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Modelling land use change by an integrated Cellular Automata and Markov Chain Model (case study; Azadshahr County) | ||
| Environmental Resources Research | ||
| دوره 11، شماره 1، مهر 2023، صفحه 97-106 اصل مقاله (1.45 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22069/ijerr.2023.13458.1218 | ||
| نویسندگان | ||
| Chooghi Bairam Komaki* 1؛ Hamidreza Asgari2؛ Habib Nazarnejad3؛ Mohammad Alinezhad4 | ||
| 1Assistant Professor, Department of Arid Zone management, Faculty of Rangeland and Watershed management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 2Associate Professor, Department of Arid Zone management, Faculty of Rangeland and Watershed management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Associate Professor, Department of Watershed management, Faculty of Rangeland and Watershed management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 4Ph.D student, Department of Arid Zone management, Faculty of Rangeland and Watershed management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| چکیده | ||
| In this research, land-use changes in Azadshahr County were investigated from 1998 to 2009, using the imageries from Landsat 5 satellite and an integration of the Markov chain and Cellular Automata methods. Using the object-based support-vector-machine image classification method, land-use maps were classified into three major categories, namely agriculture fields, forest lands and built-up areas for the years of 1987, 1998 and 2009; their overall accuracies have been obtained 91.0%(1987), 91.0% (1998) and 88.8% (2009), with the respective Kappa values of 86.5%(1987), 86.5% (1998) and 83.2%(2009). The built-up areas had the greatest changes by increasing 2.02% and 2.17% for the periods of 1987-1998 (as first period) and 1998-2009 (as second period), respectively. During the first period, forest area has shrunk by approximately -1.80%. However, as a result of the afforestation project during 1998-2009, forest area has increased 1.59%, while over the 22-year period the total area of forest has merely reduced by -0.21%. Agricultural areas on the one hand has shrunk in favor of the built-up areas, and on the other hand, increased by the conversion of the forest lands, making a total reduction of -0.22 and -3.75% for the first and second periods, respectively. The land-use pattern of 2020 was simulated using the MULOSCE extension of the QGIS software based on the integrated cellular automata and Markov chain technique. It is expected for this period to encounter a 0.62% increase in built-up areas, with 0.48% and 0.15% reduction in agriculture fields and forest lands, respectively. | ||
| کلیدواژهها | ||
| Landsat؛ Object-Oriented Classification؛ Support-Vector Machine؛ Markov Chain؛ Azadshahr | ||
| مراجع | ||
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Alavipanah, S.K., Ch. B Komaki, A. Goorabi., and Matinfar, H.R. 2007. Characterizing Land Cover Types and Surface Condition of Yardang Region in Lut Desert (Iran) Based upon Landsat Satellite Images. World Applied Sciences Journal. 2 (3), 212–228.
Alimohamadi, A., Motkan, A. A., Ziaeian, P., and Tabatabaei, H. 2009. Comparison of pixel-based classification, object-based classification methods and decision tree mapping for forest types mapping, using data from remote sensing (Case Study: Astara Forest). Applied research in geographic (Geographical Sciences). 10 (13), 7 -26.
Aspinall, R.. 2004. Modelling Land Use Change with Generalized Linear Models—a Multi-Model Analysis of Change between 1860 and 2000 in Gallatin Valley, Montana. Journal of Environmental Management, Modelling land use change and environmental impact. 72 (1–2), 91–103.
Azizi, M. 2014. Estimation of accuracy from a combination of cellular automata, SVM and ANFIS models in predicting the physical development of the city based on GIS Case Study: Shiraz - District Six. M.Sc Thesis, Hormozgan University. http://ganj.irandoc.ac.ir/ articles/805458.
Badjana, H.M., Helmschrot, J., Selsam, P., Wala, K., Flügel, W.A., Afouda, A. and Akpagana, K., 2015. Land cover changes assessment using object‐based image analysis in the Binah River watershed (Togo and Benin). Earth and Space Science. 2(10), 403-416.
Colgan, M. S., Baldeck, C. A., Féret, J. B., and Asner, G. P. 2012. Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data. Remote Sensing. 4 (11), 3462–80. doi:10.3390/rs4113462.
Deilmai, B.R., Ahmad, B.B. and Zabihi, H. 2014. Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia. In IOP Conference Series: Earth and Environmental Science. 20(1), 012052.
Jensen, J. R. 1995. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd ed. Prentice Hall.
Jensen, J.R., Im, J., Hardin, P. and Jensen, R.R. 2009. Image Classification. The SAGE Handbook of Remote Sensing. 269–281.
Khoshgoftar, M.M., and Talei, M. 2010. Urban growth modeling in Tehran using CA-Markov Model. Remote Sensing and GIS in Iran. 2 (26), 17-34.
Komaki, Ch.B., and Alavipanah, S.K. 2005. An assessment of the spectral resolution in data classes from the Lut Desert using satellite data. Geographical Research. 37 (54), 13-28.
Mahini, A. S., Nadali, A., Feghhi, J., and Raizi, B. 2012. Classification of forest areas in Golestan province using maximum likelihood method from ETM + satellite sensor in 2001. Journal of Environmental Science and Technology. 14 (3), 56-47.
Mas, J.F. 1999. Monitoring Land-Cover Changes: A Comparison of Change Detection Techniques. International Journal of Remote Sensing. 20 (1), 139–152.
Molamehr Alizadeh, F., Janati, M., and Shayan, S. 2005. Feasibility of remote sensing (RS) mapping of landforms and its role in environmental planning. Humanities teacher, 9 (443), 49-80.
Motkan, A.A., and Hajeb, M. 2014. Comparison between the ability of support vector machine, neural network and maximum likelihood classification methods for improving LiDAR data filtering algorithms. Iranian Journal of Remote Sensing and GIS. 5 (2), 1-16.
Qingsheng,L., Guo, Y., Liu, G., and Zhao, J. 2014. Classification of Landsat 8 OLI Image Using Support Vector Machine with Tasseled Cap Transformation. In 2014 10th International Conference on Natural Computation (ICNC), 665–69. Xiamen: Natural Computation (ICNC). doi:10.1109/ICNC.2014.6975915.
Qixia, M., Guo, H., Dong, P., Liu, G., and Shi, R. 2014. Support Vector Machines and Maximum Likelihood Classification for Obtaining Land Use Classification from Hyperspectral Imagery. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Québec, Canada, 2878–2881. ftp://ftp.legos.obs-mip.fr/pub/tmp3m/IGARSS2014/pdfs/0002878.pdf.
Ranjbar, P., Shataei, Sh., and Mahini, A.S. 2014. The comparison of forest area changes detection methods using data from TM and ETM+ sensors. Wood Research Science and Technology.20 (3), 1-22.
Richards, J. A., and Jia, X. eds. 2006. Image Classification Methodologies. In Remote Sensing Digital Image Analysis: An Introduction, 295–332. Berlin, Heidelberg: Springer Berlin Heidelberg. DOI:10.1007/3-540-29711-1_11.
Richards, J.A. and Jia, X., 1999. The Interpretation of Digital Image Data. In Remote Sensing Digital Image Analysis: An Introduction, 67–82. Berlin, Heidelberg: Springer Berlin Heidelberg. DOI:10.1007/3-540-29711-1_3.
Riley, S.J., DeGloria, S.D., and Elliot, S.D. 1999. A Terrain Ruggedness Index That Quantifies Topographic Heterogeneity. Internation Journal of Science 5 (January), 23–27.
Saghafian, B., Farazjou, H., Sepehr, A., and Najafinejad, A. 2006. The effect of land use changes on flood in Golestan dam basin. Iran Water Resource Research. 2 (1), 18-28. | ||
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