Investigating the transmission potential of land use and land cover using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods (Case study: Bastam basin, Selseleh city)

Subject Areas : natural resorces

soheila naseri rad ¹ , Hamed Naghavi ² , Javad Soosani ³ , seyed ahmadreza nouredini ⁴ , sasan vafaei ⁵

1 - M.Sc in Forestry Engineering, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran.
2 - ‌Assistant Professor in Forestry Engineering, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran. *( Corresponding author)
3 - Associate Professor in Forestry Engineering, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran.
4 - Ph.D. in Forestry Engineering, Faculty of Agriculture and Natural Resources, University of Guilan, Rasht, Iran.
5 - ‌Ph.D. in Forestry Engineering, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran.

Received: 2019-04-22 Accepted : 2019-06-26 Published : 2021-01-20

Keywords: land cover, Markov chain, Remote sensing, land use, Modeling,

Abstract :

Background and Objective: Assessing and estimating the high-accuracy transmission potential is an important step in the process of land use and land cover changes modeling and predicting. The aim of this study is to investigate the transmission potential of land use and land cover changes using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods. Method: The land use and land cover maps for a 30-year period (1985-2015) were prepared using Landsat 5 and 8 satellite imagery. Land use and land cover transmission potential modeling was done using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods and effective variables in the process of change. The accuracy of the results obtained from the models was determined by comparing with ground reality map for mentioned year. Findings: The Kappa coefficient of Similarity Weighted Instance based Learning, Logistic regression and Geomod were 0.84, 0.76 and 0.67, respectively. The investigating predicted maps for 2030 prepared by Similarity Weighted Instance based Learning and Markov chain showed that the area of residential areas, gardens and agricultural lands is increasing and the area of bare land, forests, pastures and water resources will have a decrease trend. Discussion and Conclusion: Finally, the results indicate a relatively high accuracy of three methods in estimating the transmission potential for land use and land cover changes, but according to the kappa coefficients, the accuracy of Similarity Weighted Instance based Learning method more than the other two methods.  

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