Object-oriented classification of Ikonos satellite data for the identification of tree species composition

https://doi.org/10.17221/4500-JFSCitation:Hájek F. (2006): Object-oriented classification of Ikonos satellite data for the identification of tree species composition. J. For. Sci., 52: 181-187.
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This paper describes the automated classification of tree species composition from Ikonos 4-meter imagery using an object-oriented approach. The image was acquired over a man-planted forest area with the proportion of various forest types (conifers, broadleaved, mixed) in the Krušné hory Mts., Czech Republic. In order to enlarge the class signature space, additional channels were calculated by low-pass filtering, IHS transformation and Haralick texture measures. Employing these layers, image segmentation and classification were conducted on several levels to create a hierarchical image object network. The higher level separated the image into smaller parts regarding the stand maturity and structure, the lower (detailed) level assigned individual tree clusters into classes for the main forest species. The classification accuracy was assessed by comparing the automated technique with the field inventory using Kappa coefficient. The study aimed to create a rule-base transferable to other datasets. Moreover, the appropriate scale of common image data and utilisation in forestry management are evaluated.
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