The future spatial development of the city and the suburbs of Bandar Abbas

Document Type : Original Article

Authors

1 PhD candidate in Geography and Urban Planning, Yazd Branch, Islamic Azad University, Yazd, Iran

2 Associate professor, remote sensing and GIS, Yazd, branch , department, Islamic Azad University , Yazd, Iran

3 Associate professor, GIS-RS department and natural engineering, Meybod Branch, Islamic Azad University, Meybod, Iran

4 Associate Professor, department of natural resources engineering, faculty of agricultural engineering and natural resources, Hormozgan University, Hormozgan, Iran

Abstract

Urban expansion has led to the formation of complex forms of the spatial existence of cities. The consequences of this expansion have been shown in the form of agricultural land destruction, environmental damage, and uneven and scattered urban growth. In recent years, several programs have been prepared to organize, manage and guide the expansion of the city, which due to the lack of sufficient knowledge of the influencing and driving factors of the development, and the state and manner of the future expansion of the city, make decision-making in this field a serious challenge. As a result of the increasing growth of cities, the physical expansion of cities to the surrounding areas as well as the increa se of density and accumulation within the cities will be inevitable hence the peri-urban spaces are expanding When the population increases and urbanization accelerates, new residential spaces are created in the periphery of cities from the "city-rural" confrontation, which is strongly influenced by urban spaces. Such residential spaces are called "Pirashahr". This urban growth will bring serious and countless problems. Since the instability of the development of human societies in the last two centuries (after the industrial revolution) and its harmful consequences, which are a function of population variables, per capita and consumption patterns, attention to the principle of sustainability is being questioned more and more
Table 2 shows the results of the accuracy evaluation of the produced maps. According to this table, four parameters are presented for overall accuracy, kappa coefficient, producer accuracy and consumer accuracy. By examining the obtained statistical results, we find that the accuracy and parameters are often above 90% (except for the kappa coefficient of 2000 and the forecast kappa coefficient of 2020), which is very appropriate and in several cases these statistics are close to 100%. The results of this research are also in line. The results of their research show that vegetation lands have declined sharply in the time interval. Also, based on the results of the sacrificing and colleagues (2014), farmers' land has increased and urban growth has increased the study of the study, and the results of the overwhelming land change means also indicated the continuation of this process. Research Results Majid (1393) in evaluating changes in land margin in Urmia during the years 1989 to 2013, and then predicting the trend of changes by 2035 of the combination of Markov chain and automated cells showed that the growth of Urmia city has always caused the destruction of agricultural lands and gardens of this city and becoming residential lands. Ali Mohammadi et al The population growth of the city and followed by the change in land cover and destruction of vegetation. Growth of urban population and a tongue to build a building on the populations of the crowd; During the destruction of crops, ranges, green space, the field of changes and significant changes in land cover, as well as the change in the climate. This research, using the modeling and analyzing chain of the Markov and automated cells, was modeled and analyzed and analyzed the changes coated on the city of Bandar Abbas and its penetration area. For this purpose, the first Latest satellite imagery was classified for 2000, 2005 and 2010, 2015 and 2020 with the most similar similarity approach. The classifications were analyzed and the results showed a coaxial coefficient of 90% and the high accuracy of the classification. In the following, changes in land coverings were carried out during the period (2000-2005), (2005 to 2010) and (2010 to 2015) (2015 to 2020) and (2015 to 2020). In the next step, using the Markov chain analysis and automated cells, modeling and predicting changes in landing coating in the future were taken. , the results showed that the increase in significant percentage of changes in the water, which indicates that this year increased by the year in 2015 by 2020. In general, the area of land is provided to the research below to be reviewed in the following charts. The vegetation was reduced from 4707 hectares in 2000 to 3518 hectares in 2005 and 3321 in 2010 and 2693 in 2020. According to the model, this decline will continue to be a downtrend, as it will reach 1984 hectares in 2025. The city and the built-in land has been ascending since 2000, as of 5879 hectares in 2000 with about 582 hectares reached 6461 hectares in 2005. This land ups will be continued, and the prediction map shows that these land will increase to 7976 hectares in 2025. According to the findings obtained from the results of the random forest model, the highest parameter influenced in the development of the city was the main way of the index. The results of this research and using the processing of moonlight images showed that the Bangea Abbas city city during the 20 years (2000 to 2020) were changed over the range of farmers and the city center. Study of land use maps in this study showed that the trends and development of the city of Bandar Abbas did not comply with a uniform trend and in terms of changes in fluctuations. The city's growth is out of range, in the southern part, given that there is a major range as a sea that is the ability to expand in these areas very low; However, as the city's growth in the southern margin, due to government decisions for tourism growth as well as marine trade, has led to the expansion of human structures in this area. On the other hand, in the northern areas of Bandar Abbas, due to the possibility of developing the city, during the study period, it has been continuously taken to these populations. A point of view can be concluded that the land-related activities of the land market have made a large growth in the north of the city, while the region is in line with the shape of 17, the proportion of these areas for urban development is weak.

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امان­پور، سعید. علیزاده، مهدی. دامن باغ، صفیه. 1399. شناسایی و تحلیل الگوی گسترش کلان‌شهر اهواز در بازه زمانی 1360 تا 1400. مطالعات توسعه پایدار شهری و منطقه­ای. دوره 1. شماره 1. صص 77-96.
اسماعیلی، فاطمه.ایلانلو، مریم. 1400. مدلسازی تغییرات کاربری اراضی بر پایه زنجیره مارکوف درروش lcm  رامهرمز.آمایش محیط. شماره 54.
توکلی، مرتضی. نعیم‌آبادی، نازنین. 1398. خزش شهری و تغییرات کاربری اراضی فضاهای پیراشهری نیشابور. مجله توسعه فضاهای پیراشهری. سال اول. شماره دوم. صص 151-165.
ثروتی، محمدرضا. 1383. تنگناهای طبیعی توسعه شهر لار (جنوب استان فارس). جغرافیای سرزمین، دوره 11. شماره 4. صص 3-22.
حیدریان ، پیمان، رنگزن، کاظم، ملکی، سعید، نقی­زاده، ایوب. 1393. تلفیق تکنیک‌های سنجش‌ازدور و مدل LCM  بارویکرد مدل‌سازی توسعه شهری نمونه موردی: کلان‌شهر تهران، مطالعات جغرافیایی مناطق خشک، پاییز93، شماره هفدهم، صص87-100 .
رهنما، محمد رحیم. عباس­زادگان، غلامرضا. 1387. اصول، مبانی و مدل­های سنجش فرم کالبدی شهر، نشر جهاد دانشگاهی مشهد.
شکویی، حسین. 1382. دیدگاه­های نو در جغرافیای شهری، تهران: انتشارات سمت.
علمی­زاده، هیوا. 1388. کاربرد ژئومورفولوژی در توسعه و محدودیت شهر کرج. فصلنامه سپهر. 18 (71). صص 67-63.
فاضلی فارسانی، آرش.، قضاوی، رضا.، و فرزانه، محمدرضا. 1394. بررسی عملکرد الگوریتم­های طبقه­بندی کاربری اراضی با استفاده از تکنیک‌های ادغام تصاویر (مطالعة موردی: زیرحوزه بهشت‌آباد). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی. 6(1)، صص 91-106.
فردوسی، بهرام. 1384. امکان­سنجی و کاربرد سیستم پشتیبانی تصمیم­گیری در توسعه فیزیکی شهر، نمونه موردی سنندج. پایان‌نامه ارشد، تهران. دانشگاه تربیت مدرس.
کمالی­باغراهی، اسماعیل. سمندری، امید. صیدبیگی، صادق. سرحدی، مرتضی. 1401. آینده­پژوهی تعیین اراضی بالقوه جهت توسعه شهری و ارائه الگوی بهینه توسعه شهر کرمان. نشریه تحقیقات کاربردی علوم جغرافیایی، سال 22. شماره 65.
محمدزاده­خانی، سیما. خاکپور، براتعلی. مداحی، سید مهدی. 1399. مکان­یابی بهینه توسعه فیزیکی شهر بجنورد با استفاده از نرم‌افزار GIS و روش تحلیل شبکه­ای. مجله جغرافیا و توسعه فضای شهری. سال 7. شماره 1. شماره پیاپی 12.
میرزاحسین، حمید. زمانی، امیرحسین. 1400.مروری بر کاربرد روش‌های یادگیری ماشین و عامل مبنا در برنامه­ریزی کاربری زمین. نشریه علمی علوم و فنون نقشه‌برداری، دوره یازدهم ، شماره2، آذرماه.
نصیری­هندخاله. اسماعیل؛ امیرانتخابی. شهرام؛ تاج. سروش .1400. پایش زیست‌پذیری سکونتگاه‌های ناکارآمد پیراشهری کلانشهر رشت مورد محله عینک. مجله توسعه فضاهای پیراشهری. دوره 3 . شماره6 . صص 129-1.
نوفل، سید علیرضا. کلبادی، پارین. 1392. باز توسعه زمین­های قهوه­ای، رهیافتی به‌سوی توسعه محلی پایدار. نشریه علمی-پژوهشی انجمن علمی معماری و شهرسازی ایران. 5. صص 133-146.
Alhedyan, M. A. 2021. Change detection of land use and land cover, using landsat-8 and sentinel-2A images (Doctoral dissertation, University of Leicester).120-145.
Kaya, S., & Curran, P. J. 2006. Monitoring urban growth on the European side of the Istanbul metropolitan area: A case study. International Journal of Applied Earth Observation and Geo information, 8(1) : 18 -25.
Batisani, N., & Yarnal, B. 2009. Urban expansion in Centre County, Pennsylvania: Spatial dynamics and landscape transformations. Applied Geography, 29(2) : 235 -249 .
Breiman, Leo. Friedman, Jerome; Olshen, Richard A, and Stone, Charles J. 1984. Classification and regression trees Chapman and Hall. New York.
C.kamusoko, J.Gamba .2018. Simulating urban growth using o random forest –cellular automata. Journal of geo information.
Esri .2011. GIS for urban and regional planning, 68p.
Guan, Qingfeng. Wang, Liming; and Clarke, C. Keith.2005. An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32(4): 369-380.
Gutman, Garik. Janetos, Anthony. C., Justice, Christopher. O., Moran, Emilio. F., Mustard, John. F., Rindfuss, Ronald. R., Skole, David. Turner, Billy Lee., Cochrane, Mark. A.2004. remote sensing and digital image processing, Volume 6, land change science: observing, monitoring and understanding trajectories of change on the earth’s surface, Springer.
Hadly, C. C.2000. “Urban sprawl Indicators, Causes and solution”, WWW. CITY. BLOMINGTON.
Hall, Tim.2005. urban geograghy, 3rd edition, regional planning, routledgem London.
Jaeger, J. A., Bertiller, R., Schwick, C., & Kienast, F. 2010. Suitability criteria for measures of urban sprawl. Ecological Indicators, 10(2): 397 -406.
Jamali; A. Zarekia; S and Randhir’ O.T .2018.Risk assessment of sand dune disaster in       realation to geomorphic properties and vuluerability in the saduq YAZD Erg. Applied ecology and environmental research.16 (1)’ 579-590.
Law, Stephen. Seresinhe, Chanuki Illushka; Shen, Yao. and Gutierrez-Roig, Mario.2020. Street-Frontage-Net: urban image classification using deep convolutional neural networks. International Journal of Geographical Information Science, 34(4): 681-707.
Maleki, M., Rahmati, M., Sadidi, J., & Babaee, E. .2014., (November). Landslide risk zonation using AHP method and GIS in Malaverd catchment, Kermanshah, Iran. In International Conference on Geospatial Information Research (GI Research 2014) (pp. 15-17).
Murgante, B., Borruso, G., Lapucci, A.2009. Geocomputation and Urban Planning, Springer, 280 p.
Netzband, M., W. L. Stefanov, and C. Redman.2007. Applied Remote Sensing for Urban Planning, Governance and Sustainability, Springer, Berlin, 278 p.
Yang, X., J. Li.2013.Advances in mapping from remote sensor imagery: techniques and applications, CRC Press, Taylor & Francis Group, 414 p.
Zare Naghadehi, S., Asadi, M., Maleki, M., Tavakkoli-Sabour, S. M., Van Genderen, J. L., & Saleh, S. S. 2021. Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data. ISPRS International Journal of Geo-Information, 10(8), 513.