Simulation of Urban Land Use Growth Scenarios Using the Cellular Automata Method of SLEUTH

Accelerating urban expansion is increasingly challenging the sustainable use of land, since modeling urban growth is important in order to adapt to balanced development. Among all the models for urban simulation, dynamic models based on cellular automata have significant usage in urban modeling because of its applications in different places and times. One of the most widely used spatial models based on cellular automata is the SLEUTH model, which in recent years has improved its accuracy and efficiency for performing calculations, and today it is widely considered in predicting the development trend of different cities in the world.This study was carried out with the aim of simulating the future urban expansion of Birjand Metropolitan from 2020 to 2050 using Cellular Automata (CA) methodology in the SLEUTH modeling considering two scenarios: historical and environmental growth.

Study areaBirjand Metropolitan is the capital of South Khorasan province. The area of Birjand is 14.265 km2 and located between 53', 32° N and 59', 12° E in the northeastern margin of The Lut Desert, which is surrounded by mountains. In the last decade, and especially after the division of Khorasan province into three provinces of North Khorasan, South Khorasan and Khorasan Razavi, Birjand Metropolitan as center of South Khorasan province faced to socio-economic and political changes that led to rapid urban growth and physical and functional changes.SLEUTH modelIn this research, modeling the expansion of Birjand has been considered using cellular automata by SLEUTH method. SLEUTH name is derived from the names of input layers: Slope, Land use, Excluded areas, Urbanization, Transportation and Hillshade. The main attribute of SLEUTH is that it be calibrated on the base of the region changes in the past and therefore reasonably predicts the future. SLEUTH starts with the oldest data (first year of control) and generates growth cycles. Each growth cycle is assumed to represent one year. A run is the set of growth cycles from the first control year to the final year. Considering the comparison the simulated image with the real image in the control years, evaluation indexes are generated.SLEUTH modeling performed in four steps:1- Data gatheringAll layers (Slope, Land use, Excluded areas, Urbanization, Transportation and Hillshade) were georeferenced to the same geographical references system (UTM-40N) and pixel size 30*30. Slope and Hillshade layers extracted from DEM. Land use and Urbanization provided from classification of landsat_TM images 1990, 2000, 2001, 2020 years. These images were belonged to row and path 159 and 37, respectively and classification was performed using ‎Support Vector Machine (SVM) algorithm. In preparing the road layer for four mentioned years, in addition to using satellite images, the existing maps and Google Earth were used for updating. Two Excluded areas layer in this study were prepared according to two scenarios: historical growth and environmental growth. In the historical growth scenario, roads and cities and in the environmental growth scenario, vegetation and high slopes (slops higher than 30%) were considered as Excluded areas.2- Confirmation of the correct execution of the modelThe SLEUTH model was downloaded from the Gigalapolis project from the website: http://www.ncgia.ucsb.edu/projects, and the required simulator in the Windows (Cygwin) on the PC was installed. To ensure the correct execution of the model, SLEUTH was performed by experimental data and data from the study area. Surveying the results and outputs of these performances confirms accuracy of execution.3- The calibration stepCalibration is one of the most important steps in simulating urban growth using SLEUTH. In the calibration step, based on the historical data, the best set for the five global parameters/coefficients (Diffusion, Bread, Spread, Slope and Road Gravity) is extracted. These coefficients, which indicate the contribution of factors to the expansion of the study area, vary from zero to 100 and according to the Brute Force method in four stages, which consist of coarse, fine, final and average, are obtained based on cell size, search range and Monte Carlo execution number. In each forward stage the range of search become narrow and number of Monte Carlo execution become more. ‎Optimum Sleuth Metric (OSM) and Leesalee metrics in each stage were used to determining the calibration coefficients for the next stage. These metrics show the overall precision of the simulation too.4- Prediction the Birjand growthFour kinds of growth simulate by SLEUTH consist of spontaneous, spread, organic and road influenced. This growth rules and the coefficients obtained from previous step constitute the transfer rules in the Cellular Automata of SLEUTH model for prediction. Output image of prediction was reclassified to 1: urbanization probability> 80% and 0: urbanization probability< 80%.

In this study, using the SLEUTH model, the expansion of Birjand city was predicted according to historical and environmental growth scenarios from 2020 until 2050. During the study period, the growth of the urbanization is quite evident and it is mainly happening in the northeast and southwest of the city. One scenario of this research was based on the historical growth and the past trend, which means that all the drivers that have caused the growth of the city in the past will continue in the future. In the environmental growth scenario, urban growth will not located in vegetation cover and slopes above 30%. The largest increase will be happened around roads in the historical growth scenario and in the vicinity of urban in the environmental scenario. The Calibration results showed that there was different coefficients for scenarios. High diffusion in historical growth scenario (87) rather than environmental growth scenario (20) denoted that probability of a new urban center by 2050 is high and spontaneous growth/outlying growth will occur by 2050 but, in the environmental growth scenario the growth will has a more coherent pattern. Bread coefficients in historical growth scenario (73) show the probability of filling around the new urban points by 2050 is high in the future, but in environmental growth scenario (4) does not support new points for urban expansion. Spread coefficient in environmental growth scenario (32) was more than historical growth scenario (20) that means probability of edge (organic) growth in environmental growth scenario is more than historical growth scenario in the future. Slope Gravity in both scenarios was very low (1) so slope isn’t the control factor in this area. The main reason for low rule of slope is the uniformity of the slope and the scarcity of high slopes in the study area. Road Gravity in historical growth scenario (22) and in environmental growth scenario (37) that shows there are high eventuality of linear growth type in both scenario and road-influenced growth is significant for the future growth.The simulation results showed that urban expansion in the historical and environmental growth scenarios would be 2201.85 and 2150.91 hectares, respectively. Although the extent of the urbanization area is close to each other, the probable places for urbanization are more compact with organic pattern in the environmental scenario and more scattered in the historical scenario. Therefore urban expansion in environmental scenario has lower influence on surrounding environment rather than historical scenario and is more close to sustainable development.The results can provide useful information for the decisions of land managers and municipalities in the direction of sustainable urban development.

The new political division of Khorasan Province had significant changes on urban growth of Birjand that turn it to metropolitan. According to simulation of urban growth increasing the area of Birjand city is inevitable in both historical and environmental scenarios. A comparison of the two scenarios denoted that in the historical growth scenario, the urban growth rate is higher, the vegetation destruction and spontaneous settlements is maximal. The findings of this study can help policy makers and managers in formulating informed urban planning strategies to have the least destructive effect on the environment in the future.