Simulating the spatiotemporal changes of forest extent for the Chehelchay watershed (Golestan province), using integrated CA-Markov model

Document Type : Scientific article

Authors

1 PhD student of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources

2 Assistant professor, Faculty of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources

3 Associate professor, Faculty of Fisheries and Environmental Sciences, Gorgan University of Agriculture Sciences and Natural Resources

Abstract

The importance of land-use/ land-cover (LULC) as a dynamic factor and effective on environmental conditions, makes it necessary to get precise quantitative and qualitative information from it and to identify its changes through short time periods. Modeling LULC changes provides useful information for better understanding of the changes process, determining driving forces and predicting areas under change conditions. In this study, the integrated CA-Markov model was used to simulate forest cover changes in the Chehelchay Water catchment at minodasht area of Golestan province of Iran. CA-Markov is a combined Cellular Automata/Markov chain land cover prediction procedure that adds an element of spatial contiguity to the stochastic Markov chain analysis. Landsat images of 1987 and 2009 and land-use map of 2001 were used to derive forest extent maps of the Chehelchay Water catchment and characterize changes through time. To investigate the relationships between forest extent changes and some environmental and human-related factors and to produce transition suitability maps, a logistic regression analysis was applied between forest extent changes as response variable and the deriving factors as explanatory variables. Future LULC types for 2009 were then predicted using CA–Markov model, based on the land-cover changes between 1987 and 2001. In order to evaluate the modeling results, prediction for 2009 was compared with the observed 2009 land cover map. The computed accuracy coefficient indicated high efficiency of CA-Markov for simulating forest extent changes in the Chehelchay Water catchment (Kappa = 0.92). Finally, assuming current trends in LULC changes continue, forest cover map for the year 2020 was developed. The results indicate that there will be a notable decrease in forest area.

Keywords

References

- Anonymous, 2005. Watershed Management and Detailed Implementation Plan for the Chehelchay Watershed. Golestan province watershed management bureau, 502 p.
- Azarmehr, M.R., Mesgary, M.S. and Karimi, M., 2010. Using GIS and CA method in spatio-temporal simulation of Malaria propagation. Proceedings of Geomatics 89 (National Conference & Exhibition), 10 p.
- Bagheri, R. and Shatae Joibari, Sh., 2010. Modeling forest areas decreases, using logistic regression (case study: Chehl-Chay catchment, Golestan province). Iranian Journal of Forest, 2 (3): 243-252.
- Balzter, H., 2000. Markov chain models for vegetation dynamics. Ecological Modelling, 126: 139-154.
- Brown, D.G., Pijanowski, B.C. and Duh, J.D., 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.
- Cabral, P. and Zamyatin, A., 2006. Three land change models for urban dynamics analysis in Sintra-Cascais area. 1st EARSeL Workshop of the SIG Urban Remote Sensing, Humboldt-Universität zu Berlin, 2-3 March, 8 p.
- Clark, W.A. and Hosking, P.L., 1986. Statistical Methods for Geographers. New York: John Wiley & Sons, 518 p.
- Congalton, R., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35-46.
- Eastman, J.R., 2006. Idrisi for windows user’s guide ver.32. Clark University, 328 p.
- Hakimipoor, A., 1997. Managerial Decision Making. The Publication of Astan Quds Razavi, 200 p.
- He, Z. and Lo, C., 2007. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31 (6): 667-688.
- Jensen, J.R., 2007. Remote Sensing of The Environment: An Earth Resource Perspective. Pearson Prentice Hall, 592 p.
- Khoshgoftar, M.M., Talei, M. and Malekpour, P., 2010. Spatio-temporal modeling of urban sprawl: an approach based on integrating cellular automata and Markov chains. Proceedings of Geomatics 89 (National Conference & Exhibition), 9 p.
- Koomen, E., Stillwell, J., Bakema, A. and Scholten, H.J., 2007. Modelling Land-use Change, Progress and Applications. Netherlands, Springer, 410 p.
- Mahiny, A.S. and Turner, B.J., 2003. Modeling past change in vegetation through remote sensing and GIS: A comparison of neural networks and logistic regression methods. Proceeding of Geocomputation conference, Southampton, UK, 24 p.
- Norris, J.R. 1997. Markov Chains. Cambridge University Press, 237 p.
- Peterson, L.K., Bergen, K.M., Brown, D.G., Vashchuk, L. and Blam, Y., 2009. Forested land-cover patterns and trends over changing forest management eras in the Siberian Baikal region. Forest Ecology and Management, 257: 911-922.
- Pirbavaghar, M., 2004. Investigating forest extent changes and related topographic and human factors (case study: eastern forests of Gilan). M.Sc. Thesis, University of Tehran, 136 p.
- Pontius, R.G. and Schneider, L.C., 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85 (1-3): 239-248.

Files

Share

How to cite

Statistics