Evaluation of the most efficient supervised classification algorithm in monitoring growth changes in Tehran

Subject Areas : landuse

Aida Ashjaee ¹ , Seyed Masoud Monavari ² , Jalil Imany Harsini ³ , Maryam Robati ⁴ , Zahra Azizi ⁵

1 - PhD. Student of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.
2 - Associate Professor, Department of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran. *(Corresponding Author)
3 - Assistant Professor, Department of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.
4 - Assistant Professor, Department of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.
5 - Associate Professor, Remote Sensing and Geographical Information System, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.

Received: 2023-03-07 Accepted : 2023-06-28 Published : 2023-09-23

Keywords: Neural Network, Jajroud protected area, Urban sprawl growth, Maximum Likelihood, Tehran,

Abstract :

Background and Objectives: The urban sprawl is a dynamic and complex phenomenon, and the most effective factor is land use-cover change Coordinated by with the growth of population and economy, and the resulting changes affect vegetation and the functioning of urban ecosystems. In this paper, identification of the most appropriate classification algorithm to investigate the effect of urban sprawl growth in the east of Tehran city in the time period of 1986 to 2016 on land use-cover changes of Jajroud protected area has been studied. Material and Methodology: In this research, the land cover-use changes map was prepared using the supervised classification method and the comparison of three neural network algorithms, minimum distance and maximum likelihood was done in ENVI 5.3.1 software environment. Findings: Land use-cover changes from 1986 to 2016 (period of 30 years) shows the increase of land use-cover area including compact rangelands 58.45%, arid region 91/19%, urban 65/57%, and forest 74/47%. In 2016 compared to 1986. Discussion and Conclusion: By comparing and examining three supervised classification algorithms including neural network, minimum distance, maximum likelihood, the neural network method has been the most suitable algorithm to identify land use-cover changes.

References:

1. Biney, Ernest; Boakye, Ebenezer. (2021). Urban sprawl and its impact on land use land cover dynamics of Sekondi- Takoradi metropolitan assembly, Ghana. Environmental Challenges, Volume 4


2. Shirvani Moghadam, Sousan; Saeedi Mofrad, Sanaz. (2018). Urban sprawl trend analysis using statistical and remote sensing approach case study: Mashhad city. Creative City Design, Volume 1, Number 1.


3. Wiatkowska, Barbara; Slodczyk, Janusz; Stokowska, Aleksandra. (2021). Spatial- temporal land use and land cover changes in urban areas using remote sensing images and GIS analysis: The case study of Opole, Poland. GeoSciences.


4. Sayyahnia, Romina; Farkhondeh, Majid; Faryadi, Shahrzad. (2014). the role of remote sensing in land use studies - study area: Tehran metropolis and its boundaries. Journal of Space Science & Technology, Volume 7, Number 3. (In Persian)


5. Gao, Bi; Huang, Qingxu; He, Chunyang; Sun, Zexiang; Zhang, Da. (2016). How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data. Landscape and Urban Planning, Volume 148.


6. Dadashpoor, Hashem; Salarian, Fardis. (2020). Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran. Environment, Development and Sustainability.


7. Zheng, Qing; Yang, Xuan; Wang, Ke; Huang, Lingyan; Shahtahmassebi, Amir reza; Gan, Muye;Valerie Weston,Melanie. (2017). Delimiting urban growth boundary through combining land suitability evaluation and cellular automata. Sustainability.


8. Khalaji, Saba. (2021). Effects of land use change on land use planning. Journal of Place and Space Research, Number 3. (In Persian)


9. Hegazy, Ibrahim; Kaloop, Mosbeh. (2015). Monitoring urban growth and land use change detection with GIS and Remote Sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, Volume 4.


10. Arsanjani, Jamal; Helbich, Marco; Vaz, Eric. (2013). Spatiotemporal simulation of urban growth patterns using agent- based modeling: The case Tehran. Cities, Volume 32.


11. Liu, Yong; Fan, Peilei; yue, Wenze; Song, Yan. (2018). Impacts of land finance on urban sprawl in china: The case of Chongqing. Land use Policy, Volume 72.


12. Alshalabi, Mohammad; Billa, Lawal; Pradhan, Biswajeet; Mansor, Shattri. (2013). Modelling urban growth evolution and land use changes using GIS based Cellular automata and SLEUTH models: The case of Sana^, a metropolitan city, Yemen. Environmental Earth Sciences, Volume 70, Number 1.


13. Amini, Saeid; Saber, Mohsen; Rabiei Dastjerdi, Hamidreza; Homayouni, Saeid. (2022). Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sensing, Volume 14, Number 11.


14. Saeifar, Mohammad Hamed; Mohammadnia, Mousa. (2015). Land use/ land cover change detection in Tehran city using Landsat satellite images. Journal of Applied Environmental and Biological Sciences, 5 (12).


15. Eskandari, Saeedeh; Jaafari, Mohammadreza; Oliva, Patricia; Ghorbanzadeh, Omid; Blaschke, Thomas. (2020). Mapping land cover and tree canopy cover in Zagros forests of Iran: Application of Sentinel-2, Google earth, and Field data. Remote Sensing, 12 (12).


16. Meratifar, Mehdi; Kabood vandpour, Shahram; Amanollahi, Jamil. (2022). Evaluating the ability of Ca- Markov model to predict of the land use changes (case study: Bijar protected area). Journal of Geographic Space, 22 (79).


17. https://earthexplorer.usgs.gov/


18. Ebadi, Rohallah. (2021). Landscape of Jajrood protected area.


19. Jaefari, Shirkoo; Alizadeh Shaebani, Afshin; Danekar, Afshin; Nazari Samani, AliAkbar. (2012). Evaluation of residential use changes in protected areas (case study: Jajrud Protected Area). The second national conference on health, environment and sustainable development. (In Persian)


20. Jaefari,Shirkoo; Alizadeh Shaebani, Afshin; Danekar, Afshin; Nazari Samani, AliAkbar. (2013).


21. Comparison of two national parks, Khojir and Sorkheh Hesar, in terms of the change in cover-use. Journal of Natural Environment, Volume 4. (In Persian)


22. Nejadi, Athare; Hashemi, Nasim. (2016). Application of remote sensing data and geographic information system in the extraction of land use maps (case study: Jajroud protected area). The second international conference on landscape ecology. (In Persian)


23. Ahmadi Nodashan, Mojgan; Saianian, Alireza; Khajehaldin, Jamaleddin. (2010). Preparation of land cover map of Arak city using artificial neural network and maximum likelihood classification methods. Physical Geography Research, Volume 69. (In Persian)