Land Cover Change Detection and Prediction in Sefiddasht-Borujen Basin Using Ca-Markov

Document Type : Original Article

Authors

1 MSc. in Combating Desertification, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran.

2 Associate Prof., Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.

3 Assistant Prof., Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.

Abstract

The aim of this study is to evaluate the land cover changes in the basin of Sefiddasht-Borujen using remote sensing Using remote sensing data, land cover maps of satellite images of 1998, 2009, and 2018 were prepared and classified. Then, using the image differencing method, land cover changes for the time periods of 1998 to 2018 were detected. Finally, predicted land cover changes were investigated in each land cover using a CA-Markov model. To predict the probable changes for the year of 2028, the 2018 land cover was modeled using 1998-2009 images by applying of the CA-Markov method of change detection. Next, the resulted of modeled 2018 land cover map were compared with the ground truth map of this year. The results of both maps showed relatively similarity and there was a slight difference between these predicted and classified images of 2018. Therefore, this method was used to predict 2028 land cover image too. The results of change detection for the years 1998 to 2018 indicates the reduction of 8339 hectares of agricultural lands in the study area, as well as 11824 ha from rangelands. Conversely, the bare land increased 14601 ha. According to predicted map for 2028, the largest incremental change in the bare land will be 16476 ha. Estimates show that 8664 hectares of these lands will be from agricultural lands, but approximately 8580 ha will be transformed into the bare land and about 224 ha to residential-industrial lands. Rangelands also will be reduced by13055 ha including 11663 ha to bare land and 1069 ha will be transformed into residential-industrial areas. 16476 ha will be added to bare land and 1420 ha to residential-industrial areas. The results of the present study can be used for future planning for the study area.

Keywords


  1. Ahmadi, H. )1995(. Applied geomorphology. Tehran University Press, Iran. (in Farsi)
  2. Ahmad, A. (2012). Analysis of maximum likelihood classification on multispectral data,Applied Mathematical Sciences6(129), 6425- 6436.
  3. Alavi Panah, S. K. (2005). Application of remote sensing in geo sciences. Tehran University Press, Iran. (in Farsi)
  4. Aplin, P., & Atkinson, P. M. (2004). Predicting missing field boundaries to increase per field classification Accuracy. Photogrammetric Engineering and Remote Sensing70(1), 141-14.
  5. Ara, H., Kianian, M. K., Sohrabi, H., & Ahmadabadi, A. )2020.( Studying effectiveness of Landsat ETM+ satellite images classification methods in Identification of desert pavements (case study: south of Semnan), Environmental Erosion Research Journal10(2), 1-20 (in Farsi)
  6. Arzani, H., Mirakhorlou, K. H., & Hosseini, S. Z. (2009). Land use mapping using Landsat7 ETM data (case study in middle catchment's of Taleghan). Range and Desert Research16(2), 150-160 (in Farsi)
  7. Asghari Zamani, A., Ahad Nejhad Roshani, M., & Khodavandi, A. (2016). Analytical assessment of the spatial-spatial extent of urban areas and its effect on land use changes using GIS and RS (Case study: Shiraz during the period of 1950-1987), Geographical Space53: 57-76. (in Farsi)
  8. Ashraf, M., & Yasushi, Y. D. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Applied Geography29(3), 390-401.
  9. Bakr, N., Weindorf, D. C., Bahnassy, M. H., Marei, S. M., & El-Badawi, M. M. (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography, 30(4), 592-605.
  10. Bonyad, A. A., & Hajyghaderi, T. (2007). Inventorying and mapping of natural forest stands of Zanjan province using landsat ETM+ image data. Crop Production and Processing, 10(42), 627-638. (in Farsi)
  11. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: a review. Remote Sensing, 25(9), 1565-1596.
  12. Dawelbait, M., & Morari, F. (2012). Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. Arid Environments, 80, 45-55.
  13. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), 390-401.
  14. Doski, J., Shattri, A. l., Mansor, B., Shafri, H. Z., (2013), Change detection process and techniques, Civil and Environmental Research, 3(10), 632-635.
  15. Du, Y., Teillet, P. M., & Cihlar, J. (2002). Radiometric normalization of multi-temporal high-resolution satellite images with quality control for land cover change detection. Remote sensing of Environment, 82(1), 123-134.
  16. Eastman, J. R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA.
  17. Fathizad, H., Rostami, N., & 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(10), 629. 1-12.
  18. Hager W. H. (1987). Lateral outflow over side weirs. Hydraulic Engineering, 113(4), 491-504.
  19. Heidarizadi, Z., & Mohamadi, A. (2016). Predicting the land use change using markov- cellular automata model in Mehran plain. The Ecosystem of Desert Engineering, 5(10), 57-68. (in Farsi)
  20. Johnson, R. D., & Kasischke, E. S. (1998). Change vector analysis: A technique for the multispectral monitoring of land cover and condition. Remote Sensing, 19(3), 411-426.
  21. Jafari, M., Majedi, H., Monavari, S. M., Alesheikh, A. A. & Zarkesh, M. K. (2016). Dynamic simulation of urban expansion through a CA-Markov model case study: hyrcanian region, Gilan, Iran. European Journal of Remote Sensing, 49(1), 513-529.
  22. Khazaee, M., Zare, M., Mokhtari, M. H., Rashtian, A., & Arabi Aliabad, F. (2019). Comparing the accuracy of different classification methods in preparing land use map (Case study: Yazd city). Geographical Exploration of Desert Areas7 (1): 165-178. (in Farsi)
  23. Kileshye Onema, J.-M., & Taigbenu, A. (2009).  NDVI–rainfall relationship in the Semliki watershed of the equatorial Nile. Physics and Chemistry of the Earth34 (13-16), 711–721.
  24. Khosravi, D. A., Mirabbasi, N. R., Samadi, B. H., & Ghasemi, D. A. (2019). Monitoring and Forecasting of Groundwater Drought Using Groundwater Resource Index (GRI) and First to Third-Order Markov Chain Models (Case study: Boroujen Plain). Water and Soil conservation, 26(2), 117-136. (in Farsi)
  25. Lambin, E. F., & Geist, H. (2006). Land-Use and Land-Cover Change: Local Processes and Global Impacts. Springer.
  26. Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological Indicators, 2(1), 3-15.
  27. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. Remote Sensing, 28(5), 823-870.
  28. Melchiade, B. (2009). Secretariat of the convention to combat desertification. CSD-17 Intergovernmental Preparatory Meeting Panel on Desertification. New York, February 26.
  29. Mosayebi, M., & Maleki, M. (2014). Change detection in land use using remote sensing data and GIS (Case study: Ardabil County).  Applied RS and GIS Techniques in Natural Resource Science, 5(1), 75-86. (in Farsi)
  30. Mousavi, S. H., Vali, A. A., Moayeri, M., & Ranjbar, A. (2013). Monitoring the desertification status of Haji Ali Gholi Playa (1987-2006). Quantitative Geomorphological Research, 1(4), 85-102. (in Farsi)
  31. Norozi, M., (2013), Investigation and forecasting land use change using LCM model (Case Study: Part of Tajan and Black River Rivers), Master's thesis, Faculty of AgriculturalSciences and NaturalResources, Sari University. (in Farsi)
  32. Peijun, D. U., Xingli, L., Wen, C., Yan, L., & Huaping, Z. (2010). Monitoring urban land over and vegetation change by multi-temporal Remote Sensing information. Mining Science and Technology, 20(6), 922-932
  33. Prabaharan, S., Srinivasa, R. K., Lakshumanan, C., & Ramalingam, M. (2010). Remote sensing and GIS applications on change detection study in coastal zone using multi temporal satellite data. Geomatics and Geosciences, 1(2),159-166
  34. Safari Shad, M., Habibnejad Roshan, M., Soleimani, K., Ildermi, A. R., & Zeinivand, H. (2019). The maximum Likelihood method valuation in detecting land use change using NDVI index (Case study of Hamedan-Bahar watershed). Geographical Space18(64), 159-176. (in Farsi)
  35. Schulz, J. J., Cayuela, L., Echeverria, C., Salas, J., & Benayas, J. M. R. (2010). Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008). Applied Geography, 30(3), 436-447.
  36. Shataee, S., & Abdi, O. (2007). Land cover mapping in Mountainous lands of Zagros using ETM+ data, case study: Sorkhab watershed, Lorestan province. Agricultural Sciences and Natural Resources, 14(1), 129. (in Farsi)
  37. Singh, P., & Khanduri, K. L. (2011). Use and land cover change detection through Remote Sensing & GIS technology: case study of pathankot and dhar kalan tehsils, Punjab. Geomatics and Geosciences, 1(4), 839-846.
  38. Vahidi, M. J., Jafarzadeh, A. A., Fakheri, A., Sadeghi, H. R., Moghadam, M. H., & Valizade, K. H. (2015). Study of land use and land cover change in Lighvan watershed, East Azerbaijan Province, Geograghical Space15(49), 75-100. (in Farsi)
  39. Zhang, F., Tiyip, T., Feng, Z. D., Kung, H-T., Johnson, V. C., Ding, J. L., Tashpolat, N., Sawut, M., & Gui, D.W. (2015). Spatio-temporal patterns of land use/cover changes over the past 20 years in the middle reaches of the Tarim River, Xinjiang, China. Land Degradation and Development, 26(3), 284- 299.