Abstract
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 producer’s accuracies ranged between 33% (European hornbeam) and 94% (European beech) and the user’s accuracies between 57% (European hornbeam) and 92% (Lawson’s 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.
Highlights
Tree species diversity is a key parameter to describe forest ecosystems
We focus on the following research questions: (1) Which tree species can be separated by WorldView-2 data and which accuracies can be achieved? (2) Do the 4 additional bands of WorldView-2 improve the classification accuracy significantly compared to the 4 standard bands? (3) Is the classifier Random
We examined the explanatory power of the 8 spectral bands in the classification of the 10 tree species using the following measures of variable importance: mean decrease in accuracy (MDA), mean decrease in Gini (MDG), mean discriminant function coefficients (MDFC), and Wilks’
Summary
Tree species diversity is a key parameter to describe forest ecosystems. It is a relevant parameter for various ecological issues such as wildlife habitat modeling and it is becoming more and more important in sustainable forest management [1,2,3]. In close-to-nature forest management pure stands are replaced by heterogeneous, mixed stands. Spatially detailed tree species information is of high importance. Traditional forest inventories and other field-based data acquisition methods, such as stand estimation, are not suitable for such tasks. It is nearly impossible to acquire detailed tree species information over large areas purely on the basis of field assessments. Enhanced methods are required to get spatially-explicit information on the tree species composition and distribution patterns
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