SHadman Darvishi; Karim Solaimani; Morteza Shabani
Abstract
Extended Abstract Introduction Urbanization is a continuous process and the spatial patternsof urban growth havealways played an important role in the transformation of human life throughout history. Urban growth has two dimensions: demographic and spatial, meaning that with increased urban population, ...
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Extended Abstract Introduction Urbanization is a continuous process and the spatial patternsof urban growth havealways played an important role in the transformation of human life throughout history. Urban growth has two dimensions: demographic and spatial, meaning that with increased urban population, the need for shelter increases and cities are faced with spatial growth. Expansion of cities in the spatial dimensions have several consequences,including changes in land use and land covers of areas surrounding cities.Land use change is currentlyone of the major concerns ofthe environmental approach, since land use changes in areas surrounding cities have led to changes in the economic structure of cities and the destruction of vegetation and agricultural lands as one of the main foundations of production in these areas. They have also seriously damaged other water resources, wildlife habitats, and resulted in the reduction of soil organic matter, changes in soil humidity and saltiness, increased energy consumption, increased urban heat islands, climate changes, as well as negative effects on the mental and physical health of urban residents. Nowadays, rapid growth in remote sensing technology and geographic information system, as well as the advancements in computer science and its application in environmental sciences and urban planning have created spatial modeling techniques such as Markov chain, Cellular Automata, intelligent neural networks and statistical models. Due to its dynamic nature, the capability of showing spatial distribution of land use changes, as well as its unique characteristics in modeling of natural and physical geographic featureson the ground and simpler adaptation with remote sensing data and GIS, a combination of Markov chain model and Cellular Automata are used as an important supporting toolfor decision making in urban planning and environmental sciences in many studies performedrecently. Over the past few decades, the population of Iranhas increased from 27 million in 1955 to 79 million in 2016. And according to the 2016census, 74 percent of the population lives in urban areas. In recent years, the population of Kurdistan province has experienced a 1.42% (2011 to 2016)average annual growth rate (especially in Baneh, Marivan and Saghez), which isaround 0.18% more than the average annual growth rate of the country (1.24%). Investigating census data shows that Baneh, Marivan and Saqezhave experienced a higher urban growth rate as compared to other cities in the province, and thus monitoring this growth and predicting its negative effects on the surrounding land use seems crucial.Destruction of vegetation and agricultural lands not only results in climate change, but also directly affect the lives of residents in the region. Therefore, understanding the growth rate is necessary for properplanning and managementofthese areas. Materials and Methodology Images received from Landsat in 1987, 2002 and 2017 were downloaded from the US Geological Surveywebsite and used in the present study. Google Earth images, land useand topography maps, and ground control points (GCP) were also used to perform imagepreprocessing, classification operations, and accuracy assessment. The study area includesBaneh, Marivan and Saqqez cities, which have recently experienced a high level of population growth. Considering the impact of population growth on increased rate of construction and physical development of urban areas, it is therefore necessary to study urban growth. In order to reduce the city’s impact on land use in future, it is necessary to modelurban growth. Using these models, planners can guide the urban development back to the optimal and appropriate routes and minimize the destruction of the land use.Image pre-processing in the present research was performed in ENVI5.3 environment. Then, using Maximum Likelihood algorithm, the images were categorized into five classes of water, residential areas, vegetation, agriculture and open spaces. Then, the overall accuracy of the classification maps was assessed using ground control points. To predict the urban growth, CA-Markov model was used in the IDRISI TerrSet software. Results and Discussion Findings indicate that the classified images have an accuracy of above 80%, and thus, land use maps of the study areas are valid.Investigations shows that the growth inMarivan and Baneh has most severely affected vegetation and agricultural land use. In the time period of 1987 to 2017, 897. 39 and 801 hectares of vegetation in Marivan and Banehhave been transformed into urban areas, respectively.During the same time period, 790.38 hectares of agricultural land in Marivan and 772.29 hectaresinBanehhave changed into urban areas. It is also important to note that unlike Saqez, the degradation of vegetation and agricultural lands in Marivan and Banehwas more severe than bare lands. In other words, bare landsinSaqez were more severely affected (as compared to vegetation and agricultural land), and about 1249,29 hectares of bare lands have turned into urban areas, while only 121.50 hectares of vegetation, and 509.04 hectaresof agriculture lands haveexperienced such a change.Also, results of the CA-Markov model showed that the growth of Baneh and Marivan cities in the 2017-2032 period will be in the Northeast and East directions, respectively. Results also indicate that this urban growth will affect agricultural and bare landsmore significantly. It is predicted that about 511.29 hectares of agricultural lands and 722.70 hectares of bare lands (in Baneh city) and 1080 hectares of agricultural lands and 2402.101 hectares of bare lands (in Marivan city) will turn into urban areas in this time period. Conclusion Based on the findings, it can be concluded that planning urban growth inthe study areas should be performed in a way that vegetation and especially the surrounding agricultural lands are preserved, and the negative effects of land use changesare minimized. Also,plannerscan apply the results of the present study in their future plansto guide the development of Baneh, Marivan and Saqeztoward optimal ways and reduce land use degradation.
Amir Hossein Kazem; Farhad Hosseinali; Ali Asghar Ale-sheikh
Abstract
Modeling urban growth and land use changes are an integral part of planning for sustainable development. The present research intends to model the urban growth and development for Tehran metropolis from the aspect of timeand spatial distribution. To this end, land-use maps for the years 1988, 2002 and ...
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Modeling urban growth and land use changes are an integral part of planning for sustainable development. The present research intends to model the urban growth and development for Tehran metropolis from the aspect of timeand spatial distribution. To this end, land-use maps for the years 1988, 2002 and 2013 were categorized with the object-based approach using Landsat satellite time series images. In the next step, using the logistic regression model, the effect of independent variables in relation to urban growth including 14 variables in the form of two groups of environmental-natural and socio-economic variables during the period of 1988 to 2002 was calculated as the coefficient in the regression equation, and the potential map of urban expansion was produced. The evaluation of the logistic regression function using two Pseudo R2 and ROC indexes with values of 0.32 and 0.89 showed good regression fit and proper description capability. Subsequently, the area of change for the expected year was quantitatively predicted using Markov chain analysis.Finally, by using the outputs of the two models of logistic regression and Markov chain analysis and using the Cellular Automata Model, urban growth was modeled for the year 2013, comparison of which with the 2013 classified image, shows that the used model with a 93% relative accuracy for the estimated area and a Kappa coefficient of 0.87 has been a successful model. Accordingly, the same model was used to estimate the urban growth in 2025,using images from the years of 2002 and 2013.
Tahereh Ghaemi rad; Mohammad Karimi
Abstract
Forest fire is one of the most common ecological hazards whoseproper prediction of spreading is a vital issue in minimizing its destructive effects.This phenomenon depends on factors such as topography, vegetation and climate. Among the existing models, the definite empirical models presented in the ...
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Forest fire is one of the most common ecological hazards whoseproper prediction of spreading is a vital issue in minimizing its destructive effects.This phenomenon depends on factors such as topography, vegetation and climate. Among the existing models, the definite empirical models presented in the form of raster including cellular automata are more populardue to their modeling simplicityand the ability to model complex systems. Different simulation systems have been developed to simulate and predict the spread of fire using cellular automata. The quality of the results obtained from these systems, in addition to the complexity of the model, depends on the accuracy and reliability of the input parameters, most of which have a degree of uncertainty. One of the constructive suggestions to overcome the uncertainty problem is the use of a two-stage simulation approach. In this approach, all of theexisting parameters in the model are first optimized by comparing the results derived from the simulation with the reality, then,the related simulation model will performthe simulation of the next step fire spread by considering the optimal values obtained for the parameters. One of the most important points in designing this system is the use ofdesirable optimization method. In this research, two optimization methods namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have been used to overcome the uncertainty problem and enhance the accuracy of forest fire spread modeling and implementation of two-stage simulation approach for a part of the forests of Gilan province. The results show that the Artificial Bee Colony (ABC) algorithm optimization method has abettercapability than the Particle Swarm Optimization (PSO) to produce optimal parameters of the desired model.
