Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
International Journal of Remote Sensing
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Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi-arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high-resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field-measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan-sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south-western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer-aided automatic classification methods were tested. After delineation, stem volume as a function of these image-measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R 2 = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R 2 = 0.51, with a RMSE of 15.2%. The results showed that pan-sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.