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Simulation of land use map in 2026 using CLUE-s model in Rahim-Abad Basin

Document Type : Research Paper

Authors

1 PhD Student, Faculty of Natural Resources and Earth Sciences, University of Kashan, Iran

2 Associate Professor, Faculty of Natural Resources and Earth Sciences, University of Kashan, Iran

3 Associate Professor, Faculty of Agriculture, Lorestan UniversityUniversity, Khoramabad, Iran

4 Associate Professor, Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO) of Tehran

Abstract

Today, with the increase in population, land use has been changed to meet the needs, which has made it more important to know the future status of land use. Investigating land use changes plays a fundamental role in environmental studies, modeling and simulation of land use changes and water resources management and helps managers in better land use planning. So, in this research, first, the land use map of Rahim-Abad Basin was extracted for 1999 and 2016, using ETM+ and Landsat 8 sensors in ENVI5.3 software environment, with a kappa coefficient of 95% and overall accuracy of 97% that indicates the high accuracy of the 2016 map.Then, the maps of affecting factors on land use change including maps of distance from the road, distance from the river, distance from the city, distance from the village, distance from the fault, geology, soil texture, rainfall, evaporation, elevation digital model, gradient, groundwater level and the amount of solar radiation were prepared in Arc-GIS 10.6.After, using logistic regression, the role of effective factors on land use was determined and the Relative Operating Characteristics curve (ROC) was used to evaluate the logistic regression. Finally, the land use map of the study basin was simulated for 2026 using the CLUE-s model.Results showed that the area under the ROC curve was 0.9, 0.88, 0.9, 0.92 and 0.91 for grasslands, rain-fed lands, irrigated lands, water and, residential zones, respectively, which expresses the acceptable accuracy of the regression method in investigation of affecting factors on land use.Also, the most changes of land use in 2026 would be related to conversion of rangelands to rainfed lands, and 6.47% rangelands would decrease and 18% rainfed lands would increase.

Keywords

References

  1.  

    1. Asghari Zamani, A., Maleki and A. Movahhed. 2011. Predicting land use changes using CLUE_s software package: the case of Zanjan. Journal of Geography and Regional Development, 15: 39-64 (in Persian).
    2. Babaei Aghdam, F., N. Azimi and Hosseini. 2011a. Modeling land use patterns using with CLUE_s model, a case study of Meshkinshahr City. Journal of Studies of Human Settlements Planning, 6(14): 1-18 (in Persian).
    3. Babaei Aghdam, F., U. Esmaili and A. Heidari Sarban. Modeling the land use pattern of Sarein City at the horizon of 1400 using the CLUE-s model. Geographical Researches Quarterly Journal, 26(4): 17595-17619 (in Persian).
    4. Babaei Aghdam, F. and Ebraheemzade Asmin. 2011c. Modeling agricultural and arid land use changes into built-up in Ardabil urban region using CLUE-s model. Geography and Development Iranian Journal, 10(26): 21-34 (in Persian).
    5. Brinkmann, K., J. Schumacher, A. Dittrich, I. Kadaore and A. Buerkert. 2012. Analysis of landscape transformation processes in and around four West African cities over the last 50 years. Landscape and Urban Planning, 105: 94–105.
    6. Chen, Y., Y. Xu and Y. Yin. 2009. Impacts of land use change scenarios on storm runoff generation in Xitiaoxi Basin, China. Quaternary International, 208: 121-128.
    7. Foody, G.M. 2002. Status of land covers classification accuracy assessment. Remote Sensing of Environment, 80(1): 185-201.
    8. Fox, J., J.B. Vogler, O.L. Sen, T.W. Giambelluca and A.D. Ziegler. 2012. Simulating land cover change in montane mainland Southeast Environmental Management, 49: 968-979.
    9. Geist, H., W. McConnell, E.F. Lambin, E. Moran, D. Alves and T. Rudel. 2006. Causes and trajectories of land-use/cover change. Land-use and land-cover change, 2: 41-70.
    10. Ghasemiamin, N., N. Arman and H. Zeinivand. 2018. Simulation of land use map related to years of 2025 by CLUE-s, GIS and RS models in Nojian Watershed. Watershed Engineering and Management, 10(3): 294-303 (in Persian).
    11. Gibreel, T.M., S. Herrmann, K. Berkhoff, E.A. Nuppenau and A. Rinn. 2014. Farm types as an interface between an agro-economical model and Clue-Naban land change model: application for scenario modeling. Ecological Indicators, 36: 766-778.
    12. Hietel, E., R. Waldhardt and A. Otte. 2004. Analyzing land-cover changes in relation to environmental variables in Hesse Germany. Landscape Ecology, 19: 473–489.
    13. Hosmer, D.W. and S. Lemeshow. 2000. Applied logistic regression. John Wiley and Sons, New York, 511 pages.
    14. Hosmer, D. 2000. Applied logistic regression. Wiley Intercedence Publication, 396
    15. Huang,, J. Huang and T. Liu. 2019. Delimiting urban growth boundaries using the CLUE-s model with village administrative boundaries. Land Use Policy, 82: 422-435.
    16. Huixia, L., L. Guohua and F. Bojie. 2012. Estimation of regional evapotranspiration in Alpine area and its response to land use change, a case study in three-river headwaters region of Qinghai-Tibet Plateu. China Geographical Sciences, 22(4): 437-449.
    17. Liu,, Q. Jin, J. Li, L. Li, Ch. He, Y. Huang and Y. Yao. 2017. Policy factors impact analysis based on remote sensing data and the CLUE-S model in the Lijiang River Basin, China. Catena, 158: 286-297.
    18. Luo, G., Ch. Yin, X. Chen, W. Xu and L. Lu. 2010. Combining system dynamic model and CLUE-s model to improve land use scenario analyses at regional scale, a case study of Sangong Watershed in Xinjiang, China. Ecological Complexity, 7: 198–207.
    19. Mohammadi, M., H. Moradi and H. Zainiwand. 2013. Simulation of land use in Salahian Basin in future years using CLUE-s model to optimize land use. Proceedings of the First National Conference on Natural Resources Management, March 8, Gonbad-e-Kavos University.
    20. Mohammady, M., H.R. Moradi, H. Zeinivand, A.J.A.M. Temme and M.R. Yazdani. 2018. Modeling and assessing the effects of land use changes on runoff generation with the CLUE-s and WetSpa models. Theoretical and Applied Climatology, 133(1-2): 459-471.
    21. Nefeslioglu, H.A., T.Y. Duman and S. Durmaz. 2008. Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Easten Black Sea region of Turkey). Geomorphology, 94: 401-418.
    22. Pappas, E.A., D.R. Smith, C. Huang, W.C. Shuster and J.V. Bonta. 2008. Impervious surface impacts to runoff and sediment discharge under laboratory rainfall simulation. Catena, 72(1): 146-152.
    23. Schmitt-harsh, M. 2013. Landscape change in Guatemala: driving forces of forest and coffee agroforest expansion and contraction from 1990 to 2010. Applied Geography, 40: 40-50.
    24. Verburg, P.H. and A. Veldkamp. 2004. Projecting land use transitions at forest fringes in the Philippines at two spatial scales. Landscape Ecology, 19: 77–98.
    25. Verburg, P. 2010. CLUE model. University Amesterdam, IVM Institute for Environmental Studies, 53 pages.
    26. Yecui, H., Z. Yunmei and Z. Xinqi. 2013. Simulation of land-use scenarios for Beijing using CLUE-s and Markov composite models. China Geographical Sciences, 23(1): 92-100.
    27. Zhang, X.Q., L. Zhao, W.N. Xiang, N. Li, L.N. Lv and Yang. 2012. Coupled model for simulating spatio-temporal dynamics of land-use change, a case study in changing Jinan China. Landscape and Urban Planning, 106: 51-61.
Watershed Engineering and Management
Volume 12, Issue 4
January 2021
Pages 1102-1121
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