Investigation of Vegetation Cover Crown Percentage Changes Pridection using Marcov Chain using RS and GIS

Document Type : علمی - پژوهشی

Abstract

Markov chain is a model that use for prediction future conditions based on the rates of past change. The method is based on probability that a given piece of land will change from one cover to another. These probabilities are generated from past changes and then applied to predict future change. The main objective of this paper is to evaluate the validation of Markov model in vegetation cover crown percentage change simulation. In this study, TM images sensor for 1989, 1998 and LISS III image sensor for 2006 from Landsat and IRS satellite from Mouthe wild life refuge were used to provide maps of vegetation cover percentage. In order to prediction of vegetation cover in 2006, Markov chain model were used by vegetation map of 1989 and 1998, during 9 years. Validation of all maps was evaluated. Finally the map for 2006 that had provided by image was used to evaluate the map from Markov model. Kappa for this map was 53 %. Post classification was used to investigate the area with high accurate prediction and error in prediction. Results have shown because of high decrease in vegetation cover in non-core zone and increase vegetation cover in core zone this model couldn’t predict by high accuracy. But this model is valuable in small scale, in order to general view of future. Keywords: Marcove chain, satellite images, change detection, prediction accuracy.

References

  1. Adhikari, S., Southworth, J., 2012, Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach, Remote Sensing, 4(10), PP. 3215-3243.
  2. Azarmehr, M.R., Mesgary, M.S., Karimi, M., 2010, Using GIS and CA Method in Spatio-temporal Simulation of Malaria Propagation, Proceedings of Geomatics 89 (National Conference & Exhibition), 10 p.
  3. Balzter, H., 2000, Markov Chain Models for Vegetation Dynamics, Ecological Modelling, 126, PP. 139-154.
  4. Darvish Sefat, A., 1999, Thematic GIS Data Based Maps Accuracy Assessment, 5th Geographic Information System conference.
  5. Falahatkar, S., Soffianian, A.L., Khajeddin, S.J., Ziaee, H.R., Ahmadi Nadoushan, M., 2011, Integration of Remote Sensing Data and GIS for Prediction of Land Cover Map, International Journal of Geomatics and Geoscience, 1, (4), PP. 74-83
  6. Jensen, J.R., 2007, Remote Sensing of The Environment: An Earth Resource Perspective, Pearson Prentice Hall, 592p.
  7. Kamusoko C., Aniya, M., 2006, Landuse/cover Change and Landscape Fragmentation Analysis in the Bindura District Zimbabwe, Land Degradation & Development, 24, PP. 389-401
  8. Khajeddin, S.J., 1995, Asurvey of the Plant Communities of the Jazmorian, IRAN, Using Landsat MSS Data, university of reading.
  9. Kityuttachai, K., Kumar Tripathi, N., Tipdecho, T., 2013, CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand, Sustainability journal, 5,
  10. PP. 1480-1500.
  11. Masoud, A.A., K., Koike, 2006, Arid Land Sanilization Detected by Remotely-sensed Land Cover Changes: A case Study in the Siwa Region, NW Egypt, Arid Environment, 66, PP. 151-167.
  12. Memarian, H., Balasundram, S.K., 2012, Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia, Journal of Geographic Information System, 4,
  13. PP. 542-554.
  14. Norris, J.R., 1997, Markov Chains, Cambridge University Press, 237 p.
  15. Peterson, L.K., Bergen, K.M., Brown, D.G., Vashchuk, L. and Blam, Y., 2009, Forested Land-cover Patterns Andtrends over Changing Forest Management Eras in the Siberian Baikal Region, Forest Ecology and Management, 257, PP. 911-922.
  16. Rahdari, V., 2006, Land Use/cover Change Detection in Mouteh Wildlife Refuge Using RS and GIS, M.Sc. thesis, Isfahan University of Technology.
  17. Rahdari, V., Maleki, S., Afsari, K., Abtin, E., Piri, H., 2011, Land Use and Land Cover Change Detection During 1987 to 2009 in Hamoun Wildlife Refuge Using Remote Sensing Data and Geographic Information System, Iranian Remote Sensing & GIS, 3, (2), PP. 59-70.
  18. Shahidul Islam, M.D., Ahmed, R., 2011, Land Use Change Prediction in Dhaka City Using GIS Aided Markov Chain Modeling, journal of Life Earth Sci, 6,
  19. PP. 81-89.
  20. Zare Garizi, A., Sheikh, V., Sadoddin, A., Mahiny, A.R., 2012, Simulating the Spatiotemporal Changes of Forest Extent for the Chehelchay Watershed (Golestan Province), using integrated CA-Markov model, Iranian Journal of Forest and Poplar Research, 20 (2), PP. 89-98.
  21. Weng, Q., 2001, Land use Change Analysis in the Zhujiang Delta of China Using Satellite Remote Sensing, GIS and stochastic modeling, 64, PP. 273–284.

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