Zagros Forest Canopy Density Classification by Image Texture Quantization based on First and Second Statistical and Geostatistical Methods Using Arial Panchromatic Images

Author

Photogrammetry and Remote Sensing Departmnt, K.N.Toosi University of Technology, Tehran, Iran.

Abstract

Forest canopy density classification map is one of the main sources of information used in forest management. In conventional methods multispectral images are used to generate the map. In this study, aerial panchromatic images as a valuable data source are used to generate this map. Statistical image texture quantization methods including first statistical and second statistical based on GLCM matrix and also geostatistical method used to generate new features from high spatial resolution image. Generated features beside main image used as classification input feature space. Supervised classification was used and about 90% accuracy was obtained. This method is mainly usable in areas with low variety in forest cover type like Zagros and Iran-Turanian region.

Keywords

Journal of Soft Computing and Information Technology
Volume 6, Issue 1 - Serial Number 1
January 2019
Pages 16-25

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