Preparation of Land Use Map Using ETM+ Landsat (A Case Study in Hendodar Watershed)

Subject Areas : GIS

Rezvan davodpour ¹ , Hamid Toranjzar ²

1 - Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran *(Corresponding Author)
2 - Department of Envionment, Arak Branch, Islamic Azad University, Arak, Iran

Received: 2017-06-12 Accepted : 2018-12-05 Published : 2020-08-22

Keywords: ETM+ data, Remote sensing, Hendodar watershed, land use,

Abstract :

Background and Objective: High population growth rate has led to excessive use of capacity and double pressure on natural resources, resulting in rapid land use changes. Therefore, quick and accurate identification of types of land cover can play an effective role in planning and management. Satellite data because of vast and integrated sight covering with different electromagnetic spectrums and updated images are very suitable for making applicable Land use maps. The aim of this study is preparation of land use map using ETM+ landsat (a Case Study in Hendodar Watershed) Method: The Landsat 7 satellite images were used to determine the land use changes of Hendodar watershed in Markazi province. The GPS was used to determine the position of land use and land cover types on the basis of taking test and ground control points on field investigation. Obtained samples were used for supervised classification with four different algorithms including maximum Likelihood, minimum distance, Minimum Mahalanobis Distance and Box Classification.  Findings: The optimum index factor (OIF) for the main bands and PCA (principal coordinate analysis) were used to select the optimum combination of three bands in a satellite image to create a color composite, sample set and other operation and classification. Among the algorithms, the maximum likelihood classification algorithm had better results from the types of coverage and lands use on the images.  Discussion and Conclusion: The maximum likelihood classification algorithm with combination of b7, b4, b1 bands with 81.25% accuracy is the best algorithms of land use determination and classification comparing with real ground map of the area.

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