynamics and their consequences. (Mundia & Murayama, 2010)
Remote sensing techniques have shown their value in mapping urban dynamics and as data sources for the analysis and modelling of urban growth and land use change (Batty and Howes 2001; Clarke et al. 2002; Donnay et al. 2001; Herold et al. 2001; Jensen and Cowen 1999). Remote Sensing provides spatially consistent data sets that cover large areas with both high spatial detail and high temporal frequency. These kinds of data sets are necessary for land use land cover analysis, which is an essential element of ecological studies. As urbanisation occurs, changes in land use land cover accelerate and land making up the natural resource base such as forests and agricultural land are replaced, leading to fragmentation and land degradation (Mundia and Aniya 2006).
The study of land use land cover changes is essential not only for land use management but also in detecting environmental change and in formulating sustainable development strategies (Barnsley and Barr 1997). Accurate information on land use changes is needed for documenting growth, making policy decisions and improving land-use planning. Information concerning land use changes is also required for predictive modelling (Bullard and Johnson 1999; Gross and Schott 1998; Jacobson 2001; Epstein et al. 2002).
Models are the best way of considering the land change phenomenon and predict correct planning activities for sustainable cities. This is a vital topic in present research agenda and a noteworthy number of scientists are offering their efforts in the study of this phenomenon. Among all developed urban growth models, cellular automata (CA) urban growth models have better performance in simulating urban development than conventional mathematical models. CA simplifies the simulation of complex systems. Its appropriateness in urban modelling is due to the fact that the process of urban spread is entirely local in nature. Models based on cellular automata are impressive in terms of their technological evolution in connection to urban applications. Development of a CA model involves rule definition and calibration to produce results consistent with historical data, and future prediction with the same rules (Batty & Xie, 1994; Waldrop, 1992; Clarke & Gaydos, 1998; Yang & Lo, 2003; Clarke et al, 1997).
Many CA-based urban growth models are reported in the literatures. CA model involves reduction of space into square grids, (White & Engelen, 1992). They implement the defined transition rules in recursive form to match the spatial pattern. CA models are usually designed based on individual preference and application requirements with transition rules being defined in an ad hoc manner (Li & Yeh, 2003). Most of the developed CA models need intensive computation to select the best parameter values for accurate modelling. In this research, cellular automata will be used to study land use change and prediction of future trends in Nakuru Municipality as Kenya attains Vision 2030 in the year 2030. The model for Nakuru will utilise multi-temporal Landsat data, which will be calibrated using multistage Monte Carlo method and a 30 year prediction simulation will run until 2030. The model aims at predicting the future land use development under the existing urban planning policies.
Statement of the problem
On a global basis, nearly 6.8 million km2 of forest, woodlands and grasslands have been converted to other land uses in the last three centuries (Agarwal et al. 2002) and most of the changes were into urban land use. These changes in land use have significant implications on the Earth's resources and climate.
On a local basis, the growth of Nakuru municipality began in the 1970s due to rural urban migration and increased towards the 21st Century. The land use land cover in Nakuru Municipality has been noted to have changed significantly; urbanisation began at a very high rate between 1973 and 1986, but still continued into year 2000 at a moderate rate. Nevertheless there has been a decrease in agricultural land implying that such land has changed into urban land. Furthermore vegetation has decreased implying destruction of forests through deforestation and changing climatic conditions which inhibit natural growth of vegetation. Economic development and the rising population have been noted to be the major factors influencing land use land cover changes. (Mubea et al, 2009) Thus, consideration and careful assessment are required for monitoring and planning land management, urban development and decision making.
This research on modelling urban land using GIS as well as detailed and comprehensive assessment of the land use land cover changes, socio-economic and environmental impacts of human activities in Nakuru Municipality is expected to provide a better understanding and give perspective into the impacts of human activities and associated land use land cover changes simulated over time till the year 2030 when Kenya attains Vision 2030. Specifically, the impacts of human activities and environmental conflicts that are arising as a result of competition over the limited resources in Nakuru Municipality will be investigated. This in turn will allow comprehensive land use land cover planning and an integrated management of resources, so that they are managed in such a way as to ensure sustainability of land and to achieve social equity, economic efficiency and environmental sustainability.
Research Questions
How will the model perform?
How will the accuracy of results be?
What kind of process can lead to changes?
What will be the extent of the land use changes in the future?
Objectives
Main objective
To develop an effective CA based urban growth model for better planning and management of resources
Specific objectives
To develop a calibration algorithm that takes into consideration spatial and temporal dynamics of urban growth
To establish how land use has been changing over time
Analyse the specific issues of the urban environment and put forward a recommendation that may form the basis for a sound solution for sustainable land management.
The study area
Nakuru municipality roughly lies between latitudes 0° 15' and 0° 31' South, and longitude 36° 00' and 36° 12' East, with an average altitude of 1,859 meters above sea level, covering an area of 290km² (Figure 1). Within Nakuru municipality is Nakuru town, Lanet town and Lake Nakuru National Park. The town of Nakuru is located 160 km North West of Nairobi along the twin east-west railroad transport route from Mombasa to Kampala and is the fourth largest urban centre in Kenya following Nairobi, Mombasa and Kisumu.
Nakuru town, being one of the fastest urbanising towns in Africa, is ideal for testing the model's flexibility to adapt and evolve over time depending on the changing characteristics of the town. The administratively defined town has land uses divided roughly into urban use, agriculture, rangeland and remnants of evergreen tropical forests. The urban pattern of Nakuru town and its environs are characterised by intense urban pressures, first along the main highways and through the development of sub urban areas. Nakuru population has been growing at the rate of 5.6% per annum. From a population of 38,181 in 1962, the population reached 163,927 in 1989, and 289,385. By the year 2015, the population is projected to rise to 760,000, which is approximately 50% above the present levels. Figure : Nakuru Municipality
(Mwangi, W., 2007)
Significance of the study
One of the major impacts of urban land cover dynamics is a shrinking amount of cultivated land through the development of infrastructures and various development projects. Therefore, urban land use change studies are important tools for urban or regional planners and decision makers to consider the impact of urban sprawl. The results of this study will provide information relevant to contribute in the environmental management plans and improve urban planning issues. It is also expected to: provide information on the status and dynamics of the urban land use of the area and the use of remote sensing from satellite imagery for such analysis for planners; assist environmentalist, regional and urban planners, and decision makers to consider the potential of geospatial tools for monitoring and planning urban environment; provide elements for long term bench-mark monitoring and observation relating to resource dynamics; and provide a base line for eventual research follow-up, by identifying specific and important topics that should be considered in greater detail by those interested in the area
CHAPTER TWO: LITERATURE REVIEW
Introduction
Urban development and the migration of much of the population from rural to urban areas are significant global phenomena. Increasingly, more small isolated population centres are changing into large metropolitan cities at the expense of prime agricultural land and the destruction of natural landscape and public open space. This has attracted a lot of attention to the study of urban development under the theme of global environmental change. Various urban models have been built for this purpose. Amongst these, models based on the principles of cellular automata are developing most rapidly.
Urban development resembles the behaviour of a cellular automaton in many aspects. The space of an urban area can be regarded as a combination of a number of cells, each cell taking a finite set of possible states representing the extent of its urban development with the state of each cell evolving in discrete time steps according to some local transition rules.
In this research, a simulation model of urban development will be developed based on the principles of the cellular automata. An innovative feature of the model is the incorporation of the fuzzy set and fuzzy logic approach. Instead of defining the state of cells as a binary mode of either non-urban or urban, urban development was regarded as a spatially and temporally continuous process. In this process, a cell might be in a non-urban (or rural) or a fully urban state, or it can also be in a state that is not rural/natural but yet not fully urbanised, that is, it is to some extent urbanised. Based on the fuzzy set theory, the extent to which a cell has undergone an urban development process can be represented by a fuzzy membership grade. Within this membership grade, a cell can be non-urban or fully urban with a membership grade of 0 or 1 respectively, or it can be at any stage of converting from non-urban to urban land use, in which case the membership grade is between 0 and 1 exclusively.
Role of GIS in Urban Studies
Urban GIS application, is an essential decision support system for any local Urban authority, which handles complex urban issues varies from road development, solid waste management, drainage, hospitals, the property tax collection and the tax assessment etc. All of the various urban issues, the urban authorities has to involve in the planning, development and monitoring of the various infrastructure facilities.
The modern technology of remote sensing which includes both aerial as well as satellite based systems, allow us to collect lot of physical data rather easily, with speed and on repetitive basis, and together with GIS helps us to analyse the data spatially, offering possibilities of generating various options, thereby optimising the whole planning process.
These information systems also offer interpretation of spatial data with other socioeconomic data, and thereby providing an important linkage in the total planning process and making it more effective and meaningful. The purpose of using GIS is that, maps provide an added dimension to data analysis which brings us one step closer to visualizing the complex patterns and relationships that characterise real world planning and policy problems.
Modelling with Cellular automata
Many cellular automata (CA) models have been developed so far especially in growth urban process studies. The models differ depending on their transition rules, and calibration methods. Examples of CA models include SLEUTH, fuzzy cellular automata, Artificial Neural Network model, and MCE CA model.
One of the earliest and most well-known models in the literature is Clarke's et al (1997) CA-based model "SLEUTH" that has four major types of data: land cover, slope, transportation, and protected lands, (von Neumann; 1966; Hagerstrand, 1967; Tobler; 1979 and Wolfram; 1994). A set of initial conditions in "SLEUTH" is defined by `seed' cells which were determined by locating and dating the extent of various settlements identified from historical maps, atlases, and other sources. These seed cells represent the initial distribution of urban areas. A set of complex behaviour rules is developed that involves selecting a location randomly, investigating the spatial properties of the neighbouring cells, and urbanizing the cell based on a set of probabilities. Despite all the achievements in CA urban growth modelling, the selection of the CA transition rules remains a research topic (Batty, 1998).
Wu (1996; 1998a) in his study introduced Fuzzy CA model which was a unique approach that defined transition rule with CA to simulate rural-urban land conversion in a fast growing metropolis. Unlike previous studies in which transition rule is defined by a mathematical equation this approach introduced the concept of the fuzzy logic control (FLC) into mimicking land conversion process. Preconditions of an action are described by fuzzy sets and state changes are simulated according to these fuzzy sets.
Artificial Neural Network (ANN) CA model provides a way to get parameter values automatically (Li et al. 2001a). When simulation is done or multiple land use changes ANN is considered as a promising tool. (Li et al. 2002a) The model based on ANN has been successfully applied by Li et al (2002) to the simulation of multiple land use changes in a fast growing area in southern China.
In Multi criterion (MCE) CA model, the transition rules are defined in various ways. One of the novel ways has been proposed by Wu (1998c). The combination of three elements, GIS, CA, and MCE, as claimed by the author has several advantages: visualization of decision-making, easier access to spatial information, and the more realistic definition of transition rules in CA.
Cellular Automata and Land Use Change
Latest development of GIS technology improves the systematic power required for the study of land cover change and land use. Current land use data is being digitised while much more data is being readily created in digital format, that is, remotely sensed imagery. The applications and improvement of digital land use database have been quite triumphant as evident in many reports (Lay, 2000). Using GIS, these data can be easily transferred and provide the necessary information needed for situational evaluation.
However, the methodology for modelling and predicting land use change is somewhat immature. Land use change is a vibrant spatial process that involves compound interactions between many aspects at different spatial extents. The intricacy of this dynamic process makes the formation of a broad model quite demanding. Additionally, the recent development of cellular automata brings in a new point of view for land use change. Moreover, there are raising interests in using CA for land use study (Clarke et al, 1997). The notion of cellular Automata began from research for a self-replicating machine, a robot type machine equipped with visions and encoded Turing machine that can gather a copy of itself from module parts (Firebaugh, 1988). Though such a machine has not been effectively constructed, a theoretical model of this self-replicating system was created as two-dimensional cellular automata. Additionally, cellular automata may be characterised by a set of simple production rules while its result may imitate a very complex system (Firebaugh, 1988).
Additionally, from the application side, cellular automata are a dynamic model that naturally integrates temporal and spatial dimension. CA is made up of four elements as described below (White & Engelen, 2000). Forecast of future land cover is fundamental for a variety of restoration and conservation goals, including evaluating the impacts of possible restoration and mitigating scenarios, targeting areas for restoration, and determining the weaknesses of certain resource lands to future land conversion.
CHAPTER THREE: METHODOLOGY
Land Use land cover change analysis
The approach adopted for the analysis of land use land cover will involve three sets of clear, cloud-free Landsat images for 1986, 2000 and 2006 will be selected to classify the study area. Nakuru municipality is entirely contained within Landsat TM path 169, rows 60. The data sets include Landsat TM, and ETM+ images. Reference data will be developed for each of the three years and then randomly divided for classifier training and accuracy assessment. Black and white aerial photos acquired in 1987 and 1995 will be used as reference data for the 1986 and 2000 classifications while GPS points will be used for 2006 classification. Stratified random sampling was adopted for selecting samples.
The classification scheme contain six classes namely forest, urban land, agriculture, water, rangeland, barren land, and vegetation, based on the land use land cover classification system developed by Anderson et al. (1976) for interpretation of remote sensing data at various scales and resolutions. Several of the factors considered during the design of categorisation scheme will incorporate: the major land use land cover groups within the study area, disparities in spatial decrees of the sensors, and the want to always discriminate land use land cover classes irrespective of seasonal disparities (Anderson et al, 1976). A combination of the reflective spectral bands from images (i.e., stacked vector) will be used for classification of the 1986, 2000 and 2006 images. A hybrid supervised-unsupervised training approach referred to as ''guided clustering'' in which the classes are clustered into subclasses for classifier training will be used with maximum likelihood classification (Bauer et al., 1994). Training samples of each class will be clustered into 5-10 subclasses. Class histograms will be checked for normality and small classes deleted. Following classifications, the subclasses will be recorded to their respective classes. Post-classification refinements will be enforced to diminish categorisation errors as a result of the similarities in spectral responses of certain classes such as bare fields and urban areas and some crop fields and wetlands. Spatial modeller and additional rule based procedures were adopted to overcome these classification challenges and differentiate between classes.
Independent samples of about 100 pixels for each class will be randomly selected from each classification category to assess classification accuracies. Error matrices as cross-tabulations of the mapped class versus the reference class will be used to assess classification accuracies (Congalton & Green, 1999). Overall accuracy, user's and producer's accuracies, and the Kappa statistic will then be derived from the error matrices. The Kappa statistic incorporates the off diagonal elements of the error matrices (i.e., classification errors) and represents agreement obtained after removing the proportion of agreement that could be expected to occur by chance. Following the classification of imagery from the individual years, a GIS based multi-date post-classification comparison change detection strategy will be employed to determine changes in land use land cover.
Cellular automata
The Clarke Urban Growth Model, which integrates biophysical factors with Cellular Automata spatial modelling, will be modified and calibrated for modelling urban growth of Nakuru municipality. The model uses suitability analysis, constraints analysis, land use land cover change analysis and cellular automata to address urban growth. The suitability analysis, constraints analysis and land use land cover change analysis will be determined using geographical information system while the cellular automata will be useful for defining cellular automata neighbourhood, for model calibration and for applying transition change rules. The model will utilise five input layers namely slope, land use, areas excluded from development, urban areas and road network layers. The slope will be derived from triangulated irregular network and used to implement topographic constraints on the model while the urban extents will be extracted from land use land cover maps obtained from remotely sensed images for 1986, 2000 and 2006.
The input layers will enable various types of urban land use change to be simulated. These included: spontaneous growth, new spreading centres growth, edge growth, and road-influenced growth. These growth types will sequentially be applied during each growth year and controlled through the interactions of five growth parameters, which describe an individual growth characteristic and when combined with other characteristics can describe several different growth processes.
Model Calibration
Before undertaking model calibration, data set preparations such as geo-registration, data type standardisation and resolution check will be necessary to ensure that all data sets have the same extent in terms of longitude and latitude, the same data standards and the same number of rows and columns. Using these data sets, calibration will be done to derive parameters for forecasting urban growth.
Model calibration will be achieved through a brute force Monte Carlo calibration method. This method ( Clarke et al., 2002), determines, given an initial starting image of urban extent, a set of initial control parameters that lead to a model run that best fits the observed known data. The method steps through the coefficient space in a complete, regular and irreducible manner.
When the model is run, a set of control parameters will be refined in the sequential calibration phases (coarse, fine, and final calibrations). Between calibration phases, attempts will be made to extract the value that best matched the five coefficient factors that control the behaviour of Nakuru municipality namely; diffusion (overall scatter of growth), breed (likelihood of new settlements being generated), spread (growth outward and inward from existing spreading centres), slope resistance (flat more preferred), and road gravity (attraction of urbanisation to roads and diffusion of urbanisation along roads).
