Performance comparison of Geomod and LCM models to predict land use changes (case study: Abughovair plain, Ilam province) | ||
| تحقیقات مرتع و بیابان ایران | ||
| Article 13, Volume 26, Issue 3, September 2019, Pages 660-674 PDF (1.25 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijrdr.2019.120012 | ||
| Authors | ||
| Zahedeh Heidarizadi1; Ali Mohammadian Behbahani* 2 | ||
| 1PhD student of Combat Desertification, Arid Zone Management, Gorgan University of Agricultural Science and Natural Resources, Iran | ||
| 2Assistant Professor, Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Iran | ||
| Abstract | ||
| Protecting an ecosystem and conserving natural resources requires knowledge of the conditions and land-use changes. The purpose of this study was to monitor land-use changes in the past and evaluate the performance of GEOMOD and LCM models in simulating land-use changes in order to select a more appropriate model for predicting land-use changes in the future. Landsat satellite images were used during the periods of 1990, 2003, and 2016 and land-use changes were monitored by using these images. The simulation of land use status by the LCM model for 2016 was done using the maps of the years 1990 and 2003. Using MLP and Markov chain, the land use map was simulated for 2016. To run the GEOMOD model, the image of the single-user map of 2003 and 2016 were used and the map of "appropriateness of changes" was made by the use of variables affecting land-use change and they were introduced into the model. The results of the accuracy of the simulation map of 2016 showed that LCM and GEOMOD had Kappa coefficients of 81% and 71%, respectively. Therefore, the LCM model was chosen as the most appropriate model and the map of the year 2029 was predicted and prepared. | ||
| Keywords | ||
| MLP Method; Markov Chain; Landsat; Land use; Prediction | ||
| References | ||
|
- Arzani, H., Mirakhorlou, K. h., Hosseini, S. Z., 2009. Land use mapping using Landsat7 ETM data (Case study in middle catchment’s of Taleghan). Iranian Journal of Range and Desert Research, 16(2): 150-160. - Bagheri, R., Mohamadi, S., Saljoghi, M., 2016. Land use change effects on some soil physical properties (Case study: Baft city of Kerman province). Iranian Journal of Range and Desert Research, 23(2):231-243. - Heidarian, p., Rangzan. K., Maleki, S. and Taghizade, A., 2014. Integration of GIS and LCM measurement techniques with Urban development modeling approach (Case Study: Tehran Metropolis). Journal of Arid Environment Research, 5(17). - Darvishsefat, A., 1988. Remote sening. Agriculyural Faculty of Tehran University, 166 p. - Gholamalifard, M., Jorabianshoshtari, S. H., Hosseini, S. H. and Mirzaei. M., 2012. Modeling land use change in the coast of Mazandaran province using LCM in GIS environment. Journal of Ecology, 38 (64):109-124. - Kamyab, H., Mahini, S., Hosseini, A. and Gholamalifard, M., 2011. Application of artificial neural network in urban development modeling (Case Study: Gorgan City). Journal of Human Geography Research, 76: 99-113. - Kamyab, H., Mahini, S., Hosseini, A. and Gholamalifard, M., 2010. Adoption of information-based approach using logistic regression methodology for modeling urban development in Gorgan. Journal of Ecology, 36 (54): 89-96. - Fatemi, S. B. and Rezaei, Y., 2012. Basics of remote sensing, third edition, Azadeh Publisher. - Mirzapour, H., 2016, Performance comparison of CA-Markov, Geomod and LCM models to predict land use changes in Badavar-Nurabad watershed, Lorestan. MS.c thesis, Lorestan university. - Arekhi, S., 2014. Predicting spatial change of land uses using LCM in GIS (case study: Sarable). Iranian Journal of Forest and Range Protection Research,12 (1): 1-19. - Eastman, J. R., Van Fossen, M. E. and Solarzano, L. A. 2012. Transition potential modeling for land cover change. 48-65. In: Maguire, D., Good Child, M., Batty, M. (Eds.), GIS, Spatial analysis and modeling. ESRI Press, Redlands, California. - Fraser, R. H., Abuelgasim, A. and Latifovic, R., 2005.A method for detecting large-scale forest cover change using coarse spatial resolution imagery. Joirnal of Remote Sensing of Environment, 95:414-427. - Geri, F., Amici, V. and Rocchini, D., 2011. Spatially-based accuracy assessment of forestation prediction in a complex Mediterranean landscape. Journal of Applied Geography, 31 (3): 881-890. - Jafari, M., Zehtabian, G. H. and Ehsani, A. H., 2011. Effect of thermal bonding and supervised classification algorithms of satellite data in making land use maps (Case study: Kashan). Iranian Journal of Range and Desert Research, 20 (3): 72-87. - Kim, O. S., 2010. An assessment of deforestation models for reducing emissions from deforestation and forest degradation (REDD). Journal of Transactions in GIS, 14:631-654. - Liew, C. S., 1997. Effects of atmospheric aerosol models on the single scattering point spread function in optical remote sensing. Remote Sensing, A Scientific Vision for Sustainable Development. IEEE International‚ (4): 1914-1916. - Mas, J. F., Kolb, M., Paegelow, M., Camacho Olmedo, M. T. and Houet, T., 2014. Inductive pattern-based land use/cover change models: a comparison of four software packages. Journal of Environmental Modellingand Software, 51:94-111. - Mas, J. F., Puig, H., Palacio, J. L. and Sosa- Lopel, A., 2004. Modeling deforestation using GIS and artificial neural networks. Journal of Environmental Modeling and Software, 19 (5): 461- 471. - Nedjai, R., Nghiem, V. T., Do, T. P. T. and Nasredine, M. N., 2016. The impact of land use and climate change in the center region of France on the physico-chemical status of aquatic systems. International Journal of Spatial, Temporal and Multimedia Information Systems, 1 (1):102-117. - Pijanowski, B., Pithadia, S., Shellito, B. y. and Alexandridis, K., 2005. Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science 2: 197–215. - Pontius Jr, R. G. and Malanson, J., 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science. (19): 243-265 - Pontius Jr, R. G.; Cornell, J. D. and Hall, C. A. S., 2001. Modeling the spatial pattern of land- use change with Geomod2: Application and validation for CostaRica. Journal of Agriculture Ecosystems and Environment. 1775: 1-13 - Saifullah, K., Barus, B. and Rustiadi, E., 2017. Spatial modelling of land use/cover change (LUCC) in South Tangerang City, Banten. In IOP Conference Series. Journal of Earth and Environmental Science, 54(1) : 12-18. - Subudhi, B. N., Bovolo, F., Ghosh, A. and Bruzzone, L., 2014. Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images. Journal of Optics and Laser Technology, 57: 284–292. - Václavík, T. and Rogan, J., 2009. Identifying trends in land use/land cover changes in the context of post-socialist transformation in central Europe. Journal of GIS Science and Remote Sensing, 49(1):1-32. - Vafaiee, S., 2013. Assaying and predicting the land uses changes using remote sensing and GIS (The studied area: Marivan), M.Sc. Thesis, University of Tehran. - Wang, W., Zhang, C., Allen, J. M., Li, W., Boyer, M. A., Segerson, K. and Silander, J. A., 2016. Analysis and prediction of land use changes related to invasive species and major driving forces in the state of Connecticut. Journal of Land, 5 (3), 25. | ||
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