Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Monitoring and predicting of land use changes and physical expansion of Rudsar city using LCM and CA-Markov model

Document Type : Research Paper

Authors
Roya Ramezani Kiasejmahaleh 1
Esmaiel Salehi 2
1 PhD student, Department of Environmental Planning, Management and Education, Faculty of Environment, University of Tehran, Tehran, Iran
2 Associate Professor, Department of Environmental Planning, Management and Education, Faculty of Environment, University of Tehran Tehran, Iran
10.22131/sepehr.2024.2023758.3064
Abstract
Extended Abstract
Introduction
The increase in population and the rapid increase in urbanization is one of the key factors of land use change, which leads to the destruction of forests and the conversion of fertile land into urban construction with a significant impact on ecosystems. Land use change is a change in the way land is used, and these changes are shaped by numerous factors, such as politics, management, economics, culture, and the environment. It has serious consequences for the environment. Many cases, such as soil erosion, water scarcity, impact on ecosystem services, decreased biodiversity and habitat loss, and impact on surface temperatures are among the environmental problems caused by land use change. In addition to reducing natural resources, these changes could have serious socio-political consequences in the region by affecting food supply. Hence, recognizing land use change and its driving forces is critical to environmental protection, resource management, land use planning, and sustainable development. To this end, the use of geographic data in combination with land surface modelers (e.g. The LCM and CA-Markov model) offers an effective tool in understanding the dynamics of land use changes in place and time, which can be a guide in formulating policies for the sovereignty of sustainable land.
Materials & Methods
The present study is applied research, which is prepared by a descriptive-analytical method. In order to prepare a land use map of the studied area, Landsat satellite images sensors of TM, ETM+ and OLI from three time periods, for 1997, 2010 and 2023, respectively, were used by the American Geological Site (USGS). After receiving satellite images, radiometric corrections were performed and then atmospheric corrections were performed using FLAASH command in ENVI 5.3 software. In the next step, points were selected as educational examples in Google Earth Pro software to collect ground information. These samples were collected in six classes: agricultural land, garden land, urban and built land, forest cover, barren land and river. Then, using Maximum Likelihood, which is a supervised classification method, was used to classify the images. In the next step, the maps were entered into ArcGIS 10.7.1 software and necessary calculations such as determining the area of each user were determined. Then, the maps were entered on the IDRISI TerrSet software and the trend of land use changes was investigated using the LCM model. Finally, using the CA-Markov model, land use changes were predicted first for 2023 and then for 2036.
Results & Discussion
The results showed that from 1997 to 2010, agricultural land increased by 1.8%, garden land by 3.3%, urban and built land by 0.8%, and bareland by 0.75%. But the forest cover shows a decrease of about 6%. In the analysis of time changes between 2010 and 2023, garden land has increased by 2.25%, urban and built land by 9.9%, bareland by 0.85%. Forest cover decreased by 2.36%. During this period, the area of agricultural land has decreased dramatically by about 10%. This decline was caused by the conversion of agricultural land into land (especially Kiwi gardens) and even built land. Also, the gardens and agricultural land that were near the coast, due to the receding of the Caspian Sea, have become urban and built land, and the process of urban development has spread towards the coast. According to the projected land use map of 2036, the area of agricultural use shows a significant decrease compared to 2023. In 2036, 6.48% of the agricultural area used was reduced and added to the area for other uses. These results show that we will see a decline in agricultural land in the coming years, and this is predictable given the annual increase in the price of agricultural tools and inputs, the increase in the problems of farmers in selling crops and the lack of savings in land cultivation, the increase in land prices and the increase in the number of immigrants from the central and southern provinces of the country to this region to buy land and housing, which requires more attention from planners and managers in the decision-making process.
Conclusion
The results showed that in different years, the area of agricultural land has decreased due to the conversion of this land into garden land and urban and built land. Due to the increase in the price of agricultural inputs in recent years and the lack of sales or sales at low crop prices, in the future, the decrease in agricultural land area will continue at a faster rate, and these lands will either become garden land and built land, or they will increase the area of bareland due to the lack of land cultivation. Over the past years, with the retreat of the Caspian Sea, urban land has expanded towards the coast, and this trend will continue in the future. Therefore, since these changes have significant effects on sustainability, food security, biodiversity and socio-economic vulnerability of the region, it is necessary for planners to make appropriate management decisions to preserve agricultural land and control urban development in the region and prevent the serious damage that will be done to the region following these changes.
Keywords
Modeling
Maximum likelihood classification
Remote sensing
Landsat satellite
Simulation
Subjects
1- Akdeniz, H. B., Sag, N. S., & Inam, S. (2023). Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey. Environmental Monitoring and Assessment, 195(1), 135.
2- Alawamy, J. S., Balasundram, S. K., Mohd. Hanif, A. H., & Boon Sung, C. T. (2020). Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya using time-series landsat data from 1985 to 2017. Sustainability, 12(11), 4490.
3- Aljenaid, S. S., Kadhem, G. R., AlKhuzaei, M. F., & Alam, J. B. (2022). Detecting and assessing the spatio-temporal land use land cover changes of Bahrain Island during 1986–2020 using remote sensing and GIS. Earth Systems and Environment, 6(4), 787-802.
4- Al-sharif, A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian journal of geosciences, 7, 4291-4301.
5- Aniah, P., Bawakyillenuo, S., Codjoe, S. N. A., & Dzanku, F. M. (2023). Land use and land cover change detection and prediction based on CA-Markov chain in the savannah ecological zone of Ghana. Environmental Challenges, 10, 100664.
6- Benito, P. R., Cuevas, J. A., de la Parra, R. B., Prieto, F., Del Barrio, J. G., & de Zavala Gironés, M. Á. (2010). Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through CORINE Land Cover data and Markov models. Forest Systems, 19(3), 315-328.
7- Benkhelif, A., Setti, M. H., Sehl, B., Djeddaoui, F., & Nazrul, I. (2024). Predicting urban growth and its impact on fragile environment using Land Change Modeler (LCM): a case study of Djelfa City, Algeria. GeoJournal, 89(2), 72.
8- Dadashpoor, H., Zarei, A. (2013). Forecasting of urban development changes in Noshahr County using LUCIA model. Research and Urban Planning, 3(11): 37-58.
9- Derakhsh, M., Sobhan Ardakani, S. (2022). Simulation of the Spatial Pattern of Land Use Change in the City of Gachsaran Using Cellular Automata Model. Human & Environment, 3(20): 67-81.
10- Eastman, J. R., & Toledano, J. (2018). A short presentation of the Land Change Modeler (LCM). Geomatic approaches for modeling land change scenarios, 499-505.
11- Ebrahimy, H., Rasuly, A., & Ahmadpour, A. (2019). Modeling dynamic changes of Land Use with Object Based Image Analysis and CA-Markov approach (Case study: Shiraz city). Scientific- Research Quarterly of Geographical Data (SEPEHR), 27(108), 137-149.
12- Elagouz, M. H., Abou-Shleel, S. M., Belal, A. A., & El-Mohandes, M. A. O. (2020). Detection of land use/cover change in Egyptian Nile Delta using remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 57-62.
13- Foody, G. M., & Mathur, A. (2004). Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment, 93(1-2), 107-117.
14- Gallego, F. J. (2004). Remote sensing and land cover area estimation. International Journal of Remote Sensing, 25(15), 3019-3047.
15- Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9).
16- Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421.
17- Hegazy, I. R., & Kaloop, M. R. (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, 4(1), 117-124.
18- Heidari Mozaffar M, Saleh Joneghani E. (2021). Modeling Urban Expansion and Development of Isfahan City by Using Remote Sensing Data in LCM Model. JGST, 10 (4) :179-190.
19- Heidarizadi, Z., & Mohammadian Behbahani, A. (2019). Performance comparison of Geomod and LCM models to predict land use changes (case study: Abughovair plain, Ilam province). Iranian Journal of Range and Desert Research, 26(3), 660-674.
20- Houet, T., & Hubert-Moy, L. (2006). Modeling and projecting land-use and land-cover changes with Cellular Automaton in considering landscape trajectories. EARSeL eProceedings, 5(1), 63-76.
21- Hua, A. K. (2017). Land use land cover changes in detection of water quality: a study based on remote sensing and multivariate statistics. Journal of environmental and public health, 2017.
22- Hyandye, C., & Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International journal of remote sensing, 38(1), 64-81.
23- Karimi Firozjaei, M., Kiavarz, M., & Kalantari, M. (2018). Monitoring and prediction of land use changes and physical expansion of Babol city during 1985-2040 using multi-temporal Landsat imagery. Physical Social Planning, 5(3), 32-52.
24- Karimzadeh Motlagh, Z., Lotfi, A., Pourmanafi, S., & Ahmadizadeh, S. (2022). Evaluation and Prediction of Land-Use Changes using the CA_Markov Model. Geography and Environmental Planning, 33(2), 67-84.
25- Khalil, A. A., Essa, Y. H., & Hassanein, M. K. (2014). Monitoring agricultural land degradation in Egypt using MODIS NDVI satellite images. Nat. Sci, 12(8), 15-21.
26- Khoshnood Motlagh, S., Sadoddin, A., Haghnegahdar, A., Razavi, S., Salmanmahiny, A., & Ghorbani, K. (2021). Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran. Land Degradation & Development, 32(10), 3092-3105.
27- Khwarahm, N. R., Qader, S., Ararat, K., & Fadhil Al-Quraishi, A. M. (2021). Predicting and mapping land cover/land use changes in Erbil/Iraq using CA-Markov synergy model. Earth science informatics, 14(1), 393-406.
28- Kisamba, F. C., & Li, F. (2023). Analysis and modelling urban growth of Dodoma urban district in Tanzania using an integrated CA–Markov model. GeoJournal, 88(1), 511-532.
29- Koko, A. F., Yue, W., Abubakar, G. A., Hamed, R., & Alabsi, A. A. N. (2020). Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov). Sustainability, 12(24), 10452.
30- Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in physical g Alam et al., 2019eography, 21(3), 375-393.
31- Lambin, E. F., & Geist, H. J. (Eds.). (2008). Land-use and land-cover change: local processes and global impacts. Springer Science & Business Media.
32- Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (Lcm) in nashe watershed, upper blue nile basin, Ethiopia. Sustainability, 13(7), 3740.
33- Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.
34- Mishra, P. K., Rai, A., & Rai, S. C. (2020). Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. The Egyptian Journal of Remote Sensing and Space Science, 23(2), 133-143.
35- Mohammadi, S., Tavakoli, M., Zarafshani, K., Mahdizadeh, H., & Amiri, F. (2021). Investigation of the process of land use change in Mahidasht plain watershed using Remote Sensing images. Sustainability, Development & Environment, 2(2): 53-70.
36- Patel, S. K., Verma, P., & Shankar Singh, G. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental monitoring and assessment, 191, 1-17.
37- Sam, S. C., & Balasubramanian, G. (2023). Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India. Geodesy and Geodynamics, 14(2), 172-181.
38- Shekar, P. R., & Mathew, A. (2023). Detection of land use/land cover changes in a watershed: A case study of the Murredu watershed in Telangana state, India. Watershed Ecology and the Environment, 5, 46-55.
39- Solaimani, K., Shokrian, F., & Darvishi, S. (2023). Monitoring and Forecasting of Spatiotemporal Changes in Land Use and the Growth of Kermanshah Township Using Remote Sensing and the CA-Markov Model. Urban Structure and Function Studies, 10(35), 57-82.
40- Tariq, A., Yan, J., & Mumtaz, F. (2022). Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. Physics and Chemistry of the Earth, Parts A/B/C, 128, 103286.
41- Wang, S. W., Gebru, B. M., Lamchin, M., Kayastha, R. B., & Lee, W. K. (2020). Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and GIS. Sustainability, 12(9), 3925.
42- Yeneneh, N., Elias, E., & Feyisa, G. L. (2022). Detection of land use/land cover and land surface temperature change in the Suha Watershed, North-Western highlands of Ethiopia. Environmental Challenges, 7, 100523.
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