Abstract
Imaging spectroscopy is a promising tool for airborne tree species recognition in hyper-diverse tropical canopies. However, its widespread application is limited by the signal sensitivity to acquisition parameters, which may require new training data in every new area of application. This study explores how various pre-processing steps may improve species discrimination and species recognition under different operational settings. In the first experiment, a classifier was trained and applied on imaging spectroscopy data acquired on a single date, while in a second experiment, the classifier was trained on data from one date and applied to species identification on data from a different date. A radiative transfer model based on atmospheric compensation was applied with special focus on the automatic retrieval of aerosol amounts. The impact of spatial or spectral filtering and normalisation was explored as an alternative to atmospheric correction. A pixel-wise classification was performed with a linear discriminant analysis trained on individual tree crowns identified at the species level. Tree species were then identified at the crown scale based on a majority vote rule. Atmospheric corrections did not outperform simple statistical processing (i.e., filtering and normalisation) when training and testing sets were taken from the same flight date. However, atmospheric corrections became necessary for reliable species recognition when different dates were considered. Shadow masking improved species classification results in all cases. Single date classification rate was 83.9% for 1297 crowns of 20 tropical species. The loss of mean accuracy observed when using training data from one date to identify species at another date in the same area was limited to 10% when atmospheric correction was applied.
Highlights
The Amazon forest, the largest tropical forest basin on earth, covers an area of 5.5 million km2 and harbours an estimated 16,000 tree species [1]
We evaluate the impact of various levels of image pre-processing steps on the classification accuracy of 20 tree species of tropical forest using Linear Discriminant analysis (LDA)
We report the effect of various pre-processing steps on species specific signal noise ratio (SNR) as captured by the R2 of the ANOVA of the different wavelengths
Summary
The Amazon forest, the largest tropical forest basin on earth, covers an area of 5.5 million km and harbours an estimated 16,000 tree species [1]. It plays a major role in global climate regulation, through the cycling and storage of carbon [2] and it constitutes an extraordinary terrestrial reservoir of biodiversity [1]. The Amazon faces degradation threats [3] from unsustainable logging [4], climate change [5], land use change [6], agricultural [7] and other human activities [8]. The mere number of tree species in the Amazon is a matter of debate [10]
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