Characterization of an antarctic penguin colony ecosystem using high-resolution UAV hyperspectral imagery

Penguin colonies significantly influence the distribution and diversity of vegetation communities in Maritime Antarctica, as they serve as vital sources of nutrients for both terrestrial and aquatic ecosystems. Remote sensing techniques are becoming increasingly common for monitoring these vast Antarctic areas, especially with Unmanned Aerial Vehicles (UAVs) imagery, which provides the highest spatial resolutions to date. In fact, the use of hyperspectral (HS) sensors is crucial for accurately identifying and distinguishing between the main ground characteristics and vegetation communities in an Antarctic penguin colony, making this study one of the first UAV-based HS approaches to our knowledge. Consequently, this study provides a spectral library covering the entire spectral range from 400 to 2500 nm for the five main vegetation communities found at Hannah Point penguin colony (Livingston Island, Antarctica). Through this library, two valuable wavelength regions have been identified for distinguishing these communities based on pigments composition, specifically in the green (495–570 nm) and near-infrared (800–900 nm) ranges, that served as a reference for validating the results using 35 ground reference spectrometry data collected in 1667 wavelength bands within the 320–876 nm range. In addition, the supervised classification approach known as the “Spectral Angle Mapper” has been employed to monitor the coverage of each vegetation community based on the information provided by the aforementioned spectral library. The observed ecological gradient, which reveals an increase in vegetation complexity away from the high-nutrient content guano areas, highlights the influence of guano on the distribution of the main vegetation patterns across the entire penguin colony. The results of this study could serve as a reference point for more sophisticated research involving the use of UAV-based HS or MS sensors in Antarctica, offering unique opportunities to detect small variations in these remote ecosystems resulting from climate change.

This research was funded by the Spanish Government projects RTI2018-098048B-100 and PID2021-1257830B-100. Equipment was funded by Spanish Government Infraestructure projects EQC2018-004446-P, EQC2018-004275-P and EQC2019-005721. A. Román is supported by grant FPU19/04557 funded by Ministry of Universities of the Spanish Government. B. Fernández-Marín is supported by RYC2021-031321-I grant funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationFU/PRTR. This work represents a contribution to CSIC Thematic Interdisciplinary Platforms POLARCSIC and PTI TELEDETECT.

Peer reviewed