Modeling the Effects of Land Use and Land Cover Changes on Desertification Intensity in Mokhtaran Plain Using CA-Markov Method

Document Type : Original Article

Authors

1 Ph.D. student of Combating Desertification, Department of Combating Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran.

2 Associate Professor, Department of Combating Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran.

3 Assistant Professor, Desert studies faculty, Semnan University, Semnan, Iran.

4 Associate Professor, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran.

5 Assistant Professor, Department of Combating Desertification, Faculty of Desert Studies, Semnan University, Semnan, Iran.

Abstract

Land use and land cover (LULC) is one of the most important factors that affect the desertification risk in this study, the desertification sensitivity of the Mokhtaran basin in South Khorasan province was estimated by integrating LU/LC scenarios with the MEDALUS environmentally sensitive areas (ESAs) model to predict the desertification risk. The four main MEDALUS criteria including soil, climate, vegetation, and management were examined to assess the sensitive areas to the desertification. Land use maps were categorized using the Landsat satellite imageries of TM, ETM+, and OLI sensors for 1987, 1998, 2003 as a past scenario, and 2015 as a current scenario. Land use maps for 2025 and 2035 were produced as the future scenario based on the simulation of CA-Markov models. The validation results confirmed the model accuracy by calculating the kappa coefficient of 0.95. The land use map was predicted for the years 2025 and 2035 based on the transition rules and a transition area matrix. The results showed that the rainfed area was reduced by 68.29 km2 and the agricultural land was increased by 25.35 km2 during theperiod. In the protection area of playa-bare lands, the changes showed this area was increased by 26.86 km2. The rangeland has also experienced positive changes with an increase of 18.83 km2. Compared to the current scenario, the desertification trend in the future scenario was positively predicted by increasing the area of critical areas from 30.9% to 48.7% over 20 years. The most susceptible lands to desertification were known as playa-bare lands.

Keywords


  1. Adamo, S. B., & Crews-Meyer, K. A. (2006). Aridity and desertification: exploring environmental hazards in Jachal, Argentina. Applied Geography, 26(1), 61-85.
  2. Agarwal, C. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice. US Department of Agriculture, Forest Service, Northeastern Research Station, 297.
  3. Araya, Y. H., & Cabral, P. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), 1549-1563.
  4. Bakhshandehmehr, L., Sultani, S., & Sepehr, A. (2013). assessment of present status of desertification and modifying the MEDALUS model in Segzi plain of Isfahan. Range and Watershed Management, 66(1), 27-41. (in Farsi)
  5. Boudjemline, F., & Semar, A. (2018). Assessment and mapping of desertification sensitivity with MEDALUS model and GIS–Case study: basin of Hodna, Algeria. Water and Land Development, 36(1), 17-26.
  6. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46.
  7. De Oliveira Barros, K., Ribeiro, C. A. A. S., Marcatti, G. E., Lorenzon, A. S., de Castro, N. L. M., Domingues, G. F., de Carvalho, J.R. & dos Santos, A. R. (2018). Markov chains and cellular automata to predict environments subject to desertification. Environmental Management, 225, 160-167.
  8. Ding, H. P., Chen, J. P., & Wang, G. W. (2009, February). A model for desertification evolution employing GIS with cellular automata. In 2009 International Conference on Computer Modeling and Simulation (pp. 324-328). IEEE.
  9. Directorate General of Natural Resources and Watershed Management of South Khorasan Province., (2005). Basic Reports of Watershed Management Studies of Mokhtaran plain. (in Farsi)
  10. Eastman, J., (2012). IDRISI Selva Tutorial, 45. Worcester, MA: Idrisi Production, Clark Labs-Clark University.
  11. Falsoleiman, M., Hajipour, M., & Sadeghi, H. A. (2013). Comparison of the efficiency of AHP and TOPSIS multivariate methods for determination of areas suitable for pistachio cultivation in Birjand Mokhtaran plain. Applied Geographical Science Research, 13(31), 155-133. (in Farsi)
  12. Hadeel, A. S., Jabbar, M. T., & Chen, X. (2010). Application of remote sensing and GIS in the study of environmental sensitivity to desertification: a case study in Basrah Province, southern part of Iraq. Applied Geomatics, 2(3), 101-112.
  13. He, C., Shi, P., Chen, J., Li, X., Pan, Y., Li, J., Li, Y. & Li, J. (2005). Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model. Science in China Series D: Earth Sciences, 48(11), 1979-1989.
  14. Hyandye, C., & Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. Remote Sensing, 38(1), 64-81.
  15. Jiang, M., & Lin, Y. (2018). Desertification in the south Junggar Basin, 2000–2009: Part I. Spatial analysis and indicator retrieval. Advances in Space Research, 62(1), 1-15.
  16. Joseph, O., Gbenga, A. E., & Langyit, D. G. (2018). Desertification risk analysis and assessment in Northern Nigeria. Remote Sensing Applications: Society and Environment, 11, 70-82.
  17. Kahangwa, C., Nahonyo, C., & Sangu, G. (2020). Monitoring Land Cover Change Using Remote Sensing (RS) and Geographical Information System (GIS): A Case of Golden Pride and Geita Gold Mines, Tanzania. Geographic Information System, 12(5), 387-410.
  18. Keshtkar, H., & Voigt, W. (2016). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2, 10.
  19. Kosmas, C., Ferrara, A., Briasouli, H., & Imeson, A. (1999). Methodology for mapping Environmentally Sensitive Areas (ESAs) to Desertification, in: The Medalus project: Mediterranean desertification and land use. Manual on key indicators of desertification and mapping environmentally sensitive areas to desertification, Kosmas C., Kirkby M. & Geeson, N. (Eds.). European Union 1999.
  20. Ladisa, G., Todorovic, M., & Liuzzi, G. T. (2012). A GIS-based approach for desertification risk assessment in Apulia region, SE Italy. Physics and Chemistry of the Earth, Parts a/B/C, 49, 103-113.
  21. Markov, A. A. (1907). Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain, The Notes of the Imperial Academy of Sciences of St. Petersburg VIII Series, Physio-Mathematical College, 22(9).
  22. Memarian, H., Balasundram, S. K., Talib, J. B., Sung, C. T. B., Sood, A. M., & Abbaspour, K. (2012). Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia. Geographic Information System, 4(6), 542-554.
  23. Memarian, H., Balasundram, S. K., Abbaspour, K. C., Talib, J. B., Boon Sung, C. T., & Sood, A. M. (2014). SWAT-based hydrological modelling of tropical land-use scenarios. Hydrological Sciences, 59(10), 1808-1829.
  24. Miller S. L., & Childers, D. (2004). Markov Processes, In: Probability and Random Processes. United States: Academic Press.
  25. Mirdavoodi, H., Zahedi Pour, H. Moradi, M., Goodarzi, G. (2008). Determination of agricultural and rangeland ecological capability of Markazi using GIS. Range and Desert Research, 15(2), 255-242. (in Farsi)
  26. National Research Council (2001). Grand challenges in environmental sciences. Canada: National Academies Press.
  27. Peng, B., Wen, Z., Fan, H., Niu, Q., Guo, Y., & Gu, J. (2020). Dynamic simulation of land use change in Bashang desertification region of Hebei Province using CA-Markov Model. Geomatics, 14(1), 10-18.‏
  28. Rubio, J. L., & Bochet, E. (1998). Desertification indicators as diagnosis criteria for desertification risk assessment in Europe. Arid Environments, 39(2), 113-120.
  29. Salman Mahini, A. & Kamyab, H. R. (2018). Remote sensing and application of GIS with Idrissi software. 1ed Edition. Gorgan: Gorgan University of Agricultural Sciences and Natural Resources Publications.
  30. Salvati, L., & Bajocco, S. (2011). Land sensitivity to desertification across Italy: past, present, and future. Applied Geography, 31(1), 223-231.
  31. Schulz, J. J., Cayuela, L., Rey Benayas, J. M., & Schröder, B. (2011). Factors influencing vegetation cover change in Mediterranean Central Chile (1975–2008). Applied Vegetation Science, 14(4), 571-582.
  32. Silakhori, E., Ownegh, M., Salman Mahini, A., & Babaiyan, E. (2018). Assessing the effects of observational changes and land use and climate scenarios in developing Management Program of desertification risk in the Esfarayen-Sabzevar. PhD, Gorgan University Agricultural Sciences and Natural Resources, Gorgan.
  33. UNCCD, (2007). Climate Change and Desertification. Bonn, Germany: United Nations Convention to Combat Desertification.
  34. Vieira, R. M. D., Tomasella, J., Barbosa, A. A., Martins, M. A., Rodriguez, D. A., Rezende, F. S., Carriello, F. & Santana, M. D. (2021). Desertification risk assessment in Northeast Brazil: Current trends and future scenarios. Land Degradation & Development, 32(1), 224-240.
  35. Wang, G., Jiang, H., Xu, Z., Wang, L., & Yue, W. (2012). Evaluating the effect of land use changes on soil erosion and sediment yield using a grid‐based distributed modelling approach. Hydrological Processes, 26(23), 3579-3592.
  36. Wijitkosum, S. (2012). Evaluation of impacts of spatial land use changes on soil loss using remote sensing and GIS in Huay Sai Royal Development Center, Thailand. Environmental Research and Development, 6(3), 487-493.
  37. Wijitkosum, S. (2014). Critical factors affecting the desertification in Pa Deng, adjoining area of Kaeng Krachan National Park, Thailand. Environment Asia, 7(2), 87-98.
  38. Wijitkosum, S. (2016). The impact of land use and spatial changes on desertification risk in degraded areas in Thailand. Sustainable Environment Research, 26(2), 84-92.
  39. Wu, J., Liu, Y., Wang, J., & He, T. (2010). Application of Hyperion data to land degradation mapping in the Hengshan region of China. Remote Sensing, 31(19), 5145-5161.
  40. Wu, R., & Tiessen, H. (2002). Effect of land use on soil degradation in alpine grassland soil, China. Soil Science Society of America, 66(5), 1648-1655.
  41. Xiao-Li, L. I. U., Yuan-Qiu, H. E., Zhang, H. L., Schroder, J. K., Cheng-Liang, L. I., Jing, Z. H. O. U., & Zhang, Z. Y. (2010). Impact of land use and soil fertility on distributions of soil aggregate fractions and some nutrients. Pedosphere, 20 (5), 666-673.
  42. Yang, X., Zhang, K., Jia, B., & Ci, L. (2005). Desertification assessment in China: An overview. Arid Environments, 63(2), 517-531.
  43. Zehtabian, Gh. R. Khosravi, H& Masoudi (2014). Desertification assessment models (criteria and indicators), 1st edition, Tehran University Press. (in Farsi)