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.
Shirin Mohammahkhan; Hamid Ganjaeian; Somaieh Shahri; Amirali Abbaszade
Abstract
Extended Abstract Introduction Cities have always been under the influence of various factors and developed under such conditions. Countries around the world are increasingly moving toward urbanization. Physical development of cities occurs in the form of human activities or changes in urban (or rural) ...
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Extended Abstract Introduction Cities have always been under the influence of various factors and developed under such conditions. Countries around the world are increasingly moving toward urbanization. Physical development of cities occurs in the form of human activities or changes in urban (or rural) land use, and lead to widespread use of lands and adverse environmental effects. In some cases, urban growth leads to environmental hazards and threats human societies. Although the effects of natural factors such as geomorphological phenomena have not been scientifically considered in the development of the study area, there factors had a leading role in this development. Due to geomorphological situation, elevations and steep areas, scattered fault lines and rivers full of water, development of human settlements in the study area faces many constraints. Therefore, it is necessary to plan urban development in the study area based on the geomorphological situation of the region. Accordingly, the present study seeks to evaluate the trend of changes occurred from 1992 to 2017 in the residential districts of Marivan. It also aims to determine the extent of urban growth towards areas facing geomorphological hazards, and finally to predict this trend for 2035. Materials and Methods The present study takes advantage of an analytical and statistical research method, along with the necessary software. Moreover, it seeks to study the trend of urban development from 1992 to 2017, and also predict the future trend of development for 2035. Thus, satellite images received in June 1992, 2001, 2011, and 2017 are collected. After preprocessing the images, a land use map is extracted based on the situation of the study area in 1992, 2001. 2011 and 2017. Then, based on these maps and using effective variables, a map is produced based on the predictions made for the residential areas in 2035 by LCM model. Modeling and prediction are performed using LCM model in four steps: 1. Examination of Land Use Changes; 2. Mapping Potential Transfer using Markov Chain. 3. Extracting a predictive map. 4. Evaluating the accuracy of prediction. After predicting and extracting a map of residential areas for each time period, distribution of geomorphologic hazards in these areas is evaluated. In fact, development trend of high risk residential areas has been evaluated. Discussion and Results A large part of the study area is mountainous, and these elevations have somehow limited the development of human settlements. Since the present study seeks to determine the trend of human settlements development in areas facing geomorphological hazards, a map has been extracted for these prohibited areas before evaluating the trend of development. These prohibited areas have been mapped in order to identify hazardous areas, and to evaluate development of residential settlements toward these areas. To prepare this map, multiple criteria have been selected based on the situation in the region and experts’ opinion. Then in accordance with the purpose of this research, an information layer was produced using these criteria. Regarding geomorphology, regions with an altitude of more than 1700 m, slopes of more than 30%, north-south direction of the slope, area within 1000 m radii around fault lines and within 200 m radii around rivers are referred to as prohibited areas. After determining prohibited areas, human settlements in the study area were mapped based on 1992, 2001, 2011, and 2017 information. Then, trend of settlement development in prohibited areas was estimated and projected for 2035. Conclusion Based on the evaluation of results, there is an increasing demographic trend from 1992 to 2017, so that residential area has increased from 7.8 km in 1992 to 10.9 km in 2017. Maximum development occurred from 2001 to 2011. During this period, settlements developed 3.6 km2 and reached around 14.5 km2 in 2011. From 2011 to 2017, settlements area reached 16.6 km2. Apart from the increasing trend of development in residential areas during these years, this development has mostly occurred toward hazardous areas. So that in 1992, around 1.7 km2 of total residential area was located in prohibited areas, most of which included steeped areas and rivers’ border lines. In 2001 and 2011, this trend has increased from 2.3 to 2.9 km2, and reached 3.3 km2 in 2017. Considering the increasing trend of population toward Marivan, increased constructions in peri-urban and rural areas of Marivan and also along the main road of this city, development of settlements toward prohibited areas has mostly occurred in these areas. According to the main purpose of the present research, development of residential areas is projected for 2035 based on land use in pre-specified years. Results indicate that total area of settlements will increase to about 24.3 km2 in 2035, about 5.7 km2 of which will be in prohibited areas.