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Selected Publication:

Immitzer, M; Atzberger, C; Koukal, T.
(2012): Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data
REMOTE SENS-BASEL. 2012; 4(9): 2661-2693. FullText FullText_BOKU

Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pixel-based) using spectra of manually delineated sunlit regions of tree crowns. The overall accuracy for classifying 10 tree species was around 82% (8 bands, object-based). The class-specific producerxxxs accuracies ranged between 33% (European hornbeam) and 94% (European beech) and the userxxxs accuracies between 57% (European hornbeam) and 92% (Lawsonxxxs cypress). The object-based approach outperformed the pixel-based approach. We could show that the 4 new WorldView-2 bands (Coastal, Yellow, Red Edge, and Near Infrared 2) have only limited impact on classification accuracy if only the 4 main tree species (Norway spruce, Scots pine, European beech, and English oak) are to be separated. However, classification accuracy increased significantly using the full spectral resolution if further tree species were included. Beside the impact on overall classification accuracy, the importance of the spectral bands was evaluated with two measures provided by RF. An in-depth analysis of the RF output was carried out to evaluate the impact of reference data quality and the resulting reliability of final class assignments. Finally, an extensive literature review on tree species classification comprising about 20 studies is presented.
Authors BOKU Wien:
Atzberger Clement
Immitzer Markus
Koukal Tatjana

Find related publications in this database (Keywords)
tree species classification
temperate forest
Random Forest
linear discriminant analysis
variable importance measures
classification reliability

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