Document Type : Research Paper
Authors
1
M.Sc. Graduate, Department of Forestry, Faculty of Natural Resources, University of Kurdistan, Sanandaj, I.R. Iran
2
Assoc. Prof., Department of Forestry, Faculty of Natural Resources, The Center for Research and Development of Northern Zagros Forestry, University of Kurdistan, Sanandaj, I.R. Iran
3
Assist. Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, I.R. Iran
Abstract
This study aims at investigating the possibility of using remote sensing to estimate the woody species diversity which plays an important role in the sustainability of forest ecosystems. For this purpose, Sentinel-2 imagery data was used over a part of the Marivan forests, Iran. The study site with an area of about 500 ha was investigated through field observation. 89 field-plots with a size of 40 m × 40 m were selected over the whole area. Within each plot, tree species and number of each species were recorded. Different woody species diversity indices i.e. Simpson, Shannon-Wiener, Margalef, Fisher-alpha, and Dominance were calculated for each sample plot. Satellite imagery of sentinel-2 level 1C product was provided. Different vegetation indices were generated from the original bands of satellite imagery, and the digital values were extracted from spectral bands, and vegetation indices based on the field sample plots. In the next step, the correlation between extracted digital values (i.e. spectral data) and species diversity indices was investigated for 66 sample plots, and stepwise multiple regression was applied. The validation procedure based on 23 sample plots showed that the Sentinel-2 data predict Simpson index (R2 = 0.57, RMSEr = 21.39%), Dominant index (R2 = 0.55, RMSEr = 22.63%) and Shannon-Wiener index (R2 = 0.50, RMSEr = 23.16%) were more accurate than other species diversity indices. Based on the results of this study, it could be concluded that Sentinel-2 images have a moderate ability to estimate species diversity in Zagros forests.
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