Investigating the Effects of Land Use Changes on Dust Storms in the Sistan Region Using Markov Chain Forecasting

Document Type : Original Article

Authors

1 PhD student, Faculty of Agriculture and Natural Resources, Hormozgan University, Hormozgan, Iran.

2 Associate Professor, Department of Natural Resources Engineering, Hormozgan University, Hormozgan, Iran.

3 Assistant Professor, Department of Natural Resources Engineering, Hormozgan University, Hormozgan, Iran.

4 Assistant Professor, Department of Environment, Faculty of Natural Resources, Zabul University, Zabol, Iran.

Abstract

Introduction
Population growth and the excessive use of natural resources have caused significant changes in natural ecosystems, including a decrease in rainfall and an increase in temperature. The potential exists for them to decrease vegetation and increase barren areas. Serious economic, social, and environmental damage can occur in natural ecosystems due to the destruction of land cover and other damages, such as dust storms. Therefore, ecosystem changes are taking place worldwide, both at the temporal and spatial scale, due to human activities and natural factors. So, investigating the amount of land use/cover changes, their effect on dust storms, and predicting these changes for the coming years can be an important step in reducing and controlling unprincipled changes, planning, and optimizing resource. Climate change and human activities, such as drought, human activities, and non-compliance with water rights, have a significant impact on the Hamon wetland area, so that the dry bed of the wetland has become the main sources of dust. This research is focused on investigating the impact of land use changes on dust storms and forecasting land use changes in the Sistan region for the next 20 years.
 
Material and Methods
The impact of land use changes on dust storms in the Sistan region was examined using Markov chain forecasting methods. For this purpose, first of all, the land use maps of 2002, 2011 and 2022 were prepared using satellite images. An anomalous method was used to investigate climatic parameters, including temperature, rainfall, and the number of days with dust, in the next step. To evaluate climatic changes, it is necessary to use a method that shows long-term changes. The anomaly method was employed for this purpose. The values of this index can be


 
either positiveor negative. In order to predict land use changes for the next 20 years, the combination of the maps of 2002 and 2022 for severe drought conditions were used by using Markov chain and Cell models. The Markov model was predicted to generate multiple images. The transfer probability matrix allows for the expression of the probability that any type of land cover will be found in any location in the future. Despite the accuracy of transmission probabilities for each user is unknown, due to the lack of information on the spatial distribution of users, the Markov model does not have any spatial dependence information.  In contrast, to the automatic network, it is an agent that has the ability to change its state based on the application of the law that shows the new state in accordance with the previous state and the state of its neighbors.
 
Results and Discussion
This study examined the impact of land use change on dust in the Sistan region. At first, climatic changes of temperature, rainfall and number of dusty days were investigated and the results showed that the temperature has increased and rainfall has decreased in the Sistan region during the last two decades. The land use maps also showed that in the years when the Hamon wetland has been drained, pastures and dense vegetation have increased and barren lands and salt marshes have decreased. But due to the recent droughts like the year 2022, when a drought has occurred in the region, the use of vegetation and pasture has decreased and barren and salt marshes have increased. These conditions cause an increase in the level of dust in the region. The land use map for severe drought conditions in the next 20 years was predicted using the Markov model.  It showed that in the future, pastures and dense vegetation will decrease, but barren lands and salt marsh areas will increase dramatically. As desertification and wind erosion increase, dust storms will also increase as a result of these conditions. The economic, social, environmental, and health conditions of residents in the region are adversely affected by dust storms. Therefore, proper planning and management can reduce the damages caused by dust storms in the Sistan region.

Keywords


  1. Azizi Qalati, S., Rangzan, K., Sadidi, J., Heydarian, P., & Taghizadeh, A. (2016). Predicting the trend of spatial changes in land use using the Markov chain model-CA (case study: Kohmera Sorkhi region of Fars province), Remote Sensing and Geographic Information System in Natural Resources. 7(1), 59-71. [In Persian].
  2. Arabi Aliabad, F., & Zare, M., & Ghafarian Malamiri, H R. (2021). Land use Change Prediction using Markov Chain Compilation Model and Automated Cells (Casestudy: Shirkuh). Geography and Development, 19 (62), 251-270. DOI: 10.22111/J10.22111.2021.6022 [In Persian]
  3. Ayodeji Opeyemi, Z. (2006). Change detection in land use and land cover using remote sensing data and GIS, (A case study of Ilorin and its environs in Kwara State), The department of Geography, University of Ibadan in Partial Fulfillment for the award of master of science.
  4. Alimohamadi, A., Moosivand, A.J., & Shayan, S. (2010). Prediction of land use and land cover changes using satellite images and Markov chain models. Humanities Teacher – Spatial Planning, 14(3), 117-130. [In Persian]
  5. Aslami, F., Ghorbani, A. Sobhani. B., & Panahandeh, M. (2015). Comparing artificial neural network, support vector machine and object-basedmethods in preparation land use/cover maps using landsat-8 images. RS & GIS techniques innaturalresources. 6(3),1-14, [In Persian]
  6. Bahak, b. (2018). Spatial analysis of the occurrence of dust phenomenon in Sistan and Baluchistan province with statistical methods. Quarterly Scientific Research Journal of Geography. 8(3), 109-97. DOI: 1001.1.22286462.1397.8.3.28.9 [In Persian]
  7. Brown D. G., Pinjanowski B. C., & Duh J. D. (2000). Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management. 59(4), 247-263. DOI:1006/jema.2000.0369
  8. Coppin P I., Jonckheere K., Nackaerts B., & Muys, S. (2004). Digital change detection methods in ecosystem monitoring: a review. Remote Sensing, 25(9), 1565–1596. DOI: 1080/0143116031000101675
  9. Chen, M., Su, W., Li. L., & Zhang, C. (2008). A comparison of pixel-based and object-oriented classification using spot5 imagery, Wseas Transactions on Information Science & Applications, 6(3), 477-489.
  10. Dahmardeh Ghaleno, M., Nohtani, M., & KHaledi, S. (2019). Effect anthropogenic factors on winderosion intensification in Hirmand Hamoon Region. Journal of Watershed Engineering and Management, 3(11), 609-618. DOI: 10.22092/ijwmse.2018.108923.1250 [In Persian]
  11. Ehsani A.h., & Shakeryari, M. (2019). Determining the optimal method for classification and mapping of land use/land cover through comparison of artificial neural network and support vector machine algorithms using satellite data (Case study: International Hamoun wetland). Environmental Science and Technology, 20(4) ,193-207. Doi:22034/JEST.2019.13711 [In Persian]
  12. Fathizade, H., Karimi, H., Tazeh, M., &Tavakoli, M. (2014). Prediction of land use change and land cover Changes in Arid and Semi-Arid Regions using satellite Images and Markov chain model (Case study: Doviraj Basin, Ilam Province). Desert Management. 2(3), 76-61. DOI: 22034/JDMAL.2014.17062 [In Persian]
  13. Farzadmehr, J., Arzani, H., Darvish Sefat, A.A. & Jafari, M. (2006). Assessment Landsat 7 satellite data ability to estimate the canopy and plant production (Case study: Semi-steppe zone henna Samirom). Iranian Journal of Natural Resources, 57(2), 1-15. [In Persian]
  14. Fartot Enayat, M., & Jaberzadeh, M. (2015). Investigating the effect of Hamon wetland on the formation of sand and dust storm phenomenon in Sistan. Seventh International Conference on Comprehensive Crisis Management, Tehran. [In Persian]
  15. Gholam Ali Fard, M., Jvrabyan Shushtarî Sh., Hosseini Kahnooj S.H., & Mirzaee, M. (2013). Modeling land use changes in the coastal province using LCM. Ecology, 4(4), 124-109. DOI: 10.22059/jes.2013.29867 [In Persian]
  16. Huang, Y., Yang, B., Wang, M. Liu.b., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Environmental Earth Sciences, 79(60), 1-12. DOI: 1007/s12665-019-8785-z
  17. Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 4(7), 101-112. DOI: 10.1016/j.apgeog.2015.06.015
  18. Ilderami, A., Nouri, H., Naderi, M., Aghabeigi, S., & Zainiwand, H. (2017). Prediction of land use changes using the Markov chain model. Watershed Management Research Paper. 8(16), 232-240. ‎DOI:29252/jwmr.8.16.232 [In Persian]
  19. Jenerette, D., & Jianguo. Wu. (2001). Analysis and simulation of land use change in the central ArizonaPhoenix region, USA. Landscape Ecology. 16(7), 611- 626. DOI: 1023/A:1013170528551
  20. Karimi, K., Zahtabian, G., Faramarizi, M., & Khosravi, H. (2016). Monitoring and changes in land use using Markov chain in order to predict it. Pasture and Watershed, Journal of Natural Resources of Iran. 69(3), 711-724. [In Persian]
  21. E., Massah Bavani, A.R., Saadi, T., & Javadi. S. (2020). Detection and attribution of climate change effects on inflow to Karaj dam in the past period. Iran-Water Resources Research. 16(3), 306-321. DOI: 20.1001.1.17352347.1399.16.3.21.9 [In Persian]
  22. Khoshgoftar, M.M., Tallei, M., & Malek pour. P. (2010). Spatial-temporal modeling of urban scattering, by automated cell based approach and Marcov chain. National geomatic conference. Abadan. Iran. [In Persian]
  23. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365- 2401. DOI: 10.1080/0143116031000139863
  24. Mir, M., Maliki, S., & Rahdari, V. (2021). Investigating changes in the intensity and scope of the impact of dust storms in the Sistan plain. Scientific Research Journal of Desert Ecosystem Engineering. 10(30), 111-125. DOI: 10.22052/deej.2021.10.30.59 [In Persian]
  25. Mir, M., Maliki, S., & Rahdari, V. (2022). Ecosystem restoration and degradation monitoring using ecological indices. International Journal of Environmental Science and Technology. 62(20), 1713-1724. DOI: 10.1007/s13762-022-04694-8
  26. Nikpour, A., Amonia, H., & Nurpasandi, A. (2021). Monitoring and forecasting land use changes using Landsat satellite images using automatic cells and Markov chain (case study: Abbas Abad region, Mazandaran province). Remote Sensing and Geographic Information System in Natural Resources. 12(2), 53-35. DOI:30495/GIRS.2021.678602 [In Persian]
  27. Najafi, A., Azizi. S &. Mokhtari .M. (2017). Application of support vector machine in land use classification of Cheshmeh Kileh-Chalkroud area. Journal of Watershed Management Research, 15(8), 92-96. DOI: 10.29252/jwmr. 8.15.92 [In Persian]
  28. Norris, J.R. (1997). Markov Chains. Cambridge University Press.
  29. Patino, J.E., & Duque, J.C. (2013). A review of regional science applications of satellite remote sensing in urban settings. computers, Environment and Urban Systems, 8(3), 1-17. DOI: org/10.1016/j.compenvurbsys.2012.06.003
  30. Rahmani, N., Shahedi, K., Soleimani, K., & Yaghoubzadeh. M.H. (2016). Evaluation of the Land use Change Impact on Hydrologic Characteristics (Case Study: Kasilian Watershed). Journal of Watershed Management Research, 7(13), 23-32. DOI: 10.18869/acadpub.jwmr.7.13.32 [In Persian]
  31. v., Maleki, S., & Mir, M. (2021). Presenting a sensitivity model to wind erosion using a multi-criteria evaluation method in Hamon wildlife sanctuary. Desert Management. 10(2), 54-39. DOI: 10.22034/JDMAL.2022.551548.1382 [In Persian]
  32. Richards, J. A., & Richards, J. (1999). Remote sensing digital image analysis (Vol. 3), Springe
  33. Salman Mahini, A., & Kamiyab, R. (2011). Remote sensing and applied geographical information systems with idrisi software, Mehr Mahdis Publications, Tehran.
  34. Soffianian, A., Ahmadi Nadoushan, M., Yaghmaei L., & Falahatkar, S. (2010). Mapping and analyzing urban expansion using remotely sensed imagery in Isfahan, Iran. World Applied Sciences Journal, 9(12), 1370-1378.
  35. Sistan and Baluchestan Regional Water Company. (2014). Status report of Sistan and Baluchestan province, 41 pages.
  36. Wang, S., Zheng, X., & Zang, X. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13(1), 1238-1245. DOI: 1016/j.proenv.2012.01.117
  37. Youssef, A. M., Pradhan, B., & Tarabees, E. (2011). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arabian Journal of Geosciences, 4(3), 463-473. DOI: 1007/s12517-009-0118-1
  38. Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13(6), 1238-1245. DOI: org/10.1016/j.proenv.2012.01.117