Volume 2, Issue 4 (6-2018)                   NMCE 2018, 2(4): 1-9 | Back to browse issues page


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Mirakhorlo M S, Rahimzadegan M. Integration of SimWeight and Markov Chain to Predict Land Use of Lavasanat Basin . NMCE 2018; 2 (4) :1-9
URL: http://nmce.kntu.ac.ir/article-1-147-en.html
1- M.Sc graduated of Water Resources, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
2- Assistant Professor, Department of Water Resources, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran , rahimzadegan@kntu.ac.ir
Abstract:   (1376 Views)
Production and prediction of land-use/land cover changes (LULCC) map are among the significant issues regarding input of many environmental and hydrological models. Among various introduced methods, similarity-weighted instance-based machine learning algorithm (SimWeight) and Markov-chain with lower complexity and proper performnce are frequently used. The main aim of this study is utilizing SimWeight along with Markov chain to predict land-use map of Lavasanat basin located in north-east of Tehran for the year 2018. In this regrad, eight driver variables and two land-use maps of the sudy area which were created from two Landsat-5 TM image sensor for the years 2000 and 2011 were considered as input. To evaluate the result of SimWeight, Receiver Operating Characteristic was used. The Land-use map of year 2018 was predicted using the proposed method. To evaluate this map, a land-use map of 2018 was produced using classification of a Landsat-8 OLI image. The results of model and value of area under curve (AUC) for transition potential map was about 0.78, which indicated  good performance. Furthermore, the comparison of two produced and predicted land-use maps of 2018 shows great similarity. Generally, the results indicated the proper performance of the propsed method to predict LULCC.
Full-Text [PDF 1125 kb]   (1076 Downloads)    
Type of Study: Research | Subject: General
Received: 2018/02/6 | Revised: 2018/04/8 | Accepted: 2018/05/6 | ePublished ahead of print: 2018/05/19

References
1. [1] BELL, E. J. & HINOJOSA, R. 1977. "Markov analysis of land use change: continuous time and stationary processes", Socio-Economic Planning Sciences, 11, 13-17. [DOI:10.1016/0038-0121(77)90041-6]
2. [2] BISHOP, C. M. 1995. "Neural networks for pattern recognition", Oxford university press. [DOI:10.1201/9781420050646.ptb6]
3. [3] BUNUNU, Y. A. 2017. "Integration of Markov chain analysis and similarity-weighted instance-based machine learning algorithm (SimWeight) to simulate urban expansion", International Journal of Urban Sciences, 21, 217-237. [DOI:10.1080/12265934.2017.1284607]
4. [4] DOWNLOAD.GEOFABRIK.DE 2018. "Geofabrik Download Server. [online] Available at: http://download.geofabrik.de/ [Accessed 4 Dec. 2018]",
5. [5] EASTMAN, J. 2015. "TerrSet: Geospatial Monitoring and Modeling Software", Clark Labs, Clark University.
6. [6] EASTMAN, J. R. 2015. "TerrSet manual", Accessed in TerrSet version, 18, 1-390.
7. [7] FIELDING, A. H. & BELL, J. F. 1997. "A review of methods for the assessment of prediction errors in conservation presence/absence models", Environmental conservation, 24, 38-49. [DOI:10.1017/S0376892997000088]
8. [8] uthor 1951. "Discriminatory analysis-nonparametric discrimination: consistency properties". California Univ Berkeley.
9. [9] GAGO-SILVA, A., RAY, N. & LEHMANN, A. 2017. "Spatial Dynamic Modelling of Future Scenarios of Land Use Change in Vaud and Valais, Western Switzerland", ISPRS International Journal of Geo-Information, 6, 115. [DOI:10.3390/ijgi6040115]
10. [10] GHOSH, P., MUKHOPADHYAY, A., CHANDA, A., MONDAL, P., AKHAND, A., MUKHERJEE, S., NAYAK, S., GHOSH, S., MITRA, D. & GHOSH, T. 2017. "Application of Cellular automata and Markov-chain model in geospatial environmental modeling-A review", Remote Sensing Applications: Society and Environment. [DOI:10.1016/j.rsase.2017.01.005]
11. [11] HYANDYE, C. & MARTZ, L. W. 2017. "A Markovian and cellular automata land-use change predictive model of the Usangu Catchment", International Journal of Remote Sensing, 38, 64-81. [DOI:10.1080/01431161.2016.1259675]
12. [12] LI, S., JIN, B., WEI, X., JIANG, Y. & WANG, J. 2015. "Using Ca-Markov Model to Model the spatiotemporal change of land use/cover in Fuxian Lake for decision support", ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 163. [DOI:10.5194/isprsannals-II-4-W2-163-2015]
13. [13] MOGHADAM, H. S. & HELBICH, M. 2013. "Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model", Applied Geography, 40, 140-149. [DOI:10.1016/j.apgeog.2013.01.009]
14. [14] MOZUMDER, C., TRIPATHI, N. K. & LOSIRI, C. 2016. "Comparing three transition potential models: A case study of built-up transitions in North-East India", Computers, Environment and Urban Systems, 59, 38-49. [DOI:10.1016/j.compenvurbsys.2016.04.009]
15. [15] MURAYAMA, Y. 2012. "Progress in geospatial analysis", Springer Science & Business Media. [DOI:10.1007/978-4-431-54000-7]
16. [16] OLMEDO, M. C. 2018. Multi-objective land allocation (MOLA). Geomatic Approaches for Modeling Land Change Scenarios. Springer.
17. [17] PONTIUS JR, R. G. & SCHNEIDER, L. C. 2001. "Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA", Agriculture, Ecosystems & Environment, 85, 239-248. [DOI:10.1016/S0167-8809(01)00187-6]
18. [18] RODRIGUES, H. & SOARES-FILHO, B. 2018. A short presentation of dinamica ego. Geomatic Approaches for Modeling Land Change Scenarios. Springer [DOI:10.1007/978-3-319-60801-3_35]
19. [19] SANGERMANO, F., EASTMAN, J. R. & ZHU, H. 2010. "Similarity Weighted Instance‐based Learning for the Generation of Transition Potentials in Land Use Change Modeling", Transactions in GIS, 14, 569-580. [DOI:10.1111/j.1467-9671.2010.01226.x]
20. [20] SHRESTHA, B., COCHRANE, T. A., CARUSO, B. S. & ARIAS, M. E. 2018. "Land use change uncertainty impacts on streamflow and sediment projections in areas undergoing rapid development: A case study in the Mekong Basin", Land Degradation & Development, 29, 835-848. [DOI:10.1002/ldr.2831]
21. [21] SOARES-FILHO, B., MOUTINHO, P., NEPSTAD, D., ANDERSON, A., RODRIGUES, H., GARCIA, R., DIETZSCH, L., MERRY, F., BOWMAN, M. & HISSA, L. 2010. "Role of Brazilian Amazon protected areas in climate change mitigation", Proceedings of the National Academy of Sciences, 107, 10821-10826. [DOI:10.1073/pnas.0913048107]
22. [22] SOARES-FILHO, B. S., RODRIGUES, H., COSTA, W. & SCHLESINGER, P. 2009. "Modeling environmental dynamics with Dinamica EGO", Centro de Sensoriamento Remoto. Universidade Federal de Minas Gerais. Belo Horizonte, Minas Gerais, 115.
23. [23] TAUD, H. & MAS, J. 2018. Multilayer perceptron (MLP). Geomatic Approaches for Modeling Land Change Scenarios. Springer. [DOI:10.1007/978-3-319-60801-3_27]
24. [24] TRAN, D. X., PLA, F., LATORRE-CARMONA, P., MYINT, S. W., CAETANO, M. & KIEU, H. V. 2017. "Characterizing the relationship between land use land cover change and land surface temperature", ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119-132. [DOI:10.1016/j.isprsjprs.2017.01.001]
25. [25] ZADBAGHER, E., BECEK, K. & BERBEROGLU, S. 2018. "Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey", Environmental monitoring and assessment, 190, 494. [DOI:10.1007/s10661-018-6877-y]

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