Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city)

Document Type : Original Article

Authors

1 Department of Remote Sensing and Geographic Information Systems, Faculty of Environmental Sciences, Aban Haraz Institute of Higher Education, Amol, Mazandaran, Iran

2 Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Mazandaran, Iran

Abstract

Introduction: Monitoring and evaluation of land surface condition is one of the basic needs in investigating the changes occurring at different levels, including global, regional and local, which include environmental changes. Today, the rapid growth of remote sensing technology, GIS and computer science has led to the emergence of many models to present current and future patterns of land use change. In order to high population growth in large cities and the population's need for land resources, this provides for the destruction of land use, especially vegetation. Kermanshah city as one of the growing areas in recent years has experienced a large population growth and due to the role of population in land use changes and vegetation cover, this issue requires awareness of the vegetation status of this area for proper management of natural resources. The purpose of this study is to monitor and predict vegetation changes in Kermanshah city using NDVI index and CA-Markov model.
Material and methods: In this study, vegetation density of Kermanshah city using NDVI index in four classes of low, medium, dense and highest dense vegetation was extracted from Landsat images in 1987, 2002 and 2017 and then the results were validated using ground control points. Also, in order to predict vegetation density for 2032, vegetation map of 2017 was first simulated by applying CA-Markov model and then results were validated using actual vegetation map of the same year using validate module in IDRISI Terrset software followed by validation results and by applying the mentioned model, vegetation density map was predicted in 2032.
Results and discussion: The results of vegetation maps with over 85% accuracy show that the area with low, medium and highest dense vegetation classes had a decreased and dense vegetation class had an increased trend during the period of 1987 to 2017. Changes in vegetation classes in elevation classes over the 30 year period show low vegetation in classes of 1042 to 1587 and 2133 to 2678 meters, medium vegetation in classes of 1042 to 1587, 1587 to 2133 and 2678 to 3224 meters, dense vegetation in classes of 2133 to 2678 meters and highest dense vegetation in classes of 1042 to 1587 and 1587 to 2133 meter had a decreased trend. Also, vegetation density in slope classes showed that slope of 0-25% had the highest and slopes of 50-75% and more than 75% had the lowest vegetation density. Also, CA-Markov model results with more than 80% accuracy show that vegetation density in 2032 will be similar to previous periods and medium vegetation cover will have the highest vegetation area in Kermanshah city. The increasing and decreasing trend of vegetation classes indicates that the medium vegetation class will decrease compared to 2017 and the classes with low, dense and high dense vegetation will increase and assessment of vegetation classes in elevation and slope classes shows that at altitudes of 1042 to 1587, 1587 to 2133 and 2133 to 2678 meters and slopes of 0 to 25 percent, the highest vegetation density was related to medium and dense vegetation classes but at altitudes of 2678 to 3224 meters and the slope of 50 to 75 and more than 75 percent the highest vegetation density will be the low vegetation class.
Conclusion: In general, the results of this study showed that using NDVI and CA-Markov models with respect to the validation results of these methods can provide acceptable results from the vegetation status of an area.

Keywords


  1. Amini Parsa, V. and Salehi, E ., 2016. Spatiotemporal analysis and simulation pattern of land use/cover changes, case study: Naghadeh, Iran. Journal of Urban Management. 5, 43-51.
  2. Anonymous., 2019. Satellite monitoring greenness and vegetation. Available online at: https://isa.ir
  3. Askarizadeh, D., Arzani, H., Jafary, M., Bazrafshan, J. and Prentice, I.C., 2018. Surveying of the past, present, and future of vegetation changes in the central Alborz ranges in relation to climate change, Iranian journal of RS and GIS for Natural Resources. 9, 1-18 (In Persian with English abstract)
  4. BedawiAhmed, G. and Shariff, A. R. M., 2016. Predicting the Vegetation Expansion in Selangor, Malaysia using the NDVI and Cellular Automata Markov chain, world congress on civil Structural and Environmental Engineering (CSEE’16), March 30th -31th,Clarion Congress Hotel Prague, Prague, p.1-6.
  5. Brown, D. G., Pijanowski, B. C. and 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, 247-263
  6. Bowen, R. L., Cox, L. G. and Fox, M., 1991. The Interface between Tourism and Agriculture. Journal of Tourism Studies, 2, 43-54.
  7. Booth, T.D. and Tueller, P. T., 2003. Rangeland monitoring using remote sensing. Arid Land Research and Management. 17, 455–467.
  8. Bhat. P, A., Shafiq, M., Mir, A. A. and Ahmed, A., 2017. Urban sprawl and its impact on land use/land cover dynamics of Dehradun City, India. International Journal of Sustainable Built Environment. 6, 513-521.
  9. Cabral, P. and Zamyatin, A., 2009. Markov Processes in Modeling Land Use and Land Cover Changes in Sintra-Cascais, Portugal. Dyna. 76,191–198.
  10. Campbell, J.B. and Wynne, R., 2011. Introduction to remote sensing. fifth ed, Guilford Press., New York.
  11. Celik, A., 2005. Land-use effects on organic matter and physical properties of soil in a southern Mediterranean highland and of Turkey. Soil and Tillage Research. 83, 270-277.
  12. Cheng, J. and Masser, I., 2003. Urban Growth Pattern Modeling: A Case Study of Wuhan City, PR China. Landscape and Urban Planning. 62, 199-217.
  13. Clarke, K.C., Hoppen, S. and Gaydos, L., 1997. A Self modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area. Environment and Planning B: Planning & Design. 24, 247-261.
  14. Dadi, D., Azadi, H., Senbeta, F., Abebe, K. and Taheri, F., 2016. Urban sprawl and its impacts on land use change in Central Ethiopia. Urban Forestry & Urban Greening. 16, 132-141.
  15. Dadhich, P. N. and Hanaoka, S., 2010. Remote sensing, GIS and Markov’s method for land use change detection and prediction of Jaipur district. journal of Geomatics. 4, 9-15.
  16. Darvishi, SH., Solaimani, K. and Rashidpour, M., 2019. Impact of vegetation indices and urban surface characteristics on land surface temperature changes (Case study: Sanandaj city). Iranian journal of RS and GIS for Natural Resources. 10, 17-35 (In Persian with English abstract)
  17. Emami, S. and Emami, E., 2017. Detecting and predicting vegetation cover changes using sentinel 2 Data (A Case Study: Andika Region). Journal of Radar and Optic Remote Sensing. 1, 38-54.
  18. Frumkin, H., 2002. Urban sprawl and public health. Public Health Report. 117, 201-207.
  19. Glenn-Lewin, D. C., Peet, R.K. and Veblen, T. T., 1993. Plant Succession: Theory and Prediction, first ed, Springer Netherlands., London.
  20. .Guan, D, J., Li, H. F., Inohae, T., Su, W., Nagaie, T. and Hokao, K., 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modeling. 222, 3761-3772.
  21. Hathout, S., 2002. The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal of Environmental Management. 66, 229-238.
  22. Hendrik Prinz, J., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J. D., Schütte, C. and Noe, F., 2011. Markov models of molecular kinetics: Generation and validation. Journal of chemical physics. 134, 1-23.
  23. Herold, M., Goldstein, N.C. and Clarke, K. C., 2003. The Spatiotemporal Form of Urban Growth: Measurement. Analysis and Modeling, Remote Sensing of Environment, 86, 286–302.
  24. Irwin, E. G. and Geoghegan, J., 2001. Theory, data, methods: developing spatially explicit economic models of land use change, Agriculture. Ecosystems & Environment. 85, 7-24.
  25. Jenerette, G. and Wu, J., 2001. Analysis and simulation of land use change in the central Arizona-Phonix region, USA. Landscape ecology. 16, 611-626.
  26. Jimenez-Muñoz, J. C., Sobrino, J.A., Plaza, A., Guanter, L., Moreno, J. and Martinez, P., 2009. Comparison between fractional vegetation cover retrievals from vegetation indices and spectral mixture analysis: Case study of PROBA/CHRIS data over an agricultural area. Sensors. 9, 768-793.
  27. Jiang, Z., Huete, A. R., Didan, K. and Miura, T., 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment. 112, 3833-3845.
  28. Karimi, A., Abdollahi, S., Kabiri Balajadeh, H. R., Askari, O. A. K., Eslamian, S. and Singh, V., 2018. The Use of Remote Sensing Techniques in Detecting and Predicting Forest Vegetation Change Using MODIS Satellite Data, Golestan, Iran. American Journal of Engineering and Applied Science. 11, 387.396.
  29. Kumar S., Radhakrishnan, N. and Mathew, S., 2014. Land use change modeling using a Markov model and remote sensing, Geomatics. Natural Hazards and Risk. 5, 145-156.
  30. Lu, D., Mausel, P., Brondi´zio, E. and Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing. 25(12):2365-2401.
  31. Lambin E. F., Turner, B. L. and Geist, H. J., 2001. The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change. 11, 261-269.
  32. Mather, P. and Tso, B., 2009. Classification methods for remotely sensed Data, second ed, Taylor& Francis publisher., London.
  33. Malarvizhi, K., Kumar, S. V. and Porchelvan, P., 2016. Use of high resolution Google earth satellite imagery in landuse map preparation for urban related applications. Procedia Technology. 24, 1835-1842.
  34. Mubea, K. W., Ngigi, T. and Mundia, Ch., 2010. Assessing application of Markov chain analysis in predicting land cover change: a case study of Nakuru municipality. Journal of Agriculture, Science and Technology. 12, 126-143.
  35. Memarian, H., Balasundram, S. K., Bin Talib, J., Teh Boon Sung, Ch., MohdSood, A. and Abbaspour, K., 2012. Validation of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia. Journal of Geographic Information System. 4, 542-554.
  36. Mohammadyari, F., Pourkhabaz, H., Tavakoli, M. and Aghdar, H., 2015. MappingVegetation and monitoring its Changes using Remote Sensing and GIS Techniques (Case study: Behbahan city). Iranian journal of Scientific - Research Quarterly of Geographical Data (SEPEHR). 23, 23-34 (In Persian with English abstract)
  37. Mirzaeizadeh, V., Niknejad, M. and Haydari, M., 2016. Monitoring and predicting changes in vegetation density using remote sensing (Case study: Venet watershed, Ilam province). Iranian journal of Zagros forest research. 3,19-32 (In Persian with English abstract)
  38. Omar, N. Q., Sanusi, S. A. M., Hussin, W. M.W., Samat, N. and Mohammed, K. S., 2014. CA-Markov model using analytical hierarchy process and multi regression technique, 7th IGRSM International Remote Sensing & GIS Conference and Exhibition, IOP Conference Series: Earth and Environmental Science, 21th-22th April, Kuala Lumpur, Malaysia. P. 1-16.
  39. Pointius, R. G., 2000. Quantification Error versus location Error in comparison of categorical maps. Photogrammetric engineering & Remote sensing. 66, 1011-1016.
  40. Pijanowskia, B.C., Brown, D.G., Shellitoc, B.A. and Manikd, G. A., 2002. Using Neural Networks and GIS to Forecast Land Use Changes: A Land Transformation Model. Computers, Environment and Urban Systems. 26, 553-575.
  41. Quintero, G. V., Moreno, R. S., Garcia, M. P., Guerrero, F. V., Alvarez, C. P. and Alvarez, A. P., 2016. Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata. Sustainability. 8, 1-13.
  42. Rafiee, R., Salman Mahiny, A. and Khorasani, N., 2009. Assessment of changes in urban green spaces of Mashhad city using satellite data. International Journal of Applied Earth Observation and Geoinformation. 11, 431-438.
  43. Sobrino J. A., Jimenez-Muñoz J. C. and Paolini, L., 2004. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment. 90, 434-440.
  44. Smits, P. C., Dellepiane, S. G. and Schowengerdt, R .A., 1999. Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing. 20, 1461-1486.
  45. Singh, A., 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing. 10, 989-1003.
  46. Shayesteh, K. and Mohammadyary, F., 2018. Evolution and prediction of changes in vegetation using landscape metrics and Markov model. Iranian journal of geography and development. 16, 85-104 (In Persian with English abstract)
  47. Shamsipour, A. A., Heydari, S. and Bagheri, K., 2017. Monitoring the Process of Land Use/cover Changes Using Markov CA Model: a Case Study of Kermanshah City. Iranian journal of geography urban planning researches. 5, 495-514 (In Persian with English abstract)
  48. Salehi, N., Ekhtesasi, M. R and Talebi, A., 2019. Predicting locational trend of land use changes using CA-Markov model (Case study: Safarod Ramsar watershed). Iranian journal of RS and GIS for Natural Resources.10, 106-120 (In Persian with English abstract)
  49. Subedi, P., Subedi, K. and Thapa, B., 2013. Application of a hybrid cellular automaton – Markov (CA-Markov) model in land-use change prediction: A case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences. 1,126–132.
  50. Surabuddin Mondal, M. D., Sharma, N., Garg, P. K. and Kappas, M., 2016. Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results, The Egyptian Journal of Remote Sensing and Space Sciences, 19 , 259–272,
  51. Thapa, R. B. and Murayama, Y., 2012. Scenario based urban growth allocation in Kathmandu Valley, Nepal. Landscape and Urban Planning. 105, 140-148.
  52. Tilahun A. and Teferie B., 2015. Accuracy assessment of land use land cover classification using Google earth. American Journal of Environmental Protection. 4, 193-198.
  53. Ustin, S. L., 2004. Remote Sensing for Natural Resources Management and Environmental Monitoring, Manual of remote sensing, third ed, John Wiley & Sons., New York.
  54. Verburg P. H., Schot, P., Dijst M. and Veldkamp, A., 2004. Land use change modeling: current practice and research priorities. GeoJournal. 61, 309-324.
  55. Wang, L., Yu, D., Liu, Z. and Yang, Y., 2018. Study on NDVI changes in Weihe Watershed based on CA–Markov model, Geological Journal. 53, 435-441.
  56. Yu, L., Liu, T., Bu, K., Yan, F., Yang, J., Chang, L. and Zhang, S., 2017. Monitoring the long term vegetation phenology change in Northeast China from 1982 to 2015. Scientific Reports. 7, 1-8.
  57. Zhang, R., Tang, Ch., 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 Modeling. 54, 924-930.