10.57647/j.jrs.2025.1503.29

Forecast of temporal-spatial changes of Iran's vegetation using Markov Chain- Automated Cell Model

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  • Fereshteh Pormarefat1,
  • Mahdi Tazeh1,
  • Majid Sadeghinia1,
  • Vahid Moosavi2,
  1. Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, Ardakan, Iran
  2. Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran

Received: 2024-04-15

Revised: 2024-07-06

Accepted: 2024-11-06

Published in Issue 2025-07-20

Copyright (c) 2025 Fereshteh Pormarefat, Mahdi Tazeh, Majid Sadeghinia, Vahid Moosavi (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Pormarefat, F., Tazeh, M., Sadeghinia, M., & Moosavi, V. (2025). Forecast of temporal-spatial changes of Iran’s vegetation using Markov Chain- Automated Cell Model. Journal of Rangeland Science, 15(3). https://doi.org/10.57647/j.jrs.2025.1503.29
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Abstract

Vegetation changes are the main criteria for desertification and that is important in land management and monitoring. This research aimed to investigate the vegetation cover changes in different provinces of Iran. In the present study, vegetation changes were forecasted for 2024 using the Normalized Difference Vegetation Index (NDVI) obtained from MODIS satellite images to monitor temporal-spatial vegetation changes in different provinces of Iran and apply a Markov forecast model. To this end, vegetation maps were initially drawn for 2000, 2008 and 2016, and then changes were detected based on the comparison after classification according to different provinces. Results revealed that more descending vegetation changes belong to Markazi, Lorestan, Khuzestan, North Khorasan, Razavi Khorasan, Bushehr and Ilam Provinces, respectively. Finally, vegetation changes were forecasted for 2024 using Markov chain-automated cell model. The results of the vegetation changes matrix based on maps from 2008 to 2016 showed that the vegetation classes of no vegetation, low vegetation, medium vegetation, and high vegetation with probabilities of 95, 90, 89, and 93%, respectively, would remain unchanged from 2016 to 2024 so that the class without vegetation will have the highest stability, and on the other hand, the normal class will have the lowest stability.

Keywords

  • MODIS,
  • NDVI,
  • Remote Sensing,
  • Simulation,
  • Change Detection
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