Providing poplar plantation map by Indian remote sensing (IRS) satellite imagery in Northern Iran

Abstract

Using satellite imagery processing technique, we can separate planting poplar lands with a low cost, high speed and precision. The present study was conducted in northern Iran using Indian Remote Sensing (IRS) satellite data in July 2006 and ILWIS software to identify the cultivated surfaces of poplar. Field visitation recorded 548 ground control points by GPS in the way of features in various areas of Guilan Province. Ground control point map was overlaid on satellite color composite (sample sat) as training points map (pixels). In supervised classification, box classifier, maximum likelihood, minimum distance and minimum mahalanobis distance were used to determine user’s accuracy, overall accuracy and kappa coefficient of each method, separately. Results indicate that spectrum reflex of poplar species is different from other vegetations and separable; but it has very close interference with conifer forest, natural forest, rice field and canebrake. By determining suitable training points, poplar cultivated surfaces could be identified. Among the classification methods, maximum likelihood with 91.48% of overall accuracy and 90.75% of kappa coefficient is the best method for separating planting poplar lands compared to the other methods. Key words: Poplar planting, supervised classification, maximum likelihood method, Indian remote sensing (IRS), Guilan.