Estimation of Tree Density with High-resolution Imagery in the Zarbin Forest of North Iran (cupressus Sempervirence Var. Horzontalis)

Cupressus sempervirence var. horzontalis belong to the Cupressus family. This species is evergreen and grows within an elevation range of 290 meters and 1200 meters above sea level in the North of Iran. The extraction of texture features from high-resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally similar landscape features. High spatial resolution satellite imagery and Aerial photography to estimate tree density have been applied. In this paper, objects and questions are; identification and delineation of tree crown, estimation of tree density by counting trees per hectare, the analyses of image segmentation, classification, texture and comparison with aerial photography, estimation of vegetation index and introduce a new method for tree density on base of pixel-based of classification. A new method of pixel-based classification has been used for tree density per hectare. A sample plot area about 900 ha was selected incorporating 15 sub sample plots chosen with 9 ha (300 ×300m) equal to 120 × 120 pixels for counting trees per hectare. Our method of measuring tree density was useful for estimation of biomass, enabling better decision making for natural resource managers in environmental fields. The results show that the proposed method could effectively reduce the over classification effect and achieve more accurate classification results, compared to existing method. The results of these analyses should be noted. We recommend a new method pixel based classification approach for extracting tree in the forest. * Corresponding author : Fadaei, Department of Social Informatics, Graduate School of Informatics, Kyoto University, Kyoto, Japan 606-8501, Phone: +81-75-753-3137 Fax: +81-75-753-3133, Email: [email protected]