Abstract
Abstract. In order to investigate the capability of satellite images for Pistachio forests density mapping, IRS-P6-LISS IV data were analyzed in an area of 500 ha in Iran. After geometric correction, suitable training areas were determined based on fieldwork. Suitable spectral transformations like NDVI, PVI and PCA were performed. A ground truth map included of 34 plots (each plot 1 ha) were prepared. Hard and soft supervised classifications were performed with 5 density classes (0–5%, 5–10%, 10–15%, 15–20% and > 20%). Because of low separability of classes, some classes were merged and classifications were repeated with 3 classes. Finally, the highest overall accuracy and kappa coefficient of 70% and 0.44, respectively, were obtained with three classes (0–5%, 5–20%, and > 20%) by fuzzy classifier. Considering the low kappa value obtained, it could be concluded that the result of the classification was not desirable. Therefore, this approach is not appropriate for operational mapping of these valuable Pistachio forests.
Highlights
Pistachio (Pistacia vera) natural forests are one of the most important forest reserves in Iran which are considerable for their conservational and economical conditions
The main objective of this study is to investigate the capability of IRS-P6-LISS IV image for Pistachio forests density mapping in Khorasan province of Iran
To assess the capability of IRS-P6-LISS IV images for forest density mapping, an accurate ground truth was prepared through fieldwork, since there is no update aerial photo or other very high resolution images
Summary
Pistachio (Pistacia vera) natural forests are one of the most important forest reserves in Iran which are considerable for their conservational and economical conditions. Providing spatial information and thematic maps are essential for recognize recognizing and manage managing these valuable forests. There is not enough and comprehensive information related to this area. Information describing forest canopy is essential for monitoring and sustainable management. There are a variety of approaches that have been used to map forest canopy: (1) object-based classification [Dorren et al, 2003], (2) Maximum likelihood forest canopy density mapper (4) artificial neural network [Joshi et al, 2006]. Due to importance of forest density maps, various satellite data and forest stands, continuing of these researches is needed. The main objective of this study is to investigate the capability of IRS-P6-LISS IV image for Pistachio forests density mapping in Khorasan province of Iran
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