Modeling Trends Land Use Changes Local by Using LCM Model Based on Artificial Neural Networks and Markov Chain Analysis (Case Study: BeheshtAbad Watershed)

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Mohammad Yousefi Majid; Pajoohesh Mehdi; HONARBAKHSH AFSHIN

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Journal: JOURNAL OF WATERSHED MANAGEMENT RESEARCH Year:2020 | Volume:11 | Issue:21 Page(s): 129-142

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Title

Modeling Trends Land Use Changes Local by Using LCM Model Based on Artificial Neural Networks and Markov Chain Analysis (Case Study: BeheshtAbad Watershed)

Author(s)

Mohammad Yousefi Majid | Pajoohesh Mehdi | HONARBAKHSH AFSHIN | Issue Writer Certificate

Keywords

BeheshabadQ1

Classification of ImagesQ1

Landsat Satellite ImagesQ1

Monitor ChangesQ1

Prediction ChangesQ1

Spatial VariablesQ1

Transmission ForceQ1

Abstract

The prediction and modeling of land use changes is important for understanding the quantity and quality of possible future changes. The purpose of this research is to monitor land use changes in the past and to investigate the possibility of predicting them in the future using the LCM model in the Beheshabad watershed of Chaharmahal va Bakhtiari province. In this study, Landsat 5 TM sensors images of 1991 and 2008 and Landsat 8 OLI sensors images in 2016 were used and analyzed. Images of all three periods were classified into five categories of range lands, urban-construction areas, agricultural lands, garden lands and Bare lands. The prediction of land use status for 2016 was carried out using user maps of 1370 and 1387 using the LCM model based on artificial neural networks and Markov chain analysis. For this purpose, the Spatial Variables of the distance from the pastures, the distance from the residential and urban areas, the distance from the agricultural land, the distance from the garden, the distance from the Bare Lands, distance from the river, distance from the road, elevation or digital elevation model, slope and direction of the slope, are used as factors affecting changes in artificial neural network. The results of modeling the Transmission Force using artificial neural network in most. of the sub-models showed high accuracy (62 to 94 percent). The total error in modeling for the year 2016 obtained approximetly 23%, which reflects the large mismatch of the predicted image of the model with the image of the Earth reality and the acceptability of the model. The prediction results for the years of 1420 and 1429 showed that the area of range land and bare lands would be reduced and the area of urbanconstruction areas, garden lands and agricultural lands would becincrease.

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APA: Copy

Mohammad Yousefi, Majid, Pajoohesh, Mehdi, & HONARBAKHSH, AFSHIN. (2020). Modeling Trends Land Use Changes Local by Using LCM Model Based on Artificial Neural Networks and Markov Chain Analysis (Case Study: BeheshtAbad Watershed). JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 11(21 ), 129-142. SID. https://sid.ir/paper/407119/en

Vancouver: Copy

Mohammad Yousefi Majid, Pajoohesh Mehdi, HONARBAKHSH AFSHIN. Modeling Trends Land Use Changes Local by Using LCM Model Based on Artificial Neural Networks and Markov Chain Analysis (Case Study: BeheshtAbad Watershed). JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2020;11(21 ):129-142. Available from: https://sid.ir/paper/407119/en

IEEE: Copy

Majid Mohammad Yousefi, Mehdi Pajoohesh, and AFSHIN HONARBAKHSH, “Modeling Trends Land Use Changes Local by Using LCM Model Based on Artificial Neural Networks and Markov Chain Analysis (Case Study: BeheshtAbad Watershed),” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 11, no. 21 , pp. 129–142, 2020, [Online]. Available: https://sid.ir/paper/407119/en

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