Volume 8, Issue 16 (2-2018)
jwmr 2018, 8(16): 232-240 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
(2018). Land use change prediction using Markov chain and CA Markov Model
(Case Study: Gareen Watershed)
. jwmr. 8(16), 232-240. doi:10.29252/jwmr.8.16.232
URL: http://jwmr.sanru.ac.ir/article-1-919-en.html
URL: http://jwmr.sanru.ac.ir/article-1-919-en.html
Abstract: (4354 Views)
Land use change prediction is an important factor in appropriate planning and integrated ecological management of watersheds. There are various methods for modeling and prediction of land use, i.e. Markov chain and CA Markov. In this research, for predicting the land use of Gareen watershed in Iran in 2042, Landsat satellite images of 1986, 2000 and 2014, and the Markov chain and CA Markov was used. In addition, for assessing the accuracy of land use classification, overall accuracy and Kappa coefficient were used. Overall accuracy and Kappa coefficient was obtained higher than 90% that indicate high accuracy of images classification. Results showed that in case of the land use fixed trend during 1986 to 2014, the watershed forest area will increase 5.77% and rangeland area will decrease 6.34% in 2042.
Type of Study: Research |
Subject:
Special
Received: 2018/01/30 | Revised: 2018/02/26 | Accepted: 2018/01/30 | Published: 2018/01/30
Received: 2018/01/30 | Revised: 2018/02/26 | Accepted: 2018/01/30 | Published: 2018/01/30
References
1. Abd El-Kawy, O. R., J.K. Rod, H. A. Ismail and A.S. Suliman.2011. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2): 483-494. [DOI:10.1016/j.apgeog.2010.10.012]
2. Akbari, M. 2010. Evaluation and classification of desertification by RS and GIS in the arid area of north of Isfahan. Master thesis, Natural Resources Faculty, Isfahan University, 68-71(In Persian).
3. Brown, D. G., B.C. Pijanowski and J.D. Duh. 2000. Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59: 247-263. [DOI:10.1006/jema.2000.0369]
4. Balzter, H. 2000. Markov chain models for vegetation dynamics. Ecological Modelling, 126: 139-154. [DOI:10.1016/S0304-3800(00)00262-3]
5. Baker,W. L. 1989. A review of models of landscape change. Landscape Ecology, 2: 111-133. [DOI:10.1007/BF00137155]
6. Feizi Zade, B. 2007. Comparison of pixel-based and object-oriented methods in the availability of land use maps. Master thesis, Literature and Human Sciences faculty, Tabriz University (In Persian).
7. Gholam Ali Fard, M., Sh. Jvrabyan Shushtarî, S.h. Hosseini Kahnooj and M. Mirzaee. Modeling land use changes in the coastal province using LCM. Journal of Ecology. 4: 124-109.
8. Hakimpour, A. 1997. Managerial decision making. Ghods Razavi press, 200p. (In Persian).
9. Hathout, S. 2002. The use of GIS for monitoring and predicting urbangrowth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal ofEnvironmental Management, 66: 229-238. [DOI:10.1006/jema.2002.0596]
10. Henríquez, C., G. Azócar and H. Romero. 2006. Monitoring and Modeling the urbanGrowth of Two Mid-sized Chilean Cities, Habitat International, 30:945-964. [DOI:10.1016/j.habitatint.2005.05.002]
11. Jenerette, G. Darrel and Wu. Jianguo. 2001. Analysis and simulation of landuse change in the central Arizona-Phonix region, USA Landscapeecology, 16: 611-626. [DOI:10.1023/A:1013170528551]
12. Kamusoko, C., M. Aniya, B .Adi and M. Manjoro. 2009. Rural Sustainability underThreat in Zimbabwe - Simulation of Future Land Use /cover Changes in theBindura District Based on the Markov-Cellular Automata Model, AppliedGeography, 29: 435-447. [DOI:10.1016/j.apgeog.2008.10.002]
13. Khoshgoftar, M.M., M. Tallei and P. Malek pour. 2010. Spatial-temporal modeling of urban scattering, by automated cell based approach and Marcov chain. National Geomatic conference. 9. ( In Persian)
14. Lu, D., P. Mausel, E. Brondi'Zio and E. Moran. 2004. Change detection techniques. International Journal of Remote Sensing, 25: 2365-2407. [DOI:10.1080/0143116031000139863]
15. Lefsky, M.A. and W.B. Cohen. 2003. Selection of remotely sensed data, pp: 13-46. In: Wulder, M.A., and Franklin, S.E. (eds.), remote sensing of forest environments: concepts and case studies, Kluwer Academic Publishers, Boston, USA. [DOI:10.1007/978-1-4615-0306-4_2]
16. Muller, M.R. and J. Middleton. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario and Canada. Landscape Ecology, 9: 151-157.
17. Norris, J.R. 1997. Markov Chains. Cambridge University Press, 237 p. [DOI:10.1017/CBO9780511810633]
18. Pontius, R. G and J. Malanson. 2005. Comparison of the Structure andAccuracy of Two Land Change Models, International Journal of Geographical Information Science, 19(2): 243-265. [DOI:10.1080/13658810410001713434]
19. Parker, D.C., S. M. Manson., M.A, Janssen. M.J, Hoffmann and P, Deadman. 2002. Malti agent systems for the simulation of land use and land cover change: a Review. 43.
20. Rezai Lyvary, V. 2012. Comparison Pixel-Based, Object-Oriented Methods and Effective Parameters in Classification Land Cover/ Land Use of West Province Azerbaijan. Physical Geography Resear Quarterly, Tehran University, 71:73-84 (In Persian).
21. Rahmani Naseh, Shahedi Kaka, Soleimani Karim and Miryaghoubzadeh Mirhassan. Evaluation of the Land use Change Impact on Hydrologic Characteristics (Case Study: Kasilian Watershed). Journal ofWatershed Management Research, 7(13):23-32. [DOI:10.18869/acadpub.jwmr.7.13.32]
22. Shanani Hoveyzeh Seyyedeh Maedeh and Zarei Heidar. Investigation of Land Use Changes During the Past Two Last Decades (Case Study: Abolabas Basin), Journal of Watershed Management Research, 7(14): 237-244. [DOI:10.29252/jwmr.7.14.244]
23. Shaabani, H.M. 2003. The impact of land use in sediment yield of watersheds (case study: Taleghan watershed). Master thesis, Natural Resources faculty, Tehran University, 145 pp. (In Persian)
24. Ustine, S.L. 2004. Remote sensing for natural resource management and environmental monitoring. Third Edition, 4, John Wiley and Sons, Inc., Hoboken, New Jersey. USA, 522p.
25. Yousefi, P., D. Taze, Q. Mirzaee, H.F. Moradi and Sh. Rich. 2011. Comparison of different classification algorithms for satellite images to map land use (Case of the city of light). The use of remote sensing and GIS in Natural Resource Sciences, Second year, 2: 15-25 (In Persian).
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
