Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 13; p. 2470
• Main Authors: Chi, Junhwa, Lee, Hyoungseok, Hong, Soon Gyu, Kim, Hyun-Cheol
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Antarctic region; Antarctic vegetation; Antarctica; Barton Peninsula; Biodiversity; case studies; Climate change; Contact potentials; Discriminant analysis; Environmental conditions; field spectroscopy; Image classification; Islands; King George Island; Laboratories; Libraries; lichen; moss; Mosses; mosses and liverworts; Object recognition; Polar environments; Polls & surveys; Principal components analysis; Remote sensing; Research centers; Sensors; Short wave radiation; Snow; Spectra; spectral analysis; Spectral resolution; spectrometers; Spectrum analysis; Unmanned aerial vehicles; Vegetation; Vegetation mapping; Wavelengths; Antarctica; South Shetland Islands; Antarctic Peninsula; Arctic region; King George Island; Antarctic region
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13132470

Spectral information is a proxy for understanding the characteristics of ground targets without a potentially disruptive contact. A spectral library is a collection of this information and serves as reference data in remote sensing analyses. Although widely used, data of this type for most ground objects in polar regions are notably absent. Remote sensing data are widely used in polar research because they can provide helpful information for difficult-to-access or extensive areas. However, a lack of ground truth hinders remote sensing efforts. Accordingly, a spectral library was developed for 16 common vegetation species and decayed moss in the ice-free areas of Antarctica using a field spectrometer. In particular, the relative importance of shortwave infrared wavelengths in identifying Antarctic vegetation using spectral similarity comparisons was demonstrated. Due to the lack of available remote sensing images of the study area, simulated images were generated using the developed spectral library. Then, these images were used to evaluate the potential performance of the classification and spectral unmixing according to spectral resolution. We believe that the developed library will enhance our understanding of Antarctic vegetation and will assist in the analysis of various remote sensing data.