Analysis and Prediction of Land Use Change in Yazd-Ardakan Plain

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Department of management the arid and desert regions, College of Natural Resources and Desert, Yazd University, Iran

2 Desert management, natural resources, Yazd University, Yazd, Iran

3 Agriculture and Natural Resources Department, Ardakan University, Yazd, Iran

4 Department of arid and desert regions management, College of Natural Resources and Desert, Yazd University, Iran

چکیده

Land use maps provide a large fragment of the information required by planners for basic decision-making. Detection of changes as well as prediction of land use changes play a critical role in providing a general insight into better management and conservation of natural resources. This study aimed to simulate land use and land changes using the automatic cell model and Markov Chain in a 30-year period (1986-2016) in the Yazd-Ardakan plain, Iran. In this regard, the object-oriented classification technique, Landsat satellite images (MSS) of 1986, Landsat (TM) of 1999, Landsat (ETM+) of 2010, and Landsat 8 (OLI) of 2016 were employed to create the land use maps, including seven land use types ( afforestation, agricultural land and garden, barren land, poor rangeland, residential land, rocky land and sand dune). To validate the model accuracy, the simulated land use map of 2010 was compared to the actual map obtained by mapping of the satellite image of the same year. The Kappa coefficient obtained showed that the CA-Markov chain model had a high ability (81%) in simulation of land use changes in the Yazd-Ardakan plain. Based on the results, it is likely that, at the interval of 2016-2030, 80% of afforestation land, 55% of agricultural land and gardens, 41% of barren land, 34% of poor rangeland, 47% of residential land, 43% of sand dune, will be 93% unchanged. Additionally, from 2016 to 2030, the conversion of barren lands to afforestation (55%) as well as poor rangeland to agricultural lands and gardens (43%) is highly probable. Based on the area obtained from each land use in 2030 compared to 2016, the areas of afforestation, agricultural land and gardens, residential land and sand dune will increase, and the barren land and poor rangeland will decline. The excessive growth of the population and the increasing need for food and new energy sources as well as the need for residential areas lead to unconventional and extreme exploitation of the natural resources of the Yazd-Ardakan plain.

کلیدواژه‌ها


عنوان مقاله [English]

Analysis and Prediction of Land Use Change in Yazd-Ardakan Plain

نویسندگان [English]

  • Hassan Fathizad 1
  • Mohamadali Hakimzadeh ardakani 2
  • Rouhollah Taghizadeh Mehrjardi 3
  • Hamid Sodaie Zadeh 4
1 Department of management the arid and desert regions, College of Natural Resources and Desert, Yazd University, Iran
2 Desert management, natural resources, Yazd University, Yazd, Iran
3 Agriculture and Natural Resources Department, Ardakan University, Yazd, Iran
4 Department of arid and desert regions management, College of Natural Resources and Desert, Yazd University, Iran
چکیده [English]

Land use maps provide a large fragment of the information required by planners for basic decision-making. Detection of changes as well as prediction of land use changes play a critical role in providing a general insight into better management and conservation of natural resources. This study aimed to simulate land use and land changes using the automatic cell model and Markov Chain in a 30-year period (1986-2016) in the Yazd-Ardakan plain, Iran. In this regard, the object-oriented classification technique, Landsat satellite images (MSS) of 1986, Landsat (TM) of 1999, Landsat (ETM+) of 2010, and Landsat 8 (OLI) of 2016 were employed to create the land use maps, including seven land use types ( afforestation, agricultural land and garden, barren land, poor rangeland, residential land, rocky land and sand dune). To validate the model accuracy, the simulated land use map of 2010 was compared to the actual map obtained by mapping of the satellite image of the same year. The Kappa coefficient obtained showed that the CA-Markov chain model had a high ability (81%) in simulation of land use changes in the Yazd-Ardakan plain. Based on the results, it is likely that, at the interval of 2016-2030, 80% of afforestation land, 55% of agricultural land and gardens, 41% of barren land, 34% of poor rangeland, 47% of residential land, 43% of sand dune, will be 93% unchanged. Additionally, from 2016 to 2030, the conversion of barren lands to afforestation (55%) as well as poor rangeland to agricultural lands and gardens (43%) is highly probable. Based on the area obtained from each land use in 2030 compared to 2016, the areas of afforestation, agricultural land and gardens, residential land and sand dune will increase, and the barren land and poor rangeland will decline. The excessive growth of the population and the increasing need for food and new energy sources as well as the need for residential areas lead to unconventional and extreme exploitation of the natural resources of the Yazd-Ardakan plain.

کلیدواژه‌ها [English]

  • Land use
  • Landsat satellite imagery
  • Object-oriented classification
  • CA-Markov
  • Yazd-Ardakan plain
  1. Al-Bakri, J., Duqqah, M., and Brewer, T, 2013. Application of Remote Sensing and GIS for Modeling and Assessment of Land Use. Cover Change in Amman. Jordan; 5 (5):509 - 519. doi.org/10.4236/jgis.2013.55048
  2. Amiraslani, F. and Dragovich, D. 2011. Combating desertification in Iran over the last 50 years: An overview of changing approaches; J Environ Manage.92 (1):1-13. doi:1016/j.jenvman.2010.08.012
  3. J.J., Kainz, W., and Mousivand. A. J. 2011. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran, International Journal of Image and Data Fusion, 2:4: 329-345, DOI:10.1080/19479832.2011.605397
  4. Baatz M. and Schape. A. 1999. Object-oriented and multi-scale image analysis in the semantic network, in Proc. 2nd Int. Symposium on operalization of remote sensing. Ensched ITC. 148-157
  5. Baker, W.L, 1989. A review of models of landscape change. Landscape Ecol. 2 (2):111-133. doi.org/10.1007/BF00137155
  6. Baysal, G. 2013. Urban land use and land use change analysis and modeling a case study area Malatya, Turkey. Diss., Mathematics, University of Jaume, Castellon.
  7. Behera, D., Borate, S. N., Panda, S. N., Behera, P. R. and Roy, P. S, 2012. Modeling and Analyzing the Watershed Dynamics Using Cellular Automata (CA)–Markov Model–A Geo-Information Based Approach. Journal of Earth System Science. 121 (4): 1011-1024. Doi: 10.1007/s12040-012-0207-5.
  8. Blaschke T. and Lang. S. 2006. Briding remote sensing and GIS-what are the main supportive pillars; 1st International Conference on Object-based Image Analysis. Page 6.
  9. Blaschke, T, 2010. Object-based image analysis for remote sensing. ISPRS Journal of photogrammetry and remote sensing. 65(1): 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
  10. Blaschke, T. 2003. Object-based contextual image classification built on image segmentation. In Advances in Techniques for Analysis of Remotely Sensed Data. 2003 IEEE Workshop on: 113-119. IEEE. doi:1109/WARSD.2003.1295182
  11. Bouziani, M., Goita, K. and He, D.C, 2010. Rule-Based Classification of a Very High-Resolution Image in an Urban Environment Using Multispectral Segmentation Guided by Cartographic Data. IEEE Transactions on Geoscience and Remote Sensing. 48 (8): 3198-3211. doi:1109/TGRS.2010.2044508
  12. 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. J. Environ. Manage. 59 (4): 247-263; org/10.1006/jema.2000.0369
  13. Bruce, C. M. and Hilbert, D. W, 2004. Pre-processing Methodology for Application to Landsat TM/ETM+ Imagery of the Wet Tropics. Cooperative Research Centre for Tropical Rainforest Ecology and Management. Rainforest CRC, Cairns, 38 pages.
  14. Chavez P.S.J.R. and Mackinnon D.J. 1994. Automatic detection of vegetation changes in the Southwestern United States using remotely sensed images. Photogrammetric Engineering and Remote Sensing. 60 (5): 571–583.
  15. Claudia, M. A., Iris M. S., Claudia, D.A., Carolina, M. D., Madalena, N. P. and Raul, Q.F. 2007. Multilevel Object-Oriented Classification of Quick Bird Images for Urban Population Estimates, Proceedings of the 15th International Symposium on Advances in Geographic Information Systems ACM GIS 2007. pp. 5. doi: 1145/1341012.1341029
  16. Collingwood, A., Steven, E.F., Guo, X. and Stenhouse, G, 2009. A medium-resolution remote sensing classification of agriculture areas in Alberta grizzly bear habit. Can. J. Remote sensing. 35 (1): 23-36 HTTP://doi.org/10.5589/m08-076
  17. Definiens Imaging Gmb, H. 2006. Definiens Professional 5 User Guide, http: definiens.-com.User guide. Pdf, 249 Pp.
  18. Eastman, J.R. 2006. Idrisi for windows user’s guide ver.32. Clark University, 328 Pp.
  19. Feizizadeh, B., Blaschke, T., Tiede, D. and Moghaddam, M. H. R, 2017. Evaluating fuzzy operators of object-based image analysis for detecting landslides and their changes. Geomorphology. 293: 240-254. https://doi.org/10.1016/j.geomorph.2017.06.002
  20. Fonji S F. and Taff G.N. 2014 Using satellite data to monitor land - use land – cover change in North - eastern Latvia; Springer Plus. 3: 61. org.10.1186.2193-1801-361.
  21. Gao, Y., Mas, J.F. Navarrete, A. 2009, The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data. International Journal of Digital Earth. 2 (3): 219-236. https://doi.org/10.1080/17538940902818311
  22. Gilmore Pontius J.R. and Chen H.2006. GEOMOD Modeling. Clark Lab, Clark University, Worcester.
  23. Hathout, S. 2002. The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. J. Environ. Manage. 66 (3):229-238. org/10.1006/jema.2002.0596
  24. He Z. and Lo C. 2007. Modeling urban growth in Atlanta using logistic regression. Comput. Environ. Urban Syst 31 (6): 667-688; org/10.1016/j.compenvurbsys.2006.11.001.
  25. Higgins J. and Keller-McNulty S. 1995. Concepts in Probability and Stochastic Modeling, Duxbury Press; first edition.
  26. Hill, J. P., Hostert, G., Tsiourlis, P., Kasapidis, T., Udelhoven, C. and Diemer, C, 1998. Monitoring 20 years of increased grazing impact on the Greek island of Crete with earth observation satellites, Journal of Arid Environment. 39 (2):165-178. https://doi.org/10.1006/jare.1998.0392
  27. Houet T. and Hubert-Moy L. 2006. Modeling and Projecting Land-Use and Land-Cover Changes with Cellular Automaton in considering Landscape Trajectories. Earsel Proceedings. 5 (1): 63–76.
  28. Huang L. and Ni L. 2008. Object-Oriented Classification of High-Resolution Satellite Image for Better Accuracy, Proceedings of the 8th International Symposium on Spatial Accuracy Assessment Natural Resources and Environmental Sciences, Shanghai, P. R. China, June 25-27: 211-218.
  29. Jawak, S. D., Raut, D. A. and Luis, A. J, 2015. Iterative spectral index ratio exploration for object-based image analysis of Antarctic coastal oasis using high resolution satellite remote sensing data. Aquatic Procedia. 4: 157-164. https://doi.org/10.1016/j.aqpro.2015.02.022
  30. Jenerette Darrel G. and Wu J. 2001. Analysis and simulation of land use change in the central Arizona-Phonix region, USA. Landscape Ecol. 16 (7):611-626. doi.org/10.1023/A:101317052855.
  31. Kamusoko, C., Aniya, M., Adi, B. and Manjoro, M, 2009. Rural sustainability under threat in Zimbabwe – Simulation of future land use. Cover changes in the Bindura district based on the Markov -cellular automata model. Appl. Geogr. 29 (3):435-447. org/10.1016/j.apgeog.2008.10.002.
  32. Kityuttachai, K., Tripathi, N.K., Tipdecho, T. and Shrestha. R. 2013. CA-Markov Analysis of Constrained Coastal Urban Growth Modeling: Hua Hin Seaside City, Thailand. Sustainability. 5: 1480-1500. doi:10.3390/su5041480
  33. Lambin, E.F. 1997 Modelling and monitoring land-cover change processes in tropical regions. Phys. Geogr. 21 (3):375-393. doi.org/10.1177/030913339702100303.
  34. Lillesand T.M. and Kiefer R.W. 1994 Remote Sensing and Image Interpretation, John Wiley and Sons, New York, 750.
  35. López E, Bocco G, Mendoza M, Duhau E (2001) Predicting Land-Cover and Land-Use Change in the Urban Fringe: A Case in Morelia City, Mexico. Landscape and Urban Planning; 55 (4): 271–285. doi:10.1016/S0169-2046(01)00160-8.
  36. Luo, G., Yin, C., Chen, X.., Xu, W. and Lu, L, 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. Eco- logical Complexity. 7 (2): 198-207. doi: 10.1016/j.ecocom.2010.02.001
  37. Mas, J.F., Kolb, M., Paegelow, M., Teresa, M., Olmedo, C. and Houet, T, 2014. Inductive pattern-based land use. Cover change models: A comparison of four software packages. Environ. Modell. Software. 51: 94-111. org/10.1016/j.envsoft.2013.09.010.
  38. Memarian, H., Balasundram, S. K., Talib, J. B., Sung, C. T. B., Sood, A. M. and Abbaspour, K, 2012. Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Journal of Geographic Information System. 4 (6): 542-554. doi:10.4236/jgis.2012.46059.
  39. Mubea, K. W., Ngigi, T.G. and Mundia, C.N, 2010. Assessing application of Markov e chain analysis in Predicting land cover change: A case study of NAKURU municipality. JACSTR. 12 (2): 19
  40. Myint S.W. and Wang L. 2006. Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Can. J. Remote Sensing. 32 (66): 390-404. Doi: 10.5589/m06-032.
  41. Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J. and Deadman, P, 2003. Multi-agent systems for the simulation of land use and land cover change: A Review; AAG. 93 (2):314-337. doi.org/1111/1467-8306.9302004.
  42. Platt R. V. and Schoennagel T. 2009. An object-oriented approach to assessing changes in tree cover in the Colorado Front Range 1938–1999. Forest Ecology and Management. 258 (17): 1342-1349. https://doi.org/10.1016/j.foreco.2009.06.039
  43. Puigdefabregas J. and Mendizabal T. 1998. Perspectives on desertification: Western Mediterranean. Journal of Arid Environment. 39 (2): 209-224. https://doi.org/10.1006/jare.1998.0401
  44. Richards, J.A, 1993. An Introduction to Remote Sensing Digital Image Analysis, Springer-Verlag New York, Inc.
  45. Samat, N, 2009. Integrating GIS and CA-MARKOV Model in Evaluating Urban Spatial Growth. Malaysian Journal of Environmental Management. 10 (1): 83–99.
  46. Sang, L., Zhang, C., Yang, J., Zhu, D. and Yun, W, 2011. Simulation of land uses spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling. 54 (3): 938-943. https://doi.org/10.1016/j.mcm.2010.11.019
  47. Sayemuzzaman M. and Jha M.K. 2014. Modeling of future land covers land use change in North Carolina using Markov chain and Cellular automata model. American Journal of Engineering and Applied Sciences, 7 (3): 2 95-306
  48. Sohl T.L. and Claggett P R. 2013. Clarity versus complexity: Land-use modeling as a practical tool for decision-makers. J. Environ. Manage. 129 (15): 235-243. org/10.1016/j.jenvman.2013.07.027
  49. Thapa R. B. and Murayama Y. 2011 Urban growth modeling of Kathmandu metropolitan region, Nepal. Computers. Environment and urban systems. 35 (1): 25-34. org/10.1016/j.compenvurbsys.2010.07.005
  50. Tong, X., Lin, X., Feng, T., Xie, H., Liu, S., Hong, Z. and Chen, P, 2013. Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 79: 53–67. https://doi.org/10.1016/j.isprsjprs.2013.01.012
  51. Tudun-Wada, M.I., Tukur, Y.M., Hussaini, Y., Sani, M.Z., Musa, I. and Lekwot, V.E, 2014. Analysis of forest cover changes in Nimbia Forest Reserve, Kaduna State, Nigeria using Geographic Information System and Remote Sensing techniques. IJEMA. 2 (2): 91-99. doi: 10.11648/j.ijema.20140202.15.
  52. UNEP, 1991. Status of desertification and implementation of the United Nations plan of action to combat desertification. Nairobi, Kenya.
  53. Upadhyay, T., Solberg, B. and Sankhayan, P.L, 2006. Use of models to analyze land-use changes, forest. Soil degradation and carbon sequestration with special reference to the Himalayan region: A review and analysis. For. Policy Econ. 9 (4):349-371; org/10.1016/j.forpol.2005.10.003
  54. Verburg, P.H., Schot, P. P., Dijst, M. J. and Veldkamp, A, 2004. Land Use Change Modelling: Current Practice and Re- Search Priorities. Geo Journal. 61 (4): 309-324. doi:10.1007/s10708-004-4946-y
  55. Wang, J., Zhou, W., Qian, Y., Li, W. and Han, L, 2017. Quantifying and characterizing the dynamics of urban greenspace at the patch level: A new approach using object-based image analysis. Remote Sensing of Environment. 24: 94-108. https://doi.org/10.1016/j.rse.2017.10.039
  56. Yan, G, 2003. Pixel Based and Object-Oriented Image for Coal Fire Research, http://www.ITC.com (accessed in July 2008). 3-99
  57. Zhaocong, W., Lina, Y. and Maoyun, Q, 2009. Granular Approach to Object-Oriented Remote Sensing Image Classification. Rough Sets and Knowledge Technology. 563–570. https://doi.org/10.1007/978-3-642-02962-2_71