Abstract
Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.
Highlights
The accurate classification of tree species is a key element for forest management, policy implementation, and the conservation sector for planning strategies and actions to address biodiversity loss [1,2]
The best performance belonged to the independent component analysis (ICA) transformation, and the poorest performance belonged to the original airborne prism experiment (APEX) as an input
The ICA transformation was able to derive independent components which can be viewed as a set of mutually exclusive classes (Figure 11)
Summary
Hyperspectral imagery ( known as imaging spectroscopy) provides detailed spectral information, utilizing contiguous narrow spectral bands that can be used for tree species classification [4,5,6,7,8,9,10]. According to Harrison [14], the environmental monitoring of vegetation has widely used the visible (VIS; 400–700 nm) and the near and shortwave infrared (NIR: 700–1400 nm; SWIR: 1400–2500 nm) of the electromagnetic spectrum (EM) Each of these spectral regions provides different information; for instance, in the VIS, chlorophyll reflects in the green band (495–570 nm) and absorbs in the red and blue bands (620–750 nm and 450–495 nm, respectively). In the NIR, plants are strongly reflective, and their reflectance is driven by leaf thickness and internal morphology; few studies have utilized these features for species classification in the same sites and seasons [14,15,16]
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