08. Applying mathematical models and GIS techniques in forecasting land cover, land use changes in Quy Nhon city

Thơ Phan Văn, Tú Ngô Anh


In recent years, the urbanization rate of Quy Nhon city has increased rapidly. The assessment and forecast of land use change is an important prerequisite solution that helps the local government of Quy Nhon city to plan, manage and use this resource properly, maintain a sustainable ecosystem and develop the economy and society. In this study, Landsat images for the period 2010 - 2020 were used to generate the land cover, land use maps. The Artificial Neural Network (ANN) with Markov - CA was also used to model the land use change of Quy Nhon city. The results showed that the modeling results were highly accurate (compared to the results of land use, land cover classification in 2020, the accuracy rate >85%). The model therefore was used to predict land use changes in Quy Nhon city in 2025, 2035 and 2050. Accordingly, urban land is still rapidly increasing in Quy Nhon city in years to come. Unused land and vacant land will be used economically for the development of the city.

Full text article

Generated from XML file


[1]. M. Ridd and J. Hipple (2006). Remote sensing of human settlements: Manual of remote sensing (Vol. 5). Bethesda: American Society for Photogrammetry and Remote Sensing.
[2]. Vu Kim Chi and Nguyen Thi Thuy Hang (2016). Coastal urban development in Quy Nhon, Vietnam, in the context of climate change. DOI:10.4324/9781315620701.
[3]. I. Nahib, R. W. Turmudi, J. Suryanta, R. S. Dewi, and S. Lestari (2018). Comparing of Land Change Modeler and Geomod Modeling for the Assessment of Deforestation.
[4]. F. Jahanishakib, S. H. Mirkarimi, A. Salmanmahiny, and F. Poodat (2018). Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting. Environmental monitoring and assessment, Vol. 190, No. 6, pp. 1 - 19, Art. no.
[5]. N. Q. Omar, M. S. S. Ahamad, W. Hussin, and N. J. I. Samat (2014). Modelling land-use and land-cover changes using Markov - CA, and multiple decision making in Kirkuk city. Vol. 2, No. 1, pp. 29 - 42, Art. no.
[6]. A. Gharaibeh, A. Shaamala, R. Obeidat, and S. J. H. Al-Kofahi (2020). Improving land - use change modeling by integrating ANN with Cellular Automata - Markov Chain model. Vol. 6, No. 9, p. e 05092, Art. no.
[7]. M. H. Saputra and H. S. J. S. Lee (2019). Prediction of land use and land cover changes for north sumatra, indonesia, using an artificial-neural-network-based cellular automaton. Vol. 11, No. 11, p. 3024, Art. no.
[8]. E. Buğday and S. E. J. C. Buğday (2019). Modeling and simulating land use/cover change using artificial neural network from remotely sensing data. Vol. 25, No. 2, pp. 246 - 254, Art. no.
[9]. V. M. Tuấn (2011). Ứng dụng Viễn thám và GIS đánh giá biến động và dự báo đất đô thị tại phường Hiệp Bình Phước, quận Thủ Đức. Kỷ yếu hội thảo GIS toàn quốc năm 2011, Vol. quyển 1.
[10]. D. V. V. Quân (2007). Đánh giá tổng hợp điều kiện địa lý phục vụ xác lập mô hình hệ kinh tế sinh thái khu vực ven biển huyện Triệu Phong, tỉnh Quảng Trị. Luận văn thạc sĩ, Trường Đại học Khoa học Tự nhiên, Đại học Quốc gia Hà Nội.
[11]. Giles M. Foody (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Vol. 239, p. 111630, Art. no.
[12]. S. K. Singh, S. Mustak, P. K. Srivastava, S. Szabó, and T. J. E. P. Islam (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Vol. 2, No. 1, pp. 61 - 78, Art. no.
[13]. H. Khawaldah, I. Farhan, N. J. G. J. o. E. S. Alzboun, and Management (2020). Simulation and prediction of land use and land cover change using GIS, remote sensing and CA - Markov model. Vol. 6, No. 2, pp. 215 - 232, Art. no.
[14]. H. Memarian, S. K. Balasundram, J. B. Talib, C. T. B. Sung, A. M. Sood, and K. Abbaspour (2012). Validation of CA - Markov for simulation of land use and cover change in the Langat Basin, Malaysia.
[15]. I. Santé, A. M. García, D. Miranda, R. J. L. Crecente, and u. planning (2010). Cellular automata models for the simulation of real - world urban processes: A review and analysis. Vol. 96, No. 2, pp. 108 - 122, Art. no.
[16]. R. G. Pontius Jr and M. J. I. J. o. R. S. Millones (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Vol. 32, No. 15, pp. 4407 - 4429, Art. no.
[17]. H.-h. FENG, H.-p. LIU, B.-x. ZHOU, X.-g. MAO, X.-f. J. G. ZHAO, and G.-I. Science (2012). Study on the Parameters Behavior of the SLEUTH Model. P. 06, Art. no.


Thơ Phan Văn
phanvantho@qnu.edu.vn (Primary Contact)
Tú Ngô Anh
Phan Văn, T., & Ngô Anh, T. (2021). 08. Applying mathematical models and GIS techniques in forecasting land cover, land use changes in Quy Nhon city. Science Journal of Natural Resources and Environment, (37), 73–84. Retrieved from https://tapchikhtnmt.hunre.edu.vn/index.php/tapchikhtnmt/article/view/347

Article Details

Similar Articles

1 2 3 4 5 6 > >> 

You may also start an advanced similarity search for this article.

01. Monitoring mining activities by using satellite imagery data and UAV images: a case study in Yen Bai province

Huệ Lê Minh, Hiên Vũ Thị Thanh, Thảo Đỗ Thị Phương
Abstract View : 39
Download :19

06. Study on soil salinity by using Sentinel-2 imagery data: a case study in Dong Nai Province, Vietnam

Huy Chu Xuân, Ngọc Nguyễn Minh, Đạt Đinh Ngọc, Thủy Lê Thu, Hải Hoàng, Huy Bùi Quang, Phong Trần...
Abstract View : 61
Download :14