Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data
By Markus Immitzer, Clement Atzberger, Tatjana Koukal. Remote Sensing, 2012
754 citations recorded by the source corpus.
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.
Subfields: Ecological Modeling, Ecology, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/79a73d0ae35aa305a6b6829295f1a7fec4f605e8
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In Ecological Modeling
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In Environmental Engineering
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Citations in the corpus
- Flood hazard risk assessment model based on random forest (2015, background): “…classification (Solomon and Liu, 2010), prediction of rock burst classification (Dong et al., 2013), genomic data analysis (Chen and Ishwaran, 2012), tree species classification (Immitzer et al., 2012), gene selection (Deng and Runge, 2013), and computer-aided diagnosis (Mihailescu et al., 2013).”
- A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China (2017, background): “The main advantage of LDA is its straightforward interpretability (Immitzer et al. 2012) which was clearly seen in the current study.”
- Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery (2017, background): “Several remote sensing studies have successfully mapped specieslevel distributions, though largely at highly localized spatial scales (Carleer and Wolff, 2004; Immitzer et al., 2012; Ke et al., 2010; Martin et al., 1998; Plourde et al., 2007).”