ارزیابی و آینده‌نگری تغییرات زمانی و مکانی شوری خاک با استفاده از مدل ترکیبیCA-Markov در مناطق خشک (مطالعه موردی: دشت میناب)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه احیاء مناطق خشک و کوهستانی دانشگاه تهران، کرج، ایران

2 گروه احیای مناط‌ق‌ خشک‌ وکوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

3 گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، ، کرج، ایران

4 گروه احیای مناط‌ق‌ خشک‌ وکوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، ، کرج، ایران

5 گروه مدیریت و توسعه کشاورزی، دانشکده اقتصاد و توسعه کشاورزی، دانشگاه تهران، ، کرج، ایران

چکیده

شوری خاک در چند دهه اخیر به دلیل استفاده نامناسب و غیراصولی از منابع پایه به‌شدت رو به افزایش است. این معضل در مناطق مختلف کشور به‌ویژه مناطق خشک و نیمه‌خشک آثار زیان‌بار شدیدی را پدید آورده است. به‌طوری‌که در این مناطق با تجمع نمک‌های محلول در سطح خاک عملکرد محصول کاهش می‌یابد و در نهایت باعث ازبین‌رفتن زمین‌های کشاورزی می‌شود. باتوجه‌به اهمیت موضوع در این پژوهش به بررسی روند تغییرات زمانی و مکانی شوری خاک در دشت میناب پرداخته شد. بدین منظور از تصاویر ماهواره‌ای مربوط به سال‌های 1380، 1390 و 1400 استفاده گردید. برای تهیه نقشه‌های شوری خاک از نرم‌افزار ENVI5.1  و برای بررسی تغییرات و پیش‌بینی آن در دوره آتی از مدل ترکیبیCA-Markov  در نرم‌افزار TerrSet استفاده شد. نتایج نشان داد که با گذشت زمان بر میزان شوری اراضی در این منطقه افزوده می‌شود به‌طوری‌که مساحت کلاس شوری خیلی زیاد در سال‌های 1380، 1390 و 1400 به ترتیب برابر است با 21/12، 14 و 51/19 درصد می‌باشد که این میزان افزایش در بخش‌های جنوب و جنوب غرب دشت بیش‌تر رخ داده است. همچنین نقشه پیش‌بینی نیز نشان‌دهنده گسترش شوری در منطقه موردمطالعه می‌باشد به‌طوری‌که بیش‌ترین وسعت افزایش نرخ تغییر شوری در سال 1420 مربوط به کلاس شوری خیلی زیاد و برابر 24/20 درصد است. مساحت اراضی با شوری خیلی زیاد در سال 1380 تا 1420 از 20/12 درصد به 62/29 درصد افزایش‌یافته، درحالی‌که مساحت اراضی با شوری متوسط از 47/60 درصد در سال 1380 به 88/13 درصد در سال 1420 کاهش‌یافته است. در حالت کلی یکی از راهکارهای مدیریتی جهت جلوگیری از افزایش شوری خاک در این منطقه تغییر سیستم آبیاری می‌باشد تا به کمک آن به توان از مصرف شدید آب و کاهش کیفیت آب‌وخاک جلوگیری کرد.

کلیدواژه‌ها


عنوان مقاله [English]

An assessment and Prediction of Temporal and Spatial Variations of Soil Salinity Using the Hybrid CA-Markov Model in Arid Regions: A Case Study of Minab Plain

نویسندگان [English]

  • Hamed Eskandari Damaneh 1
  • Gholamreza Zehtabian 2
  • Hassan Khosravi 3
  • Hossein Azarnivand 4
  • Aliakbar Barati 5
1 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
2 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
3 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
4 Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
5 Department of Agricultural Management and Development, Faculty of Agricultural Economics and Development, University of Tehran, Karaj, Iran
چکیده [English]

Soil salinity has sharply been increasing in recent decades due to improper use of basic resources. This issue has had severe harmful effects in different parts of Iran, especially in arid and semi-arid regions where the accumulation of soluble salts in soil surface has reduced crop yields and destroyed arable lands. Given the significance of this issue, the present research investigated the trend of temporal and spatial variations of soil salinity in Minab Plain for which the satellite images of 2001, 2011, and 2021 were used. The Envi5.1 software package was used to develop the soil salinity maps, and the hybrid CA-Markov model in the TerrSet software package was employed to study the soil salinity changes and predict it for the future period. The results showed that the land salinity would increase in these regions over time so that the area of very high salinity class has been 39.46, 45.26, and 63.09 km2 in 2001, 2011, and 2021, respectively. This increase was even greater in southern and southwestern parts of the plain. Furthermore, the prediction map showed the expansion of salinity in the studied region so that the highest area of salinity change rate in 2021 was found to be related to the very high salinity class (20.24%) and the area of very highly saline lands has increased from 12.20% to 29.62% from 2001 to 2021 whereas the area of moderately saline lands has decreased from 60.47% in 2001 to 13.88% in 2021. In general, an approach for preventing soil salinity aggravation in this region is to change the irrigation system to prevent severe water use and the loss of water quality, which would finally influence the soil to a lesser extent.

کلیدواژه‌ها [English]

  • Land degradation
  • Minab plain
  • Satellite images
  • Soil Salinity
Amini, D., Tavakoli, M., and faramarzi, M. (2020). Investigation of the Relationship Between Soil Salinity Trend, Land Use and Climatic Factors Change (Case Study: Shadegan, Khuzestan). Journal of Environmental Science and Technology, 22(9), 13-58. (In farsi).
Arsanjani, 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.
Asfaw, E., Suryabhagavan, K. V., and Argaw, M. (2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 17(3), 250-258.
Asgari, H. R., rashno, A., bairramkomaki, C., boali, A. (2020). Investigation Study of Soil Salinity Mapping using Landsat Data (Case Study: Dashli Borun, Golestan Province). Degradation and Rehabilitation of Natural Land. 1 (1), 72-81
Asghari Sarasekanrood, S., and Asadi, B. (2021). Analysis of land use changes and their effects on the creation of thermal islands in Isfahan City. The Journal of Geographical Research on Desert Areas, 8(2), 217-246.
Asghari Sereskanrood, S., and Ardeshirpey, A.A. (2020). Prediction of Land Use Changes Using CA-Markov: A Case Study of Yasuj City. Town and Country Planning, 12(2), 407-430.
Azareh, A. (2016). Modeling the Climate Change Effects on Underground Water Damage and Land Destruction (Case Study: Ghazvin Plain). Ph.D. Department of Natural Resources, University of Tehran. (In Farsi).
Eskandari Damaneh, H., Eskandari Damaneh, H., Khosravi, H., and Gholami, H. (2019). Analysis and monitoring of drought using NDVI index (Case study: the west basin of Jaz Murian wetland). Rangeland, 13(3), 461-475. (In Farsi).
Eskandari Damaneh, H., Gholami, H., Khosravi, H., Mahdavi Najafabadi, R., Khoorani, A., and Li, G. (2020). Modeling Spatial and Temporal Changes in Land-Uses and Land Cover of the Urmia Lake Basin Applying Cellular Automata and Markov Chain. Geography and Environmental Sustainability, 10(2), 57-72.
Eskandari Damaneh, H., Zehtabian, G., Salajegheh, A., Ghorbani, M., and Khosravi, H. (2018). Assessing the effect of land use changes on groundwater quality and quantity (Case study: west basin of Jazmoryan wetland). Journal of Range and Watershed Managment, 71(3), 563-578. (In farsi).
Eskandari Damaneh, H., Zehtabian, G.R., Khosravi, H., Azarnivand, H., and Barati, A.A. (2020), Investigating the Influence of Drought on Trend of Vegetation Changes in Arid and Semiarid Regions, Using Remote Sensing Technique: A Case Study of Hormozgan province), Desert Ecosystem Engineering Journal, 9(28), 13-28. (In Farsi).
Eskandari, H., Borji, M., Khosravi, H., Nakhaee Nejadfar, S. and Eskandari, H. (2016). Change Detection of of Bakhtegan and Tashk Basin during 2001-2013. International Journal of Forest, Soil and Erosion (IJFSE), 6(2), 67-71.
Eskandari Damaneh, H., Zehtabian, G.R, Khosravi, H., Azarnivand, H., and Barati, A. (2021). Simulation of future spatial and temporal changes in land uses and cover in arid areas (Case study: Minab plain). Journal of Range and Desert Research, 28(3), 520-536.
Fatemi, S. B., and Rezaei, Y. (2010). Principles of Remote Sensing, Tehran, p, 257. (In Farsi).
Gashaw, T., Bantider, A., and Mahari, A. (2014). Evaluations of land use/land cover changes and land degradation in Dera District, Ethiopia: GIS and remote sensing based analysis. International Journal of Scientific Research in Environmental Sciences, 2 (6), 199.
Gorji, T., Tanik, A., and Sertel, E. (2015). Soil salinity prediction, monitoring and mapping using modern technologies. Procedia Earth and Planetary Science,15, 507-512.
Hallbian, A., and Soltaniyan, M. (2016). Estimation and prediction of desertification changes in the east and south of Isfahan with CA-Marcov model, Environmental Spatial Spatial Analysis Journal, Third Year, 4,71-88. (In Farsi).
Hassen, E. E., and Assen, M. (2018). Land use/cover dynamics and its drivers in Gelda catchment, Lake Tana watershed, Ethiopia. Environmental Systems Research, 6 (1), 4.
Khosravi, H., Azareh, A., Dameneh, H. E., Sardoii, E. R., and eskandari Dameneh, H. E. (2017). Assessing the effects of the climate change on land cover changes in different time periods. Arabian Journal of Geosciences, 10(4), 93.
Khosravi, H., Eskandari Damaneh, H., Azarnivand, H., and Barati, A. (2020). Simulation and prediction of climatic parameters of temperature and precipitation in arid regions (Case study: Minab Plain, Iran). Geography, 66(66), 110-227.
Khosravi, H., Zahtabian, G.h., Azareh, A., and Eskandari, H. (2018). An Investigation and Comparison of the Effects of Agricultural Activities on Soil Properties (Case Study: Khatam)., Scientific Journal of Rangeland, 2 (2), 232-241. (In Farsi).
Kumar, K. S., Kumari, K. P., and Bhaskar, P. U., (2016). Application of Markov Chain & Cellular Automata based model for prediction of urban transitions. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 12(4), 4007-4014.
 Matin Far, H., Sarmedian, F., and Alavi Panah, S.K. (2010). Identification of saline soils of Kashan region based on digital processing of IRS-1D satellite data and field studies, Journal of Engineering and Watershed Management, 2 (4), 220-211. (In Farsi).
Mombeni, M., Arkhi, P., and Arami, S. A. (2015). Changes in Salinity Process Using Remote Sensing and GIS (Case Study: South of Khuzestan), Journal of the Desertification Ecosystem Engineering, Fourth Year, 6, 34-27. (In Farsi).
Mombeni, M., Karamshahi, A., Graii, P., Azadnia, F., and Khosravi, H. (2015). Assessing the actual status of desertification, with emphasis on water, climate and soil criteria using the IMDPA model (Case study: Abbas Plain), 19 (72), 360-334(In Farsi).
Morshed, M. M., Islam, M. T., and Jamil, R. (2016). Soil salinity detection from satellite image analysis: an integrated approach of salinity indices and field data. Environmental monitoring and assessment, 188(2), 1-10.
Parvian, N. (2015). Assessing and monitoring the status of drought in Khorasan Razavi province using satellite imagery. "First International Conference on Environment and Natural Resources. (In Farsi).
Rafiee Imam, A., and Zahtabian, G.h. (2006). Investigation of Factors Affecting Land Destruction in Varamin Plain. Natural Resources of Iran, 59 (2), 289-298. (In Farsi).
Ranjbar, R., Olayee, H., Ranjbar, H., and Adhami, A. (2018). Monitoring Soil Salinity Change Using Remote Sensing in Zahed Shahr, Fars Province, Remote Sensing and Geographic Information System in Natural Resources, 9 (3), 116-128. (In Farsi).
Refiei sharifAbad, J., Khosravi, H., and Alamdarlou, E.H. (2014). Assessment the effects of land use changes on soil physicochemical properties in Jafarabad of Golestan province, Iran. Bulletin of Environment, Pharmacology and Life Sciences, 3(3), 296-300.
Seydehmet, J., Lv, G., Nurmemet, I., Aishan, T., Abliz, A., Sawut, M., and Eziz, M. (2018). Model prediction of secondary soil salinization in the Keriya Oasis, Northwest China. Sustainability, 10(3), 656.
Shayan, S., and Zare, G.h. (2011). Explaining the concept of erosion from the point of view of geomorphology and its comparison with the perspective of natural resources. Journal of Environmental Erosion Research, 1 (1), 77-92. (In Farsi).
Shirovi, M., Sepehr, A., Mosaedi., A., and Proian, N. (2015). Investigation of spatial and temporal changes in salinity in Khorasan Razavi province using digital data, the first international conference on environment and natural resources. (In Farsi).
Singh, N. and Punia, M. (2018). Geospatial Approach for Land Use/Land Cover Change Prediction: A case study of Bhagirathi Basin, Uttarakhand, INDIA. cosp, 42, A3-1.
Surabuddin Mondal, M., Sharma, N., Kappas, M., and Garg, P. K. (2013). Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques. Geocarto International, 28 (7), 632-656.
Vali Pour, M., Karimian Iqbal, M., Melkoty, M., and Khosgoftar Manesh, A. (2008). The trend of salinity development and agricultural land degradation in Shamsabad region of Qom province, Journal of Soil and Water Sciences, 12 (46), 691-683. (In Farsi).
Yaghobi, S., komaki, C. B., and karimzadeh, H. (2020). Zoning and Studying of the Soil Salinity Trend by using Remote Sensing Data and Land Statistics (Case Study: Segzi Plain, Isfahan). Degradation and Rehabilitation of Natural Land, 1 (1), 92-104
Yahiaoui, I., Douaoui, A., Qiang, Z.H., and Ziane, A. (2015). Soil salinity prediction in the Lower Cheliff plain(Algeria) based on remote sensing and topographic feature analysis. Journal Arid Land, 7(6), 794–805.
Zehtabian, G., khosravi, H., Eskandari Damaneh, H., and Abolhasani, A. (2018). An Iranian Model of Desertification Potential Assessment for Sustainable Regional Development, Journal of Environmental Erosion Research, 8 (1), 21-3