پیش‌بینی تغییرات کاربری زمین و اولویت بندی مناطق ناپایدار زیست محیطی حوضه آبریز رودخانه هلیل‌رود

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه اقتصاد کشاورزی، دانشکده کشاورزی ، دانشگاه شیراز، شیراز، ایران

2 استاد گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

چکیده

پیش­بینی تغییر کاربری زمین می­تواند اطلاعات مهمی را برای تصمیم­سازی مدیریت زیست محیطی و برنامه­ریزی آینده مهیا سازد. اهداف اصلی این مطالعه ارزیابی تغییر کاربری زمین و پیش­بینی تغییر برای سال­های 2030، 2042 و 2054 با استفاده از مدل زنجیره مارکف و اولویت بندی زیرحوضه­ها با بکارگیری فرایند تحلیل سلسله مراتبی فازی (FAHP) برای شناسایی مناطق ناپایدار زیست محیطی در آینده است. تغییرات کاربری زمین با استفاده از سنجش از راه دور و سامانه اطلاعات جغرافیایی در حوضه آبریز رودخانه هلیل رود بررسی شد. برای این منظور، از تصاویر لندست TM، ETM و OLI و الگوریتم طبقه بندی حداکثر راستنمایی برای سال­های 1994، 2006 و 2018 استفاده شد و پنج نوع کاربری زمین شامل مرتع متوسط، مرتع فقیر، زمین کشاورزی (زراعی-باغی)، پوشش آبی (شامل آب رودخانه، سد و تالاب) و سایر کاربری­های فاقد پوشش گیاهی (شامل رخنمون سنگی، کوه، خاک، مرتع بسیار فقیر و مناطق مسکونی) در حوضه مشخص شد. نتایج نشان داد که طی سال­های 2018-2054، مراتع متوسط، فقیر و پوشش آبی به زمین­های کشاورزی و فاقد پوشش گیاهی تغییر خواهند یافت، به گونه­ای که سهم پوشش مرتعی متوسط و فقیر به میزان 12/65 درصد کاهش خواهد یافت. همچنین، نتایج حاصل از بکارگیری رهیافت FAHP نشان داد که زیرحوضه­های رودبار-جنوب و قلعه گنج در 36 سال آینده با شدیدترین بحران­های زیست محیطی روبرو خواهند شد. نهایتاً اینکه در سال 2054، شرایط کاربری زمین و زیست محیطی تمامی زیرحوضه­ها، بحرانی پیش­بینی می­شود. بنابراین، اعمال سیاست­هایی (از جمله مدیریت منابع آب) در جهت تغییر در روند تخریب زیست محیطی سطح حوضه پیشنهاد می شود.

کلیدواژه‌ها


  1. Ali Mohammadi, A., Mosivand, A. J. and Shayan, S. (2010) Prediction of land use change and land cover using satellite imagery and Markov chain model. Lecturer in Humanities - Space Planning and Design. 14 (3): 117-130. (Persian)
  2. Al-sharif, A. A. and Pradhan, B. (2015) A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov Chain and cellular automata models in GIS. Geocarto International. 30: 858–881.
  3. Asadpour, H., Yousefpour, F. and Feyzabadi, Y. (2016) Comparison between definitive and fuzzy decision models and their application in appointing the priority of agricultural production combination. Iranian Journal of Agricultural Economics and Development Research. 64 (4): 833-846. (Persian)
  4. Ayanlade, A. and Drake, N. (2016) Forest loss in different ecological zones of the Niger Delta, Nigeria: evidence from remote sensing. Geographical Journal. 81 (5): 717–735.
  5. Ayanlade, A. and Howard, M. T. (2017) Understanding changes in a Tropical Delta: A multi-method narrative of landuse/landcover change in the Niger Delta. Ecological Modelling. 364: 53-65.
  6. Baby, S. (2015) Monitoring the coastal land use land cover changes (LULCC) of Kuwait from space-borne Landsat sensors. Indian Journal of Geo-Marine Sciences. 44 (6): 1–7.
  7. Barati, A. A., Asadi, A., Kalantari, KH., Azadi, H. and Mamoorian, M. (2015) Analyzing the impact of agricultural land use change according to the experts opinion of agricultural land organization in Iran. Iranian Journal of Agricultural Economics and Development Research. 45 (4): 639-650. (Persian)
  8. Brown, D. G., Pijanowski, B. C. and Duh, J. (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.
  9. Butt, A., Shabbir, R., Ahmad, S. S. and Aziz, N. (2015) Land use change mapping and analysis using Remote Sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. Egyptian Journal of Remote Sensing and Space Sciences. 18: 251–259.

10. Chang, D. Y. (1996) Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research. 95 (3): 649-655.

11. Department of Natural Resources and Watershed Management of South Kerman. (2017). (Persian)

12. Fathizad, H., Rostami, N. and Faramarzi, M. (2015) Detection and prediction of land cover changes using Markov Chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment. 187: 1-12.

13. Gadrani, L., Lominadze, G. and Tsitsagi, M. (2018) Assessment of landuse/landcover (LULC) change of Tbilisi and surrounding area using remote sensing (RS) and GIS. Annals of Agrarian Science. 163-169.

14. Gogus, O. and Boucher, T. O. (1998) Strong transitivity, rationality and weak monotonicity in fuzzy pairwise comparisons. Fuzzy Sets and Systems. 94 (1): 133-144.

15. Hashem, N. and Balakrishnan, P. (2015) Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar. Annual GIS. 21: 233-247.

16. Huang, Y., Nian, P. and Zhang, W. (2015) Prediction of interregional land use differences in Beijing: a Markov model. Environmental Earth Science. 73: 4077-4090.

17. Jain, M. K. and Das, D. (2010) Estimation of sediment yield and areas of soil erosion and deposition for watershed prioritization using GIS and remote sensing. Water Resource Management. 24: 2091-2112.

18. Jaiswal, R., Thomas, T., Galkate, R., Ghosh, N. and Singh, S. (2014) Watershed prioritization using Saaty's AHP based decision support for soil conservation measures. Water Resources Management. 28: 475-494.

19. Jaiswal, R. K., Thomas, T., Galkate, R. V. and Singh, S. (2013) Rainfall analysis & assessment of irrigation water in a command of drought affected Bundelkhand Region (M.P.) Indian Nationality Conference on Sustainable Water Resource Development and Management (SWARDAM 2013) Aurangabad (India). pp. 20-27 (Sept 30 Oct 01 2013).

20. Joshi, R. R., Warthe, M., Dwivedi, S., Vijay, R. and Chakrabarti, T. (2011) Monitoring changes in land use land cover of Yamuna riverbed in Delhi: a multi-temporal analysis. International Journal of Remote Sensing. 32 (24): 9547-9558.

21. Kaliraj, S. and Chandrasekar, N. (2012) Spectral recognition techniques and MLC of IRS P6 LISS III image for coastal landforms extraction along South West Coast of Tamilnadu, India. Bonfring International Journal of Advances in Image Processing. 2 (3): 01-07.

22. Kaliraj, S., Chandrasekar, N., Ramachandran, K. K., Srinivas, Y. and Saravanan, S. (2017) Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Sciences. 20: 169-185.

23. Kaliraj, S., Chandrasekar, N., Simon Peter, T., Selvakumar, S. and Magesh, N. S. (2014) Mapping of coastal aquifer vulnerable zone in the south west coast of Kanyakumari, south India, using GIS-based DRASTIC model. Environmental Monitoring and Assessment.

24. Kawakubo, F. S., Morato, R. G., Nader, R. S. and Luchiari, A. (2011) Mapping changes in coastline geomorphic features using Landsat TM and ETM imagery: examples in southeastern Brazil. International Journal of Remote Sensing. 32 (9): 2547-2562.

25. Kuenzer, C., Van Beijma, S., Gessner, U. and Dech, S. (2014) Land surface dynamics and environmental challenges of the Niger Delta, Africa: remote sensing-based analyses spanning three decades (1986–2013). Applied Geography. 53: 354-368.

26. Kundu, S., Khare, D. and Mondal, A. (2017a) Landuse change impact on sub-watersheds prioritization by analytical hierarchy process (AHP). Ecological Informatics. 42: 100-113.

27. Kundu, S., Khare, D. and Mondal, A. (2017b) Past, present and future land use changes and their impact on water balance. Journal of Environmental Management. 197: 582-596.

28. López, E., Bocco, G., Mendoza, M. and 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, 271-285.

29. Mahapatra, M., Ratheesh, R., Rajawat, A. S. (2013) Shoreline change monitoring along the South Gujarat coast using remote sensing and GIS techniques. International Journal of Geography, Environment and Earth Science. 3 (2): 115-120.

30. Meyfroidt, P., Lambin, E. F., Erb, K. H. and Hertel, T. W. (2013) Globalization of land use: distant drivers of land change and geographic displacement of land use. Current Opinion in Environmental Sustainability. 5 (5): 438-444.

31. Mishra, V. N. and Rai, P. K. (2016) A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences. 9: 1-18.

32. Misra, A., Murali, R. M. and Vethamony, P. (2013) Assessment of the land use/land cover (LU/LC) and mangrove changes along the Mandovi-Zuari estuarine complex of Goa, India. Arabian Journal of Geosciences. 8 (1): 267-279.

33. Mohammadzade, SH., Sedighi, H., Pezeshki Rad, GH., Makhdom, M. and Sharifi Kia, M. (2015) Analyzing the impact of changing agronomic land use to orchard from the viewpoint of orchardist in the west of Urmia lake basin. Iranian Journal of Agricultural Economics and Development Research. 45 (4): 775-785.

34. Mukhopadhyay, A., Mondal, A., Mukherjee, S., Khatua, D., Ghosh, S., Mitra, D. and Ghosh, T. (2014) Forest cover change prediction using hybrid methodology of geo informatics and Markov Chain model: a case study on sub-Himalayan town Gangtok, India. Journal of Earth System Science. 123: 1349-1360.

35. Peterson, L., Bergen, K., Brown, D., 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.

36. Rawat, J. S., Biswas, V., Kumar, M. (2013) Changes in land use/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand. The Egyptian Journal of Remote Sensing and Space Sciences. 16: 111-117.

37. Rawat, J. S., Kumar, M. (2015) Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egyptian Journal of Remote Sensing and Space Sciences. 18 (1): 77-84.

38. Rezaei Moghadam, M. H., Andaryani, S., Vali zadeh, K. H. and Almaspour, F. (2016) Determine the best land cover and land-use extraction algorithm and discovery of changes from Landsat satellite imagery (Case study: Maragheh Sufi Basin). Geographical Space Journal. 16 (55): 65-85. (Persian)

39. Romijn, E., Lantican, C. B., Herold, M., Lindquist, E., Ochieng, R., Wijaya, A., Murdiyarso, D. and Verchot, L. (2015) Assessing change in national forest monitoring capacities of 99 tropical countries. Forest Ecology and Management. 352: 109-123.

40. Santhiya, G., Lakshumanan, C. and Muthukumar, S. (2010) Mapping of landuse/landcover changes of Chennai coast and issues related to coastal environment using remote sensing and GIS. International Journal of Geomatics and Geosciences. 1 (3): 563-576.

41. Şener, E. and Şener, Ş. (2015) Evaluation of groundwater vulnerability to pollution using fuzzy analytic hierarchy process method. Environmental Earth Sciences. 73 (12): 8405-8424.

  1. Sharma, S., Rajput, G., Tignath, S. and Pandey, R. (2010) Morphometric analysis and prioritization of a watershed using GIS. Journal of Indian Water Resources Society. 30: 33-39.

43. Shinde, V., Tiwari, K. and Singh, M. (2010) Prioritization of micro watersheds on the basis of soil erosion hazard using remote sensing and geographic information system. International Journal of Water Resources and Environmental Engineering. 5: 130-136.

44. Weng, Q. (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management. 64: 273-284.

45. Yeh, C. K. and Liaw, S. C. (2015) Application of landscape metrics and a Markov Chain model to assess land cover changes within a forested watershed, Taiwan. Hydrological Process. 29: 5031-5043.

46. Zhang, X., Kang, T., Wang, H. and Sun, Y. (2010) Analysis on spatial structure of land use change based on remote sensing and geographical information system. International Journal of Applied Earth Observation and Geoinformation. 125: 145-150.

47. Zhang, R., Tang, C., Ma, S., Yuan, H., Gao, L. and Fan, W. (2011) Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain, China. Mathematical and Computer Modelling. 54: 924-930.

48. Zhang, R. and Zhu, D. (2011) Study of land cover classification based on knowledge rules using high-resolution remote sensing images. Expert Systems with Application. 38 (4): 3647-3652.

49. Zoran, M. E. (2006) The use of multi-temporal and multispectral satellite data for change detection analysis of Romanian Black Sea Coastal zone. Journal of Optoelectronics and Advanced Materials. 8: 252-256.