Analysis of Land Cover Changes in the Corridor of Larestan-Evaz using Spatial Analysis Techniques by RS and GIS

Document Type : Research Paper

Authors

Department of Urban Planning, Faculty of Art and Architecture, Shiraz University, Shiraz, Iran

10.22059/jtcp.2025.403740.670528

Abstract

This research aimed to analyze land cover and land use changes in the Lar-Evaz communication axis during the period from 2004 to 2024 and to investigate the factors affecting these changes. To achieve the research objectives, Landsat 7 and 8 satellite images (ETM+ and OLI sensors) were used. Land cover and land use classification was performed using the maximum likelihood method for the years 2004, 2014, and 2024 in ENVI 5.6 software. The kappa coefficient was evaluated to examine the accuracy of the maps as 94%, 97%, and 96%, respectively. Subsequently, the changes that occurred were examined using the post-classification comparison method. In the next step, to examine the factors affecting the occurrence of changes, the logistic regression model was used in the TerrSet software, and the parameters under study were examined as independent variables, including altitude, land slope, proximity to roads, proximity to residential centers, and proximity to water sources (water wells). The results indicated that barren lands decreased by 46.9% over a 20-year period, from 276.0005 km² in 2004 to 249.89 km² in 2024. The largest changes were related to built-up areas, which increased from 7656.35 km² in 2004 to 4785.60 km² in 2024, a  69.1% growth. The vegetation cover of the region has also increased by about 47.3%, from 2879.40 km² in 2004 to 6851.41 km² in 2024. In examining the factors affecting the occurrence of changes using the logistic regression model, it was found that proximity to water sources (wells), slope, and proximity to the road had the greatest impact on causing changes in the Lar-Evaz region. The evaluation of the logistic regression model with ROC and Pseudo-R² indices of 0.939 and 0.426, respectively, indicated the high capability of the model in describing changes and determining areas susceptible to change.

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Main Subjects

References

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Town and Country Planning
Volume 17, Issue 2
Autumn & Winter
October 2025
Pages 209-228

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  • Receive Date: 07 October 2025
  • Revise Date: 28 December 2025
  • Accept Date: 28 December 2025

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