Spectral unmixing-based Arctic plant species analysis using a spectral library and terrestrial hyperspectral Imagery: A case study in Adventdalen, Svalbard

Abstract

Remote sensing is an invaluable tool for monitoring the rapid changes in Arctic vegetation distribution caused by global warming. Although hyperspectral data consisting of contiguous spectral bands enables the quantitative analysis of remote sensing data, mapping Arctic vegetation using hyperspectral remote sensing remains challenging due to the difficulty in acquiring data from the region. Additionally, the mixed pixel issue, which represents a mixture of more than one plant species due to low-spatial resolution, hinders accurate mapping of Arctic vegetation. To address these limitations, we collected hyperspectral information on the dominant plant species, such as shrubs and graminoids, in Adventdalen, Svalbard, and investigated effective methods for mapping Arctic vegetation using spectral unmixing. First, labeled datasets were constructed for Arctic plant species by extracting pixel data from terrestrial hyperspectral images. A spectral library was developed using the labeled datasets and used for spectral unmixing as endmembers. We employed three established classifiers, random forest, support vector machine, and one-dimensional convolutional neural network (1D-CNN) for hyperspectral image classification. Subsequently, we quantitatively and qualitatively compared the classification performances of these machine learning-based classifiers to determine the optimal classification method for validation purposes. The first derivative spectra of smoothed reflectance (RS,FD) and the 1D-CNN classifier were used to identify ground truth by classifying pixels of Arctic vegetation because they achieved the highest statistical accuracies of 0.9892 (Kappa = 0.9880) and 0.9352 (Kappa = 0.9280) for the two independent test sets and produced the most accurate vegetation maps. The spectral library using the RS,FD spectrum showed high spectral discriminability and potential for estimating the abundance of classes from simulated mixed pixels, resulting in good agreement with the ground truth. Accordingly, our findings provide valuable insights and references for large-scale mapping studies using remote sensing data and offer effective monitoring strategies for Arctic vegetation changes in response to climate change.

Keywords

• Hyperspectral image
• Arctic vegetation mapping
• Machine learning classification
• Spectral library
• Spectral unmixing