(Gis) and Cellular Automata (Ca) for Land Use Change Modeling in Singaraja City and Its Surrounding Area

SPATIAL SIMULATION BASED ON GEOGRAPHIC INFORMATION SYSTEM (GIS) AND CELLULAR AUTOMATA (CA) FOR LAND USE CHANGE MODELING IN SINGARAJA CITY AND ITS SURROUNDING AREA

Nyoman Arto Suprapto1, Takahiro Osawa2, I Dewa Nyoman Nurweda Putra3 1Master Student of Environmental Science, Udayana University, 2Master Lecturer of Environmental Science, Udayana University, 3Lecturer of Marine and Fisheries Faculty, Udayana University. E-mail : [email protected]

ABSTRACT Singaraja is the second largest city after Denpasar in Bali. The magnitude of the potential of the region both trade and services, agriculture and tourism in Buleleng Regency has given a very broad impact not only on the economy but also the use of land. Economic development in the city of Singaraja cause some effects such as population growth, an increasing number of facilities (social, economic, health, and others), as well as changes in land use. Changes in land use have a serious impact on the environment in the city of Singaraja. The development of urban areas of Singaraja has given the excesses of increasing the land conversion. Suburb dominated by wetland agriculture has now turned into buildings to meet the needs of shelter, trade and services as well as urban utilities. This study was conducted by mean to determine how changes in land use from agricultural land into build up land during twelve years (period of 2002 - 2014) and the prediction of land use within the next 12 years (period of 2020 and 2026). Prediction of land use changes will be done using spatial simulation method which is integrating Cellular Automata (CA) and Geographic Information Systems (GIS) which analyzed based on land requirement, the driving variable of land use changes (population and road) and the inhabiting variable of land use change (slope steepness and rivers). Keywords : Land Use Change, Land Use Change Modeling, Celullar Automata, GIS

1. Introduction Effects) model used by Veldkamp and Buleleng is the largest regency in Sresco (1995) to analyze land use Bali with a total area of 1365.88 km2 changes in Costa Rica on a local, or 24.25% of the area of Bali Province. regional and national. By using a The capital of Buleleng regency is different size scale, this model shows Singaraja city which is composed of 2 that at local, regional and national districts, namely district of Buleleng levels can produce an opposite effect. and Sukasada. Singaraja is the second Cellular Automata (Markov Chain) in largest city in the Bali province after this study is used to determine the Denpasar. Based on the spatial location or any area of agricultural structure of Bali Province (Bali land use that could potentially turn into Provincial Regulation No. 16 of 2009), a built up region. Geographic the urban areas of Singaraja is included Information System (GIS) used to as PKW (Center of Regional Activity). develop a spatial aspect and Spatial structure mandated by the Bali constructed the driving variables that Provincial Spatial Plan sets the urban affect changes. Some of the variables areas of Sarbagita as PKN (Center of that led to change in land use are the National Activity) and urban areas of distance to roads, distance to rivers, Singaraja as PKW. Thus, the distance to settlements, slope, climate, governments of both central and population density and revenue. In this regional level will focus on the study there are four variables used as development of infrastructure in the factors driving and inhibiting changes urban areas. Enactment of Singaraja in land use such as distance to road, urban areas as PKW gives positive and distance to rivers, road network density negative impacts of the development and slope. Therefore, by combine because the economic activity will be Cellular Automata method with GIS is focused on urban areas. Increased expected to give a better answer in economic activity is one of the positive modeling the land use change. impacts on the provision of supporting This research was conduct to infrastructure. However, land use answer several questions, how much change is also a threat to the growth change of agriculture land into non- and development of the region. agriculture land in Singaraja City and Various methods for modeling its surrounding area occurred during land use change have been applied by the period 2002 – 2014 and also how is several researchers. Wijaya (2011) was the prediction of land use changes modeling using Multinomial Logistic from agriculture land into non- Regression (MLR) method. Wu et al. agriculture land in Singaraja City and (2006) used regression analysis to its surrounding areas in 2016 and 2026. model the land use changes in the city of Beijing China and predicting land use 20 years into the future with Markov Chain models. CLUE

(Conversion of Land Use and its

determine the allocation of space in the future became a major inhibitor in preparing spatial plans in the city of Singaraja. Spatial planning has been drawn up for Buleleng and also the detailed plans in each districts, it would require a method of evaluation of the spatial plan. In Law 26 of 2007 explained that each 5-year spatial plan product must be evaluated. Land use projection methods are indispensable in the preparation of a detailed plan and evaluation of spatial planning in the future. Spatial simulation based Geographic Information Systems (GIS) and Cellular Automata (CA) is

intended to examine the land use Figure 1. Research Location projected system and also projecting land use in the city of Singaraja to find 2. Framework of Research out how is the change of agriculture land use into the built up area to ten Land use change caused by many years into the future. For more details, factors, including, the value of land, research framework of this study is the potential of the region, presented in the following diagram. infrastructure and population.

Population has a very important role in land use. Population is the most dominant variable that determines the changes in land use. Land use change is also determined by the regional development policy, so that the value of land, the potential of the area, infrastructure and population is an important part in the development of the area in the city of Singaraja.

Under Law No. 26 Year 2007 on spatial planning, every region in

Indonesia must be set by spatial planning to guide utilization on space.

Spatial planning is also needed for the city of Singaraja, especially with the potential for greater flotation area.

Lack of analytical methods to

3.1 Land Use Change in period 2002, 2008, and 2014 Land use change from 2003 to 2013 would be calculated using overlay technique using ArcGIS. By using ArcGIS, an area for agricultural and non-agricultural land can be calculated for each year. Land use change from 2003 to 2008 and from 2008 to 2013 can be calculated by subtracting the amount of non- agricultural or agricultural land in the years thereafter.

LUC = LU 2014 – LU 2002……….(1) Figure 2. Research Design Where: LUC = Land Use Change LU 2014 = Land Use in 2014 3. Method LU 2002 = Land Use in 2002 The dynamics of land use changes in this study detected using 3.2 Prediction of Land Use Change Landsat ETM+ in 2002 and 2014 with in 2020 and 2026 based on land a spatial resolution of 30 mx 30 m and requirement ALOS AVNIR2 imagery with a To calculate the projected resolution of 10 mx 10 m. Changes changes in land use between 2020 and focused on the use of agricultural land 2026 assuming the land requirement into built up land by calculating the for housing, infrastructure, facilities, amount of land use in each year. trade and services etc. The number of Prediction of land use will be land use in the built up area in 2002, done using spatial simulation method 2008, and 2014 will be analyzed which is integrating the Geographic regression to determine how the Information System (GIS) and Cellular relationship and the influence of Automata (CA). Basic aspects are population on land use of built up area. considered in this spatial simulation is According to Steel at al. (1980) the need of land, land quality and equation of non-linear regression use environmental conditions in the to calculate the built up area needs in vicinity. 2018 and 2023 is as follows. Some analysis to be performed in this study are : Y = a + blnX……..(2) Where: Y = land use change A = constantan B = intercept parameter X = population

To calculate the projected land 3.4 Mapping Analysis of Land use by 2020 and 2026 then it should Quality use the projected population in 2020 The quality of land is an and 2026. Thus, by including the value important aspect to be considered in of the projected population of the selecting land for a specific use. Land above equation will be changed as quality can be assessed based on a follows. number of factors or a particular indicator, depending on the type of

Y = a +blnPt………(3) land use to be commercialized. Soil Where: fertility, availability of irrigation is an Y = projected land use change in 2020 important factor to be considered for and 2026 the use of agricultural land. Location a = constantan and accessibility, on the other hand, is b = intercept parameter a more important factor for the use of Pt = projected population in 2020 and non-agricultural land. 2026 Indicator of land quality is relative depending on the purpose of 3.3 Prediction of Land Use Change assessing the quality of the land. In in 2018 and 2023 based on land accordance with the research requirement. objectives, quality land assessed based To calculate the projected on the accessibility of the land. The population in 2020 and 2026 should be assumption used is the higher calculated first population growth from accessibility of land, more likely to 2003 to 2013. By using a model of experience changes in land use. This exponential population growth can assumption is based on a number of then be calculated. After the growth of studies on changes in use, particularly the population is known, then the change of agricultural land into non- calculation of population projections agriculture. The parameter used to can be done by entering the population assess the accessibility is the slope, growth rate in exponential models. distance to roads, density of road According Kachigan (1986) formula network and distance to the river. for exponential models are as follows: Analysis and mapping of four parameters is done by using a

Pt = a * exp(b * P0)……….(4) geographic information system (GIS). Where: Analysis and mapping of

Pt = prediction of the total parameter of land quality values population in 2020 and 2026 produce different unit. Slope parameter

P0 = the total population in 2002, using units of percent (%), distance 2020 and 2026 parameter using unit of meters (m) and a = population growth the density parameter using units of b = intercept parameter kilometers per square kilometer (km / exp = exponential km2). Parameter values need to be compared for the further analysis. Equalization is done by means of the

6 process of standardization or 3.5 Prediction of Land Use Change normalization. The method used to Location standardize the value of the parameter Location of land use changes is a linear transformation using the predicted by spatial simulation using equation (7) and (8) (Kachigan, 1986). Cellular Automata (CA). As the name implies, CA contains a number of

( )7 cells, which has a certain value. Each cell can be changed to follow a certain principle of transition (transition rule). ( )8 CA consists of four components that interact with each other namely (Liu, Where: 2009): = the result of standarisation

= parameter value U (universe) = space dimensional of 푋 = parameter of maximum value the cell 푋 = parameter of minimum value S (state) = circumstance that may achieve by the cell Equation (5) is used to N (neighborhood) = the number of standardize the distance parameter neighborhood cell that values to the river and the density of considered to determine the road network. Using equation (5), the cell value the value of the parameter will have a T (transition) = a couple of rule used range of 0 to 1 Land, in the study area to determine the value of which has the farthest distance from each cell the rivers and the highest density of road network will have a value of 1. Instead of land closest to the river and CA in this study is used to lowest road network density, will have simulate changes in land use from a value of 0. agricultural land into non-agricultural Equation (6) is used to land. The unit used is the simulation standardize the value of the parameter cell (pixel) size of 30 mx 30 m. Each distance to roads and slope. Using cell has a value of U (universe) and S equation (6), the value of the parameter (state). U value of a cell is the location will have a range of 0 to 1, but the of the cell indicated by the coordinates result is contrast with the use of (x, y). S value of a cell is a category of equation (5). Land which has farthest land use. In accordance with the distance from the road and the steep research objectives, then there are only slopes will have a value of 0. Instead two possible values of S, namely of land closest to the road and flat agricultural land and non-agricultural slope would have a value of 1. land. Component T is a rule in the simulation. An example of a rule is "change only applies in one direction, namely from the state agricultural land into non-agricultural land". Other

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