Mohsen Shaterian; Seyed Hojjat Mousavi; Zahra Momenbeik
Abstract
Extended Abstract Introduction Knowing type and percentage of each land use and land cover are considered to be a fundamental need for understanding and managing an area. Given the ever-increasing changes in land use, managers and experts need to be aware of past changes and developments. This is because, ...
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Extended Abstract Introduction Knowing type and percentage of each land use and land cover are considered to be a fundamental need for understanding and managing an area. Given the ever-increasing changes in land use, managers and experts need to be aware of past changes and developments. This is because, policy making and solving existing problems require detecting changes and determining the trend of changes over time. Satellite data is one of the quickest and least expensive methods available based on which researchers can produce different land use map. In this regard, Landsat Satellite imageries are one of the most important data sources used to study different types of land use and land cover changes, such as deforestation, agricultural expansion and urban growth. Extracting information from satellite imagery through classification is one of the most widely used methods. One of the most important applications of remote sensing data is for investigating and discovering changes in phenomena with a spatial-temporal nature (i.e. phenomena whose position and status changes over time). In fact, change detection is the process of identifying and determining the type and extent of land cover or land use in a given period of time based on remote sensing images. The present study seeks to monitor land use changes in Shahr-e Kord during the period of 1985 to 2017, and to prepare land use maps of the area using Landsat satellite imageries. Materials & Methods In the present study, satellite imageries received from TM, ETM+, and OLI sensors of Landsat satellites in 1985, 2000, 2015, and 2017 were extracted from the United States Geological Survey (www.usgs.gov) and analyzed using different remote sensing software and geographical information systems like ENVI 4.7 and ArcGIS 10.4. In order to produce land use changes map, error correction was first performed. Then, images were processed using supervised classification method and maximum likelihood algorithm, which based on previous studies have a higher accuracy compared to other algorithms. In order to classify land use/land covers, a training sample was produced for each land use based on field observations, topographic maps (1:25000) produced by Iran National Cartographic Center, Google Earth imageries, and visual study of the imageries. Then, classification results were corrected using auxiliary data, visual interpretation, experiential knowledge, and GIS techniques. Prior familiarity with the region, visual study of imageries, previous experience and field operations revealed that following land uses exist in the region and are detachable on the images as well: a) urban, b) agricultural, c) industrial, d) meadow, e) airport, and c) other land uses (including pasture, rocky areas and areas without any specific land cover). Confusion or error matrix –including overall accuracy, producer’s accuracy, user accuracy and kappa coefficient- was also used to evaluate the accuracy of the classification. Also, urban land use changes were monitored using image differentiation functions. Results & Discussion After production of land use maps based on imageries received in 1985, 2000, 2015, and 2017, area of the six land cover classes was obtained. Results indicate that during these four periods (1985 to 2000), urban, industrial, agricultural and airport land uses have increased to 13, 111.7, 5.2 and 3.4 km2 (1.26, 10.16, 0.51 and 0.4 % increase) respectively, while meadows and other land uses have faced a decreasing trend. In other words, it can be concluded that most changes during this 15-year period occurred in meadows and other land uses. Since development of the airport have resulted in destruction of a large part of meadows, this land use have faced more severe changes. Land use changes from 1985 to 2017 indicate that 7.8 km2 of agricultural lands were transformed into urban land use, 1.4 km2 to industrial land use, 1.08 km2 to airport and 7.7 km2 to other land uses. Also, 20.5 km2 of other land uses were transformed into urban land use, 203.1 km2 to agricultural land use, 0.03 km2 to dried meadows, 0.17 km2 to airport and 14.5 km2 to industrial land use. 2.8 km2 of meadows were also transformed into agricultural land use, 0.05 km2 to industrial land use and 2.04 km2 to airport. During this period, urban and industrial land uses have remained unchanged. Conclusion Generally, results indicate that urban, industrial and agricultural land uses have developed over time, and these land uses have always had a positive increasing trend. While meadows and other land uses have had a decreasing and negative trend. This is due to the construction of Shahr-e Kord Airport, uncontrolled exploitations, digging wells and drought phenomena, which have led to a decrease in the level of water in aquifers and destruction of natural ecosystem in this region. In this way, previous meadows have turned into the source of intense dust generation in the city, which is a sign of desertification and ecosystem destruction. Due to drought and water scarcity in recent years, new deep wells have been dug with the aim of supplying water. This have occurred despite the critical condition of the meadows, and thus, have resulted in repeated protests by farmers and livestock farmers. Dramatic decrease in other land uses, including pastures, can also be attributed to recent droughts in Iran and intense dust generation. Increased population, increased human pressure on natural resources and also development of agricultural lands are among other causes of the present situation. Based on existing maps and satellite imageries, Shahr-e Kord is developing towards North and North West. In some areas, this development has occurred in pastures. Therefore, due to very high population density in the region which is still increasing, and also ongoing migration of villagers to the city, supplying appropriate accommodation and occupation for this population requires finding new suitable locations for urban and industrial development of the city. This development process should happen with correct management and according to the goals of sustainable development.
Yones Gholami Bimargh; Sayed Ahmad Hosseini; Mohsen Shaterian; Akram Mohammadi; Abolfazl Dehghan jazi
Abstract
Introduction
Nowadays, rapid urbanization; mismatch between modern streets and the demands of population; population attracting land uses along streets; and vicinity of incompatible land uses have resulted in traffic congestion in cities. Traffic is one of the major problems in most large cities, and ...
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Introduction
Nowadays, rapid urbanization; mismatch between modern streets and the demands of population; population attracting land uses along streets; and vicinity of incompatible land uses have resulted in traffic congestion in cities. Traffic is one of the major problems in most large cities, and even medium and small cities. It is also one of the social problems of modern societies and cities. Although, extensive studies have been carried out on the network structure and land use separately, their interaction has been disregarded. Like other modern cities, the city of Kashan faces this problem. The central texture of Kashan attracts a large population throughout the day, and especially during rush hours. This is on the one hand, due to the presence of historical elements, such as Kashan historical bazaar, historical buildings and schools, and on the other hand, because of population attracting land uses like commercial, educational, and therapeutic land use. Therefore, it is necessary to consider this problem, and the spatial redistribution of population attracting land uses.
Materials & Methods
The present study applies a descriptive-analytic methodology. The necessary information was collected using library research, documentary method, and expert interview. Then, the data was entered in GIS software. GIS software and network analysis model were used for data analysis.
Results & Discussion
In this study, the role of educational and therapeutic land use in traffic congestion in central areas of Kashan was investigated. To carry out network analysis, the network map of Kashan streets and their operating speed were required. The street network was depicted in GIS software. Then, the maximum operating speed of the main streets of Kashan was determined based on the master plan. Table 1 presents operating speed in five main axes of Kashan based on the master plan. These include main and crowded streets of Kashan. The operating speed of other streets was collected through expert interviews. After designing the network and determining operating speed of streets, (educational and therapeutic) land uses with the most significant impact on the traffic congestion of Kashan were identified by interviewing ten experts, with the aim of determining service areas. For each sample land use, a test was performed to determine service areas in the network analysis phase. To conduct this test, the standard service radius of educational and sanitation land uses in Iran was used. In network analysis, the test was separately conducted for each primary school (minimum operating radius of 4 minutes/ maximum operating radius of 5 minutes), middle school (minimum access radius of 6 minutes/ maximum access radius of 7 minutes), high school (minimum access radius of 8 minutes/maximum access radius of 10 minutes), and therapeutic land uses (minimum access radius of 7 minutes/ maximum access radius of 8 minutes).
Conclusion
Based on the analysis of service provision range in Kashan downtown, we conclude that compared to other areas in the city, primary schools (in their minimum access radius) face 2.25% increase in traffic congestion; middle schools in their minimum radius of access face 4.67%, increase and in their maximum radius of access face 1.83% increase; high schools in their minimum radius of access face 3.25% increase, and in their maximum radius of access face 7.95% increase, and therapeutic land use in their minimum radius face 7.46% increase, and in their maximum radius face 6.16 % increase in traffic congestion. However, primary schools in other areas of the city face 0.24% higher traffic congestion in maximum access radius as compared to downtown. Thus, downtown attracts 13.13% more unnecessary urban commutes and traffic in its minimum radius of access. This reaches 20.68% in the maximum radius of access, which is due to a larger overlap between educational and therapeutic land use.