Modeling and predicting land use changes using Markov chain Model (Case study: Ghaleh Jogh, Torbat-e-Heydarieh City)

Document Type : Complete scientific research article

Authors

1 M.Sc. Graduate, Dept. of Nature Engineering and Medicinal Plants, Faculty of Agriculture, University of Torbat Heydarieh, Khorasan-Razavi, Iran.

2 Corresponding Author, Assistant Prof., Dept. of Nature Engineering and Medicinal Plants, Faculty of Agriculture, University of Torbat-Heydarieh, Khorasan-Razavi, Iran.

3 Assistant Prof., Soil Conservation and Watershed Management Research Department, Khorasan-Razavi, Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Khorasan-Razavi, Iran.

Abstract

Modeling and predicting land use changes using Markov chain Model (Case study: Ghaleh Jogh, Torbat-e-Heydarieh City)
Abstract:
Background and objectives: Prediction of land use changes in explaining the interactions between ecosystems and human activities is important for helping decision makers. A Land use map is considered an information resource in natural resource management. Optimal resource management needs to be investigated, as recognition of changes and resource degradation in the past, and proper and principled planning in order to control and control possible future degradation. The purpose of this study was to evaluate the performance of the Markov chain model (CA Markov) in determining and predicting land use changes for the future in the Ghaleh Jogh area located in the city of Torbat-e-Heydarieh.
Materials and Methods: Satellite imagery, remote sensing, and the Markov chain model were used for modeling and detecting land use change. The study area of the Ghaleh Jogh Watershed is from Torbat-e-Heydarieh province. A topographic map with a scale of 1: 25,000 and OLI, ETM +, and Landsat MSS images of 1987, 2002, and 2015 were used to prepare land use maps and the changes process. Satellite imagery with first order polynomial equation was corrected by the method of closest neighboring geometric correction and corrected using linear and histogram explanations. For the categorization of images, the controlled classification method and the maximum probability algorithm with acceptable accuracy are used. A Land use map was prepared in 4 ranges of pasture land, Bayer lands, crops, and gardens. In this research, using the CA Markov model, the 2015 forecast map was compared with the monitored map. The percentage of land use, garden, rangeland, lawn, and arable land use varies from 5, 54, 24, and 17 percent in 1987 to 7, 4, 7, 36, 51, and 7.6 percent in 2015, and the user's map Lands for 2025 and 2040 are foreseen. Model calibration was used for predicting the 2015 model.
Results and Conclusion: The results showed that according to the Kappa coefficient, the prepared map has high accuracy. Based on the results of the detection and simulation of the pasture and cultivating lands, they are among the most unstable classes. Garden lands due to more attention and economic significance, there have not been any significant changes in their extent. In general, the largest changes occurred in pasture and agricultural land. The corrective work done in the region has reduced the area of arable land and added to the rangelands.

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References

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Journal of Water and Soil Conservation
Volume 30, Issue 2
July 2023
Pages 75-96

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